Dissertations / Theses on the topic 'Management of consumer demand for electric power'

Thesis (MBA (Business Management))--University of Stellenbosch, 2009.
ENGLISH ABSTRACT: The purpose of this study is to determine whether residential electricity consumers within the Cape Peninsula would be willing to voluntarily purchase green electricity if it is sold at a premium price. International experience in the field of green marketing shows that while niche markets for green electricity clearly existed, few programmes however exceeded a 5% penetration in the residential market. This study therefore methodologically drew on recent development in the literature of norm-motivated behaviour to identify testable factors that could influence residential consumers’ willingness to purchase premium-priced green electricity. After identifying these core testable factors, they were used to test various hypotheses. This was done through the testing of primary data that was collected through a telephone market survey of 405 respondents within the Cape Peninsula. These respondents were all identified as financial decision makers within their electricity consuming households. This study subsequently found that residential electricity consumers in the Cape Peninsula are very concerned about the future of the environment and that a large percentage of them (more than 40%) from almost all income levels might voluntary buy premium-priced green electricity. However, as it did identify that consumers must truly be convinced of the positive effects that green electricity would have on the environment before voluntarily supporting such a campaign, it found that consumers might not be well enough informed on environmental and climate change issues to ensure their actual support. To be at all successful, such a green electricity marketing campaign should be very informative and specifically focused on the positive effects that such a purchase would have on the environment. This study also found that supportive residential consumers would on average be willing to pay a maximum premium of 26% or approximately 15c/kWh. The combined maximum potential value of these premiums amount to R39 million per month. This serves as indication that there is much room for future development of the green electricity market. This study also identified that the majority of residential consumers believe that excessive users of electricity should be forced to make a larger financial contribution towards the generation of green electricity than low usage consumers. Based on its findings, the study closes with recommendations to role players in the green electricity market, i.e. the City of Cape Town Municipality, Darling Wind Farm and Eskom.
AFRIKAANSE OPSOMMING: Die doel van hierdie studie is om te bepaal of residensiële elektrisiteitsverbruikers in die Kaapse Skiereiland gewillig sou wees om vrywilliglik groen elektrisiteit teen ’n premie aan te koop. Internasionale ervaring op die gebied van groen elektrisiteit het getoon dat, alhoewel daar verseker nismarkte vir groen elektrisiteit bestaan, baie min programme meer as 5% van die residensiële mark kon wen. Hierdie studie steun dus metodologies op onlangse verwikkelinge in die literatuur rakende normgemotiveerde gedrag om sodoende toetsbare faktore te identifiseer wat moontlik verbruikers se bereidwilligheid om groen elektrisiteit teen ’n premie te koop, kan verbeter. Na die identifisering van hierdie toetsbare faktore is hulle gebruik om verskeie hipoteses te toets. Dit is gedoen deur die toets van primêre data wat deur middel van telefoon-marknavorsing by 405 respondente binne die Kaapse Skiereiland ingesamel is. Hierdie respondente was almal geïdentifiseer as finansiële besluitnemers van huishoudings wat elektrisiteit gebruik. Hierdie studie het bevind dat residensiële elektrisiteitsverbruikers in die Kaapse Skiereiland baie besorg is oor die toekoms van die omgewing en dat ’n groot hoeveelheid van hierdie huishoudings (meer as 40%) van amper alle inkomstegroepe moontlik gewillig sou wees om groen elektrisiteit teen ’n premie aan te koop. Die studie het ook bevind dat omdat hierdie bereidwilligheid van die residensiële verbruikers onderhewig is aan hul oortuiging dat groen elektrisiteit ’n werklike positiewe effek op die omgewing uitoefen, residensiële verbruikers dalk huidiglik nie werklik goed genoeg ingelig is rakende omgewingsbewaring- en klimaatsveranderingskwessies nie. Hierdie gebrek aan kennis kan dus moontlik hul bereidwilligheid om groen elektrisiteit teen ’n premie aan te koop, negatief beïnvloed. Om suksesvol te wees sal groen elektrisiteit-bemarkingsveldtogte baie volledige inligting moet verskaf en sterk gefokus moet wees op die omgewingsvoordele wat die aankoop van groen elektrisiteit inhou. Die studie het ook bevind dat residensiële ondersteuners bereid sou wees om gemiddeld ’n maksimum premie van 26% of 15c/kWh te betaal. Die gesamentlike maksimum potensiële waarde van hierdie premies is R39 miljoen per maand wat daarop dui dat daar heelwat ruimte mag wees vir toekomstige uitbreiding van die mark vir groen elektrisiteit. Hierdie studie het ook geïdentifiseer dat die meerderheid residensiële elektrisiteitsverbruikers glo dat oormatige elektrisiteitsverbruikers gedwing moet word om ‘n groter finansiële bydrae tot die opwekking van groen elektrisiteit te maak as lae elektrisiteitsverbruikers. Gebaseer op die bevindinge van hierdie studie, sluit dit af met aanbevelings tot verskeie rolspelers in die mark vir groen elektrisiteit, soos die Kaapstadse Munisipaliteit, Darling Windplaas en Eskom.

Thesis (MBA)--University of Stellenbosch, 2010.
Climate change is one of the most serious issues the world is facing today. With an economic slowdown globally, huge food shortages and record-high fuel prices, it has never been so important for countries to guard their natural resources to ensure future sustainability. The South African energy generation industry, of approximately 40 000 Mega Watt (MW), consists largely (90%) of coal-fired power stations, with the remainder comprising of nuclear and pumped storage schemes which are regarded as environmentally neutral. It is only recently that Eskom and independent power producers (e.g. Darling Independent Power Producer Wind farm with an estimated 10 MW) embarked on utilising South Africa's natural resources to generate electrical power. South Africa's access to inexpensive coal and paid off coal-fired power stations has made it difficult to justify the investment in renewable energy. However, on 31 March 2009 South Africa became the first African country to introduce a feed-in-tariff for renewable energy (Gipe, 2009). The hope is that this initiative would stimulate the investment in green energy generation. Eskom and municipalities are currently the only entities that have licences from the National Energy Regulator of South Africa (NERSA) to buy bulk electricity from power producers. The question therefore arises: if green electricity is more expensive to generate and is sold at a price premium to Eskom and municipalities, would they pass the premium on to consumers; can they differentiate the green electricity product and will consumers be willing to buy at a premium price? This research study aims to answer if businesses would be willing to pay a premium for green electricity, why they would be willing to buy it, which factors influence the purchasing decision and what barriers exist that will deter a purchase. A survey was conducted on businesses in the Western and Northern Cape of South Africa. The businesses sampled have a notified maximum demand of 50kVA or higher and excludes the re-distributor (City of Cape Town) customers. Approximately ten per cent of businesses would be willing buy green electricity. Most of these businesses have indicated that they are willing to pay a premium of five to nine per cent for green electricity. The businesses that are willing to pay the largest premiums (>10%) are in the electricity, gas, water, finance, insurance, real estate, business services, manufacturing, transport, storage and communications sector. Businesses that are willing to buy green electricity: • Have a strategy to reduce their carbon footprint; • Want to be community leaders (altruistic motivators); • Have as their biggest barrier the additional cost of green electricity; and • Feel that power utilities should be required to include a minimum percentage of green energy in their energy mix.

