Добірка наукової літератури з теми "Management of consumer demand for electric power"

The approaches to demand management by active consumers through forming a given schedule of power consumption in the required period of time based on the solution of the optimization problem in the form of maximizing the power of connected controlled electric receivers of various types are presented. The model of demand management by active consumers is justified, taking into account the following factors: load sensitivity for connecting a transformer substation to a change in consumer load; load priority; consistent load reduction levels with flexible performance and power control; permissible set of electric receivers in accordance with the technological process, network schedule, other logical conditions corresponding to adjacency lists. An algorithm has been developed for limiting power on the part of active consumers based on the widespread use of digital data processing technologies, modern technical means of measurement, control and switching of end consumers in real time. The presented research results indicate the validity of the demand management method by active consumers in the normal mode of intelligent electric power systems and the possibility of its practical implementation in an industrial enterprise with reference to the technological process.

The approaches to demand management by active consumers through forming a given schedule of power consumption in the required period of time based on the solution of the optimization problem in the form of maximizing the power of connected controlled electric receivers of various types are presented. The model of demand management by active consumers is justified, taking into account the following factors: load sensitivity for connecting a transformer substation to a change in consumer load; load priority; consistent load reduction levels with flexible performance and power control; permissible set of electric receivers in accordance with the technological process, network schedule, other logical conditions corresponding to adjacency lists. An algorithm has been developed for limiting power on the part of active consumers based on the widespread use of digital data processing technologies, modern technical means of measurement, control and switching of end consumers in real time. The presented research results indicate the validity of the demand management method by active consumers in the normal mode of intelligent electric power systems and the possibility of its practical implementation in an industrial enterprise with reference to the technological process.

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

In this paper is presented an algorithm for the power management of a smart home with electric vehicle and photovoltaic panels. The case study is performed considering the power demand of the household appliances, the charging/discharging of the electric vehicle, and the power supplied by the photovoltaic panels. The photovoltaic panels have a small installed power and benefit from the support scheme from the government for these types of generation sources, so it is a prosumer. The simulation is performed for a day. Additionally, the cost of power supplied to the consumer is also analyzed.

The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.

The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.

The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.

Electric power supply is one of the most important aspects of China’s national energy development strategy (NEDS). As major economic unit as well as major energy consumer, Jiangsu province is facing serious energy supply challenges. Under such circumstances, positive actions are taken by local government in respond to NEDS which put energy saving in the first place, and demand side management (DSM) is implemented. DSM is an important measure which can release rush hour electric supply pressure, enhance energy efficiency, optimize electric power utilization, and it is beneficial for sustainable development. This paper is based on the analysis of the current DSM situation of Nanjing, and green lighting is taken as an example. An empirical analysis is given to cost-effectiveness of the implementation of green lighting. Finally, the conclusion that the cost-effectiveness of the implementation of green lighting is economically viable to power supply companies, electric customers and the whole society is drawn.

Intelligent smart grid system undertakes electricity demand in a sustainable, reliable, economical and environmentally friendly manner. As smart grid involves, it has the liability of meeting the changing consumer needs on the day-to-day basis. Modern energy consumers like to vivaciously regulate their consumption patterns more competently and intelligently than current provided ways. To fulfill the consumers’ needs, smart meters and sensors make the grid infrastructure more efficient and resilient in energy data collection and management even with the ever-changing renewable power generation. Though cloud acts as an outlet for the energy consumers to retrieve energy data from the grid, the information systems available are technically constrained and not user-friendly. Hence, a simple technology enabled utility-consumer interactive information system in the form of a dashboard is presented to cater the electric consumer needs.

The Home Energy Management System (HEMS) is a system for the efficient electric power consumption of each household. It can provide real-time electricity cost information according to electricity consumption, and households can immediately control their consumption of electricity. In this study, we analyzed the effects of the HEMS on the stability of demand for electric power. To do this, we analyzed the causal relationship between the amounts of electric power generation and consumption, from the system dynamics perspective. From the analysis, we found that in the current structure, the fluctuation of the quantity of demand became large due to the time delay in households recognizing the electric bill and adjusting their electric power consumption. However, when the HEMS was introduced, it could be seen that electric power demand remained stable since consumers could see their electricity bill in real-time and could manage their electricity consumption by themselves.

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.

With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.

With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.

Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.

This chapter presents a model that is developed to distribute the electrical power among the home appliances efficiently with a given capacity. This chapter works only on the consumer side demand management by designing admission control of the appliances. The authors have proposed an algorithm to schedule different appliances by considering three different cases. The simulation is carried out in MATLAB/Simulink. The results show that the appliances efficiently utilize the provided power by reducing the wastage in power consumption in all cases. Finally, consumers can control the operations of the appliances according to their requirements and the available capacity using IoT.

Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.

Many researchers have focused on the reduction of electricity usage in residences because it is a significant contributor of CO2 and greenhouse gases emissions. However, electricity conservation is a tedious task for residential users due to the lack of detailed electricity usage. Home energy management systems (HEMS) are schedulers that schedule and shift demands to improve the energy consumption on behalf of a consumer based on demand response. In this chapter, valuable sequence patterns from real appliances' usage datasets are extracted in peak time and off-peak time of weekdays and weekends to get valuable insight that is applicable in the HEMS. Generated data in smart cities and smart homes are placed in the category of big data. Therefore, to extract valuable information from such data an architecture for the home and city data processing system is proposed, which considers the multi-source smart cities and homes' data and big data processing platforms.

A growing number of manufacturing industries are initiating efforts to address sustainability issues. According to the National Association of Manufacturers, the manufacturing sector currently accounts for about one third of all energy consumed in the United States [1]. Reducing energy costs and pollution emissions involves many areas within an industrial facility. Peak electric demands are a significant component in the cost of electricity. Electric demand management relates to electric tariff rates, new power generation, and incentives to curtail peak usages. Shifting some equipment/machine usage to the periods of lower cost or using stand-by local generators during the peak demand period can yield important savings. Analysis of these options is important to decision makers to avoid unnecessary high cost of energy and equipment. This paper proposes a Decision-Guided energy management in manufacturing (DG-EMM) framework to perform what-if analysis and make optimal actionable recommendations for a manufacturing facility both on (1) operational energy management including load shedding, curtailment, and local generation and (2) planning and investment decisions for introducing renewable technologies. The DG-EMM is based on the novel technology of the Decision-Guidance Query Language (DGQL), which is a tool for fast development and iterative extension of decision-guidance and optimization solutions. The proposed DG-EMM will support user-defined objectives for optimal recommendations, such as minimizing emissions and energy costs and maximizing Return on Investment (ROI). A case study of the peak demand control for an example manufacturing facility is discussed.

Demand-response programs offer a viable solution for improving the grid efficiency and reliability though the shaping of the consumer’s power demand. For the customers to fully benefit from varying electricity prices, an energy management strategy that coordinates the electrical loads is required. In this framework, this paper uses a Nonlinear Model Predictive Control (MPC) strategy to solve the coupled problem of optimally scheduling home appliances, Heating, Ventilation and Air Conditioning (HVAC) system and controlling electric vehicle charging. Simulation results are presented on selected case studies to demonstrate the ability of the Particle Swarm Optimization (PSO) to solve the optimization problem for a single home faster than real-time. Results show that this strategy is always able to provide near-optimal solutions with limited computation time and no reconfiguration of the control scheme for applications to houses equipped with different technologies.

Abstract Recent surge in demand for wearable technology products such as activity tracking smartwatches, and for medical devices has necessitated development of flexible secondary lithium ion batteries which also possess high capacity, robustness and thin form factors. Oftentimes, these power sources are only charged up to a partial state of charge (SoC) before use (shallow charge). Their usage continues until the SoC reaches almost zero, after which they are recharged again. Nowadays, the ‘fast-charge ‘feature used to charge the battery at higher C-rates, is a necessity in consumer electronics rather than an amenity. Also, in everyday use, these batteries are exposed to higher-than-ambient temperatures due to perpetual human body contact and also to the high temperatures resulting from poor thermal management in compact devices. This study investigates the compounded influence of partial charge, high temperatures and high C-rates on the capacity degradation of a flexible Li-ion power source subjected to accelerated life testing. The battery current and terminal voltage were logged for multiple charge-discharge cycles and were used to compute the battery capacity and energy efficiency. Finally, a regression model based on several parameters was developed to estimate the battery capacity as a function of the cycle number.

