Journal articles on the topic 'Management of consumer demand for electric power'
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1
Tretyakov, Evgeny. "Demand management by active consumers in intelligent electric power systems." E3S Web of Conferences 157 (2020): 05006. http://dx.doi.org/10.1051/e3sconf/202015705006.
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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.
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Tretyakov, Evgeny. "Demand management by active consumers in intelligent electric power systems." E3S Web of Conferences 164 (2020): 10004. http://dx.doi.org/10.1051/e3sconf/202016410004.
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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.
3
Hu, Yu-Chen, Yu-Hsiu Lin, and Harinahalli Lokesh Gururaj. "Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home." Processes 9, no. 9 (August 29, 2021): 1539. http://dx.doi.org/10.3390/pr9091539.
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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.
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Dulău, Lucian Ioan, and Dorin Bică. "Algorithm for Smart Home Power Management with Electric Vehicle and Photovoltaic Panels." Proceedings 63, no. 1 (December 25, 2020): 49. http://dx.doi.org/10.3390/proceedings2020063049.
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In this paper is presented an algorithm for the power management of a smart home with electric vehicle and photovoltaic panels. The case study is performed considering the power demand of the household appliances, the charging/discharging of the electric vehicle, and the power supplied by the photovoltaic panels. The photovoltaic panels have a small installed power and benefit from the support scheme from the government for these types of generation sources, so it is a prosumer. The simulation is performed for a day. Additionally, the cost of power supplied to the consumer is also analyzed.
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Apena, Waliu Olalekan. "A Knowledge-Based Demand Side Management: Interruptible Direct Load Approach." European Journal of Engineering Research and Science 2, no. 6 (June 30, 2017): 71. http://dx.doi.org/10.24018/ejers.2017.2.6.399.
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The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.
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Apena, Waliu Olalekan. "A Knowledge-Based Demand Side Management: Interruptible Direct Load Approach." European Journal of Engineering and Technology Research 2, no. 6 (June 30, 2017): 71–73. http://dx.doi.org/10.24018/ejeng.2017.2.6.399.
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The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.
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Alhasnawi, Bilal, and Basil Jasim. "Adaptive Energy Management System for Smart Hybrid Microgrids." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 73–85. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.11.
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The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.
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Liu, Qiu Hua, Kun Xu, and Hao Min Wang. "Study of Nanjing Demand Side Management Based on Cost-Benefit Analysis - Take Green Lighting as an Example." Applied Mechanics and Materials 737 (March 2015): 260–68. http://dx.doi.org/10.4028/www.scientific.net/amm.737.260.
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Electric power supply is one of the most important aspects of China’s national energy development strategy (NEDS). As major economic unit as well as major energy consumer, Jiangsu province is facing serious energy supply challenges. Under such circumstances, positive actions are taken by local government in respond to NEDS which put energy saving in the first place, and demand side management (DSM) is implemented. DSM is an important measure which can release rush hour electric supply pressure, enhance energy efficiency, optimize electric power utilization, and it is beneficial for sustainable development. This paper is based on the analysis of the current DSM situation of Nanjing, and green lighting is taken as an example. An empirical analysis is given to cost-effectiveness of the implementation of green lighting. Finally, the conclusion that the cost-effectiveness of the implementation of green lighting is economically viable to power supply companies, electric customers and the whole society is drawn.
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Naveen, P., W. Kiing Ing, M. Kobina Danquah, A. S. Sidhu, and A. Abu-Siada. "A Cloud Associated Smart Grid Admin Dashboard." Engineering, Technology & Applied Science Research 8, no. 1 (February 20, 2018): 2499–507. http://dx.doi.org/10.48084/etasr.1702.
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Intelligent smart grid system undertakes electricity demand in a sustainable, reliable, economical and environmentally friendly manner. As smart grid involves, it has the liability of meeting the changing consumer needs on the day-to-day basis. Modern energy consumers like to vivaciously regulate their consumption patterns more competently and intelligently than current provided ways. To fulfill the consumers’ needs, smart meters and sensors make the grid infrastructure more efficient and resilient in energy data collection and management even with the ever-changing renewable power generation. Though cloud acts as an outlet for the energy consumers to retrieve energy data from the grid, the information systems available are technically constrained and not user-friendly. Hence, a simple technology enabled utility-consumer interactive information system in the form of a dashboard is presented to cater the electric consumer needs.
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Park, EungSuk, BoRam Kim, SooHyun Park, and Daecheol Kim. "Analysis of the Effects of the Home Energy Management System from an Open Innovation Perspective." Journal of Open Innovation: Technology, Market, and Complexity 4, no. 3 (August 3, 2018): 31. http://dx.doi.org/10.3390/joitmc4030031.
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The Home Energy Management System (HEMS) is a system for the efficient electric power consumption of each household. It can provide real-time electricity cost information according to electricity consumption, and households can immediately control their consumption of electricity. In this study, we analyzed the effects of the HEMS on the stability of demand for electric power. To do this, we analyzed the causal relationship between the amounts of electric power generation and consumption, from the system dynamics perspective. From the analysis, we found that in the current structure, the fluctuation of the quantity of demand became large due to the time delay in households recognizing the electric bill and adjusting their electric power consumption. However, when the HEMS was introduced, it could be seen that electric power demand remained stable since consumers could see their electricity bill in real-time and could manage their electricity consumption by themselves.
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Gull, Muhammad S., Nasir Mehmood, Huzaifa Rauf, Muhammad Khalid, and Naveed Arshad. "Soft Load Shedding Based Demand Control of Residential Consumers." Electronics 11, no. 4 (February 16, 2022): 615. http://dx.doi.org/10.3390/electronics11040615.
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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.