Актуальність теми. На сьогоднішні день нерівномірний характер має попит споживачів на електричну потужність. Це призводить до зниження надійності та економічності функціонування енергосистеми, а також погіршенням якості електричної енергії. В зв’язку з цим виникає потреба в регулюванні попиту споживачів на електричну потужність. Викликало необхідність періодичного застосування адміністративних обмежень попиту споживачів на електричну енергію те, що в різні періоди часу енергетична галузь України була дефіцитною. Це пояснюється тим, що, з одного боку, на електростанціях недостатня кількість палива, а з іншого – фізична зношеність значної кількості генеруючого обладнання електростанції, а також електричних мереж. Несприятлива структура генеруючих потужностей спричинила те, що в об’єднаній системі України спостергіється серйозний дефіцит електричної потужності. Мається на увазі, що енергосистемa України недостатньо забезпечена необхiдною кількiстю маневруючих блокiв, які, в свою чергу, можуть швидко виходити нa робочий режим з горячого або холодного резервів, а також змінювати величину виробленої електричної потужності в широких межах. Тому, все більш складною задачею для української енергетичної галузі стає якість електричної енергії, а також надійність забезпечення попиту споживачів на електричну потужність. Відомо, що існують різні методи та способи керування попитом на електричну потужність, основними з яких є методи структурно-технологічного управління, адміністративно-правові методи, економічні та організаційні. Якщо розглядати структурно-технологічні методи, то мова йде про розвантаження енергосистеми при аварійних ситуаціях або дифіциті електричної потужності. Для цього було б доцільно збільшити кількість маневруючих потужностей в ОЕС України, а саме побудові нових гідроакумулюючих електростанцій, побудова та використання парогазових та газотурбінних генеруючих установок, а також модернізації існуючого обладнання ГЕС. Але, на жаль, цей шлях потребує значних фінансових та матеріальних витрат, а також достатньо тривалого часу. Тому необхідно застосовувати такі методи керування попитом споживачів на електричну потужність, які передбачають найменшу кількість витрат та часу, при цьому залишаються ефективними. Мова йде, про економічні методи керування. Таким чином, одним із таких засобів економічного керування попитом споживачів є тарифна система країни, а саме диференційовані за зонами доби тарифи. Диференційовані тарифи в Україні діють з 1995 року. В той час, використання таких тарифів мало сильний стимулюючий ефект у споживачів, при чому з кожним роком їх кількість ставала все більшою. Але, з часом, потреби споживачів на електричну енергію змінились, при чому, диференційовані за зонами доби, а саме коефіцієнти кожної зони, довгий час залишались не змінними. В зв’язку з цим, багато вчених аналізують характер впливу диференційованих тарифів на вирівнювання добових графіків, але разом з тим дана проблема залишається актуальною, адже зонні тарифи втратили свої стимулюючі ефекти до залучення нових споживачів, при чому, їх кількість, навіть, ставала меншою. Відповідно до цього було запропоновано новий спосіб адресного керування попитом споживачів на електричну потужність. Мета й завдання дослідження. Метою роботи є удосконалення механізму адресного керування попитом споживачів на електричну потужність. Для досягнення зазначеної мети були вирішені наступні завдання: • аналiз сучасного стaну eнергeтичної галузi України; • оцінка використання диференційованих за зонами доби тарифів; • визначення характеру та ступеню впливу груп споживачів на формування добового графіка електричного навантаження енергосистеми; • порівняння існуючих меж тарифних зон доби із сучасними потребами споживачів; • удосконалено концепцію та методичні основи побудови та використання мeхaніiму адресного кeрування рeжимами споживання eлeктричної потужностi; • удосконалено розрaхунок коeфіцієнту участi та коефіцiєнту рoзпoдiлу. Об’єкт дослідження. Процеси керування режимами споживання електричної потужності в енергетичній системі. Предмет дослідження. Методи та способи керування режимами споживання електричної потужності в енергетичній системі. Методи дослідження. Методи кореляційного та дисперсійного аналізу, а також математичної статистики застосовуються для визначення характеру та ступеню впливу основних груп споживачів на нерівномірність графіків електричного навантаження енергосистеми; дослідження характеру та ступеню протидії навантаження споживачів, які використовують диференційовані за зонами доби тарифи відповідно до зміни попиту на потужність споживачів, які не використовують ці тарифи; oцiнки пoтeнцiaлу eкoнoмії витрaт eнeргoсистеми на вирoбництвo електричної енергії, яку можливо отримати в результаті вирівнювання нерівномірності її графіка навантаження. Мeтoди встановлення довірчих інтервалів та групування годинних навантажень за допомогою критерія Стьюдента використовуються для виявлення фактичних зон доби зі статистично різним рівнем електричного навантаження енергосистеми та груп споживачів. Методи узагальнення та логічного підходу, методи моделювання графіків електричного навантаження, методи оптимального програмування застосовуються для використання засобу адресного керування режимами споживання електричної потужності в енергосистемі Наукова новизна одержаних результатів. Удосконалено метод визначення тривалості та меж фактичних зон доби зі статистично різним рівнем електричного навантаження, що базується на групуванні погодинних значень навантаження енергосистеми та споживачів електроенергії. Удосконалено показник, який дозволяє оцінити ступінь протидії електричного навантаження «дифтарифних» та «недифтарифних» споживачів. Визначено кількісні показники, а саме коефіцієнт участі та коефіцієнт розподілу, які дозволяють оцінити участь кожного із учасників запропонованого механізму адресного керування попитом споживачів у вирівнюванні нерівномірності графіка електричного навантаження енергосистеми. Практичне значення одержаних результатів. Для ефективного стимулювання споживачів до вирівнювання нерівномірності графіка електричного навантаження ОЕС України може бути застосовано запропоновану концепцію та методичні основи. Також, вона має новий напрямок розвитку ринкових методів керування режимами споживання електричнох потужності в енергосистемі.
Actuality of theme. To date, consumer demand for electricity is uneven. This leads to a decrease in the reliability and efficiency of the functioning of the grid, as well as the deterioration of the quality of electric energy. In connection with this, there is a need to regulate the demand of consumers for electric power. The necessity of the periodic application of administrative constraints on consumer demand for electricity was the fact that the energy sector of Ukraine was scarce at different times. This is explained by the fact that, on the one hand, there is insufficient amount of fuel at power plants, and on the other hand - physical deterioration of a significant amount of generating equipment of the power plant, as well as electric networks. The unfavorable structure of generating capacities was caused by the fact that in the united system of Ukraine there is a serious shortage of electric power. It is understood that Ukraine's energy system is insufficiently equipped with the required number of maneuvering blocks, which, in turn, can quickly enter the operating mode of hot or cold reserves, and also change the amount of electric power produced in a wide range. Therefore, the quality of electric energy, as well as the reliability of ensuring the demand of consumers for electric power, becomes an increasingly complex task for the Ukrainian energy sector. It is known that there are different methods and methods for controlling demand for electric power, the main of which are methods of structural and technological management, administrative and legal methods, economic and organizational. If we consider structural and technological methods, then we are talking about the unloading of the power system in emergency situations or the dipole of electric power. For this purpose, it would be advisable to increase the number of maneuvering capacities in the UES of Ukraine, namely the construction of new hydroelectric power stations, the construction and use of steam and gas turbine generating units, as well as the modernization of the existing equipment of the hydroelectric power station. But, unfortunately, this path requires significant financial and material costs, as well as a sufficiently long time. Therefore, it is necessary to apply such methods of controlling the demand of consumers for electric power, which provide the least amount of time and expenses, while remaining effective. It's about the economic management methods. Thus, one of such means of economic management of consumer demand is the tariff system of the country, namely tariffs differentiated by zones of the day. Differentiated tariffs in Ukraine have been in force since 1995. At that time, the use of such tariffs had a strong stimulating effect on consumers, with what each year their number became more and more. But, over time, the needs of consumers for electric energy have changed, with that, differentiated by zones of the day, namely, the coefficients of each zone, for a long time remained unchanged. In this regard, many scientists analyze the effect of differentiated tariffs on the equalization of daily charts, but at the same time, this problem remains relevant, because zone tariffs have lost their stimulating effects to attract new consumers, and at the same time, their number is even less. Accordingly, a new method of address management of consumer demand for electric power was proposed. The purpose and tasks of the study. The aim of the work is to improve the mechanism of address management of consumer demand for electric power. To achieve this goal, the following tasks were solved: • analysis of the current state of the Ukrainian energy sector; • estimation of the use of tariff-differentiated zones; • determination of the nature and degree of influence of groups of consumers on the formation of a daily schedule of electric load of the power system; • Comparison of the existing boundaries of the tariff zones of the day with the modern needs of consumers; • The concept and methodical bases of the construction and use of the mechanism of address control of electric power consumption modes have been improved; • The calculation of the participation coefficient and the distribution coefficient has been improved. Object of study. Processes of control of modes of consumption of electric power in the power system. Subject of study. Methods and methods for controlling the demand of electric power consumers in the energy system. Research methods. Methods of correlation and dispersion analysis, as well as mathematical statistics, are used to determine the nature and extent of the impact of major consumer groups on the unevenness of the schedules of the electric load of the power system; the study of the nature and degree of counteraction of the load of consumers, which use differentiated by the zones of the day tariffs in accordance with the change in demand for the power of consumers who do not use these tariffs; Estimating the potential of energy saving in electricity generation for electricity production, which can be obtained as a result of equalizing the unevenness of its loading schedule. Methods for establishing confidence intervals and clustering of hourly loads using Student's criterion are used to identify the actual days of the day with a statistically different level of electrical load of the power system and consumer groups. Methods of generalization and logical approach, methods of modeling electric power schedules, methods of optimal programming are used for the use of address management tools for power consumption in the grid. Scientific novelty of the obtained results. The method of determining the duration and limits of actual days of the day with a statistically different level of electric load, based on the grouping of hourly values of the load of the power system and consumers of electricity, has been improved. The indicator, which allows to assess the degree of counteraction to the electric load of "diffariffic" and "non-dipharmary" consumers, is improved. The quantitative indicators, namely, the coefficient of participation and the distribution coefficient, which allow estimating the participation of each of the participants in the proposed mechanism of address management of consumer demand in the alignment of the unevenness of the schedule of the electric load of the grid. The practical value of the results. For the effective stimulation of consumers to equalize the uneven schedule of electric loading of the UES of Ukraine, the proposed concept and methodical bases can be applied. Also, it has a new direction in developing market-based methods for controlling electricity consumption regimes in the grid.