Abstract A key approach to large renewable energy sources (RES) power management is based on implementing storage technologies, including batteries, power-to-hydrogen (P2H), pumped-hydro, and compressed air energy storage. Power-to-hydrogen presents specific advantages in terms of suitability for large-scale and long-term energy storage as well as capability to decarbonize a wide range of end-use sectors, e.g., including both power generation and mobility. This work applies a multi-nodal model for the hourly simulation of the energy system at a nation scale, integrating the power, transport, and natural gas sectors. Three main infrastructures are considered: (i) the power grid, characterized by instantaneous supply-demand balance and featuring a variety of storage options; (ii) the natural gas network, which can host a variable hydrogen content, supplying NG-H2 blends to the final consumers; (iii) the hydrogen production, storage, and re-electrification facilities. The aim of the work is to assess the role that can be played by gas turbine-based combined cycles in the future high-RES electric grid. Combined cycles (GTCCs) would exploit hydrogen generated by P2H implementation at large scale, transported through the natural gas infrastructure at increasingly admixed fractions, thus closing the power-to-power (P2P) conversion of excess renewables and becoming a strategic asset for future grid balancing applications. A long-term scenario of the Italian energy system is analyzed, involving a massive increase of intermittent RES power generation capacity and a significant introduction of low-emission vehicles based on electric drivetrains (pure-battery or fuel-cell). The analysis highlights the role of hydrogen as clean energy vector, not only for specific use in new applications like fuel cell vehicles and stationary fuel cells, but also for substitution of fossil fuels in conventional combustion devices. The study also explores the option of repowering the combined cycles at current sites and evaluates the effect of inter-zonal limits on power and hydrogen exchange. Moreover, results include the evaluation of the required hydrogen storage size, distributed at regional scale or in correspondence of the power plant sites. Results show that when extra hydrogen generated by P2H is fed to GTCCs, up to 17–24% H2 use is achieved, reaching up to 70–100% in southern regions, with a parallel reduction in fossil NG input and CO2 emissions of the GTCC plants.

Abstract The management of end-use energy loads, including commercial buildings, has been increasingly investigated as a promising source of services for the electric power grid. Lighting consumes about 17% of the total electricity use of U.S. commercial buildings; however, it may contribute significantly to services that improve the reliability and resilience of the grid due to its rapid speed of response. Connected lighting systems (CLS), which build upon solid-state light-emitting diode (LED) technology, can change their power demand more quickly than most other building electricity end-uses. But the potential of CLS to provide grid services has not been fully investigated. In this paper, we describe initial research to evaluate the potential of CLS for providing frequency regulation grid service. Frequency regulation is a reliability service that corrects in a matter of seconds for short-term changes in the balance between supply and demand that might affect the stability of the power system in a specific balancing area. Frequency regulation signals for a medium office building are generated from the normalized test signals for the PJM Regional Transmission Organization Reg-A and Reg-D regulations services. CLS are controlled in simulations to follow the Reg-A or Reg-D signal and thereby provide frequency regulation service. The performance of CLS providing frequency regulation is evaluated using the PJM 40-Minute Performance Score Template. The performance scores obtained for five different CLS categories responding to both Reg-A and Reg-D signals far exceed the minimum qualification score, a very promising result for CLS aiming to provide frequency regulation service.

Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices and consumer appetite for better products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste management in Africa. This report provides an overview of constitutes e-waste, the environmental and health impacts of e-waste, of the barriers to effective e-waste management, the opportunities associated with effective e-waste management and of the limited literature available that estimate future volumes of e-waste. Africa generated a total of 2.9 million Mt of e-waste, or 2.5 kg per capita, the lowest regional rate in the world. Africa’s e-waste is the product of Local and imported Sources of Used Electronic and Electrical Equipment (UEEE). Challenges in e-waste management in Africa are exacerbated by a lack of awareness, environmental legislation and limited financial resources. Proper disposal of e-waste requires training and investment in recycling and management technology as improper processing can have severe environmental and health effects. In Africa, thirteen countries have been identified as having a national e-waste legislation/policy.. The main barriers to effective e-waste management include: Insufficient legislative frameworks and government agencies’ lack of capacity to enforce regulations, Infrastructure, Operating standards and transparency, illegal imports, Security, Data gaps, Trust, Informality and Costs. Aspirations associated with energy transition and net zero are laudable, products associated with these goals can become major contributors to the e-waste challenge. The necessary wind turbines, solar panels, electric car batteries, and other "green" technologies require vast amounts of resources. Further to this, at the end of their lifetime, they can pose environmental hazards. An example of e-waste associated with energy transitions can be gleaned from the solar power sector. Different types of solar power cells need to undergo different treatments (mechanical, thermal, chemical) depending on type to recover the valuable metals contained. Similar issues apply to waste associated with other energy transition technologies. Although e-waste contains toxic and hazardous metals such as barium and mercury among others, it also contains non-ferrous metals such as copper, aluminium and precious metals such as gold and copper, which if recycled could have a value exceeding 55 billion euros. There thus exists an opportunity to convert existing e-waste challenges into an economic opportunity.

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy - ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications.