Добірка наукової літератури з теми "Consumers' demand for electric power"

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

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

Power generation and consumption is an instantaneous process and maintaining the balance between demand and supply is crucial since the demand and supply mismatch leads to various risks like over-investment, over-generation, under-generation, and the collapse of the power system. Therefore, the reduction in demand and supply mismatch is critical to ensure the safety and reliability of power system operation and economics. A typical and common approach, called full load shedding (FLS), is practiced in cases where electric power demand exceeds the available generation. FLS operation alleviates the power demand by cutting down the load for an entire area or region, which results in several challenges and problems for the utilities and consumers. In this study, a demand-side management (DSM) technique, called Soft-load shedding (SLS), is proposed, which uses data analytics and software-based architecture, and utilizes the real-world time-series energy consumption data available at one-minute granularity for a diversified group of residential consumers. The procedure is based on pattern identification extracted from the dataset and allocates a certain quota of power to be distributed on selected consumers such that the excessive demand is reduced, thereby minimizing the demand and supply mismatch. The results show that the proposed strategy obtains a significant reduction in the demand and supply mismatch such that the mismatch remains in the range of 10–15%, especially during the period where demand exceeds generation, operating within the utility constraints, and under the available generation, to avoid power system failure without affecting any lifeline consumer, with a minimum impact on the consumer’s comfort.

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

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

The relevance of the work is determined by the need to improve the electrical distribution grids of railways on the basis of digital technologies. The article presents advanced methods of transportation and distribution of electric power in smart power grids of railways based on multi-agent control. The analysis of the power supply system for stationary railroad consumers was performed and advanced ways of their development were defined. These methods should provide increased speed, adaptive determination of restrictions on using electric power equipment, management of mode parameters, sectioning and power flow modes in electrical distribution grids, restoration of power supply after emergency events. The method of adaptive control of transportation and distribution of electric energy in the power supply system of stationary railway consumers is developed based on the hierarchical structure of IEC 61850. This method takes into account the coordination of managing and local controllers in the data exchange environment, the control results and the variable area of responsibility of controllers and their division according to their functional purpose based on the multi-agent approach. The method of power flow control was developed to reduce power losses, increase the capacity of transport channels and ensure the restoration of the normal mode of the electric network by reconfiguring it and controlling active elements based on graph theory. The method takes into account the expected daily load curve, limits on the demand for capacity by active consumers and the possibility of a closed mode of electrical network operation through controlled cross-sections. The simulation results presented on the test circuit have showed the feasibility and efficiency of the proposed approaches.

This paper proposes a mathematical model based on mixed integer linear programming (MILP). This model aids the decision-making process in local generation use and demand response application to power demand contract adequacy by Brazilian consumers/prosumers. Electric energy billing in Brazil has some specificities which make it difficult to consider the choice of the tariff modality, the determination of the optimal contracted demand value, and demand response actions. In order to bridge this gap, the model considers local generation connected to the grid (distributed generation) and establishes an optimized solution indicating power energy contract aspects and the potential reduction in expenses for the next billing period (12 months). Different alternative sources already available or of interest to the consumer can be considered. The proposed mathematical model configures an optimization tool for the feasibility analysis of local generation use and, concomitantly, (i) checking the tariff modality, (ii) revising the demand contract, and (iii) suggesting demand response actions. The presented result shows a significant reduction in the energy and power expenses, which confirms the usefulness of this proposal. In the end, the optimized answers promote benefits for both, the consumer/prosumer and the electric utility.

One of the most critical challenges for modern power systems is to reliably supply electricity to its consumers during and in the aftermath of natural disasters. As our dependence on electrical power has increased over the years, long-term power outages can lead to devastating impacts on affected communities. Furthermore, power outages can halt the operation of water treatment plants, leading to shortages in clean water, which is essential during post-disaster recovery. One way to address this is to temporarily reconfigure power and water networks into localized networks, i.e., electric microgrids and water micro-nets, that utilize local resources to supply local demand independently of the main power grid and/or water network. Utilizing distributed energy resources such as wind and solar and treating wastewater locally for potable reuse can provide the operational flexibility for such systems to operate sustainably. However, due to uncertainties in both renewable energy generation and electric/water consumption, ensuring sustainable operation is a challenging task. In this paper, an optimal operational strategy is proposed for an islanded microgrid/micro-net, considering the stochastic nature of renewable energy resources, electric demand, and water demand. An energy storage system is modeled to address the uncertainty in power generation and demand, in conjunction with local water storage and wastewater treatment to accommodate variable water demands. A two-stage stochastic programming model is formulated and solved to determine an optimal operation strategy for the combined system.