The objective of the proposed research is to analyze automated residential energy management technology using primary energy source utilization. A residential energy management system (REMS) is an amalgamation of hardware and software that performs residential energy usage monitoring, planning, and control. Primary energy source utilization quantifies power system levels impacts on power generation cost, fuel utilization, and environmental air pollution; based on power system generating constraints and electric load. Automated residential energy management technology performance is quantified through a physically-based REMS simulation. This simulation includes individual appliance operation and accounts for consumer behavior by stochastically varying appliance usage and repeating multiple simulation iterations for each simulated scenario. The effect of the automated REMS under varying levels of control will be considered. Aggregate REMS power system impacts are quantified using primary energy source utilization. This analysis uses a probabilistic economic dispatch algorithm. The economic dispatch algorithm quantifies: fuel usage and subsequent environmental air pollution (EAP) generated; based on power system generating constraints and electric load (no transmission constraints are considered). The analysis will comprehensively explore multiple residential energy management options to achieve demand response. The physically-based REMS simulation will consider the following control options: programmable thermostat, direct load control, smart appliance scheduling, and smart appliance scheduling with a stationary battery. The ability to compare multiple automated residential energy management technology options on an equal basis will guide utility technology investment strategies.

To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid. With the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side. A bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure. As high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model. In summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand response, have been extensively studied and interactively modeled to provide more intelligent planning and management for the smart grid.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

Along with the growth of electricity demand and the penetration of intermittent renewable energy sources, electric power distribution networks will face more and more stress conditions, especially as electric vehicles (EVs) take a greater share in the personal automobile market. This may cause potential transformer overloads, feeder congestions, and undue circuit failures. Demand response (DR) is gaining attention as it can potentially relieve system stress conditions through load management. DR can possibly defer or avoid construction of large-scale power generation and transmission infrastructures by improving the electric utility load factor. This dissertation proposes to develop a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level. The proposed planning tool comprises control algorithms and a simulation platform that are designed to intelligently manage end-use loads to make the EV penetration transparent to an electric power distribution network. The proposed planning tool computes the demand response amount necessary at the circuit/substation level to alleviate the stress condition due to the penetration of EVs. Then, the demand response amount is allocated to the end-user as a basis for appliance scheduling and control. To accomplish the dissertation objective, electrical loads of both residential and commercial customers, as well as EV fleets, are modeled, validated, and aggregated with their control algorithms proposed at the appliance level. A multi-layer demand response model is developed that takes into account both concerns from utilities for load reduction and concerns from consumers for convenience and privacy. An analytic hierarchy process (AHP)-based approach is put forward taking into consideration opinions from all stakeholders in order to determine the priority and importance of various consumer groups. The proposed demand response strategy takes into consideration dynamic priorities of the load based on the consumersâ real-time needs. Consumer comfort indices are introduced to measure the impact of demand response on consumersâ life style. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration. Research findings from this work indicate that the proposed demand response strategy can fulfill the task of peak demand reduction with different EV penetration levels while maintaining consumer comfort levels. The study shows that the higher number of EVs in the distribution circuit will result in the higher DR impacts on consumersâ comfort. This indicates that when EV numbers exceed a certain threshold in an area, other measures besides demand response will have to be taken into account to tackle the peak demand growth. The proposed planning tool is expected to provide an insight into the implementation of demand response at the end-user level. It can be used to estimate demand response potentials and the benefit of implementing demand response at different DR penetration levels within a distribution circuit. The planning tool can be used by a utility to design proper incentives and encourage consumers to participate in DR programs. At the same time, the simulation results will give a better understanding of the DR impact on scheduling of electric appliances.
Ph. D.

A two-level agent-based modeling framework is proposed for the electric power system to solve the problems of renewable energy utilization and demand-side management. While in the detailed level of the framework the customers and utility companies are modeled as agents to represent electricity demand and supply performances, respectively, the high level reflects the aggregated performance of the considered electricity market via state space models. To connect the two levels, a social network is introduced as a dynamic medium for the interactions among customer agents. While the customers' consumption behaviors are modeled at lower level and affected by each other, their individual performances contribute to the system performance in the high level. This dissertation concerns three problems. First, the problem of renewable energy adoption concerns penetration process of distributed solar systems with various incentive policies (i.e., Income Tax Credits and Feed-in Tariff) for renewable energy. The proposed hybrid model incorporates agent-based modeling and system dynamics to simulate the solar system diffusion process among the residential customers. Second, the demand-side management problem focuses on scheduling the Plug-in Hybrid Electric Vehicles (PHEV) charging under different scenarios of demand response programs (i.e., Time-of Use and Real-time Pricing). For the Time-of Use (TOU) program, the decision-support analysis results from simulation-based optimization for both customers and the utility company. For the Real-time Pricing (RTP) program, the discussion is to find proper pricing functions according to different customers. Third, the problem concerns the agent interaction based on different architectures of social network (i.e., small-world and scale-free) and the network evolution based on triadic closure. Such interaction is applied to the first two problems with the effect of changing the customers' social connections, preferences in consumption behaviors and acceptable grid prices. Furthermore, to extend the demand-side management problem, this research also discusses the energy management at individual households integrating PV generation system, battery storage and electric vehicle under demand response programs. The conceptual model is based on the threshold method to suggest residential customers when to use the electricity from which sources (PV generation, storage, or local grid).

Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of electric vehicle ownership rapidly increasing, uncontrolled charging of large populations may bring about power system issues such as increased peak demand and voltage variations, while at the same time the cost of electricity generation, as well as the resulting Greenhouse Gases (GHG) emissions, will also rise. The work reported in this PhD Thesis aims to provide solutions to the three significant challenges related to EV integration, namely voltage regulation, generation cost minimisation and GHG emissions reduction. A novel, high-resolution, bottom-up probabilistic EV charging demand model was developed, that uses data from the UK Time Use Survey and the National Travel Survey to synthesise realistic EV charging time series based on user activity patterns. Coupled with manufacturers' data for representative EV models, the developed probabilistic model converts single user activity profiles into electrical demand, which can then be aggregated to simulate larger numbers at a neighbourhood, city or regional level. The EV charging demand model has been integrated into a domestic electrical demand model previously developed by researchers in our group at the University of Edinburgh. The integrated model is used to show how demand management can be used to assist voltage regulation in the distribution system. The node voltage sensitivity method is used to optimise the planning of EV charging based on the influence that every EV charger has on the network depending on their point of connection. The model and the charging strategy were tested on a realistic "highly urban" low voltage network and the results obtained show that voltage fluctuation due to the high percentage of EV ownership (and charging) can be significantly and maintained within the statutory range during a full 24-hour cycle of operation. The developed model is also used to assess the generation cost as well as the environmental impact, in terms of GHG emissions, as a result of EV charging, and an optimisation algorithm has been developed that in combination with domestic demand management, minimises the incurred costs and GHG emissions. The obtained results indicate that although the increased population of EVs in distribution networks will stress the system and have adverse economic and environmental effects, these may be minimised with careful off-line planning.

Well-designed demand response is expected to play a vital role in operatingpower systems by reducing economic and environmental costs. However,the current system is operated without much information on the benefits ofend-users, especially the small ones, who use electricity. This thesis proposes aframework of operating power systems with demand models including the diversityof end-users’ benefits, namely adaptive load management (ALM). Sincethere are a large number of end-users having different preferences and conditionsin energy consumption, the information on the end-users’ benefits needsto be aggregated at the system level. This leads us to model the system ina multi-layered way, including end-users, load serving entities, and a systemoperator. On the other hand, the information of the end-users’ benefits can beuncertain even to the end-users themselves ahead of time. This information isdiscovered incrementally as the actual consumption approaches and occurs. Forthis reason ALM requires a multi-temporal model of a system operation andend-users’ benefits within. Due to the different levels of uncertainty along thedecision-making time horizons, the risks from the uncertainty of informationon both the system and the end-users need to be managed. The methodologyof ALM is based on Lagrange dual decomposition that utilizes interactive communicationbetween the system, load serving entities, and end-users. We showthat under certain conditions, a power system with a large number of end-userscan balance at its optimum efficiently over the horizon of a day ahead of operationto near real time. Numerical examples include designing ALM for theright types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude thatwith the right information exchange by each entity in the system over differenttime horizons, a power system can reach its optimum including a variety ofend-users’ preferences and their values of consuming electricity.

