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1
Perez-Mora, Nicolas, Victor Martinez-Moll, and Vincent Canals. "DHC Load Management Using Demand Forecast." Energy Procedia 91 (June 2016): 557–66. http://dx.doi.org/10.1016/j.egypro.2016.06.198.
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Dey, Bishwajit, Soham Dutta, and Fausto Pedro Garcia Marquez. "Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System." Sustainability 15, no. 3 (2023): 1795. http://dx.doi.org/10.3390/su15031795.
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In a typical microgrid (MG) structure, the requisite of load varies from hour to hour. On the basis of the rise and fall of the load demand curve, the power system utilities fix the rate of electric power at different times of the day. This process is known as time-of-usage (TOU)-based pricing of electricity. The hourly basis load demand can be categorized into elastic hourly load demand and inelastic hourly load demand. For the duration of the peak hours, when the utility charges more, the elastic loads are shifted to low demand hours by the demand side management (DSM) to save the cost. This rebuilds the total demand model on the pillars of demand price elasticity. Keeping in view the fact that the total load in an hour in an MG structure consists of 10% to 40% of elastic loads, the paper proposes an intelligence-technique-based DSM to achieve reduction in the overall cost of using loads in an MG structure. Seven different cases are studied which cover diverse grid participation and electricity market pricing strategies, including DSM programs. The results obtained for all the MGs showcase the applicability and appropriateness of using the proposed DSM strategy in terms of cost savings.
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Elghitani, Fadi, and Ehab El-Saadany. "Smoothing Net Load Demand Variations Using Residential Demand Management." IEEE Transactions on Industrial Informatics 15, no. 1 (2019): 390–98. http://dx.doi.org/10.1109/tii.2018.2852482.
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Tabassum, Zahira, Akash D, Bavaji J, Desha R, and Anil Kumar. "Residential Load Management System using IoT." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 1225–31. http://dx.doi.org/10.51201/jusst/21/07291.
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In the recent years the technological growth is observed at a higher pace and as the technology evolves the dependency of human on technology also increases and as the result the number of appliances or devices per household increases. The increase in the number of appliances in a household contribute a major share in increasing the demand for electricity during the peak hours at a residential level. The conventional method of meeting the demand by generating power or setting up of new generating plants is one of non-feasible method and it impacts the many factors such as cost, pollution, losses etc. One of the possible solutions for the control of demand is by Demand Side Management. The paper proposes a implementation of a Demand Side Management for Residential Users using IoT.
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Djokic, Sasa Z., and Igor Papic. "Smart Grid Implementation of Demand Side Management and Micro-Generation." International Journal of Energy Optimization and Engineering 1, no. 2 (2012): 1–19. http://dx.doi.org/10.4018/ijeoe.2012040101.
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This paper analyses the influence and effects of demand side management (DSM) and micro-generation (MG) on the operation of future “smart grids.” Using the residential load sector with PV and wind-based MG as an example, the paper introduces a general methodology allowing to identify demand-manageable portion of the load in the aggregate demand, as well as to fully correlate variable power outputs of MG with the changes in load demands, including specific DSM actions and schemes. The presented analysis is illustrated using a detailed model of a typical UK LV/MV residential network.
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Hijaz Paul, Ward Ul, Anwar Shahzad Siddiqui, and Sheeraz Kirmani. "Intelligent Load Management System Development with Renewable Energy for Demand Side Management." International Journal of Advanced Engineering and Management Research 08, no. 02 (2023): 140–53. http://dx.doi.org/10.51505/ijaemr.2023.8213.
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Electric smart grid reliability and stability could be increased by the application of demand response initiatives and renewable energy resources. This study provides a brand new demand side management paradigm for smart grids with renewable energy integration that is based on intelligent optimisation. The suggested system combines real-time demand response programmes from electric utility companies and makes use of fuzzy logic to forecast consumer energy consumption patterns. Using demand response programmes, a smart energy management controller adjusts consumer energy usage forecasts to produce an operation schedule. Using simulations employing real-world data, we assess the efficacy of the suggested intelligent demand side management framework. According to the findings, compared to the load management-free method, total electricity costs and carbon emissions have significantly decreased. A potential strategy for demand side management with the integration of renewable energy, the proposed intelligent hybrid optimisation method of load management achieves superior performance in regulating energy consumption, peak loads, and carbon emissions. By presenting a useful and effective paradigm for demand-side management with renewable energy integration, this research makes a contribution to the field of energy management.
