Home Load-Side Management in Smart Grids Using Global Optimization

2021 ◽  
pp. 1017-1052
Author(s):  
Abdelmadjid Recioui
Keyword(s):  
Smart Grids ◽  
Energy Use ◽  
Peak Load ◽  
Computational Cost ◽  
Demand Side Management ◽  
Demand Side ◽  
Demand And Supply ◽  
Management Techniques ◽  
Increasing Demand

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

Home Load-Side Management in Smart Grids Using Global Optimization

2019 ◽  
pp. 127-161
Author(s):  
Abdelmadjid Recioui
Keyword(s):  
Smart Grids ◽  
Energy Use ◽  
Peak Load ◽  
Computational Cost ◽  
Demand Side Management ◽  
Demand Side ◽  
Demand And Supply ◽  
Management Techniques ◽  
Increasing Demand

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

scholarly journals Various demand side management techniques and its role in smart grid–the state of art

2022 ◽  
Vol 12 (1) ◽  
pp. 150
Author(s):  
Muthuselvi Gomathinayagam ◽  
Saravanan Balasubramanian
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Power Distribution ◽  
Electrical Power ◽  
Demand Side Management ◽  
Demand Side ◽  
Electricity Grid ◽  
Management Techniques ◽  
Increasing Demand

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

scholarly journals Demand Side Management Technique to Optimally Schedule Electric Vehicle Loads in Smart Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 10043-10046
Keyword(s):  
Electric Vehicles ◽  
Smart Grids ◽  
Peak Load ◽  
Demand Side Management ◽  
Management Technique ◽  
Demand Side ◽  
Load Curve ◽  
Load Variations ◽  
Vehicle Loads ◽  
Aggregated Load

Demand-side management (DSM) in smart grids helps the problem of reducing peak load of utilities during certain hourly periods. Based on DSM techniques, peak load hours can be equalized to non-peak load hours therefore users will have less bill payments. In this paper optimal scheduling of Electric Vehicles (EVs) is done based on an objective function formulated to minimize the load variations. Firstly, hourly consumption of load during a day at Koneru Lakshmaiah Education Foundation is considered, EVs load is assumed and flattened the aggregated load curve by optimally scheduling the EVs during off peak hours.

scholarly journals Role of Demand Side Management Techniques in Reducing Electricity Demand of Residential Users

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Ayman Uddin Mahin ◽  
Fabliha Ahmed ◽  
S. M. Ishraqul Huq ◽  
Nahid-Ur-Rahman Chowdhury
Keyword(s):  
Solar System ◽  
Transmission Lines ◽  
Electrical Energy ◽  
Demand Side Management ◽  
Environment Pollution ◽  
Demand Side ◽  
Demand And Supply ◽  
Direct Load Control ◽  
Increasing Demand

Demand of electrical energy is growing day by day worldwide. To meet this increasing demand, generation is needed to be increased subsequently. Increasing generation is not an easy task as it may require setting up new generating units, changing transmission lines, control equipments, etc. Moreover, increased generation also causes increased environment pollution. An alternate approach that can create balance between demand and supply of electricity without increasing generation is demand side management (DSM). Furthermore, demand side management has the potential to reduce the use of energy resources resulting in less environment pollution. In this paper, three DSM techniques: using solar system, load limiting, deliberate load reduction are applied for residential users of Dhaka, Bangladesh and the results are compared with two traditional techniques: energy efficiency, direct load control. It has been found that by using solar system at home significant amount of electrical energy can be saved.

scholarly journals Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1618
Author(s):  
Mohanasundaram Anthony ◽  
Valsalal Prasad ◽  
Raju Kannadasan ◽  
Saad Mekhilef ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Wind Energy ◽  
Energy Cost ◽  
Fuzzy Controller ◽  
Residential Buildings ◽  
Peak Load ◽  
Demand Side Management ◽  
Consumption Pattern ◽  
Demand Side ◽  
Pv System ◽  
Inference System

This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.

scholarly journals Distributed Demand Side Management with Stochastic Wind Power Forecasting

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Sergio Grammatico ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Smart Grids ◽  
Demand Side Management ◽  
Expected Value ◽  
Noncooperative Game ◽  
Theory Approach ◽  
Demand Side ◽  
Wind Power Forecasting ◽  
Power Forecasting

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

Application of Internet of Things (IoT) to Demand-Side Management in Smart Grids

2021 ◽  
pp. 169-183
Author(s):  
Armin Hosseini Rezaei Asl ◽  
Mir Mahdi Safari ◽  
Morteza Nazari-heris ◽  
Behnam Mohammadi-Ivatloo
Keyword(s):  
Internet Of Things ◽  
Smart Grids ◽  
Demand Side Management ◽  
Demand Side
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