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Mohamed, Afua, and Mohamed Tariq Khan. "A review of electrical energy management techniques: supply and consumer side (industries)." Journal of Energy in Southern Africa 20, no. 3 (August 1, 2009): 14–21. http://dx.doi.org/10.17159/2413-3051/2009/v20i3a3304.
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A review of electrical energy management tech-niques on the supply side and demand side is pre-sented. The paper suggests that direct load control, interruptible load control, and time of use (TOU) are the main load management techniques used on the supply side (SS). The supply side authorities normally design these techniques and implement them on demand side consumers. Load manage-ment (LM) initiated on the demand side leads to valley filling and peak clipping. Power factor correc-tion (PFC) techniques have also been analysed and presented. It has been observed that many power utilities, especially in developing countries, have neither developed nor implemented DSM for their electrical energy management. This paper proposes that the existing PFC techniques should be re-eval-uated especially when loads are nonlinear. It also recommends automatic demand control methods to be used on the demand side in order to acquire optimal energy consumption. This would lead to improved reliability of the supply side and thereby reducing environmental degradation.
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Nifatova, Оlena M., Valeriia G. Scherbak, Oleksii Yu Volianyk, and Mykhailo O. Verhun. "ПРОБЛЕМИ УПРАВЛІННЯ В СИСТЕМАХ SMART GRID УНІВЕРСИТЕТСЬКОГО ХАБА ЕНЕРГОЕФЕКТИВНОСТІ." Journal of Strategic Economic Research, no. 4 (January 10, 2022): 58–66. http://dx.doi.org/10.30857/2786-5398.2021.4.6.
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The article attempts to tackle the issues of enhancing the performance of university energy efficiency management systems. An emphasis is put that in modern realia, alternative and renewable energy sources are becoming increasingly important in the electric power sector, thus contributing to environmental protection and enabling active electricity consumers to have their own sources of energy generation. However, it is observed that the relationships between energy generation sources and electricity consumers are complicated by new demands for setting balancing modes due to certain volatility of energy generation by alternative sources as well as the need to connect additional energy storage facilities. To identify opportunities of using Smart Grid technologies to manage the University energy consumption, a power balance equation was used to determine an active power balance between generated power, generation sources and power consumed by electricity consumers. In addition, the indicators of the total active power loss in the electrical network associated with the technological consumption of energy for its transmission was included into this equation. The study presents the results of an in-depth critical analysis on Smart Grid methodology and provides argument for the relevance of using artificial intelligence techniques in Smart Grid management systems of the University energy efficiency hub, along with suggesting a notion of electricity generating consumer in the concept of intelligent networks with two-way flow of energy and information as subsystems of a different nature. It is argued that the developed conceptual model of the electricity generating consumer for multilevel smart grid management systems and their infrastructure within the University energy efficiency hub allows establishing relationships between its structural elements and objects of different character. The findings reveal that the specifics of the developed method in setting priorities and regulatory standards for optimal management by a generating consumer within the University energy efficiency hub is the possibility of its automatic adaptation to changes in the external environment subject to interactions between electricity generating consumers.
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Molla1, Tesfahun, Baseem Khan1*, and Pawan Singh2. "A comprehensive analysis of smart home energy management system optimization techniques." Journal of Autonomous Intelligence 1, no. 1 (October 14, 2018): 15. http://dx.doi.org/10.32629/jai.v1i1.14.
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Development of smart grid technology provides an opportunity to various consumers in context for scheduling their energy utilization pattern by themselves. The main aim of this whole exercise is to minimize energy utilization and reduce the peak to average ratio (PAR) of power. The two way flow of information between electric utilities and consumers in smart grid opened new areas of applications. The main component is this management system is energy management controller (EMC), which collects demand response (DR) i.e. real time energy price from various appliances through the home gateway (HG). An optimum energy scheduling pattern is achieved by EMC through the utilization of DR information. This optimum energy schedule is provided to various appliances via HG. The rooftop photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid. Under such energy management scheme, whenever solar generation is more than the home appliances energy demand, extra power is supplied back to the grid. Consequently, different appliances in consumer premises run in the most efficient way in terms of money. Therefore this work provides the comprehensive review of different smart home appliances optimization techniques, which are based on mathematical and heuristic one.
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Ogorodnikov, Nikita. "Electricity demand management within the retail electricity market in Russia." E3S Web of Conferences 289 (2021): 01006. http://dx.doi.org/10.1051/e3sconf/202128901006.
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Demand side management is an important tool for ensuring the flexibility of the electric power systems, as it maintains and regulates the balance of generation and consumption of electric energy and has a system-wide effect, which is formed by lowering electricity prices for consumers and optimizing the load and structure of generating and electric grid capacities. The article is devoted to the problem of managing the demand for electricity consumption within the framework of the retail electricity market in Russia. The author identifies and summarizes the existing demand management tools. The article substantiates the need for the development and implementation of innovative tools and mechanisms for managing the demand for electricity consumption.
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Shewale, Amit, Anil Mokhade, Nitesh Funde, and Neeraj Dhanraj Bokde. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes." Energies 15, no. 8 (April 14, 2022): 2863. http://dx.doi.org/10.3390/en15082863.
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The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.
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Hercegová, Katarína, Tatyana Baranovskaya, and Natalya Efanova. "Smart technologies for energy consumption management." SHS Web of Conferences 128 (2021): 02005. http://dx.doi.org/10.1051/shsconf/202112802005.