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Priolkar, Jayesh, and ES Sreeraj. "Optimal scheduling and demand response implementation for home energy management." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1352. http://dx.doi.org/10.11591/ijece.v14i2.pp1352-1368.
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The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.
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Priolkar, Jayesh, and ES Sreeraj. "Optimal scheduling and demand response implementation for home energy management." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1352–68. https://doi.org/10.11591/ijece.v14i2.pp1352-1368.
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The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-toaverage ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLCbased demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.
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MANOJ, D. PATIL, and BHUPAL KUMBHAR ANAND. "HEURISTIC OPTIMIZATION FOR DEMAND SIDE MANAGEMENT IN SMART GRID." JournalNX - A Multidisciplinary Peer Reviewed Journal 2, no. 7 (2016): 47–51. https://doi.org/10.5281/zenodo.1469436.
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Demand side management (DSM) is major aspects in a smart grid which allows customers to take decision based on information for their consumed energy, which also helps in reduction of peak load demand for the energy producers which enables the reshaping of the load profile. Advantage of doing this will increase the sustainability of the smart grid and benefits in reduction of operational cost and lower production of green house gases. The various strategies available for demand side management uses an traditional energy management system which possesses and certain disadvantages related to algorithms. Moreover the existing strategies can handle only a limited number of controllable loads with limited types. In this paper presentation related to demand side management is implemented and explained for future grid, termed as smart grid. Load shifting technique before 24 hour was implemented and explained in this paper. Heuristic evolutionary algorithm(EA) is used for solving the minimization problem. https://journalnx.com/journal-article/20150106
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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 (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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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 (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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Nirmala Jegadeesan, Nirmala Jegadeesan, G. Balasubramanian Nirmala Jegadeesan, N. Hemavathi G. Balasubramanian, and Yu-Chi Chen N. Hemavathi. "Empirical Validation of Fuzzy Logic Based Predictive Load Scheduling in Mimic Home Energy Management System." 網際網路技術學刊 24, no. 7 (2023): 1429–36. http://dx.doi.org/10.53106/160792642023122407004.
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<p>Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and consumers tolerance. Though many research works concentrate on load scheduling, very few works concentrated on real time scenario. On the other hand, research work concentrated on optimal load scheduling through fuzzy logic requires incorporation of fuzzy based system in either simulated or real-time home energy management system. Hence, the proposal aims to schedule the loads in a simulated home energy management system through fuzzy logic controller using MATLAB Simscape with required subsystems. The proposed simulated environment considers four different resistive loads. Intelligent scheduling is aimed to achieve efficient load scheduling. A fuzzy controller has three inputs namely integrated source, state of charge of battery, and power demand whereas probability of scheduling is considered as output. The efficiency of the proposed fuzzy-based load scheduling scheme is evaluated under various load conditions for different sub-systems with varying input power. The results exhibit the efficacy of the proposed scheme.</p> <p>&nbsp;</p>
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Raushan Kumar. "Power system load frequency management using fuzzy logic." Journal of Electrical Systems 20, no. 7s (2024): 2478–82. http://dx.doi.org/10.52783/jes.4055.
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A power system is a sizable, intricately linked network that has multiple sources and loads. The population growth causes variations and unpredictability in load demand. The utilities are responsible for meeting load demand while abiding by operational restrictions on the power system. Control of load frequency is alone in charge of providing the customers with sustainable electrical power. Its primary purpose is to control system frequency and power flow in additional areas within advised bounds by controlling tie-line power and the utility generator's output power in reaction to changes in load without compromising system frequency. The paper's recommended concept illustrates the load frequency control of two area systems using fuzzy logic, fuzzy tuned proportional integral, conventional proportional integral, and proportional integral derivative controllers. SIMULINK is used for simulation research.
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V., Manoj Kumar, Bharatiraja Chokkalingam, and Devakirubakaran S. "Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids." PLOS ONE 19, no. 3 (2024): e0300803. http://dx.doi.org/10.1371/journal.pone.0300803.
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The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid’s balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.
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Ayyub, Aimen, Mian Muhammad Hamid Nasir, Abdul Kashif Janjua, and Abraiz Khattak. "Demand Side Management Using Battery Energy Storage System for a Sample Residential Load of Pakistan." Pakistan Journal of Engineering and Technology 5, no. 1 (2022): 78–82. http://dx.doi.org/10.51846/vol5iss1pp78-82.