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

Thesis (MTech (Industrial Design))--Cape Peninsula University of Technology, 2009
Demand Side Management (DSM) within a South African context requires a transdisciplinary approach to comprehend electricity consumption. Current research suggests a technical determinism, whereby design teams fail to acknowledged social factors and cultural influences when conceptualising DSM artefacts. The result of which, is that artefacts fail to be adopted by the market, and consumer behaviour and electricity consumption remains unchanged. The thesis aims to demonstrate the hypothesis, that semiotics and ethnology may affect sustainable residential electricity management in South Africa. The ubiquitous literature on electricity management is administered by means of the theoretical lens, Sociotechnical Theory. Mixed method instrument obtain fieldwork data from three of the eleven official South African languages: Afrikaans, English and IsiXhosa.

Large penetration of distributed generation from variable renewable energy sources, increased consumption flexibility on the demand side and the electrification of transportation pose great challenges to existing and future electric distribution networks. This thesis studies the roles of several actors involved in electric distribution systems through electricity consumption data analysis and simulation models. Results show that real-time electricity pricing adoption in the residential sector offers economic benefits for end consumers. This occurs even without the adoption of demand-side management strategies, while real-time pricing also brings new opportunities for increasing consumption flexibility. This flexibility will play a critical role in the electrification of transportation, where scheduled charging will be required to allow large penetration of EVs without compromising the network's reliability and to minimize upgrades on the existing grid. All these issues add significant complexity to the existing infrastructure and conventional passive components are no longer sufficient to guarantee safe and reliable network operation. Active distribution networks are therefore required, and consequently robust and flexible modelling and simulation computational tools are needed for their optimal design and control. The modelling approach presented in this thesis offers a viable solution by using an equation-based object-oriented language that allows developing open source network component models that can be shared and used unambiguously across different simulation environments.

The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.

Under the determined impulse of the European Union to limit the environmental impact of energy-related services, the electricity sector will face several challenges in coming years. Integrating renewable energy sources in the distribution networks is certainly one of the most urging issues to be tackled with. The current grid and production structure cannot absorb the high penetration shares anticipated for 2020 without putting at risk the entire system. The innovative concept of smart grid offers promising solutions and interesting implementation possibilities. The objective of the thesis is to specifically study the technical and economic benefits that the creation of an aggregator on the Swedish island of Gotland would imply. Comparing Gotland's power system characteristics to the broad variety of solutions offered by demand side management, wind power integration enhancement by demand response appeared particularly suited. A business case, specifically oriented towards the minimisation of transmission losses by adapting the electric heat load of private households to the local wind production was designed. Numerical simulations have been conducted, evaluating the technical and economic outcomes, along with the environmental benets, under the current conditions on Gotland. Sensitivity analyses were also performed to determine the key parameters for a successful implementation. A prospective scenario for 2020, with the addition of electric vehicles, has finally been simulated to estimate the long term profitability of an aggregator on the island. The simulation results indicate that despite patent technical benefits for the distribution network, the studied service would not be profitable in the current situation on Gotland. This, because the transmission losses through the HVDC-cable concern limited amounts of power that are purchased on a market characterized by relatively cheap prices and low volatility. Besides, the high fixed costs the aggregator has to face to install technical equipment in every household constitutes another barrier to its setting up.

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No contexto de gestão e conservação de energia elétrica, ferramentas de apoio ao consumidor para gerenciar sua demanda são fundamentais para a otimização do uso dos recursos energéticos de modo a minimizar os custos com energia elétrica e ao mesmo tempo garantir o conforto do consumidor, considerando que este consumidor esteja inserido em um ambiente de Gerenciamento pelo Lado da Demanda (GLD). Assim, este trabalho propõe um novo modelo matemático de programação linear inteira mista (PLIM) para resolver o problema de gerenciamento ótimo de energia elétrica pelo lado do consumidor. O modelo matemático é baseado na minimização do custo da energia elétrica e maximização do conforto do consumidor, levando em conta a minimização da diferença entre o consumo habitual e o consumo ótimo, e a minimização da potência absorvida da rede. O modelo é implementado em linguagem de programação AMPL e resolvido utilizando o solver CPLEX. A metodologia é aplicada para gerenciar um conjunto de cargas típicas residenciais e os resultados mostram sua eficiência e potencial para gerenciar de forma ótima a demanda do consumidor, considerando a tarifa de energia elétrica com preço variável, geração distribuída, armazenamento de energia em banco de baterias e veículos elétricos.
In the context of the management and conservation of electric energy, consumer support tools to manage their demand are fundamental for optimizing the use of energy resources in order to minimize energy costs and at the same time guarantee consumer comfort, considering that the consumer is inserted in a Demand Response (DR) environment. Thus, this work proposes a new mathematical model of mixed integer linear programming (MILP) to solve the problem of optimal management of electrical energy by the consumer side. The mathematical model is based on minimizing the cost of electrical energy, maximizing consumer comfort, taking into account the minimization of the difference between habitual consumption and optimal consumption, and minimizing the power consumed by the network. The model is implemented in AMPL programming language and solved using the CPLEX solver. The methodology is applied to manage a set of typical residential loads and the results show its efficiency and potential to optimally manage the consumer demand, considering the price of electricity with variable price, distributed generation, storage of energy in bank of batteries and electric vehicles.

Orientador: Fábio Bertequini Leão
Resumo: No contexto de gestão e conservação de energia elétrica, ferramentas de apoio ao consumidor para gerenciar sua demanda são fundamentais para a otimização do uso dos recursos energéticos de modo a minimizar os custos com energia elétrica e ao mesmo tempo garantir o conforto do consumidor, considerando que este consumidor esteja inserido em um ambiente de Gerenciamento pelo Lado da Demanda (GLD). Assim, este trabalho propõe um novo modelo matemático de programação linear inteira mista (PLIM) para resolver o problema de gerenciamento ótimo de energia elétrica pelo lado do consumidor. O modelo matemático é baseado na minimização do custo da energia elétrica e maximização do conforto do consumidor, levando em conta a minimização da diferença entre o consumo habitual e o consumo ótimo, e a minimização da potência absorvida da rede. O modelo é implementado em linguagem de programação AMPL e resolvido utilizando o solver CPLEX. A metodologia é aplicada para gerenciar um conjunto de cargas típicas residenciais e os resultados mostram sua eficiência e potencial para gerenciar de forma ótima a demanda do consumidor, considerando a tarifa de energia elétrica com preço variável, geração distribuída, armazenamento de energia em banco de baterias e veículos elétricos.
Abstract: In the context of the management and conservation of electric energy, consumer support tools to manage their demand are fundamental for optimizing the use of energy resources in order to minimize energy costs and at the same time guarantee consumer comfort, considering that the consumer is inserted in a Demand Response (DR) environment. Thus, this work proposes a new mathematical model of mixed integer linear programming (MILP) to solve the problem of optimal management of electrical energy by the consumer side. The mathematical model is based on minimizing the cost of electrical energy, maximizing consumer comfort, taking into account the minimization of the difference between habitual consumption and optimal consumption, and minimizing the power consumed by the network. The model is implemented in AMPL programming language and solved using the CPLEX solver. The methodology is applied to manage a set of typical residential loads and the results show its efficiency and potential to optimally manage the consumer demand, considering the price of electricity with variable price, distributed generation, storage of energy in bank of batteries and electric vehicles.
Mestre

This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.

Les travaux de cette thèse concernent la maîtrise de l’énergie et la convergence des besoins énergétiques dans le transport et les bâtiments. Une approche basée sur les techniques d’optimisation est proposée pour la gestion conjointe de la production et de la consommation d’énergie dans un bâtiment disposant de moyens de production locale d’énergie renouvelable et d’une connexion avec voiture électrique. "Le gestionnaire d’énergie" anticipe,24 heures à l’avance, les commandes optimales pour piloter les multiples sources électriques et thermiques placées dans le bâtiment ainsi que les procédés de stockage de l’énergie dans le butde parvenir à la meilleure configuration possible des flux énergétiques minimisant le coût de la facture énergétique. Considérant la bidirectionnalité du courant électrique entre la voiture et le bâtiment dans les applications "vehicle to home" (V2H), l’électricité stockée dans la batterie de la voiture peut être appelée pour alimenter les charges installées dans le bâtiment pendant que celle-ci est en stationnement. Le gestionnaire embarque également un mécanisme d’ajustement de la demande électrique qui permet de programmer le "fonctionnement" des électroménagers en vue de diminuer la consommation des habitants aux heures des pointes électrique
This PhD dissertation addresses the power management for the convergence oftransportation and housing power requirement. An approach based on optimization techniquesis proposed for the joint management of the power production and consumption in buildingswith renewable energy generator and connection to electric vehicle. The power manageranticipates day ahead optimal control for multiple electrical and thermal sources locatedin the building as well as the processes of energy storage in order to achieve the bestpossible configuration of energy flow, which minimize the cost of energy bills. Consideringthe bidirectional energy flow between the vehicle and the building in the "vehicle to home"(V2H) applications, the energy stored in the cars battery of the car can be used to supplythe household loads while it is parked. The manager also performs an electrical demandadjustment mechanism which programs schedules the "operation" of some of home appliancesin order to reduce the consumption of the inhabitants during peak periods when the electricityprice is high