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The article aims at classifying and describing smart technologies and solutions that are currently used for energy consumption management. It stresses the positive impact of technology on the environment, with a focus on renewable energy, energy efficiency and climate change. The article analyses the green technologies used in energy generation and storage and contemplates over the Internet of Things (IoT) concept that enabled using super-fast flows of information from the generator to the consumer and back with the purpose of optimizing energy management and impacting the demand-side response of the energy consumers. Also, it describes the future smart energy systems that would combines photovoltaic (PV) panels, storage systems and batteries. In addition, it discusses the importance of peer-to-peer (P2P) energy and information exchange, virtual power plants and many other novel elements of the future smart grids that would make the transition to the low-carbon economy and electric transport smooth and effective.
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Ayub, Muhammad Ahsan, Hufsa Khan, Jianchun Peng, and Yitao Liu. "Consumer-Driven Demand-Side Management Using K-Mean Clustering and Integer Programming in Standalone Renewable Grid." Energies 15, no. 3 (January 29, 2022): 1006. http://dx.doi.org/10.3390/en15031006.
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Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, and uninterrupted electricity. However, for standalone power systems, demand-side management always remains a challenging task. In this paper, a load scheduling algorithm driven by K-mean clustering and linear integer programming to schedule consumers’ appliances for the upcoming day is proposed. In addition, the basic power to run the necessary appliances is kept available in the system all the time. Furthermore, to assist the consumer in every situation, the battery storage system and the overall system size reduction are also taken into consideration. Consumer input is also used in scheduling the appliances. The proposed method is evaluated on the publicly available real-world dataset; the simulation results demonstrate that the proposed approach performs better, due to which the reliability and continuity of the system are increased.
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Tamilarasu, Karthick, Charles Raja Sathiasamuel, Jeslin Drusila Nesamalar Joseph, Rajvikram Madurai Elavarasan, and Lucian Mihet-Popa. "Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort." Energies 14, no. 10 (May 15, 2021): 2855. http://dx.doi.org/10.3390/en14102855.
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Soaring energy demand and the establishment of various trends in the energy market have paved the way for developing demand-side management (DSM) from the consumer side. This paper proposes a reinforced DSM (RDSM) approach that uses an enhanced binary gray wolf optimization algorithm (EBGWO) that benefits the consumer premises with load scheduling, and peak demand reduction. To date, DSM research has been carried out for residential, commercial and industrial loads, whereas DSM approaches for educational loads have been less studied. The institution load also consumes much utility energy during peak hours, making institutional consumers pay a high amount of cost for energy consumption during peak hours. The proposed objective is to reduce the total electricity cost and to improve the operating efficiency of the entire load profile at an educational institution. The proposed architecture integrates the solar PV (SPV) generation that supplies the user-comfort loads during peak operating hours. User comfort is determined with a metric termed the user comfort index (UCI). The novelty of the proposed work is highlighted by modeling a separate class of loads for temperature-controlled air conditioners (AC), supplying the user comfort loads from SPV generation and determining user comfort with percentage UCI. The improved transfer function used in the proposed EBGWO algorithm performs faster in optimizing nonlinear objective problems. The electricity price in the peak hours is high compared to the off-peak hours. The proposed EBGWO algorithm shift and schedules the loads from the peak hours to off-peak hours, and incorporating SPV in satisfying the user comfort loads aids in reducing the power consumption from the utility during peak hours. Thus, the proposed EBGWO algorithm greatly helps the consumer side decrease the peak-to-average ratio (PAR), improve user comfort significantly, reduce the peak demand, and save the institution’s electricity cost by USD 653.046.
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Samadi, Mikhak, Javad Fattahi, Henry Schriemer, and Melike Erol-Kantarci. "Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization." Sensors 21, no. 1 (December 28, 2020): 130. http://dx.doi.org/10.3390/s21010130.
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The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%.
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Minhas, Daud Mustafa, Josef Meiers, and Georg Frey. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets." Energies 15, no. 5 (February 22, 2022): 1619. http://dx.doi.org/10.3390/en15051619.
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To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%.
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Rušeljuk, Pavel, Kertu Lepiksaar, Andres Siirde, and Anna Volkova. "Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management." Energies 14, no. 15 (July 28, 2021): 4553. http://dx.doi.org/10.3390/en14154553.
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Optimisation of heat and electrical load distribution, where the objective function is the maximum efficiency of the CHP unit for a given load range, can be done considering the limitations of electrical power and the heat load. Simulating a real CHP unit with a district heating network shows that demand-side management can improve the overall economic efficiency of the CHP plant and increase the unit’s operating range in the electricity spot market. Economic dispatch makes it possible to determine a reasonable additional increase in the electric power of the CHP unit, and to optimise the supply temperature and mass flow of the district heating network. The results obtained and the analysis performed indicate that the proposed methodology provides logical results and can be used to calculate the efficiency indicators of the cogeneration of electrical and thermal energy. The problem of optimising the operating mode of the CHP unit was solved, which allows us to determine the optimal additional increase in the unit’s electrical load at a given heat load of consumers, which on average increases the CHP unit’s efficiency up to an additional 1.5%.
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Aizenberg, Natalia, and Nikolai Voropai. "The Optimal Mechanism Design of Retail Prices in the Electricity Market for Several Types of Consumers." Mathematics 9, no. 10 (May 19, 2021): 1147. http://dx.doi.org/10.3390/math9101147.
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In this paper, we discuss the demand side management (DSM) problem: how to incentivize a consumer to equalize the load during the day through price-dependent demand. Traditionally, the retail market offers several electricity payment schemes. A scheme is effective when the different tariffs satisfy different consumers. At the same time, the existing and generally accepted retail pricing schemes can lead to an "adverse selection" problem when all consumers choose the same price, thereby, reducing the possible general welfare. We propose an optimal design of pricing mechanisms, taking into account the interests of the electricity supplier and different types of consumers. The results of our work are that the optimal mechanism is implemented simultaneously for several periods, including the case when the ratio of types of consumers in periods changes. In addition, the mechanism proposed by us, in contrast to the studies of other researchers, provides an equilibrium close to the socially optimal maximum. We describe the implementation algorithm of the mechanism and provide examples of its action in the electric power system with different types and numbers of consumers.