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This research is a step towards the sustainable energy development through the mitigation of the electrical energy crisis using the batteries for not only solving the intermittence but also for demand side management., mostly faced when the energy demand reaches the peak value. An algorithm is presented for the optimal usage of energy by demand side management (DSM) through peak shaving using battery energy storage system (BESS) in the residential areas for developing countries like Pakistan. The data set used is the PRECON data set based on the original demand data collected of 42 houses of Lahore, Pakistan for years 2018-2019. The load profiles for the individual and group of houses are used and analyzed. Daily, monthly and annual load factors are calculated for the groups of houses based on the stochastic data based on realistic data. Daily load factors are improved through demand side management technique such as peak shaving at individual and municipal level. Loads are categorized from the given load profiles into base load, peak load and mid-range load. The cost advantage related to the type of the battery is also considered. Research concludes to the advantages of peak shaving, load factor improvement, load profile improvement and possibility of providing optimal and reliable energy with reasonable energy prices.
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Youssef, El-Nasser S., Fabrice Labeau, and Marthe Kassouf. "Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters." Energies 15, no. 20 (2022): 7807. http://dx.doi.org/10.3390/en15207807.
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The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency.
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Talwariya, Akash, Santosh Kumar Sharma, Pushpendra Singh, and Mohan Kolhe. "GAME THEORY: DEMAND SIDE MANAGEMENT WITH DG’S AND STORAGE UNITS." International Journal of Technical Research & Science 3, no. 04 (2018): 129–33. http://dx.doi.org/10.30780/ijtrs.v3.i3.2018.020.
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DSM (Demand side management) is an approach have the objective to make our consumers energy efficient for long term. DSM can be designed to control the electricity consumption of individual users. DSM categorized the load in two groups’ base line load which are constant and uninterruptible loads and interruptible loads which can shift from peak duration to normal duration and their consumption duration is also manageable. Demand side Management for the consumer in the presence of storage units and DG’s is executed by NonCooperative Game Theory. Non-cooperative game theory deals with individual consumers without any cooperation with other consumers and provide the benefit to the active consumers who manage their load from peak hour to normal hour to reduce the PAPCR (peak to average power consumption ratio). DG’s generation is variable depending upon load forecasting and storage units also have some minimum and maximum capacity of storage will manage by machine learning with load forecasting used as learning source and storage units characteristics will manage by non-cooperative game theory. If assume that the energy provider adjudicate the electricity cost with reference to normal peak hour, consumer stop consuming electricity during peak hour and started to sell the electricity to energy provider to enhance their profit and store electricity during low cost hours and introduce a new peak at a different time frame. The system required another game for energy provider to manage the electricity cost on real time bases to stop the generation of new peak and try to minimize the PAPCR and minimize the benefit of the energy provider. Every energy provider and consumer want to earn maximum benefit. A new stackelberg game is introduce to provide maximum benefit to the provider. When a consumer shift their load to newly introduced peak duration, the energy provider will respond and accommodate the electricity price again. In stackelberg game energy provider part as leader and consumer will participate as followers. Objective of the research is to provide maximum benefit to the energy provider and active consumers; 1. INTRODUCTION design a mathematical model through the algorithm of non-cooperative game and stackelberg game for both consumer and energy provider respectively to find out the nash equilibrium between consumer and energy provider. The outcome of the problem will introduce by two defined games non-cooperative game will reduce the cost of electricity for the consumers have storage units and DG’s and manage the load and Stackelberg game will minimize the power to average consumption ratio.
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Ha, Duy Long, Stéane Ploix, Eric Zamai, and Mireile Jacomino. "Realtimes dynamic optimization for demand-side load management." International Journal of Management Science and Engineering Management 3, no. 4 (2008): 243–52. http://dx.doi.org/10.1080/17509653.2008.10671051.
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Azit, A. H., S. B. Sairan, and G. B. Jasmon. "Feeder load analysis for utility demand side management." IEE Proceedings C Generation, Transmission and Distribution 136, no. 5 (1989): 303. http://dx.doi.org/10.1049/ip-c.1989.0040.
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Sargunaraj, S., D. P. Sen Gupta, and S. Devi. "Short-term load forecasting for demand side management." IEE Proceedings - Generation, Transmission and Distribution 144, no. 1 (1997): 68. http://dx.doi.org/10.1049/ip-gtd:19970599.