El objetivo general del presente trabajo de investigación es proponer un modelo de gestión de la demanda estratégica de TI, ajustado a la realidad de la empresa, que les permita seleccionar proyectos que realmente estén alineados a los objetivos estratégicos de la compañía, tomar en consideración los recursos de TI necesarios desde un primer momento, y priorizar adecuadamente el portafolio de proyectos. En el primer capítulo presentamos el marco teórico, donde revisamos información relevante del sector eléctrico, al cual pertenece la empresa, y analizamos marcos metodológicos existentes respecto a la Gestión de Demanda Estratégica, tales como GEDEST-TI, y metodologías para la priorización de portafolios de proyecto, como Verbal Decision Analysis (VDA). En el segundo capítulo conocemos más detalles acerca de cómo está organizada la empresa, las diferentes áreas con las que cuenta, el tipo de proyectos que implementan, y nos enfocamos en el análisis de la situación problemática, ocasionada principalmente por la cantidad de proyectos cancelados, y los que son implementados pero que finalmente no son utilizados. Estos proyectos representan en promedio el 17% del presupuesto anual asignado para proyectos, tomando como base la información de los últimos 3 años. En el tercer capítulo exponemos la propuesta de solución al problema, detallando cada una de las fases del modelo, análisis de viabilidad económica, y cronograma de implementación. Finalmente, en el cuarto capítulo se detallan las conclusiones de nuestra investigación y las recomendaciones para la compañía, producto de nuestro análisis.
The main goal of this research is to propose an IT Strategic Demand Management Model adjusted to the company's realty, that allow them to select aligned to the strategic objectives’ projects, take into consideration the IT resources needed from the very first time, and properly prioritize the project portfolio. In the first chapter we present the theoretical framework, where we review relevant information about the electric sector, which the company belongs to, and analyze existing methodologic frameworks related to the IT Strategic Demand Management, such as GEDEST-TI and methodologies for project portfolio prioritization, such as Verbal Decision Analysis. In the second chapter we present more details about the company organization, its different areas, the kind of projects they implement, and we focus on the problematic situation analysis, caused by the number of cancelled projects, and implemented but not used projects. These kinds of projects represent 17% average of the annual budget assigned for projects, considering the 3 last year’s information. In the third chapter we present the proposed problem solution, detailing every one of the model phases, the economic viability and implementation schedule. Finally, in chapter 4 we detail our research conclusions and recommendations for the company, obtained from our analysis.
Trabajo de investigación

Kenntnisse über das prinzipielle Verhalten der Lasten und deren Benutzung durch die Endabnehmer sind im Wesentlichen vorhanden. Viele der aktuell notwendigen Untersuchungen benötigen jedoch Zeitreihen elektrischer Lasten, sogenannte Lastgänge. Mit der Synthese von Zeitreihen elektrischer Lasten können unter Berücksichtigung verschiedenster Anforderungen Lastgänge aufgebaut werden, wobei in dieser Arbeit der Fokus auf Haushaltsabnehmer liegt. Wichtige Eingangsdaten für die Lastgangsynthese sind die technischen Kenngrößen der elektrischen Geräte und die sozialen Kennzahlen zur Benutzung der Geräte durch die Endabnehmer. Anhand dieser Eingangsdaten wird die Lastgangsynthese durchgeführt und werden Anwendungsbeispiele dargestellt. Die Entwicklung von klassischen Versorgungsnetzen hin zu aktiven Verteilungsnetzen ist bedingt durch neue Verbraucher, wie Wärmepumpen, Elektroautos, sowie vielen dezentralen Erzeugungsanlagen. Speziell die fluktuierende Einspeisung durch Photovoltaik-Anlagen ist Anlass zur Forderung nach einem Verbrauchs- und Lastmanagement. Mit dem Verbrauchsmanagement wird die Last an die Einspeisung angepasst und das Lastmanagement berücksichtigt zusätzlich die Versorgungssituation des Netzes. Für die Lastgangsynthese werden die Haushaltsgeräte in fünf Geräteklassen unterteilt, für die spezifische Kennzahlen aus technischer und sozialer Sicht angegeben werden. Diese Kennzahlen sind Leistung pro Gerät oder Energieverbrauch pro Nutzung sowie Ausstattungsgrade, Benutzungshäufigkeiten und Zeiten für das Ein- und Ausschalten der Geräte. Damit wird ein neuer Ansatz gewählt, welcher nicht mehr auf die detaillierte Beschreibung des Bewohnerverhaltens beruht, da die Datenbereitstellung dafür äußerst schwierig war und ist. Vorzugsweise in Niederspannungsnetzen sind mit synthetischen Zeitreihen umfangreiche und umfassende Untersuchungen realisierbar. Es gibt verschiedenste Möglichkeiten, die Zeitreihen zusammenzustellen. Mit Lastgängen je Außenleiter können beispielsweise unsymmetrische Zustände der Netze analysiert werden. Zudem können auch Lastgänge für Geräte bzw. Gerätegruppen erstellt werden, welche für Potenzialanalysen des Verbrauchsmanagement essenziell sind. Der wesentliche Unterschied besteht darin, dass viele Berechnungen nicht mehr auf deterministische Extremwerte beruhen, sondern die stochastischen Eigenschaften der Endabnehmer mit den resultierenden Lastgängen berücksichtigt werden
Distributed generation and novel loads such as electric vehicles and heat pumps require the development towards active distribution networks. Load curves are needed for the appropriate design process. This thesis presents a feasible and expandable synthesis of load curves, which is performed exemplary on residential customers with a period under review of 1 year and time steps of as little as 30 s. The data is collected for up-to-date appliances and current statics examining the way of life. The main focus lies on the input data for the synthesis and distinguishes between technical and social factors. Some thirty home appliances have been analyzed and are classified into five appliance classes by incorporating switching operations and power consumptions. The active power is the key figure for the technical perspective and the data is derived from manufacturer information. For the social perspective six different customer types are defined. They differ in sizes of household and housekeeping. The social key figures are appliance penetration rate and depending on the appliance class the turn-on time, turn-off time, operating duration or cycle duration. The elaborated two-stage synthesis is efficiently implemented in Matlab®. First, artificial load curves are created for each appliance of the households under consideration of the appliance class. In the second step, the individual load curves of the appliances are combined to load curves per line conductor. The algorithms have been validated in the implementation process by retracing the input data in the load curves. Also, the feasibility of the results is shown by comparing the key figures maximum load and power consumption to data in literature. The generated load curves allow for unsymmetrical calculations of distribution systems and can be used for probabilistic investigations of the charging of electric vehicles, the sizing of thermal storage combined with heat pumps or the integration of battery storage systems. A main advantage is the possibility to estimate the likelihood of operating conditions. The enhancement to further appliances and the changeability of the input data allows for versatile further possible investigations