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Aizenberg, Natalia, and Sergey Perzhabinsky. "Generation adequacy of electric power systems in market price setting." E3S Web of Conferences 114 (2019): 03006. http://dx.doi.org/10.1051/e3sconf/201911403006.
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We propose the new model of generation adequacy optimization. Optimization criterion is a maximum of social welfare. Social welfare consists of profits of generating companies, consumer surplus, costs for development and servicing of electrical grids. In the article we present a review of existed methods of adequacy level management in liberalized electric power systems. Optimization of adequacy level is based on analysis of variants of development of the electric power system. For adequacy analysis of the variants of development we multiple estimate the electricity shortage in random hours of the system work. Analysis of the system work in every random hour is realized in two stages. At first we define values of equilibrium electricity demand in every system node and equilibrium price of electricity according to Cournot model. We consider only electricity market in the model. At the next stage we simulate failures of power generating equipment and transmission lines. The electricity shortage in a current hour is estimated on the second stage. After a whole cycle of analysis, we compute reliability indexes and profits of generating companies. Profits of generating companies are depended on the reliability of the electricity supply. The simulations of random values are based on Monte Carlo method.
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Shaban, Mahmoud, and Mohammed F. Alsharekh. "Design of a Smart Distribution Panelboard Using IoT Connectivity and Machine Learning Techniques." Energies 15, no. 10 (May 17, 2022): 3658. http://dx.doi.org/10.3390/en15103658.
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Electric load management through continuous monitoring and intelligent controlling has become a pressing requirement, particularly in light of rising electrical energy costs. The main purpose of this work is to realize a low-voltage electrical distribution panelboard that allows for real-time load monitoring and that provides a load forecasting feature at the household level. In this regard, we demonstrate the design and the implementation details of an IoT-enabled panelboard with smart features. An IoT dashboard was used to display the most significant information in terms of voltage, current, real power, reactive power, apparent power, power factor, and energy consumption. Additionally, the panel system offers visualization capabilities that were integrated into a cloud-based machine learning modeling. Among several algorithms used, the Gaussian SVM regression exhibited the best training and validation results for the load forecasting feature. It is possible for the proposed design to be simply developed to add more smart features such as fault detection and identification. This assists in an efficient management of energy demand at the consumer level.
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Naser, Beate, Franziska Schäfer, and Jörg Franke. "An Energy Ontology Focusing on Demand Side Management in Smart Homes." Advanced Engineering Forum 19 (October 2016): 124–31. http://dx.doi.org/10.4028/www.scientific.net/aef.19.124.
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By increasing the share of renewable energy sources, the volatility of available energy is rising. More and more fluctuating power generation by solar power plants and wind turbines has to be integrated into the power grid. Demand side management (DSM) represents one possible solution to achieve this goal by including energy production and energy consumption simultaneously. In this paper, we especially focus on the field of electric energy in smart homes. Considering the implementation of different DSM devices, an ontology-based approach can serve as a conceptual foundation for a necessary knowledge base. We propose an advanced energy ontology for smart homes, integrating important aspects for a successful DSM. We describe how power producers, storages and consumers are represented in our ontology. Finally, we show the scenario-based utilization of our approach.
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Koltsaklis, Nikolaos, Ioannis P. Panapakidis, David Pozo, and Georgios C. Christoforidis. "A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques." Energies 14, no. 6 (March 19, 2021): 1724. http://dx.doi.org/10.3390/en14061724.
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This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.
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Tretyakov, Evgeny. "Advanced methods of transportation and distribution of electrical power in smart power grids of railways." MATEC Web of Conferences 239 (2018): 01010. http://dx.doi.org/10.1051/matecconf/201823901010.
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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.
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Jakowski, Damian, and Marek Dzida. "Increasing Power Supply Safety in the Aspect of Supporting the Renewable Energy Sources by Conventional and Virtual Power Stores." Polish Maritime Research 25, s1 (May 1, 2018): 189–97. http://dx.doi.org/10.2478/pomr-2018-0041.
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Abstract This paper presents characteristics and purposefulness of supporting the renewable energy sources (OZE) by means of energy stores. The main emphasis was placed on analysis of virtual energy stores available for implementation in Polish economy conditions. A role which management of Demand Side Response (DSR) may play in balancing Polish electric power system, is discussed. Implementation of such solutions together with conventional energy stores may significantly influence power supply safety by assuring continuity of electric power supply at an acceptable price. Involvement of electric power consumers (DSR) should be one of the basic solutions for power markets in Poland and Europe.
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Zhang, Xiao Hong. "Research on the Mathematical Model for the Price Regulation of Power Demand-Side." Advanced Materials Research 189-193 (February 2011): 1218–21. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1218.
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Power Demand-side Management (DSM) originated in the USA and was introduced into China later. As China's rapid economic development, social electric power energy demand is increasing, therefore, strengthen DSM, using reasonable price regulation and guide consumers the rational allocation of electricity methods and structure, so that we can achieve efficient use of power resources. From the perspective of electricity consumers and suppliers, this paper analyzed the relationship between them by constructing mathematical model, expected to provide some theoretical basis and practical guidance for the practice of government and power companies.
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Afanasyev, V. V., V. G. Kovalev, and V. A. Tarasov. "Energy technology complexes as regulators of electrical power systems." Power engineering: research, equipment, technology 21, no. 5 (December 17, 2019): 50–58. http://dx.doi.org/10.30724/1998-9903-2019-21-5-50-58.
