A Framework for Demand-Side Management With Demand Response Input

The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

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Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

Applied Sciences ◽

10.3390/app10051751 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1751 ◽

Cited By ~ 1

Author(s):

Wonsuk Ko ◽

Hamsakutty Vettikalladi ◽

Seung-Ho Song ◽

Hyeong-Jin Choi

Keyword(s):

South Korea ◽

Real Time ◽

Demand Response ◽

Mean Squared Error ◽

Demand Side Management ◽

Demand Side ◽

Root Mean Squared Error ◽

Squared Error ◽

Demand Response Program ◽

Relative Root

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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Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids

Applied Energy ◽

10.1016/j.apenergy.2019.04.177 ◽

2019 ◽

Vol 250 ◽

pp. 118-130 ◽

Cited By ~ 33

Author(s):

Rui Tang ◽

Shengwei Wang ◽

Hangxin Li

Keyword(s):

Game Theory ◽

Demand Response ◽

Dynamic Pricing ◽

Smart Grids ◽

Demand Side Management ◽

Demand Side

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Role of Energy Efficiency and Conservation Including Demand Side Management for Sustainable Development in India

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12.17839 ◽

2018 ◽

Vol 7 (3.12) ◽

pp. 1210

Author(s):

Rakesh Kumar ◽

Rakesh Ranjan ◽

Mukesh Chandra Verma

Keyword(s):

Energy Efficiency ◽

Sustainable Development ◽

Demand Response ◽

Smart Cities ◽

Demand Side Management ◽

Electricity Demand ◽

Demand Side ◽

Virtual Power Plants ◽

Energy Efficiency And Conservation

Energy is used to provide cost-effective services meet the objectives of promoting sustainable development. The importance of Energy Efficiency and Conservation (EE and EC) is to conserve depleting energy resources. Energy efficiency in India has been increasing which has led the Government of India through the Energy Conservation Act (ECA) and the Bureau of Energy Efficiency (BEE) to begin several programs. Demand Side Management (DSM) and Energy Efficiency and Conservation are improving the economic growth of Indian utilities.DSM also aimed at promoting the installation and use of energy efficient equipment that consume less power having good quality of output. Energy efficiency is important for sustainable future. Demand Side Management including Demand Response (DR) Activity is utilized to limit the peak electricity demand. DSM helps grid operators to act as virtual power plants and power the utilities to transmit energy when needed through demand response measures. Demand Response (DR) measures can be adapted for a industrial and commercial facility that includes turning off air conditioning, lighting, pumps, and other non-essential equipment. Demand Response is a Demand Side Management (DSM) method in which the end users of electricity are encouraged to take part in dropping the peak load on the system by altering their normal energy consumption schedule. The basic objective of the Agriculture, Municipal and Industrial Demand Side Management (DSM) programmes are to improve the overall energy efficiency of the SLDC (State Load Dispatch Centre) and Electricity Grids which could lead to substantial savings in the electricity consumption, resulting in cost reduction and savings. The target of energy saving can be achieved by implementing acts and policies which leads to state wise DSM Regulations by Regulatory Commissions to the State Power Utilities. The Role of DSM and Energy Efficiency including conservation can fulfill the dreams projects electricity demand in Smart Cities. The Role of State Electricity Regulatory Commissions and Forum of Regulators are very important to make India’s electricity demand in future.

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Clustering-Based Energy Management of Residential Loads by using Artificial Intelligence

Engineering Proceedings ◽

10.3390/engproc2021012015 ◽

2021 ◽

Vol 12 (1) ◽

pp. 15

Author(s):

Umair Liaqat ◽

Muhammad Yousif ◽

Malik Shah Zeb Ali ◽

Muhammad Afzal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Demand Response ◽

Intelligent Systems ◽

Supply And Demand ◽

Demand Side Management ◽

Demand Side ◽

Perfect Agreement ◽

Peak Loads ◽

Artificial Neural Network Ann

Developing countries have witnessed a remarkable surge in the energy crisis due to the supply and demand gap. One of the solutions to overcome this problem is the optimal use of energy that can be achieved by employing demand side management (DSM) and demand response (DR) methods intelligently. Machine learning and data analysis tools help us create intelligent systems that motivate us to use machine learning to implement DSM/DR programs. In this paper, a novel DSM algorithm is introduced to implement DSM intelligently by using artificial intelligence. The results show an efficient implementation of an artificial neural network (ANN) along with demand side management, whereas the peak and off-peak loads were normalized to a certain range where a perfect agreement between supply and demand can be reached.

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