L'intégration massive des énergies renouvelables variables (EnRV) provoque d'importants changements dans le système électrique. Auparavant développé de manière vertical et centralisé, le système était robuste et fiable. Cependant, la production des EnRV est intermittente et peu prévisible. Ainsi, le système doit être plus flexible grâce à de nouvelles options telles que la maîtrise de la demande, le stockage ou l'effacement de la production EnRV. Cependant, le potentiel des EnRV est réparti inégalement en Europe et avec d'importants taux de pénétration d'EnRV, les échanges d'électricité entre les régions vont augmenter provoquant des congestions dans le réseau. Ainsi, les options de flexibilité ne pourront peut-être pas réduire ces congestions. Pour analyser ces effets, le travail mené dans cette thèse utilise le modèle de prospective long terme POLES (Prospective Outlook on Long-term Energy Systems) couplé avec le nouveau module du secteur électrique EUTGRID (EUropean – Transmission Grid Investment and Dispatch). Ce module inclut une représentation détaillée du réseau de transport européen d'électricité avec un calcul des flux plus réaliste. De plus, les renforcements sont déterminés suivant les coûts de congestion de chaque ligne. Ce nouveau couplage permet d'avoir une évolution dynamique du réseau de transport. Le rôle du réseau de transport est ensuite analysé et comparé avec les autres options de flexibilité. Les investissements dans le réseau augmentent ainsi fortement avec d'importants taux de pénétration des EnRV alors que les options de flexibilité ne peuvent pas intégralement remplacer le réseau. Finalement, un travail exploratoire est mené avec l'introduction de réseaux de distribution génériques (urbain, semi-urbain and rural) dans EUTGRID. Les résultats montrent que les renforcements sont légèrement décalés avec une augmentation de l'utilisation des technologies de back-up (i.e. centrales à gaz) ce qui augmente les émissions totales
The power system is facing a major shift with the large-scale development of variable renewable energy sources (VRES). This vertical and centralized architecture helped the system to be robust and reliable. However, VRES production is intermittent and less predictable. As a result, the system needs to add more flexibility with new options such as Demand Side Management, storage technologies and VREs curtailment. But renewable energies potentials are unevenly distributed in Europe and, with high shares of VREs, power flows exchanges will increase between specific regions. As a result, the existing transmission grid would face congestions and these flexibility options might not be sufficient to alleviate these bottlenecks. To analyse these impacts, the work carried in this thesis uses the long-term energy model POLES (Prospective Outlook on Long-term Energy Systems) coupled with the new European power sector module EUTGRID (EUropean – Transmission Grid Investment and Dispatch). It includes a detailed transmission grid and more realistic power flows with a DC-OPF. A grid investment mechanism is also incorporated to determine the grid investments based on nodal prices. This new coupling permits to get a dynamic evolution of the transmission grid. The role of the transmission grid is being assessed and compared with other flexibility options. The grid investments increase largely with important development of VRES while other flexibility options cannot completely replace them. Finally, an exploratory work is being carried with the introduction of generic distribution grids (urban, semi-urban and urban) in EUTGRID. The results show that the reinforcements are slightly delayed with a greater use of back-up technologies which increases the total emissions

In January 2008 the South African power grid operated by Eskom became severely constrained because electricity demand exceeded supply. Following the immediate measure of load shedding, Eskom implemented a medium term strategy of Power Conservation to encourage consumers to reduce their power usage so that overall demand could be managed. Despite the extensive campaigns the 10% electricity savings target was not met in 2008. The aim of this study was to review the experiences of other countries that had successfully implemented Power Conservation Programmes and to research the different approaches that were taken in other countries to encourage behaviour change. A survey and quantitative analysis was undertaken on a sample of electricity consumers within the eThekwini Municipality Central Region to gauge the response of consumers to the power conservation campaigns conducted within the municipality. The analysis was done to determine how the response had varied among the various groups by taking into account demographic factors like age, gender and income level. The population of the sample frame was estimated at 15 000 customers. Two hundred and eighty responses were received and analysed. A salient finding of the study was that the majority of respondents were aware of the reasons for power conservation and the campaigns undertaken, but that there was a preference for Government to lead the initiative and subsidise the required savings measures. A second important finding was that different demographic groups implemented different savings measures, for different reasons and preferred different strategies to encourage savings. The main recommendations made were that Government should lead the savings drive, with customised campaigns for different demographic groups. The campaigns should place greater emphasis on the financial benefits to be gained. It was necessary for new and different savings measures to be communicated. Further, the was a need for Government to introduce legislation to enforce electricity savings.
Thesis (MBA)-University of KwaZulu-Natal, Westville, 2009.

碩士
台南應用科技大學
國際企業經營系碩士班
105
This essay is aim to discuss the important role of electricity demand response load management. With effective electricity load management and integrate dispersed manner, the peak load power supply can be balanced, furthermore, shorten the gap of peak and trough load power. Power supply industry not only cut off the generation cost, but also elevate reserve margin. All these efforts hope to stabilize the power supply chain and avoid power crisis. This essay analyzed the “Demand bidding” method in demand response operation with case study. The most important issue is that power industry can’t control their users due to the low qualifications of participants (just to decrease the contract load up to 50kw) and low executive rate. We focus on 4 high-voltage power used company as our case study subject, including textile industry, university, traditional and general manufacturing industry, and the contract load of these company are usually between 400 kw and 18,000 kw. We received valuable information from interview, each one who engaged in this project is manager level or above. During the interview, Taipower and company representatives discussed series of question and solution. The whole process continued monthly till the end of 2016 (Table 3-1). It has been lots of problems and frustration when conducting the whole project. The result would be organized and analyzed to provide suggestion to Taipower demand bidding operation. After summarized and analyzed, the case study showed that 1. To popularize demand bidding operation more smoothly, we must access the board member, showing a sense of moderate degree of organizational loyalty. 2. To popularize demand bidding operation more smoothly, we must build trust between users and power supplier. 3. The main reason of low executive rate is the divergence of loyalty between users and power supplier. 4. The high price could have beneficial effect on electricity deduction incentives In summary, this study provided some suggestions and follow-up studies for reference by relevant department.

碩士
國立高雄大學
高階法律暨管理碩士在職專班(EMLBA)
98
Taiwan Power Company (Taipower) was chartered to establish in accordance with “The Electricity Act,” in which the general electric operators, electric power transmission companies and electric power distributers are categorized as the public utility industry. The engineerings are divided into electrical technician industry, electrical installation industry and electrical equipment inspection industry. Among them, the business license for the electric technician industry is valid without expiration while the licenses for the electrical installation industry and electrical equipment inspection industry are valid for 5 years. The competent authority carries out this regulation that will influence the marketing circumstance and the existing professional electricity relevant companies’ management direction. This study explored that after the implementation of The Electricity Act, how the Taipower’s business regulations influence the management of the electrical installation industry and electrical equipment inspection industry. Through the questionnaire survey to the general users, commercial users, electrical installation industry and electrical equipment inspection industry, this study is trying to examine the legalization and existing management problem for the electrical installation industry and electrical equipment inspection industry. Meanwhile, the study also inspected if the general users gain the right they deserved. Based on the survey results, the author proposed several ways to enhance consumers’ right, and to increase management performance for the electricity industries. Finally, this study provided the amendment suggestion for the law and offered methods to increase customers' satisfaction and demand for Taipower and releted industries. Basically, this paper is a practical study and can be applied in current circumstance. The researcher transfers the consumers and electricity industries’ problems into the research topic, and then analyzes with academic theories. Finally, according to the analysis, the author provides the consumers and electricity industries a further reference regarding management pattern and also offers the cosumers a favorable electricity value.

MSc (Statistics)
Department of Statistics
Daily peak electricity data from ESKOM, South African power utility company for the period, January 1997 to December 2013 consisting of 6209 observations were used in this dissertation. Since 1994, the increased electricity demand has led to sustainability issues in South Africa. In addition, the electricity demand continues to rise everyday due to a variety of driving factors. Considering this, if the electricity generating capacity in South Africa does not show potential signs of meeting the country’s demands in the subsequent years, this may have a significant impact on the national grid causing it to operate in a risky and vulnerable state, leading to disturbances, such as load shedding as experienced during the past few years. In particular, it is of greater interest to have sufficient information about the extreme value of the stochastic load process in time for proper planning, designing the generation and distribution system, and the storage devices as these would ensure efficiency in the electrical energy in order to maintain discipline in the grid systems. More importantly, electricity is an important commodity used mainly as a source of energy in industrial, residential and commercial sectors. Effective monitoring of electricity demand is of great importance because demand that exceeds maximum power generated will lead to power outage and load shedding. It is in the light of this that the study seeks to assess the frequency of occurrence of extreme peak electricity demand in order to come up with a full electricity demand distribution capable of managing uncertainties in the grid system. In order to achieve stationarity in the daily peak electricity demand (DPED), we apply a penalized regression cubic smoothing spline to ensure the data is non-linearly detrended. The R package “evmix” is used to estimate the thresholds using the bounded corrected kernel density plot. The non-linear detrended datasets were divided into summer, spring, winter and autumn according to the calender dates in the Southern Hemisphere for frequency analysis. The data is declustered using Ferro and Segers automatic declustering method. The cluster maxima is extracted using the R package “evd”. We fit Poisson GPD and stationary point process to the cluster maxima and the intensity function of the point process which measures the frequency of occurrence of the daily peak electricity demand per year is calculated for each dataset. The formal goodness-of-fit test based on Cramer-Von Mises statistics and Anderson-Darling statistics supported the null hypothesis that each dataset follow Poisson GPD (σ, ξ) at 5 percent level of significance. The modelling framework, which is easily extensible to other peak load parameters, is based on the assumption that peak power follows a Poisson process. The parameters of the developed i models were estimated using the Maximum Likelihood. The usual asymptotic properties underlying the Poisson GPD were satisfied by the model.
NRF