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The aim of the work is to study the processes of electrothermal gasification of solid fuels in energy technological complexes and to assess the possibilities of using energy technological complexes for regulating load schedules of electric power systems. By the methods of mathematical and physical modeling of physicochemical processes of gasification of solid carbon- containing materials and energy conversion, the main characteristics of electro-technological complexes for the processing of solid carbon-containing materials are obtained. A technological scheme of a maneuverable consumer of electricity and power is proposed, allowing to participate in demand management and increase the efficiency of electric power systems, comprehensively process any solid types of fuels, and build municipal gasification systems for areas that do not have access to natural gas sources. It is shown that the energy potential of the synthesis gas obtained by thermoelectric gasification in electrode installations is several times higher than the cost of electricity for gasification. During the hours of maximum load of the power system, the electrothermal gasifier allows to significantly reduce the consumed active power due to the transition to the autothermal gasification mode without reducing the performance of synthesis gas and work in the “market of system services” as a regulated load. Electrotechnological electrode installation allows the use of cheap electric energy of nighttime minima for the production of synthesis gas and the recovery of ferroalloys from oxides of raw materials and ore materials to be added to the coal recovery process. Electrode electrothermal installation provides a wide range of regulation of consumed electrical power, good process controllability for any type of raw materials, including combustible solid waste. High-temperature reduction processes in electrothermal gasifiers make it possible to process fuel of any composition without enrichment and grinding, to convert the mineral part of solid fuel into slag, which can be used to produce building materials. The oxides of a number of metals contained in the mineral part of the fuel are reduced and form a ferroalloy.
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Anmisha, M. "Demand Side Management Applied to a Sub Station for Energy Conservation." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3523–29. http://dx.doi.org/10.22214/ijraset.2021.37110.
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Electrical energy is an essential ingredient for the industrial and all-round development of any country. The per capita consumption of electrical energy is a reliable indicator of a country's state of development. It has been estimated that there are two billion people who still lack electricity today and the world demand in developing countries is doubling every eight years. To meet this ever-growing demand, generating plants of all types are being installed. Unfortunately, the sources of electrical energy are depleting and hence the gap between the supply and the demand is continuously increasing. Under such circumstances the only option left is optimal utilization of available resources. Initially in this direction Load Management and Energy Conservation methods were adopted to overcome the problems. But these two methods concentrate only on the problems faced by supplier alone and do not take into account the problems of consumer. To overcome this problem in 1980's, a concept of DEMAND SIDE MANAGEMENT has emerged and is being applied throughout the world. The concept of DSM has vital role in power system planning and management. The main idea of DSM technique is to discuss the mutual benefits for both supplier and consumer for minimum inconvenience. DSM is broader in scope than either Load Management or Energy Conversation. The need for Power System Planning and Management has increased enormously today. Resources crunch have made availability of power meagre to meet the demand. Power shortage is not only endemic in India but also in the world. as demand always lags with respect to supply a time factor also plays a role in bridging the gap between demand and supply. To bridge this gap the random load shedding is the usual method adopted by the supplier of electrical energy, which discourages the consumer's interest. To overcome this problem recently the concept of Demand Side Management technique has emerged and is being applied throughout the world. This paper deals with the DSM technique of Resized and Revised operation Schedule taking into consideration the load curve of a transformer. The increase in the energy efficiency of equipment is quantified in terms of Transformer Utilization Factor. A considerable increase in the energy efficiency and reduction in core losses has been observed. The work presented in this project gives the results of application of DSM technique to 33/11 KV substation in Palvancha. It has two incoming transmission lines and one 33KV outgoing transmission line and four 11KV outgoing feeders (Burgampadu, Velur, Nanprolu, Sompally). The study indicates the improvement in the energy efficiency of the system. In addition, the consumer also gets a small savings of reduction in the energy bill due to lowering of core losses/ iron losses.
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Roy, Souvik. "ENERGY MANAGEMENT SYSTEMS FOR ELECTRIC VEHICLES AND MICROGRIDS." International Journal of Students' Research in Technology & Management 6, no. 1 (January 3, 2018): 01–05. http://dx.doi.org/10.18510/ijsrtm.2018.611.
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The purpose of study Smart Grid is the evolutions of our electric grid.Our electric components are developing day by day. The purpose of research on Microgrid systems are the integration of renewable energy sources (RES),Enhanced reliability, Reduce peak demand, Smarter consumers, Lower total energy consumption, which outlines power systems interconnection differentVitalityera components (supplyside)with vitality utilization components (request side) and capacity gadgets problem. The integration of RESs and ESSs in a microgrid is studied and analyzed by several authors for different purposes.Create and apply regular cost and advantages technique over all Smart Grid field ventures.
The method employs including underlying algorithms and assumptions.Ensure that this methodology can easily accommodate algorithms and assumptions .Develop business case for investors, regulators and customers.
This project has some limitations like it takes more time to configure all over the worlds, so expensive for the consumers. The objective is to keep end-user with more reliable and increased power availability and hence keeping higher priority load connected.
This study never been done before. This study also was provided current information about smart grid and electric vehicles.
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Salkuti, Surender Reddy. "Challenges, issues and opportunities for the development of smart grid." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1179. http://dx.doi.org/10.11591/ijece.v10i2.pp1179-1186.
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The development smart grids have made the power systems planning and operation more efficient by the application of renewable energy resources, electric vehicles, two-way communication, self-healing, consumer engagement, distribution intelligence, etc. The objective of this paper is to present a detailed comprehensive review of challenges, issues and opportunities for the development of smart grid. Smart grids are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. This paper presents various challenges of smart grid development including interoperability, network communications, demand response, energy storage and distribution grid management. This paper also reviews various issues associated with the development of smart grid. Local, regional, national and global opportunities for the development of smart grid are also reported in this paper.
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Kailas, Aravind, Valentina Cecchi, and Arindam Mukherjee. "A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation." Journal of Computer Networks and Communications 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/932181.
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With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.
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Joseph, Shibily, and E. A. Jasmin. "Demand response program for smart grid through real time pricing and home energy management system." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4558. http://dx.doi.org/10.11591/ijece.v11i5.pp4558-4567.
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Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.
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Rao, Bharath, Friederich Kupzog, and Martin Kozek. "Phase Balancing Home Energy Management System Using Model Predictive Control." Energies 11, no. 12 (November 28, 2018): 3323. http://dx.doi.org/10.3390/en11123323.
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Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.
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Mukundufite, Fabien, Jean Marie Vianney Bikorimana, Etienne Ntagwirumugara, and Alex Kyaruzi. "CO2 emission reduction and energy management for an integrated smart grid — Case of study: Rwandan electrical network." E3S Web of Conferences 181 (2020): 03002. http://dx.doi.org/10.1051/e3sconf/202018103002.
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Many scholars have been focusing on the energy management by Integrating a smart grid into a conventional electrical grid. They have showed that to meet a certain power demand of the consumers, using energy management, the electric utility can turn on some generators, which may have the least operation cost, while the generators with high operation cost are left to supply extra load demand in specific peak periods. Henceforth, the operation cost of its generation units is minimized. The issue remains at a level of relating the energy management to CO2 emission. The present paper briefly discusses the Rwandan electrical network that still integrates the use of diesel generators. It estimates the amount of CO2 emission that can be avoided once a PV system is integrated into the electrical network. The paper as well proposes an algorithm for energy management with consideration of CO2 emission.
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Leme, João Vitor, Wallace Casaca, Marilaine Colnago, and Maurício Araújo Dias. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models." Energies 13, no. 6 (March 18, 2020): 1407. http://dx.doi.org/10.3390/en13061407.
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The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.
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Ahmed, Maytham S., Hussein Shareef, Azah Mohamad, Jamal Abd Ali, and Ammar Hussein Mutlag. "Rule Base Home Energy Management System Considering Residential Demand Response Application." Applied Mechanics and Materials 785 (August 2015): 526–31. http://dx.doi.org/10.4028/www.scientific.net/amm.785.526.
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The increasing number of consumer and household appliances causes the rise in home energy use. Therefore, home energy management (HEM) technology is essential to manage and reduce electricity consumption. The objective of this paper is to present an intelligent algorithm for HEM using rule base technique to manage the power consumption with demand response (DR) feature. The scheduling algorithm considers household loads according to the comfort level, customer preference setting and priority of appliance that can be managed at a given time. The algorithm guarantees the total power consumption to be below the electrical demand limit. To exhibit the performance of the proposed HEM, a number of simulations are carried out including DR signal from the network operator. The results show that the algorithm can effectively respond to DR signal, comfort level, customer preference setting and priority of appliance. Furthermore, the algorithm is simple to implement and has flexibility to control the appliances.
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Veloso, Artur Felipe da Silva, José Valdemir Reis Júnior, Ricardo de Andrade Lira Rabelo, and Jocines Dela-flora Silveira. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service." Future Internet 13, no. 11 (October 26, 2021): 271. http://dx.doi.org/10.3390/fi13110271.
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Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform traditional electric power distribution networks into smart grids (SG). With this implementation, PES will be able to remotely control electric power consumption as well as monitor data generated by smart meters (SM). However, Internet-of-Things (IoT) technologies will enable all this to happen quickly and at low cost, since they are low-cost devices that can be deployed quickly and at scale in these scenarios. With this in mind, this work aimed to study, propose, and implement a hybrid communication infrastructure with LoRaWAN and LoraMesh for the demand-side management as a service (HyDSMaaS) using IoT devices such as long range (LoRa) to provide an advanced metering infrastructure (AMI) capable of performing all these applications as a service offered by EPC to end consumers. Additionally, services such as demand-side management (DSMaaS) can be used in this infrastructure. From the preliminary results it was found that the LoRaWAN network achieved a range of up to 2.35 km distance and the LoRaMESH one of 600 m; thus, the latter is more suitable for scenarios where there is little interference and the SMs are at long distances, while the other is used for scenarios with greater agglomeration of nearby SMs. Considering the hybridized scenario between LoraWAN and LoRaMESH, it can be seen that the implementation possibilities increase, since its range was approximately 3 km considering only one hop, and it can reach 1023 devices present in a mesh network. Thus, it was possible to propose the actual implementation of LoRaWAN and LoRaMESH protocols as well as the hybridization of the two protocols for HyDSMaaS. Additionally, the results obtained are exclusively from Radioenge’s LoRa technology, which can be further improved in the case of using more powerful equipment.
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Jarmouni, Ezzitouni, Ahmed Mouhsen, Mohammed Lamhammedi, and Hicham Ouldzira. "Energy management system and supervision in a micro-grid using artificial neural network technique." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (December 1, 2021): 2570. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2570-2579.
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Nowadays, the combination of conventional and renewable energy sources such as solar energy is one of the most widespread solutions to surmount the challenge of the climate and energy crisis. In the presence of random behavior of photovoltaic systems and variable power demand by consumers, energy management is a real challenge. In this paper, we propose a new energy management technique based on artificial neural networks in a smart grid. This will ensure the continuous supply of electricity to the consumer in the presence of random operation in energy consumption and generation. The global system is modeled and simulated under the MATLAB/Simulink tool.
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Kis, Tamás, András Kovács, and Csaba Mészáros. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management." Energies 14, no. 8 (April 9, 2021): 2095. http://dx.doi.org/10.3390/en14082095.
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This paper investigates bilevel optimization models for demand response management, and highlights the often overlooked consequences of a common modeling assumption in the field. That is, the overwhelming majority of existing research deals with the so-called optimistic variant of the problem where, in case of multiple optimal consumption schedules for a consumer (follower), the consumer chooses an optimal schedule that is the most favorable for the electricity retailer (leader). However, this assumption is usually illegitimate in practice; as a result, consumers may easily deviate from their expected behavior during realization, and the retailer suffers significant losses. One way out is to solve the pessimistic variant instead, where the retailer prepares for the least favorable optimal responses from the consumers. The main contribution of the paper is an exact procedure for solving the pessimistic variant of the problem. First, key properties of optimal solutions are formally proven and efficiently solvable special cases are identified. Then, a detailed investigation of the optimistic and pessimistic variants of the problem is presented. It is demonstrated that the set of optimal consumption schedules typically contains various responses that are equal for the follower, but bring radically different profits for the leader. The main procedure for solving the pessimistic variant reduces the problem to solving the optimistic variant with slightly perturbed problem data. A numerical case study shows that the optimistic solution may perform poorly in practice, while the pessimistic solution gives very close to the highest profit that can be achieved theoretically. To the best of the authors’ knowledge, this paper is the first to propose an exact solution approach for the pessimistic variant of the problem.
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Cheremisin, V. T., and E. A. Tretyakov. "Improving the efficiency of distribution electric networks of railways based on the multi-agent mode management method." Vestnik IGEU, no. 4 (2019): 54–63. http://dx.doi.org/10.17588/2072-2672.2019.4.054-063.
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With the increase in observability and controllability of regimes, the development of methods for managing distributed objects of the electrical network is becoming more and more important. The main research directions in smart grids are based on the theory of fuzzy sets, genetic algorithms, neural networks, stochastic control, spectral graph, bilinear matrix inequality constraints. They are aimed at solving multicriterion optimization problems of electric networks with distributed objects and are computationally-demanding and time-consuming. Meanwhile, the methods of multi-agent control of the power supply system based on the parallelization of information flows and coordination of the operation of distributed linear regulators are becoming more common. The purpose of this study is to develop methods for controlling the operating modes of smart distribution electric networks of railways using an agent-based approach for stabilizing voltages within specified limits and reducing electric power losses. This goal can be achieved by solving the problems of developing an algorithm for managing power flows based on the coordinated work of active and reactive power sources and principles of demand management of active consumers. The multi-agent power flow control was realized in the AnyLogic program, the simulation modeling of the electrical network modes was performed in Matlab Simulink with assumptions of linear characteristics of voltage loads. A method has been developed to control the operation modes of smart distribution electric networks of railways based on the presented power flow control algorithm, the hallmarks of which are the use of linearized equations for determining control actions in small increments, which allows high speed data analysis in real time without calculating steady-state modes with disturbances. The obtained simulation results prove the validity of power flow control methods for voltage stabilization based on multi-agent control and the possibility of their practical implementation on modern equipment in smart distribution networks of railways.
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Singh, Pushpendra, Kedar Sharma, and Akash Talwariya. "Advancement of power generation system by instalment of solar photovoltaic system for multiple wells: A Case Study." Journal of Physics: Conference Series 2208, no. 1 (March 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2208/1/012009.
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Abstract This paper proposes the solution for electricity issues in agriculture sector of particular location in Rajasthan. The issues reported about the availability of ground water and availability of Electricity from electric Grid. The study proposes a centralized solar photovoltaic system for available nearby open wells, which are being operated by diesel engine pump-sets, which emits greenhouse gases and pollute the environment. The proposed centralized photovoltaic system will provide power for submersible pump sets. The additional available power can be stored in batteries, utilized for charging the batteries of electrical vehicles or sell back to electric grid. This paper shows the cost analysis for proposed systems that can be applied and effective centralized solar photovoltaic system in the area considered for study. Centralized solar photovoltaic generation supply to different consumers in different slots and may increase the conflict between consumers, demand side management provide suitable solution to consumers is proposed and analyse in future work.
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Jefry, N. A., and N. A. Zambri. "Suitability of Demand Response Program Equipped with Solar Energy in UTHM." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 2 (May 1, 2017): 294. http://dx.doi.org/10.11591/ijeecs.v6.i2.pp294-300.
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Demand Response Program (DRP) is a program developed in western country to cater high load demand. This system enhance the quality of the current smart grid system. This program encourage two-ways interaction between the consumer and the utility provider. The consumers are required to reduce their load consumption upon request by the utility provider, thus avoiding them from paying for the high load demand. However, the current design of DRP is seem to put the user at disadvantages. Thus, deployment of the new practice equipped with renewable energy sources will make the system more user- friendly. As DRP prove to be beneficial in many terms, this project is conducted to find DRP relevancy to be implemented in Universiti Tun Hussein Onn Malaysia (UTHM). According to the electrical consumption data disclosed by Facility Management Division, the power consumed by UTHM is remarkably high especially during 11.00 am to 12.00 pm. Thus, DRP is being proposed to be put into practise during this period. Nonetheless, it is not economically wise to implement the program to the whole campus. Therefore, three buildings with the most power consumption had been chosen for this study. They are the library, Faculty of Civil and Environmental Engineering (FKAAS) building and Faculty of Technology and Vocational Education (FPTV) building. The building power consumption and economic evaluation had been analysed using HOMER simulation. From the analysis, the combination of FPTV and FKAAS had been chosen for DRP implementation. The reason are, these buildings have superiority over the library in term of reliability and effectiveness. In terms of economy, it is almost equivalent to each other.
