Дисертації з теми "Consumers' 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.

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.

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.

Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains viii, 75 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 74-75).

An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 33-35). Also available in electronic version. Access restricted to campus users.

The threat of climate change has led to many countries and regions adopting renewable targets. Scotland's is one of the most ambitious, with an aim to generate the equivalent to 100% of its electricity needs from renewable sources by 2020. Scotland has a very large renewable resource, primarily consisting of offshore and onshore wind, tidal stream, wave and hydro power; all of which are characterised by having variable output. Over a long period, such as a year, variability in renewable generation will average out and may meet or exceed total annual demand. This thesis investigates whether matching of demand and generation is possible within a timescale meaningful to electricity system users; that is whether renewable generation can meet electricity demand in any given hour. This was established by using historic data to create an hourly generation hindcast of Scotland's renewable generation over a 30 year climate “normal” period. These outputs are then compared to a hindcast of hourly demand based on observations over the most recent few years. The results demonstrated that it is possible for Scotland to rely entirely upon renewable generation to fulfil demand in any hour of the 30 year hindcast. However, it appears that the renewable capacity and storage currently built or within the Scottish planning system is only sufficient to match demand in 65% of the hours within the hindcast. The hindcast allows judgements to be made as to how 100% of demand could be met most effectively and provides the basis of a coherent planning strategy, with security of supply at its centre. Further wave and tidal stream capacity is shown to be of higher value than additional wind power but in the latter case, addressing the geographical diversity of wind power can enable maximise phasing between sites to increase the security of supply. Importantly this work provides a means of informing decision making about where best to develop wind, wave and tidal resources and what additional storage may be required in order to provide 100% security of supply. The results are also of particular importance to Scotland's renewable generation strategy in the case of assessing where new on- and off-shore wind farms should be developed, as wind is set to dominate Scotland's renewable portfolio for the foreseeable future.

The ability for domestic consumers to provide demand response to dynamic electricity pricing will become increasingly valuable for integrating the high penetrations of renewables that are expected to be connected to electricity networks in the future. The aim of this thesis is to investigate whether domestic consumers will be willing and able to provide demand response in such low-carbon futures. A broad approach is presented in this thesis, with research contributions on subjects including data privacy, behavioural economics, and battery modelling. The principle argument of the thesis is that studying the behaviour of consumers with grid-connected photovoltaic ('PV') systems can provide insight into how consumers might respond to dynamic pricing in future low-carbon power systems, as both experience irregular electricity prices that are correlated with intermittent renewable generation. Through a combination of statistical and qualitative methods, this thesis investigates the demand response behaviour of consumers with PV systems in the UK. The results demonstrate that these consumers exhibit demand response behaviour by increasing demand during the day and decreasing demand during the evening. Furthermore, this effect is more pronounced on days with higher irradiance. The results are novel in three ways. First, they provide quantified evidence that suggests that domestic consumers with PV systems engage in demand response behaviour. Second, they provide evidence of domestic consumers responding to irregular electricity prices that are correlated with intermittent renewable generation, thereby addressing the aim of this thesis, and supporting the assumption that consumers can be expected to respond to dynamic pricing in future markets with high penetrations of renewables. Third, they provide evidence of domestic consumers responding to dynamic pricing that is similar to real-time pricing, while prior evidence of this is rare and confined to the USA.

Kyoto University (京都大学)
0048
新制・課程博士
博士(エネルギー科学)
甲第21385号
エネ博第373号
新制||エネ||73(附属図書館)
京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻
(主査)教授 手塚 哲央, 教授 下田 宏, 准教授 MCLELLAN,Benjamin
学位規則第4条第1項該当

京都大学
0048
新制・課程博士
博士(エネルギー科学)
甲第21385号
エネ博第373号
新制||エネ||73(附属図書館)
京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻
(主査)教授 手塚 哲央, 教授 下田 宏, 准教授 MCLELLAN,Benjamin
学位規則第4条第1項該当
Doctor of Energy Science
Kyoto University
DFAM