Power systems in general supply consumers with electrical energy as economically and reliably as possible. Reliable electric power systems serve customer loads without interruptions in supply voltage. Electric power generation facilities must produce enough power to meet customer demand. Electrical energy produced and delivered to customers through generation, transmission and distribution systems, constitutes one of the largest consumers markets the world over. The benefits of electric power systems are integrated into the much faster modern life in such extent that it is impossible to imagine the society without the electrical energy. The rapid growth of electric power distribution grids over the past few decades has resulted in a large increment in the number of grid lines in operation and their total length. These grid lines are exposed to faults as a result of lightning, short circuits, faulty equipment, mis-operation, human errors, overload, and aging among others. A fault implies any abnormal condition which causes a reduction in the basic insulation strength between phase conductors or phase conductors and earth, or any earthed screens surrounding the conductors. In this paper, different types of faults that affected the electric power distribution grid of selected operational districts of Electricity Company of Ghana (ECG) in the Western region of Ghana was analyzed and the results presented. Outages due to bad weather and load shedding contributed significantly to the unplanned outages that occurred in the medium voltage (MV) distribution grid. Blown fuse and loose contact faults were the major contributor to unplanned outages in the low voltage (LV) electric power distribution grid.

Power systems in general supply consumers with electrical energy as economically and reliably as possible. Reliable electric power systems serve customer loads without interruptions in supply voltage. Electric power generation facilities must produce enough power to meet customer demand. Electrical energy produced and delivered to customers through generation, transmission and distribution systems, constitutes one of the largest consumers markets the world over. The benefits of electric power systems are integrated into the much faster modern life in such extent that it is impossible to imagine the society without the electrical energy. The rapid growth of electric power distribution grids over the past few decades has resulted in a large increment in the number of grid lines in operation and their total length. These grid lines are exposed to faults as a result of lightning, short circuits, faulty equipment, mis-operation, human errors, overload, and aging among others. A fault implies any abnormal condition which causes a reduction in the basic insulation strength between phase conductors or phase conductors and earth, or any earthed screens surrounding the conductors. In this paper, different types of faults that affected the electric power distribution grid of selected operational districts of Electricity Company of Ghana (ECG) in the Western region of Ghana was analyzed and the results presented. Outages due to bad weather and load shedding contributed significantly to the unplanned outages that occurred in the medium voltage (MV) distribution grid. Blown fuse and loose contact faults were the major contributor to unplanned outages in the low voltage (LV) electric power distribution grid.

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.

On the surface it appears simple — supply electricity to meet consumer demand. In practice, however, it is a complex juggling act of matching the decisions of millions of consumers of electricity to the energy potential of over 32,000 megawatts of generation — fifth largest in the U.S. Paper published with permission.

Plug-in hybrid electric vehicles (PHEVs) have the potential of substantially reducing petroleum consumption and vehicular CO2 emissions relative to conventional vehicles (CVs). The analysis presented in this article first ascertains the cost-effectiveness of PHEVs from the perspective of the consumer. Then, the potential effects of PHEVs to an electric utility are evaluated by analyzing a simplified hypothetical example. When evaluating the cost effectiveness of a PHEV, the additional required premium is the most important financial parameter to the consumer. An acceptable amount for the additional upfront costs will depend on the future costs of gasoline and the on-board battery pack. The need to replace the on-board battery pack during the assumed vehicle lifetime also affects the allowed premium. A simplified unit commitment and dispatch model was used to determine the costs of energy and the CO2 emissions associated with PHEVs for different charging scenarios. The results show that electricity can be used to charge PHEVs during off-peak hours without an increase in peak demand. In addition, the combined CO2 emissions from the vehicles and the electric generation facilities will be reduced, regardless of the charging strategy.

This paper presents the optimal plan in an assumed cogeneration system based on Advanced Co-generation system built in Kikui-cho campus and suggests the introductory technique of co-generation system. At first, we analyzed the annual change of the electric power and cooling demand for a typical consumer and completed the assumed model by formulating. In hospital, hotel, etc., it was checked that electric power and cooling demand have the high correlation with outside temperature, and the daily maximum temperature is effective as an explaining variable especially. We verified the optimal planning according to the consumer using this assumed demand.