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Kumar, D. Sai. "Demand Side Management Techniques for Peak Reduction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2911–13. http://dx.doi.org/10.22214/ijraset.2021.36979.
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Industrial growth is the back bone for the development of any nation. Industries are mainly dependent on electrical energy. But from the various studies, the sources for electrical energy are decreasing gradually, and in turn, the gap is increasing between the supplier and the load. The solution for this scenario is optimal utilization of resources. To overcome this problem , the concept Demand Side Management (DSM) has emerged in Power System Planning and Management. The principle objective of DSM is mutual understanding between the supplier and the consumer for maximizing benefits and minimizing inconvenience. The aim of this research work is selection and application of appropriate DSM techniques to industrial and domestic loads for peak load management and energy conservation, that is to control the maximum demand during the peak hours and saving the energy by using the energy efficient and intelligent appliances like air conditioners and water heaters. DSM includes techniques like the End Use Equipment Control, the Load Priority Technique, he Peak Clipping & Valley filling, the Differential Tariff and Resizing of the equipment. Depending upon the application, all the techniques may be applied sequentially, or only a few of them can be applied. There is a lot of ambiguity in the selection of DSM techniques, because the application of each DSM technique depends on the case study and the problem associated with the respective case study. After comprehensive understanding of a particular case, a thorough investigation and subsequent data analysis pave the way for the selection of appropriate DSM technique/techniques
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Borst, Fabian, Nina Strobel, Thomas Kohne, and Matthias Weigold. "Investigating the Electrical Demand-Side Management Potential of Industrial Steam Supply Systems Using Dynamic Simulation." Energies 14, no. 6 (March 10, 2021): 1533. http://dx.doi.org/10.3390/en14061533.
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The increasing share of volatile, renewable energies, such as wind and solar power, leads to challenges in the stabilization of power grids and requires more flexibility in future energy systems. This article addresses the flexibilization of the consumer side and presents a simulation-based method for the technical and economic investigation of energy flexibility measures in industrial steam supply systems. The marketing of three different energy-flexibility measures—bivalence, inherent energy storage and adjusting process parameters—both at the spot market and at the balancing power market, are investigated from a technical as well as an economic point of view. Furthermore, the simulation-based methodology also considers pressure and temperature fluctuation induced by energy-flexibility measures. First, different energy-flexibility measures for industrial steam supply systems are introduced. Then, the physical modeling of the steam generation, distribution, and consumption as well as measure-specific control strategies will be discussed. Finally, the methodology is applied to a steam supply system of a chemical company. It is shown that the investigated industrial steam supply system shows energy-flexibility potentials up to 10 MW at peak and an annual average of 5.6 MW, which highly depend on consumer behavior and flexibility requirements.
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Jin, Zuo, Zhang, and Sun. "An Orderly Power Utilization Scheme Based on an Intelligent Multi-Agent Apanage Management System." Energies 12, no. 23 (November 29, 2019): 4563. http://dx.doi.org/10.3390/en12234563.
Full textAbstract:
Orderly power utilization (OPU) is an important measure to alleviate contradiction between supply and demand in a power system peak load period. As a load management system becomes smarter, it is necessary to fully explore the interactive ability among users and make schemes for OPU more applicable. Therefore, an intelligent multi-agent apanage management system that includes a mutual aid mechanism (MAM) is proposed. In the decision-making scheme, users’ participation patterns and the potential of peak shifting and willingness are considered, as well as the interests of both power consumers and power grid are comprehensively considered. For residential users, the charging time for their electric vehicles (EVs) is managed to consume the locally distributed power generation. To fully exploit user response potential, the algorithm for improved clustering by fast search and find of density peaks (I-CFSFDP), i.e., clusters the power load curve, is proposed. To conduct electrical mutual aid among users and adjust the schemes reasonably, a multi-objective optimization model (M2OM) is established based on the cluster load curves. The objectives include the OPU control cost, the user’s electricity cost, and the consumption of distributed photovoltaic (PV). Our results of a case study show that the above method is effective and economical for improving interactive ability among users. Agents can coordinate their apanage power resources optimally. Experiments and examples verify the practicability and effectiveness of the improved algorithm proposed in this study.
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Dalala, Zakariya M., Mohammad Alnawafa, Osama Saadeh, and Emad Alnawafa. "Reducing Commuter CO2 Footprint through Transit PV Electrification." Sustainability 12, no. 16 (August 9, 2020): 6406. http://dx.doi.org/10.3390/su12166406.
Full textAbstract:
The transport sector is a major consumer of energy, and thus a major contributor to greenhouse gas (GHG) emissions. The introduction of Electric Vehicles (EVs) has helped in mitigating some of the energy demands presented by the transportation system, though the electrical energy still needs to be secured through conventional and renewable resources. Searching for a new power source for vehicles has become necessary, due to incentives and policy initiatives to counter fossil greenhouse gas emissions. This study provides a new efficient Photovoltaic (PV) powered transportation system, which may be utilized instead of traditional public transportation systems. The main idea is to transform the transportation systems used by large campuses into green systems by deploying educated scheduling approaches and utilizing existing renewable energy infrastructures. The German Jordan University (GJU) campus was chosen as a case study. The presented work describes a comprehensive methodology to exploit the full capacity of the existing PV power plant coupled with the rescheduling of the transportation fleet to meet the energy availability and consumption demand. The proposed technique audits the existing renewable energy power plants for optimum operation. The results validate the efficiency of the proposed system and its ability to reduce carbon dioxide (CO2) emissions compared to traditional transportation systems with an acceptable payback period.