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Yan, Chun Hua, Xiang Ping Lai, Mi Fang Yan, and Mao Hua Shan. "Preliminary Design of Demand Response Management System." Applied Mechanics and Materials 448-453 (October 2013): 2769–74. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2769.
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This paper describes the concept and classification of demand response as well as the application status of demand response at home and abroad, designs the overall technical architecture of demand response system, and takes direct load control as an example, then design the entire business process, the internal and external information flow of direct load control based Demand Response system. The research results of this paper will provide China useful references for the future development of demand response management system with independent intellectual property rights.
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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 (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 (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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Li, Yu Kai, Hong Ouyang, Jia Kui Zhao, Xiu Kai Rong, and Yi Dong. "Coordinated Charging of EVs Based on Demand-Side Management." Applied Mechanics and Materials 596 (July 2014): 760–65. http://dx.doi.org/10.4028/www.scientific.net/amm.596.760.
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Electric vehicles (EVs) are adopted as an effective way to reduce the pollution of atmosphere. However, if EVs are implemented in a large scale without control, peak load would increase significantly and the grid may be overloaded. Based on Demand-Side Management (DSM), an coordinated charging method for EVs to address the problem of that is proposed. Considering load fluctuation of power grid as well as time-of-use (TOU) power price, a multi-objective optimization model is formulated to minimize the charging cost and restrain the load fluctuation. Overall power load is composed of original daily load and EV charging load, which is obtained through Monte Carlo simulations. On the basis of this, the optimal number of charging EVs in each period is worked out with NSGA-II algorithm. At last, the case study carried out shows the reasonability of this method.
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Li, Tao, Kun Peng Xu, and Xue Qing Qi. "Design of Intelligent Home Energy Management for Demand Response Applications." Advanced Materials Research 827 (October 2013): 78–83. http://dx.doi.org/10.4028/www.scientific.net/amr.827.78.
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A Home Energy Management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customers preference and load priority. This paper presents an intelligent HEM for managing high power consumption household appliances for demand response (DR) analysis. The proposed HEM manages household loads according to their preset priority and guarantees the total household power consumption below certain levels.Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.
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Serebrennikov, B., K. Petrova, and S. Serebrennikov. "Comprehensive Management of Electricity Demand Distribution in Time." Problems of the Regional Energetics, no. 2(58) (May 2023): 26–41. http://dx.doi.org/10.52254/1857-0070.2023.2-58.03.
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The paper is aimed to strengthen the controllability of electricity consumption mode at all structural layers of the country’s energy system (ES) to establish the optimal load curve in the PS. Following this goal, the energy system was broken down into seven structural layers - from the technological operation to the ES. For each layer, an expert assessment of the effectiveness of six institutional and operational methods of electricity demand-side management (DSM) was done. The integrated application of the suggested methods was tested in two industrial consumers, which proved the effectiveness of this approach for leveling their aggregated load curve. To ensure an appropriate economic impact on the electricity demand, given the influence of individual consumers on the load curve fluctuation in the ES, a particular price function considering the cross-correlation coefficient of the load curves was developed. It was proved that the complex DSM methods application significantly improved the controllability of the electricity consumption mode. To incentivize consumers to adjust their electricity consumption, a special price system functionally related to the cross-correlation coefficient of the consumer and the ES load curves was developed. The marginal price values depending on the cross-correlation coefficient were defined, while the intermediate price values were calculated by the functional transformation of the ES load curve into the price scale. The significance of the research results lies in the fact that ranking the DSM methods by the priority of application for various structural layers of ES and their integrated application almost doubled the DSM effectiveness.
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Atawi, Ibrahem E., Ahmed M. Kassem, and Sherif A. Zaid. "Modeling, Management, and Control of an Autonomous Wind/Fuel Cell Micro-Grid System." Processes 7, no. 2 (2019): 85. http://dx.doi.org/10.3390/pr7020085.