The electricity market in the South African economy uses specialised instruments in forecast-ing the energy load to be delivered. The current status quo operates with several forecasters from different offices, departments or businesses predicting for different purposes. This be-comes a challenge to derive a consolidated forecast. This study has attempted to develop a consolidating instrument that will merge all the forecasts from different offices, departments or businesses into one so-called ‘official forecast’. Such an instrument should be able to predict with accuracy the anticipated usage or demand. Article [18] examined patterns across G7 countries and forecasters to establish whether the present bias reflects the inefficient use of information, or whether it reflects a rational re- sponse to financial, reputation and other incentives operating for forecasters. This bias is particularly true for any electricity utility as forecasting is undertaken by different divisions; therefore each division has its own incentives. For instance, the generation division will tend to overstate their forecasts so as that there is no possibility of a shortage, whereas distri- bution (sales) might understate so as to give the impression of being profitable when more units are sold to consumers. Thus, the study attempts to rectify this bias by employing statistical tools in consolidating these forecasts. The results presented in this paper propose a newly developed procedure of consolidating energy demand forecasts from different users and accounting for different time horizons. Predicting for the short-term and long-term involves different measuring tools, which is one aspect of prediction this paper tackles.

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

Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.

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).

In this study, we use a nonparametric technique, locally weighted robust least squares regression (LOESS), to forecast a 24 hour demand profile at the household level and compare it to existing aggregate demand models discussed in literature. Of these aggregate demand models, a quadratic autoregressive model was selected to be used as a basis for comparison with the LOESS forecasts. It was our goal to automate the forecasting process by using the goodness of fit metric, AICCI, for smoothing parameter selection. The statistical workflow was executed using SAS and data was provided by the Glasgow Electric Plant Board of Barren County, Kentucky. Results show that LOESS outperformed the autoregressive model in roughly 80% of all cases and than using LOESS alone or as part of an ensemble model is a feasible approach to automating future household demand profile for the purpose of generating different levels of power demand profile aggregation as needed by Glasgow Electronic Plant Board.

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.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.
Includes bibliographical references (v. 2, p. 311-316).
The electric power system in the US developed with the assumption of exogenous, inelastic demand. The resulting evolution of the power system reinforced this assumption as nearly all controls, monitors, and feedbacks were implemented on the supply side. Time invariant, averaged retail pricing was a natural extension of the assumption of exogenous demand and also reinforced this condition. As a result, the market designs and physical control of the system exclude active participation by consumers. Advances in information and communications technologies enable cost effective integration of demand response. Integrating demand into the US electricity system will allow the development of a more complete market and has the potential for large efficiency gains. Without feedbacks between supply and demand, attempts to develop competitive markets for electricity will suffer from a greater potential for market power and system failure. This thesis provides an analysis of the technical, regulatory, and market issues to determine a system structure that provides incentives for demand response. An integrated, dynamic simulation model is utilized to demonstrate the effects of large scale adoption of demand response technologies. The model includes distributed decision making by both consumers and investors in generation capacity, the effects of their decisions on market prices, and the feedbacks between them. Large scale adoption of demand response technology is simulated to quantify the potential benefits of responsive demand. The effects of technology improvement via learning, long term demand elasticity, and policies to promote adoption are considered.
(cont.) The simulations show that diminishing returns for adopters and free rider effects limit the attractiveness of individual adoption. A subsidy to alleviate the costs to adopters can be justified by the significant system level savings from widespread participation. Several pernicious effects can emerge from large scale demand response, however, including increased price volatility due to reductions in generation capacity reserve margin, increases in long term demand, and increased emissions from the substitution of peak generation capacity, such as natural gas and renewables by intermediate capacity. Significant rent transfers will also occur, and stakeholder analysis is conducted to determine interests and distributional effects of large scale demand response.
by Jason W. Black.
Ph.D.

Thesis (M.S.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains x, 137 p. : ill., map Vita. Includes abstract. Includes bibliographical references (p. 99-107).

With the transport sector switching to electric energy to reduce greenhouse gas emission, the supply and demand in the energy system are impacted by this transition. Meanwhile, there are not a lot of studies focus on the electrification of the vehicle fleet in Sweden. To fill up the knowledge gap, the paper aims to identify the total required electrical energy and power for the electrification of the vehicle fleet in Sweden. This includes switching passenger vehicles, light and heavy trucks, and buses to battery electric vehicles. An Electric Vehicle Power Demand Model is designed to answer the research question. It is a simplified model that can calculate energy consumption and power demand from an electric vehicle fleet. To simulate the charging schedule, four scenarios are created with differences in charge speed and the use of smart or unregulated charging. Based on the model, the electric vehicle fleet consumes 20.4 TWh of electricity per year, accounting for 14.7% of total demand in Sweden. Combing the vehicle fleet with other energy services, an average hourly peak load of 16.2 GW in summer and 24.3 in winter can be seen, while the available capacity in Sweden is around 27.1. The result indicates that the current Swedish energy system is capable of handling demand from charging the electric vehicle fleet in terms of power capacity for most times. However, undersupply may happen in some extreme condition during the winter due to higher consumption from other energy services. Furthermore, with the increasing share of renewable power in the system, the availability of these power plants can have a direct impact on the supply. This requires smart charging to shift the charging events to prevent peak hours, which can potentially decrease the peak loads up to 2 GW in EV charging demand during peak hours. However, the actual effect of it still requires more study. Lastly, the model created for the research can be used as a research or decision-making tool to estimate the impact of a group of electric vehicles in the future, therefore, contribute to the development of the sustainable energy transition.

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.

Electric hot water tanks play a pivotal role as demand response assets within the UK's energy system by storing heat when energy is inexpensive and delivering domestic hot water when it is required. This role will become increasingly important if non-dispatchable renewable energy sources are to play a bigger part in the energy mix. Historically, the design standards relating to hot water tanks have focused primarily on minimising heat losses. However, in addition to preserving energy, a hot water tank should preserve the availability of heat above a useful temperature for as long as possible to avoid energy usage during peak times when it is costly or carbon intensive. To do this, thermal stratification within hot water tanks must be promoted. Unfortunately, thermal stratification leads to conditions that are conducive to bacterial growth due to the hospitable temperatures that arise during operation. For this reason, question marks have arisen over the extent to which more flexible control strategies, designed to allow for increasing penetrations of intermittent renewable energy sources, might lead to the growth of pathogenic bacteria within hot water tanks. The objective of the work discussed in this thesis was to understand the extent to which there is a conflict between thermal stratification and bacterial growth in practice, whether this conflict can be resolved and the potential implications for electric hot water tanks operating on a time of use tariff. A small field study demonstrated that there is prolific bacterial growth within conventional electric cylinders and that this can be attributed to thermal stratification with a confidence of (P<0.01). Fitting a de-stratification pump, to enhance sanitary performance, resulted in a 19% decrease in the recovery of useable hot water above 43°C. Given that the tanks tested during the field study were made of copper, the consequences of alternative material choices on thermal performance were explored. It was found that the rate of useable hot water loss, due to de-stratification associated with thermal diffusion across the thermocline, could be reduced by a factor of 2.7 by making the tank liner wall from stainless steel instead of copper. Further numerical work indicated that this improvement in stratifying performance was most significant for small tanks with high aspect ratios. In addition to de-stratification that arises due to vertical conduction, de-stratification due to inlet mixing was reduced by up to 30% by installing a spiral diffuser into the base of a test cylinder. In addition, by lowering the immersion heating element to ensure there is sufficient heat transfer to the base of the cylinder, sterilising temperatures could be attained throughout the stored volume of water in the tank during heating. This showed that the conflict between thermal and sanitary performance within electric tanks could potentially be resolved. A bespoke tank, made from stainless steel and fitted with a diffuser, was built and subjected to typical draw cycles that reflect real world operation. These tests showed that more useable hot water could be delivered in comparison to a commercial off the shelf copper tank and consequently the utilisation of the Economy 7 time of use tariff would be enhanced.

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.

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.

In 1982, the Bonneville Power Administration (BPA), a marketer of hydroelectric power in the Pacific Northwest, found itself in a new role which required it to acquire power resources needed to meet the demands of the region's utilities. In particular, it had to deal with the Washington Public Power Supply System's nuclear plant cost escalations. In response, BPA prepared its first independent regional power forecast. The forecast development process was intricate and multidimensional and involved a variety of interested parties. Application of the Multiple Perspective Concept uncovers strengths and weaknesses in this process by illuminating its technical, organizational and personal dimensions. Examination of the forecast from the technical perspective revealed an elaborate set of interlinked models used to develop baseline, high, and low forecasts. The organizational perspective revealed BPA to be in a transitional stage. Internally, ratemaking, forecasting, conservation, resource acquisition, and financial management swelled as new organizational functions. Interorganizationally, environmentalists, ratepayer groups, and the region's utilities all had strong interests in the decision regarding WPPSS plants. The personal perspective revealed that each of the Administrators heading BPA since the early 1980s defined the agency's approach to the resource planning problem differently, first as an engineering problem, then as a political problem, and, finally, as a business problem. Taken together, the Multiple Perspectives yielded the following conclusions about BPA's 1982 forecast. (1) BPA's range forecast constituted a major improvement over the point forecasts preceding it, but left important classes of uncertainty unexplored. (2) BPA's models were better suited to address rate and conservation issues important at the time of the 1982 forecast than their predecessors. The model of the national economy, however, remained a black box, potentially significant feedbacks were not represented, and the sheer size of the modeling system placed practical limits on its use. (3) A stronger method of dealing with forecast uncertainty is needed which utilizes a disaster-avoidance strategy and plans for high impact/low probability events. This method need not involve the use of large models, but should incorporate qualitative insights from persons normally outside the technical sphere.

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.

The transportation sector is a largest emitter of greenhouse gases in the U.S., accounting for 28.6% of all 2016 emissions, the majority of which come from the passenger vehicle fleet [1,2]. One major technology that is being investigated by researchers, planners, and policy makers to help lower the emissions from the transportation sector is the plug-in electric vehicle (PEV). The focus of this work is to investigate and model the impacts of increased levels of PEVs on the regional electric power grid and on the net change in CO2 emissions due to the decrease tailpipe emissions and the increase in electricity generation under current emissions caps. The study scope includes all of New England and New York state, modeled as one system of electricity supply and demand, which includes the estimated 2030 baseline demand and the cur- rent generation capacity plus increased renewable capacity to meet state Renewable Portfolio Standard targets for 2030. The models presented here include fully electric vehicles and plug-in hybrids, public charging infrastructure scenarios, hourly charging demand, solar and wind generation and capacity factors, and real-world travel derived from the 2016-2017 National Household Travel Survey. We make certain assumptions, informed by the literature, with the goal of creating a modeling methodology to improve the estimation of hourly PEV charging demand for input into regional electric sector dispatch models. The methodology included novel stochastic processes, considered seasonal and weekday versus weekend differences in travel, and did not force the PEV battery state-of-charge to be full at any specific time of day. The results support the need for public charging infrastructure, specifically at workplaces, with the “work” infrastructure scenario shifting more of the unmanaged charging demand to daylight hours when solar generation could be utilized. Workplace charging accounted for 40% of all non-home charging demand in the scenario where charging infrastructure was “universally” available. Under the increased renewable fuel portfolio, the reduction in average CO2 emissions ranged from 90 to 92% for the vehicles converted from ICEV to PEV. The total emissions reduced for 15% PEV penetration and universally available charging infrastructure was 5.85 million metric tons, 5.27% of system-wide emissions. The results support the premise of plug-in electric vehicles being an important strategy for the reduction of CO2 emissions in our study region. Future investigation into the extent of reductions possible with both the optimization of charging schedules through pricing or other mechanisms and the modeling of grid level energy storage is warranted. Additional model development should include a sensitivity analysis of the PEV charging demand model parameters, and better data on the charging behavior of PEV owners as they continue to penetrate the market at higher rates.

Deep decarbonisation of road transportation is challenging. One of the most potentially beneficial approaches is electrification which is the subject of this PhD thesis. A widespread penetration of electric vehicles (EVs) across a large proportion of road transport demand is needed to realise the benefits of an electrified transport sector. However, this is dependent on overcoming significant barriers. This study performs a systematic analysis of how proven power charging technologies could be used to unlock the barriers to widespread electrification of road transportation. Various road transport sectors and type of journeys are explored including aspects of autonomous operations and novel wireless power transfer technologies. For each operation, a framework is proposed that allows the exploitation of current and potential future electrification technologies to enable shifting towards EVs. Based on that, simulation tools and methods are developed to calculate the power requirements of EVs and determine a suitable charging infrastructure. The additional power demand, electric load and the implications for the electricity supply network are explored. The total expenditure needed and the CO2 emission savings are also calculated for each investigated operation. Transitional strategies include the electrification of bus routes, refuse collection functions, home deliveries and aspects of autonomous operations for public transportation within the boundaries of the cities. In the long-term, focus is given on passenger cars and freight vehicles for both urban and inter-urban journeys. A nationwide adoption of all electrification strategies proposed in this thesis would increase the peak power demand of Great Britain by approximately 38 GW (72% of the current peak) and the electricity consumption by 180 TWh per year (45% of current consumption). The total capital cost required is calculated at £225 billion which is similar to the cost of other large infrastructure projects of the country. The impact would be a significant aggregate saving of approximately 2,000 MtCO2 between the numbers calculated for today's norms (2018) and those calculated for 2050.

Приведені характеристики об'єкту електропостачання, визначено електричні навантаження, параметри і характеристики електропостачання об'єкту. Виконано розрахунок і перевірка перерізу кабелів, уставок і вимикаючої здатності апаратів захисту. В результаті виконання цього дипломного проекту було спроектовано електропостачання адміністративно-торгівельного комплексу. Для живлення будівель, що входять в комплекс, використовуються трансформаторні підстанції різної потужності. Захист трансформаторів і кабельних ліній зовнішньої системи електропостачання здійснюється вакуумними вимикачами і запобіжниками. Проведено техніко-економічне обґрунтування вибору схеми електроживлення комплексу, а також розрахунок сумарних капіталовкладень при проектуванні електропостачання. Проаналізовано небезпечні і шкідливі чинники, що діють на працівників адміністративно-торгівельного комплексу. А також організація забезпечення засобами індивідуального захисту працівників і інших категорій населення в надзвичайних ситуаціях.

O objetivo deste trabalho é estimar as elasticidades-preço e renda da demanda residencial por energia elétrica no Brasil utilizando dados em painel. A heterogeneidade da economia brasileira faz com que existam diversos padrões de consumo residencial de energia elétrica e diferentes estruturas tarifárias entre as distribuidoras. Nesse sentido, este trabalho utiliza um banco de dados em painel formado por 63 distribuidoras de energia elétrica no Brasil, para o período 1999-2006. Isso permite controlar possíveis efeitos individuais não observáveis existentes entre as distribuidoras. Três métodos de estimação em painel foram aplicados: o Pooled OLS, o de Efeitos Fixos e o de Efeitos Aleatórios. Além disso, também foi testado se no período de racionamento de energia elétrica ocorrido no Brasil em 2001/2002, ocorreu alguma alteração na sensibilidade dos consumidores com relação a variações nas tarifas de energia elétrica e na renda dos consumidores no período. Os resultados mostraram que a utilização de dados em painel produz estimativas de elasticidades preço e renda de acordo com a teoria econômica. Além disso, os resultados ficaram próximos aos da literatura nacional. Os resultados também mostraram que no período do racionamento aumentou a sensibilidade dos consumidores com relação a alterações nas tarifas de energia elétrica.
The objective of this thesis is to estimate the price and income elasticities of residential electric power demand in Brazil using panel data. The heterogeneity of the Brazilian economy leads to the existence of different patterns of residential electric power consumption and different tariffs structures among the electric power utilities companies. In this regard, this work uses a panel database composed by information about 63 electric power companies in Brazil, in the period 1996-2006. Three panel data methods were applied: Pooled OLS, Fixed Effects and Random Effects. Furthermore, it also was tested if there were some changes in the sensitivity of the consumers regarding the variations in the price and income variables during the electric power rationing in Brazil, in 2001/2002. The results showed that the panel data provides estimations of price and income elasticities in accordance with the economic theory. In addition, the results were close to those in the national literature. The estimations also showed the sensitivity of consumers increased in the period of electric power rationing.

Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012
Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.

There has been a large body of statements claiming that the large scale deployment of Distributed Energy Resources (DERs) will eventually reshape the future distribution grid operation in numerous ways. However, there is a lack of evidence specifying to what extent the power system operation will be alternated. In this project, quantitative results in terms of how the future distribution grid will be changed by the deployment of distributed generation, active demand and electric vehicles, are presented. The quantitative analysis is based on the conditions for both a radial and a meshed distribution network. The input parameters are on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. The simulation results indicate that the deployment of DERs can significantly reduce the power losses and voltage drops by compensating power from the local energy resources, and limiting the power transmitted from the external grid. However, it is notable that the opposite results (e.g., severe voltage uctuations, larger power losses) can be obtained due to the intermittent characteristics of DERs and the irrational management of different types of DERs in the DNs. Subsequently, this will lead to challenges for the Distribution System Operator (DSO).

As mudanÃas no mercado regulado pelo Estado exigiram uma adequaÃÃo do foco produtivo das concessionÃrias de energia elÃtrica em atingir as metas dos indicadores de desempenho tÃcnico e operacional, para o foco na satisfaÃÃo do consumidor dentro de suas expectativas. Desta forma, este trabalho desenvolve um estudo exploratÃrio junto a uma empresa do ramo de energia elÃtrica localizada no Estado do CearÃ, com o propÃsito de verificar as relaÃÃes entre suas aÃÃes de marketing e o nÃvel de satisfaÃÃo de seus clientes. Por meio da teoria que aborda a estrutura das caracterÃsticas intrÃnsecas à produÃÃo dos serviÃos, percepÃÃo de qualidade e satisfaÃÃo do cliente, a pesquisa dispÃe-se a responder à seguinte questÃo: qual a relaÃÃo entre as aÃÃes de marketing em uma empresa monopolista e os resultados de pesquisas de satisfaÃÃo de clientes? Para este fim, foram utilizados relatÃrios gerenciais, pesquisas de opiniÃo realizadas pela AssociaÃÃo Brasileira de Distribuidoras de Energia ElÃtrica e entrevistas aos gestores da organizaÃÃo. Conduziu-se um estudo de caso comparando a performance da empresa entre os perÃodos de 2004 e 2006, segundo a avaliaÃÃo do cliente e mediante os indicadores operacionais de desempenho. De acordo com esta abordagem, verificaram-se quais os atributos de qualidade que mais evidenciaram a influÃncia das aÃÃes mercadolÃgicas.

The demand for fossil fuels by Ontario's conventional steam power generation sector is examined. It is hypothesised that the enactment of a carbon fee policy will induce a change in the relative prices of the three fuels used in this sector (coal, natural gas and heavy fuel oil). This would lead to substantial interfuel substitution and greenhouse gas abatement. The demand share equations for the three fuels are derived from the translog functional form and set in a simulation model to estimate the value of a carbon fee necessary, to reduce carbon dioxide emissions in compliance with the Kyoto Protocol. Results suggest that a fuel specific carbon fee policy would be successful in achieving the desired emissions reduction at a negligible net cost to society.

Esse trabalho tem por objetivo aprimorar a principal atividade das empresas de distribuição no que diz respeito ao combate às perdas comerciais: as inspeções em campo às unidades consumidoras (UCs). Para tanto, é feita a proposição de um método para identificar quedas de consumo atípicas dentro do universo de faturamento de UCs de uma concessionária de energia elétrica. A proposta está fundamentada na análise dos registros históricos de consumo, de modo que os dados considerados atípicos possam ser indicados e as UCs ranqueadas de acordo com a prioridade para as inspeções em campo. Para tanto, propõe-se a utilização combinada de técnicas de previsão de demanda e de estatísticas robustas. A validade do método foi verificada através de um estudo de caso em uma empresa de distribuição de energia elétrica do sul do Brasil. Através do estudo de caso, concluiu-se que o método é capaz de identificar quedas de consumo atípicas, tendo identificado satisfatoriamente 89,38% dos casos avaliados. Ao final do trabalho, são apresentadas sugestões de estudos complementares, de modo a aperfeiçoar o desempenho do método.
This study aims to enhance the main business of distribution companies regarding to the efforts to avoid non-technical losses, that means, field inspections at the consumer units (CUs). For that, the proposition of an algorithm to identify atypical consumption falls within the universe of PAs billing of an electric facility is made. The proposal is based on the analysis of historical records of consumption, so that the data which are considered atypical can be indicated and the CUs ranked according to their priority for inspections in the field. Combined techniques of demand forecasting and statistics robust are proposed. The validity of the algorithm was verified through a case study in an electric power distribution facility in southern Brazil. Through the case study, it was concluded that the algorithm is able to identify atypical consumption falls, and satisfactorily 89.38% of the cases was identified. At the end of this paper, suggestions for further studies in order to improve the performance of the algorithm are presented.

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.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

Based on the physics of electricity transmission, proposed real-mathematical model of the power supply systems of a specific industrial power consumer, which allows determining the level of efficiency and electromagnetic compatibility of the elements of the systems.

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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