Preferencje konsumentów wobec zarządzania popytem na energię elektryczną Bernadeta Gołębiowska Streszczenie Rozprawa doktorska podejmuje problematykę zarządzania popytem na energię elektryczną (ang. Demand Side Management – DSM) w gospodarstwach domowych w Polsce. Przedstawia wyniki badań preferencji konsumentów wobec programów DSM oraz weryfikuje hipotezy o wpływie czynników psychologicznych na akceptację kontroli popytu. Zmiana klimatu prowadząca do zachwiania równowagi ekosystemu, wraz z rosnącymi potrzebami energetycznymi, skłania do poszukiwania nowych możliwości poprawy efektywności energetycznej. Systemy elektroenergetyczne na świecie nie zawsze są w stanie pokryć zapotrzebowanie na energię elektryczną a inwestycje w nowe moce wytwórcze i zwiększanie produkcji pociągają za sobą negatywne efekty zewnętrzne. Produkcja energii w Polsce opiera się głównie na spalaniu węgla, co powoduje zwiększanie emisji gazów cieplarnianych i przyczynia się do zmiany klimatu. Poza tym występowanie wysokiego obciążenia w godzinach szczytu utrudnia bilansowanie popytu i podaży. Propozycją, która może zapewnić bezpieczeństwo systemu i jednocześnie sprzyja realizacji celów klimatycznych, jest kontrola popytu – DSM. DSM pozwala wpływać na popyt na energię elektryczną, zmienia profile zużycia konsumentów za pomocą środków zachęcających do zmiany zachowania. DSM wykorzystuje programy motywujące do redukcji/przesunięcia w czasie zużycia energii, np. taryfy wielostrefowe, kontrolę urządzeń elektrycznych. Rozwój DSM w Polsce może przynieść znaczące zyski gospodarcze dla całego kraju, wynikające z ograniczenia strat przesyłowych, ograniczenia szczytowej mocy w systemie, uniknięcia poniesienia kosztów inwestycji. Mimo tak istotnego znaczenia DSM dla systemu elektroenergetycznego niektóre zagadnienia nie zostały w literaturze szczegółowo omówione i zbadane. Celem pracy doktorskiej jest uzupełnienie istniejącej luki – zbadanie preferencji polskich gospodarstw domowych wobec programów DSM oraz wpływu norm społecznych na ich akceptację. Skuteczność DSM zależy od czynników indywidualnych, takich jak normy, przekonania konsumenta, wartości, które warunkują podatność na stosowane bodźce. Analiza wpływu tych czynników umożliwia bardziej efektywne projektowanie programów DSM i przewidywanie reakcji konsumentów. Podstawą części empirycznej pracy jest internetowe badanie ankietowe (kwestionariusz + eksperyment) przeprowadzone na reprezentatywnej próbie 1000 osób mieszkających w Polsce. Z uwagi na innowacyjny charakter DSM w Polsce, zbadano świadomość energetyczną konsumentów, która ma kluczowe znaczenie we wczesnych fazach wdrażania projektów. Rozprawa doktorska składa się ze zbioru czterech prac naukowych opublikowanych w latach 2020-2021. Pierwszy artykuł to przegląd literatury na temat psychologicznych aspektów zarządzania popytem na energię elektryczną. Ukazywane w literaturze badania nt. DSM wymagają podejścia interdyscyplinarnego, które umożliwi analizę zachowania konsumentów z uwzględnieniem wielu czynników. Tymczasem w artykułach naukowych obserwuje się podziały dyscyplinarne. Zaprezentowany przegląd literatury łączy różne dziedziny nauki, uwzględnia teorię Wartości-Przekonania-Normy (ang. value-belief-norm theory), teorię aktywizacji norm Schwartza, teorię planowanego zachowania, model faz działania Rubicon, wpływ informacji zwrotnej. Ekonomiści behawioralni zwracają uwagę na ograniczenia ludzkiego umysłu i kwestionują założenie o racjonalności podejmowania decyzji. Konsumpcja energii elektrycznej stanowi przykład zachowania uwarunkowanego wieloma czynnikami a założenie o racjonalnych wyborach często nie zostaje spełnione. W przypadku projektów DSM kluczowym aspektem jest świadomość konsumentów, która jest tematem drugiego artykułu. Zaprojektowano narzędzie dla określenia poziomu świadomości energetycznej Polaków na podstawie badań prowadzonych za granicą (Blasch, Boogen, Filippini & Kumar, 2017). Do tej pory nie prowadzono badań sprawdzających wiedzę oraz przekonania składające się na świadomość energetyczną. Dodatkowo, zbadano związek pomiędzy świadomością energetyczną a normami. W gospodarstwach domowych w Polsce zaobserwowano stosunkowo niską świadomość energetyczną (mierzoną znajomością cen energii oraz skutków konsumpcji dla środowiska). Polacy nie znają dobrze cen za zużycie energii elektrycznej, ani nie mają dobrej orientacji co do kosztów korzystania ze sprzętu pobierającego energię. Używanie energii elektrycznej może mieć charakter automatyczny, ponieważ towarzyszy codziennym nawykom, np. gotowaniu, sprzątaniu. Wielkość zużycia energii nie jest widzialna, stąd część konsumentów jej nie kontroluje (por. Kowalska-Pyzalska i Byrka, 2019). Przyczynia się to do obserwowanej luki efektywności energetycznej – gospodarstwa domowe marnują część energii lub nie inwestują w energooszczędne urządzenia, nie wykorzystując możliwości oszczędzania (Abrardi, 2019). Z drugiej strony wykazano, że Polacy mają przekonanie co do pozytywnych skutków świadomej konsumpcji oraz zdają sobie sprawę z zagrożeń wynikających ze zmiany klimatu. Zwiększenie świadomości energetycznej w gospodarstwach domowych poprzez wpływ na rzeczywiste wybory konsumpcyjne jest ważnym elementem programów DSM w Polsce. W trzecim artykule przedstawiono wyniki badania preferencji konsumentów wobec programów DSM. Wykorzystano metodę wyboru warunkowego (z ang. choice experiment – CE) do analizy wyborów między różnymi umowami dotyczącymi energii elektrycznej, zakładającymi zarządzanie popytem. Głównym celem badania było oszacowanie wartości dyskomfortu w wyniku zewnętrznej kontroli zużycia energii elektrycznej i ujawniania informacji o zużyciu. Wyniki eksperymentu pozwoliły na estymację rekompensaty potrzebnej, aby ludzie zaakceptowali zewnętrzne sterowanie energią elektryczną. Dodatkowo zbadano wpływ porównania społecznego między zużyciem energii w gospodarstwie domowym a średnią w powiecie respondenta na akceptację DSM. Wbrew oczekiwaniom respondenci, którzy stwierdzili, że zużywają więcej energii elektrycznej niż przeciętna osoba w powiecie, wymagają wyższego wynagrodzenia, aby zaakceptować DSM. Badania na temat preferencji wobec DSM zostały pogłębione i opisane w czwartym artykule. Praca miała na celu zbadanie wpływu norm i motywacji finansowej na wybory konsumentów. Teoria planowanego zachowania Ajzena dostarczyła teoretycznych ram badania. W analizie ilościowej posłużono się modelem hybrydowym (ang. hybrid choice model). Osoby z wyższymi opisowymi normami społecznymi dotyczącymi zużycia energii elektrycznej były mniej wrażliwe na poziom rekompensaty za akceptację DSM. Osoby chętne do podpisania umowy z powodów finansowych były mniej wrażliwe na zewnętrzną kontrolę zużycia energii elektrycznej i mniej skłonne do wyboru opcji status quo. Rezultaty badań sugerują, że zachęty finansowe mogą zmniejszyć wpływ norm społecznych. Rozprawa doktorska dostarcza nowych empirycznych wyników, które mogą stanowić podstawę do projektowania DSM w Polsce. Artykuły wchodzące w skład pracy opisują, jakie czynniki indywidualne wpływają na zachowanie konsumentów w zakresie zużycia energii elektrycznej, tym samym stanowiąc źródło informacji dla decydentów i podstawę przyszłych analiz. Słowa kluczowe: energia elektryczna, metoda wyboru warunkowego, preferencje, zarządzanie popytem Dziedzina pracy (kody wg programu Socrates-Erasmus): Ekonomia (14300) Kody klasyfikacji JEL: C25, D19, D91, Q41, Q48
The doctoral dissertation deals with the Demand Side Management (DSM) in households in Poland. The thesis describes the results of the study on consumer preferences towards DSM programs and verifies hypotheses about the influence of psychological factors on the acceptance of demand control. Climate change, leading to imbalance in the ecosystem, along with growing energy needs, prompts a search for new solutions to improve energy efficiency. Power systems all over the world are not always able to meet the demand for electricity and investments in new production capacity and production increase negative externalities. Energy production in Poland is based mainly on coal, which increases greenhouse gas emissions and contributes to climate change. In addition, the presence of peak load times makes it difficult to balance supply and demand. Demand control through DSM is a proposal that can ensure the security of the system and helps to achieve climate goals at the same time. DSM makes it possible to influence the demand for electricity, it modifies the consumption profiles of consumers by means of incentives to change their behavior. DSM use incentive programs to reduce/shift energy consumption, e.g. multi-part tariffs, control of electrical appliances. The development of DSM in Poland may bring significant economic gains for the entire country, resulting from the reduction of transmission losses, reduction of a peak load in the system, and avoiding investment costs. Despite the importance of DSM for the energy system, some issues have not been discussed and examined in detail in the literature. The aim of the doctoral dissertation is to fill the existing gap – to examine the preferences of Polish households towards DSM programs and the impact of social norms on their acceptance. The effectiveness of DSM depends on individual factors, such as social norms, consumer beliefs, and values that determine the vulnerability to the stimuli used. By analyzing the impact of these factors, it is possible to predict consumer reactions and design DSM programs more efficiently. The empirical part of the dissertation was based on an online survey (questionnaire + experiment) conducted on a representative sample of 1,000 Poles. Due to the innovative nature of DSM, the energy literacy of Poles, which is of key importance in the early stages of project implementation, was examined. 4 The doctoral dissertation consists of a collection of four scientific publications published in 2020-2021. The first article is a literature review on the psychological aspects of electricity demand management. The research on DSM presented in the literature requires an interdisciplinary approach that will enable the analysis of consumer behavior taking into account many factors. Meanwhile, disciplinary divisions are observed in scientific articles. The presented review of the literature combines various fields of science, takes into account the value-belief-norms theory, the norm activation model of Schwartz, the theory of planned behavior, the Rubicon model of action phases, and the influence of feedback. Behavioral economists point to the limitations of the human mind and question the assumption of rational decisions. Electricity consumption is an example of behavior influenced by many factors, and the assumption of rational choices is often not met. In the case of DSM projects, the key aspect is consumer awareness, which is the subject of the second article. A tool was designed to determine the level of energy literacy of Poles based on research conducted abroad (Blasch, Boogen, Filippini & Kumar, 2017). So far, no research has been conducted to test the knowledge and beliefs that make up energy literacy. Additionally, the relationship between energy literacy and norms was investigated. Relatively low energy literacy was observed in households in Poland (measured by the knowledge of energy prices and the effects of consumption on the environment). Poles do not know well the prices of electricity consumption, nor are they well aware of the costs of using energy-consuming equipment. The use of electricity may be automatic, as it accompanies everyday habits, e.g. cooking, cleaning. The amount of energy consumption is not visible, so some consumers do not control it (see Kowalska-Pyzalska and Byrka, 2019). This contributes to the observed energy efficiency gap – households waste some of the energy or do not invest in energy-saving appliances, do not take advantage of the savings opportunities (Abrardi, 2019). On the other hand, it has been shown that Poles are convinced about the positive effects of conscious consumption and that they are aware of the dangers of climate change. Increasing the energy literacy in households, by influencing actual consumption choices, is an important element of DSM programs in Poland. The third article presents the study of consumer preferences towards DSM programs. The Choice Experiment (CE) method was used to analyze the choices between different electricity contracts involving demand management. The main objective of the study was to estimate the value of discomfort from external control of electricity consumption and sharing consumption information. The results of the experiment made it possible to estimate the compensation people need to accept the external control of electricity. Additionally, the impact of the social 5 comparison (between household energy consumption and the respondent's “powiat” average) on DSM acceptance was examined. Contrary to the expectations, the respondents who said that they use more electricity than the average person in the “powiat” require a higher compensation to accept DSM. The research on the preference for DSM has been deepened and described in the fourth article. The study was aimed at examining the impact of social norms and financial motivation on consumer choices. Ajzen's theory of planned behavior provided a theoretical framework for the study. In the quantitative analysis, the hybrid choice model was used. People with higher descriptive social norms for electricity consumption were less sensitive to the level of compensation for accepting DSM. People willing to sign a contract for financial reasons were less sensitive to external control of electricity consumption and less likely to choose the status quo option. The results suggest that financial incentives can reduce the impact of social norms. The doctoral dissertation provides new empirical results that may form the basis for designing DSM in Poland. The articles included in the work describe individual factors which influence consumer behavior in terms of electricity consumption, thus constituting a source of information for decision-makers and the base for future research. Keywords: choice experiment, Demand Side Management, electric power, preferences JEL classification codes: C25, D19, D91, Q41, Q48

Kenntnisse über das prinzipielle Verhalten der Lasten und deren Benutzung durch die Endabnehmer sind im Wesentlichen vorhanden. Viele der aktuell notwendigen Untersuchungen benötigen jedoch Zeitreihen elektrischer Lasten, sogenannte Lastgänge. Mit der Synthese von Zeitreihen elektrischer Lasten können unter Berücksichtigung verschiedenster Anforderungen Lastgänge aufgebaut werden, wobei in dieser Arbeit der Fokus auf Haushaltsabnehmer liegt. Wichtige Eingangsdaten für die Lastgangsynthese sind die technischen Kenngrößen der elektrischen Geräte und die sozialen Kennzahlen zur Benutzung der Geräte durch die Endabnehmer. Anhand dieser Eingangsdaten wird die Lastgangsynthese durchgeführt und werden Anwendungsbeispiele dargestellt. Die Entwicklung von klassischen Versorgungsnetzen hin zu aktiven Verteilungsnetzen ist bedingt durch neue Verbraucher, wie Wärmepumpen, Elektroautos, sowie vielen dezentralen Erzeugungsanlagen. Speziell die fluktuierende Einspeisung durch Photovoltaik-Anlagen ist Anlass zur Forderung nach einem Verbrauchs- und Lastmanagement. Mit dem Verbrauchsmanagement wird die Last an die Einspeisung angepasst und das Lastmanagement berücksichtigt zusätzlich die Versorgungssituation des Netzes. Für die Lastgangsynthese werden die Haushaltsgeräte in fünf Geräteklassen unterteilt, für die spezifische Kennzahlen aus technischer und sozialer Sicht angegeben werden. Diese Kennzahlen sind Leistung pro Gerät oder Energieverbrauch pro Nutzung sowie Ausstattungsgrade, Benutzungshäufigkeiten und Zeiten für das Ein- und Ausschalten der Geräte. Damit wird ein neuer Ansatz gewählt, welcher nicht mehr auf die detaillierte Beschreibung des Bewohnerverhaltens beruht, da die Datenbereitstellung dafür äußerst schwierig war und ist. Vorzugsweise in Niederspannungsnetzen sind mit synthetischen Zeitreihen umfangreiche und umfassende Untersuchungen realisierbar. Es gibt verschiedenste Möglichkeiten, die Zeitreihen zusammenzustellen. Mit Lastgängen je Außenleiter können beispielsweise unsymmetrische Zustände der Netze analysiert werden. Zudem können auch Lastgänge für Geräte bzw. Gerätegruppen erstellt werden, welche für Potenzialanalysen des Verbrauchsmanagement essenziell sind. Der wesentliche Unterschied besteht darin, dass viele Berechnungen nicht mehr auf deterministische Extremwerte beruhen, sondern die stochastischen Eigenschaften der Endabnehmer mit den resultierenden Lastgängen berücksichtigt werden.
Distributed generation and novel loads such as electric vehicles and heat pumps require the development towards active distribution networks. Load curves are needed for the appropriate design process. This thesis presents a feasible and expandable synthesis of load curves, which is performed exemplary on residential customers with a period under review of 1 year and time steps of as little as 30 s. The data is collected for up-to-date appliances and current statics examining the way of life. The main focus lies on the input data for the synthesis and distinguishes between technical and social factors. Some thirty home appliances have been analyzed and are classified into five appliance classes by incorporating switching operations and power consumptions. The active power is the key figure for the technical perspective and the data is derived from manufacturer information. For the social perspective six different customer types are defined. They differ in sizes of household and housekeeping. The social key figures are appliance penetration rate and depending on the appliance class the turn-on time, turn-off time, operating duration or cycle duration. The elaborated two-stage synthesis is efficiently implemented in Matlab®. First, artificial load curves are created for each appliance of the households under consideration of the appliance class. In the second step, the individual load curves of the appliances are combined to load curves per line conductor. The algorithms have been validated in the implementation process by retracing the input data in the load curves. Also, the feasibility of the results is shown by comparing the key figures maximum load and power consumption to data in literature. The generated load curves allow for unsymmetrical calculations of distribution systems and can be used for probabilistic investigations of the charging of electric vehicles, the sizing of thermal storage combined with heat pumps or the integration of battery storage systems. A main advantage is the possibility to estimate the likelihood of operating conditions. The enhancement to further appliances and the changeability of the input data allows for versatile further possible investigations.