Electricity generation is a major source of air pollution, contributing to nearly one-third of the total greenhouse gas emissions in the United States. As with most goods, production must keep up with the projected consumer demand, and the industry is subject to government regulations at the federal, state, and local levels. This study models the New Jersey electric grid as a market system, using agent-based modeling to represent individual consumers and power companies making utility-maximizing decisions. Each consumer agent is prescribed a unique value function that includes factors such as income, energy intensity, and environmental sensitivity, and they are able to make decisions about how much energy they use and whether they opt into a renewable energy program. Power producers are modeled to keep up with demand and minimize their cost per unit of electricity produced, and they include options to prefer either on-demand or renewable energy sources. Using this model, different scenarios are examined with respect to producer strategy and government policy. The results provide a proof-of-concept for the modeling approach, and they reveal interesting trends about how the markets are expected to react under different scenarios.

The power industry is confronting challenges with seemingly conflicting goals. Large, solid fuel power plants provide the reliability and flexibility utilities require for baseload, cycling, and on-demand situations. They provide the economy of scale needed to minimize the cost of production. Consumers/industry rely on affordable, dependable electrical energy. It’s an important part of our economy and our daily lifestyle. Reducing emission levels and conserving our finite resources are key components for achieving a sustainable environment.

Utilities in regulated energy markets manage power generation, transmission, and delivery to consumers. Matching peak demand with peak generation is costly, and the increasing penetration of renewable energy into the grid adds complexity due to fluctuations in supply. A few options exist for addressing the task of balancing supply and demand, including demand response, energy storage, and time-varying pricing (tariffs). Arizona Public Service (APS), the largest electric utility company in Arizona, employs tariffs that charge more for electricity at certain times (on-peak periods) and a demand charge for the highest power demand throughout the billing period. Such tariffs incentivize end users to lower peak demand. Arizona State University (ASU), a public university with its largest campus in Tempe, AZ, participates in a time-of-use tariff structure with APS. Analysis in this paper shows that ASU’s 16MWdc of onsite solar capacity alone can lower its monthly electricity bills by over 10% by decreasing on-peak power demand. A novel contribution of the paper is the analysis of the value of small scale, on-campus energy storage in lowering the demand charge. Most analyses consider savings from transferring off-peak electric power to peak-electric power, but this paper considers using stored electricity solely to reduce peak demand and thus lower the demand charge. Small amounts of electricity could greatly reduce overall cost. An algorithm was developed and executed in Python to decide when on-campus storage should be charged and discharged. The critical part of the algorithm is to decide when to discharge. Deploying too early, or too late, will not change peak demand. The paper’s storage dispatch model is implemented alongside a financial model that calculates the savings in electricity bills and determines the net present value (NPV) of different storage technologies as a function of storage lifetime and installed capacity (kWh). The results show that, for all storage technologies considered, a positive NPV is realized. NPVs are very sensitive to actual power demand and thus vary from year to year. This is to be expected because the storage dispatch strategy operates on extreme values, which tend to include very rare events. This analysis uses actual data from ASU, which allows us to extend the results to other universities and commercial customers. The favorable results suggest that a smarter dispatch algorithm based on machine learning would enable further cost savings by determining what can be thought of as a shadow price of electricity.

A combined electrical/mechanical and thermal power generating system is undoubtedly the way to go for optimum overall efficiency in energy conversion. Traditionally, electric power is generated in centralized power stations and subsequently supplied to consumers via extensive grid distribution networks. Waste heat is a by-product that cannot inexpensively be conveyed to meet the demand for thermal power in the communities served. Consequently, the needed thermal power is produced either using the high grade electricity delivered from the grid supply, or by combustion of expensive fuels such as natural gas and oil. Cogeneration and CHP (Cooling, Heating, and Power) systems are designed to utilize waste heat from in-house electrical/mechanical power producing devices such as micro turbines and diesel engines used in industries that are located in the community or district. The generation of mechanical and thermal power from a single fuel input significantly enhances the overall conversion efficiency, which translates to lower CO2 emissions to the environment. Greater stability and reliability in power supply at the community level is achieved via the deployment of CHP systems that provide power on the required scale as well as meet local demand for cooling and/or heating. The magnitude of efficiency gain for CHP systems is often overstated simply because the quality difference between electrical/mechanical power and thermal power is not factored into the equation. This paper focuses on a complete thermodynamic analysis of CHP systems on the basis of the first and second laws of thermodynamics.

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

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