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This paper proposes a microelectric power grid that includes wind and fuel cell power generation units, as well as a water electrolyzer for producing hydrogen gas. The grid is loaded by an induction motor (IM) as a dynamic load and constant impedance load. An optimal control algorithm using the Mine Blast Algorithm (MBA) is designed to improve the performance of the proposed renewable energy system. Normally, wind power is adapted to feed the loads at normal circumstances. Nevertheless, the fuel cell compensates extra load power demand. An optimal controller is applied to regulate the load voltage and frequency of the main power inverter. Also, optimal vector control is applied to the IM speed control. The response of the microgrid with the proposed optimal control is obtained under step variation in wind speed, load impedance, IM rotor speed, and motor mechanical load torque. The simulation results indicate that the proposed renewable generation system supplies the system loads perfectly and keeps up the desired load demand. Furthermore, the IM speed performance is acceptable under turbulent wind speed.
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Khan, Muhammad Bilal, and Tahir Mehmood. "Two-Stage Approach for Demand Side Management in Multi-Smart Grids." International Journal of Emerging Engineering and Technology 2, no. 2 (2023): 8–12. http://dx.doi.org/10.57041/7y7y7e30.
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In the face of escalating electricity demand driven by technological advancements and a growing population, efficient management becomes imperative. This study explores demand-side management (DSM) within smart grids (SGs) to minimize customer bills, alleviate peak loads, and mitigate losses. The research employs diverse strategies and compares their effectiveness, focusing on optimal load consumption across smart microgrids. Key contributions include a DSM strategy for cost reduction, efficient peak load reduction, and active power loss minimization in smart microgrids. The proposed approach is validated through simulations, with results contributing to real-world SG implementation. The study highlights a 23.4% and 31.6% cost saving for residential and commercial customers, respectively, and emphasizes the significance of load management in enhancing operational efficiency and cost-effectiveness within the energy hub framework. The research provides valuable insights for developing advanced consumption management strategies, ensuring energy efficiency and sustainability in future power systems.
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Mulyono, Mulyono. "Implementasi Demand Side Management (DSM) Pada Instalasi Pengolahan Air PDAM Mulia Baru." Energi & Kelistrikan 12, no. 1 (2020): 43–52. http://dx.doi.org/10.33322/energi.v12i1.934.
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The Mulia Baru Water Treatment Plant (IPA) is one of 6 Water Treatment Plants owned by the Ketapang Regency PDAM that is indicated to use a large amount of electrical energy. From the data obtained during one year (January to December 2017), the average monthly electricity consumption is 128,016,667 kWh with an average.Drinking Water Production Volume per month of 144,119,833 m3. Therefore the average Specific Energy Consumption (SEC) value for 2017 is 0.89. Through the application of the Peak Clipping and Strategic Concervation DSM program, a decrease in peak load occurs at a time interval between 17:00 and 22:00, in this condition a decrease in load of 0.47 (%).For greater savings, it is necessary to schedule the operation of large loads at peak load times.
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Németh, Dániel István, and Kálmán Tornai. "Electrical Load Classification with Open-Set Recognition." Energies 16, no. 2 (2023): 800. http://dx.doi.org/10.3390/en16020800.
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Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads.
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Cao, Jing Jin, and Li Jun Qin. "The Summary of Load Forecasting Method Based on Power Demand Side Management." Applied Mechanics and Materials 494-495 (February 2014): 1615–18. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1615.
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Weather, industrial structure and power demand side management can affect the load, however, the power demand side management has the most direct influence on the change of load. This paper first introduces the purpose of the electric power demand side management, then it introduces the relationship between the load forecasting and the power demand side management, furthermore it respectively introduces a variety of load forecasting method according to the time distribution, finally it compares the advantages and disadvantages of load forecasting methods for each time distribution respectively, and this will provide a new basis for power grid planning.
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Krishna, T. V., M. K. Maharana, and C. K. Panigrahi. "Integrated Design and Control of Renewable Energy Sources for Energy Management." Engineering, Technology & Applied Science Research 10, no. 3 (2020): 5857–63. https://doi.org/10.5281/zenodo.3934808.
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Growing population and expanding industry set off the demand for electrical energy and issues, such as the problem of peak load demand, emerge. To balance the supply and load demand problem, the energy management system has the vital role of Electric Peak shaving with the integration of microgrid into the utility grid. The combination of demand-side management with storable energy sources helps us resolve the matters concerned with the peak load demand. However, in a microgrid, whenever the distributed energy sources are interconnected, the DC bus link voltage will vary due to the inherent behavior of each source as they mainly depend on geographical conditions. This work proposes voltage droop control strategy to keep the DC bus link voltage at a constant value. Also, it gives an overlook of the present power sector scenario of India and a reassessment of the demand side management system and how it is utilized in electrical peak shaving.
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Akpojedje, F.O., and F.O. Odiase. "Modeling of Demand Side Management Scheme for Matching Electricity Supply with Demand in the Nigerian Power System." Nigerian Research Journal of Engineering and Environmental Sciences 6, no. 2 (2021): 618–25. https://doi.org/10.5281/zenodo.5805205.
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<em>The unserved load in the Nigerian power system currently is as a result of the ever-growing gap between electric energy demand and supply which is one of the major causes of frequent power interruptions. Thus, stabilizing the network, equalization of the available electricity supply with the energy demand becomes necessary, of which demand side management (DSM) is a veritable tool being deployed to match the ever-growing energy demand with the available electricity supply. Consequently, this paper presents a concise overview of demand side management scheme and its modeling for matching electricity supply with the ever-growing energy demand in the Nigerian power system. The residential, commercial, industrial and special customers load in the network were used for the modeling and binary particle swarm optimization (BPSO) algorithm was adopted for optimizing the models. The load shifting technique was used to control and carried out load shifting during the peak period. Both customers and utility benefits were considered using the BPSO algorithm and the results showed that the technique can match the available electricity supply with the ever-growing energy demand</em><em>.</em>
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Yu, Fan, Lei Wang, Qiaoyong Jiang, Qunmin Yan, and Shi Qiao. "Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management." Energies 15, no. 12 (2022): 4198. http://dx.doi.org/10.3390/en15124198.
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Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF.
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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 (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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McBee, Kerry D., Jacquelyn Chong, and Prasanth Rudraraju. "Demand Side Management Effects on Substation Transformer Capacity Limits." Applied Sciences 9, no. 16 (2019): 3266. http://dx.doi.org/10.3390/app9163266.
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In high penetrations, demand side management (DMS) applications augment a substation power transformer’s load profile, which can ultimately affect the unit’s capacity limits. Energy storage (ES) applications reduce the evening peaking demand, while time-of-use rates incentivize end-users to charge electric vehicles overnight. The daily load profile is further augmented by high penetrations of photovoltaic (PV) systems, which reduce the midday demand. The resulting load profile exhibits a more flattened characteristic when compared to the historical cyclic profile. Although the initial impact of PV and ES applications may reduce a unit’s peak demand, long-term system planning and emergency conditions may require operation near or above the nameplate rating. Researchers have already determined that a flattened load profile excessively ages a unit’s dielectrics more rapidly. The focus of this research was to identify an approach for establishing new transformer capacity limits for units serving flattened load profiles with a high harmonic content. The analysis utilizes IEEE standards C57.91 and C57.110 to develop an aging model of a 50 MVA SPX Waukesha transformer. The results establish a guideline for determining transformer capacity limits for normal operation, long-term emergency operation, and short-term emergency operation when serving systems with high penetrations of DSM applications.
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Eichkoff, Henrique S., Daniel P. Bernardon, Julio A. Bitencourt, et al. "Tolerance-Based Demand-Side Management for Load Shifting in Rural Areas of Southern Brazil." Energies 16, no. 8 (2023): 3395. http://dx.doi.org/10.3390/en16083395.
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In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called “irrigators”, and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distribution systems located in rural areas, employing the load shifting strategy for irrigator customers. The methodology proposed in this paper is not an entirely novel approach, but one specifically developed for the context of irrigator customers, a subject little studied in the literature. The load management model proposed by this study is segmented into three hierarchical levels. The first level is the identification of the electrical characteristics of the distribution systems, the second level is the power flow analysis of the distribution networks, and the third and last level consists in the application of load shifting to the irrigator customers of these electrical systems. The load shifting strategy is modeled by a linear programming algorithm and is only applied to irrigator customers in situations of excessive loading on power grid. The case studies were conducted on three distribution systems of a power utility, with more than 150 irrigator customers. The DSM model based on the load shifting strategy reduced the maximum demand and daily load variations on the three rural feeders evaluated. The proposed changes in load patterns can ensure the continuity of electric power supply service in future even with the high concentration of load on distribution networks, benefiting customers and power utilities.
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Sanghvi, Arun P. "Flexible Strategies for Load/Demand Management Using Dynamic Pricing." IEEE Power Engineering Review 9, no. 2 (1989): 41–42. http://dx.doi.org/10.1109/mper.1989.4310463.
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Giusti, Alessandro, Matteo Salani, Gianni A. Di Caro, Andrea E. Rizzoli, and Luca M. Gambardella. "Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management." IEEE Transactions on Smart Grid 5, no. 1 (2014): 92–101. http://dx.doi.org/10.1109/tsg.2013.2267396.
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Sanghvi, A. P. "Flexible strategies for load/demand management using dynamic pricing." IEEE Transactions on Power Systems 4, no. 1 (1989): 83–93. http://dx.doi.org/10.1109/59.32461.
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Gustafson, M. W. "Hour-by-Hour Load Management Effects on System Demand." IEEE Transactions on Power Systems 2, no. 3 (1987): 677–83. http://dx.doi.org/10.1109/tpwrs.1987.4335194.
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Abdelfattah, Asmaa I., Mostafa F. Shaaban, Ahmed H. Osman, and Abdelfatah Ali. "Optimal Management of Seasonal Pumped Hydro Storage System for Peak Shaving." Sustainability 15, no. 15 (2023): 11973. http://dx.doi.org/10.3390/su151511973.
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Power demand varies on a daily and seasonal basis. Responding to changing demands over time is challenging for energy suppliers as it causes expensive power plants to operate in high-demand seasons, usually summer, increasing the cost of electricity. Peak load shaving makes the load curve flatten by reducing the peak load and shifting it to times of lower demand, hence reducing the operation of expensive power plants. Hence, there is a need for large-scale and long-term ESS to store energy in the time of low-demand seasons for future utilization in the highest-demand ones. In this work, an energy management system (EMS) is developed to optimally manage a grid-connected pumped hydro storage (PHS) for peak shaving. The proposed model incorporates a dynamic economic dispatch (DED) over a study period of one year; hence, a DC power flow analysis considering transmission constraints is utilized to ensure a fast load flow estimation and a manageable simulation time. The framework can be adopted to assess the long-term profitability of PHS-utilizing GAMS as an optimization tool. Further, to draw conclusions that would suit the characteristics of the demand pattern. This analysis is essential to motivate the construction of new seasonal PHS plants due to the high construction costs they are identified with, especially in geographical areas where this technology is not yet considered or is hard to construct. The simulation results demonstrate that integrating 1500 MWh PHS reduced the operation of expensive thermal units by 1224 MWh annually. Further, a reduction in operation costs was recorded after integrating a PHS unit that ranged from 2.6 M to 22 M USD/year, depending on the storage capacity.
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Amir, Vahid, Shahram Jadid, and Mehdi Ehsan. "Operation of networked multi-carrier microgrid considering demand response." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 2 (2019): 724–44. http://dx.doi.org/10.1108/compel-07-2018-0276.
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Purpose Microgrids are inclined to use renewable energy resources within the availability limits. In conventional studies, energy interchange among microgrids was not considered because of one-directional power flows. Hence, this paper aims to study the optimal day-ahead energy scheduling of a centralized networked multi-carrier microgrid (NMCMG). The energy scheduling faces new challenges by inclusion of responsive loads, integration of renewable sources (wind and solar) and interaction of multi-carrier microgrids (MCMGs). Design/methodology/approach The optimization model is formulated as a mixed integer nonlinear programing and is solved using GAMS software. Numerical simulations are performed on a system with three MCMGs, including combined heat and power, photovoltaic arrays, wind turbines and energy storages to fulfill the required electrical and thermal load demands. In the proposed system, the MCMGs are in grid-connected mode to exchange power when required. Findings The proposed model is capable of minimizing the system costs by using a novel demand side management model and integrating the multiple-energy infrastructure, as well as handling the energy management of the network. Furthermore, the novel demand side management model gives more accurate optimal results. The operational performance and total cost of the NMCMG in simultaneous operation of multiple carriers has been effectively improved. Originality/value Introduction and modeling of the multiple energy demands within the MCMG. A novel time- and incentive-based demand side management, characterized by shifting techniques, is applied to reshape the load curve, as well as for preventing the excessive use of energy in peak hours. This paper analyzes the need to study how inclusion of multiple energy infrastructure integration and responsive load can impact the future distribution network costs.
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Elias, H. Ait Aissa, and UĞURENVER Abbas. "Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition." Engineering and Technology Journal 9, no. 06 (2024): 4241–48. https://doi.org/10.5281/zenodo.12065047.
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Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.
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Philipo, Godiana Hagile, Josephine Nakato Kakande, and Stefan Krauter. "Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping." Energies 15, no. 14 (2022): 5215. http://dx.doi.org/10.3390/en15145215.
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Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.
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B., Ashok Kumar, Senthilrani S., Rajeswari J., and T. Rajapandi. "Energy Management in a Standalone PV System with Priority Controller." E3S Web of Conferences 387 (2023): 02008. http://dx.doi.org/10.1051/e3sconf/202338702008.
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In many developing countries, meeting the energy demand has become a major challenge. Such problem is more prominent in rural and remote areas of the country. The load requirements in these areas are less and the same can be addressed with renewable energy sources. The proposed work deals with a MPPT based standalone PV system using a priority controller. The system can be used to meet out the critical load demands in rural areas. Due to change in weather conditions, an unregulated output in PV array is observed. Hence, maximum power is tracked using a DC-DC converter, where the tracked data is with respect to temperature and irradiance levels. To acquire the maximum power point (MPP), an incremental conductance (IC) algorithm is employed and it is executed by controlling the duty cycle of DC-DC boost convertor. Thus, the attainment of energy management in loads and battery storage is supported by priority load control algorithm. The proposed system assures better energy management and supplies energy for critical loads. The entire system has been simulated and validated using MATLAB/SIMULINK.
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Ko, Wonsuk, Hamsakutty Vettikalladi, Seung-Ho Song, and Hyeong-Jin Choi. "Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program." Applied Sciences 10, no. 5 (2020): 1751. http://dx.doi.org/10.3390/app10051751.
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In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.
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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 (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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Manan Desai. "Energy Management through V2G concept with EV Charging/Discharging Strategy." Journal of Electrical Systems 20, no. 11s (2024): 4774–85. https://doi.org/10.52783/jes.8736.
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The integration of Vehicle-to-Grid (V2G) technology has emerged as a promising solution for managing energy demand, enhancing grid stability, and reducing peak loads. This paper explores an advanced V2G-based energy management system that incorporates renewable energy sources (RES), such as solar and wind, to further optimize grid operations. By leveraging bidirectional energy flow between EVs and the grid, combined with real-time forecasting of RES generation, the system addresses renewable intermittency and maximizes energy utilization. A novel algorithm is developed to schedule the charging and discharging of electric vehicles (EVs) based on load demand profiles, EV parking plans, and RES availability. The proposed approach ensures that EVs charge during peak RES generation periods and discharge energy back to the grid during high demand hours, contributing to peak load shaving. A case study is conducted for Gujarat, India, demonstrating the effectiveness of the proposed system in reducing grid stress, improving load profiles, and supporting clean energy integration. The results reveal significant reductions in peak loads and enhanced grid reliability, making this approach a sustainable solution for smart energy management. Future work will explore machine learning-based predictive models to further refine energy scheduling and optimize real-time grid support.
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Dr., P.S.Manoharan, B.Ashok Kumar Dr., Sherif V.Navas, P.Satheeshkumar, and V.P.Thiagu. "Smart Electricity Usage Analyzer and Controller." Journal of Optoelectronics and Communication 6, no. 1 (2024): 19–27. https://doi.org/10.5281/zenodo.10691566.
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<em>Electricity boards and consumers are facing problems like unpredictability of power consumption bills, power theft, Unannounced power failure etc. The solution to all of these issues is to promptly monitor customer load, which aids in appropriate billing. Thus, a smart energy management system called Energauge is proposed to track maximum demand, prevent power theft, and optimize the energy usage of the consumer. The Static Digital Energy Meter is a system that measures the energy consumed by a load and regulates abnormal electricity usage due to multiple loads being used at once. The controller system monitors the load within a limit and uses an opto-coupler to collect the pulse from the energy meter. The consumed electricity is calculated based on the TNEB standard tariff-based slab rating, and a mobile app displays the consumed energy and cost by date. The electricity usage analyzer and controller system ensure that the maximum demand is monitored and controlled effectively. An electromagnetic power relay controls the maximum demand, which is fixed in the system. The power circuit and lighting circuits are connected to the electromagnetic relay, which activates the load only after the load has been reduced and the acknowledge push button has been pressed. The Static Digital Energy Meter is a reliable and efficient system that allows users to monitor and regulate their energy consumption. The proposed energy management system is implemented and tested in a real-time environment.</em>