A review of demand-side management: Reconsidering theoretical framework

Meyabadi, Abbas Fattahi; Deihimi, Mohammadhossein

doi:10.1016/j.rser.2017.05.207

Cited by 178 publications

(90 citation statements)

References 195 publications

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“…Demand side management (DSM) is a proactive way to increase the energy efficiency among customers in the long-term [19], and can reduce both the electricity peak power demand (kW) and the electricity consumption (kWh) [20,21]. The most prominent DSM methods include reducing peak loads (peak clipping or peak shaving), shifting load from on-peak to off-peak (load-shifting), increasing the flexibility of the load (flexible load shape), and reducing energy consumption in general (strategic conservation), as stated by Müller et al [22].…”

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confidence: 99%

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

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Cooling in the industry sector contributes significantly to the peak demand placed on an electrical utility grid. New electricity tariff structures include high charges for electricity consumption in peak hours which leads to elevated annual electricity costs for high-demanding consumers. Demand side management (DSM) is a promising solution to increase the energy efficiency among customers by reducing their electricity peak demand and consumption. In recent years, researchers have shown an increased interest in utilizing DSM techniques with thermal energy storage (TES) and solar PV technologies for peak demand reduction in industrial and commercial sectors. The main objective of the present study is to address the potential for applying optimization-based time-of-use DSM in the industry sector by using cold thermal energy storage and off-grid solar PV to decrease and shift peak electricity demands and to reduce the annual electricity consumption costs. The results show that when cold thermal energy storage and solar PV are coupled together higher annual electricity cost savings can be achieved compared to using these two technologies independently. Additionally, considerable reductions can be seen in electricity power demands in different tariff periods by coupling thermal energy storage with off-grid solar PV.

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confidence: 99%

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

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“…This paper uses first-order difference and second-order difference for bad data identification. If the jth data point X(i, j) in the ith data is a dead pixel, the correction value X'(i, j) is as shown in Equation (1).…”

Section: Short Term Load Forecasting Using the Wnn Model Based On Msdmentioning

confidence: 99%

“…Making a comprehensive survey of the market-oriented electricity reforms of various countries-although the national conditions and the paths of reform are all different-opening up the electricity-side market, and giving users the right of free choice have always been core factors in electricity market reform. The gradual opening up of the retail-side market is mainly by introducing the competition through releasing users' options, allowing users to freely choose to trade with power-retailing or power-generating enterprises, or participating in the wholesale market directly [1,2]. EU countries in 2007 completed the reform of the full liberalization of user options.…”

Section: Introductionmentioning

confidence: 99%

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

et al. 2018

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The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users' historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on "meteorological similarity day (MSD)" to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm-back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer's bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.In 2014 alone, the power trade with EEX reached an astonishing 1952 tWh. The Pennsylvania New Jersey Maryland (PJM) exchange in the USA is currently responsible for the operation and management of power systems in 13 U.S. states and the District of Columbia. As a regional Independent system operation (ISO), PJM is responsible for centrally dispatching the largest and most complex power control area in the USA and ranks third in the world in scale. The PJM controlled area accounts for 8.7% of the total population (about 23 million), load 7.5%, installed capacity of 8% (about 58,698 MW), and transmission lines up to more than 12,800 kilometers [4]. The effective application of power big data for the profitability and control of power companies has a high value. Some experts have said that whenever the data utilization increases by 10%, it can cause the power grid to increase profits by 20% to 49%. In the face of a huge power system, data needs to be processed quickly, hence data mining technology came into being, and will play a key role in supporting the sales company to participate in market competition.Power-selling is the pillar business of the retailer. As an intermediate link between production and use, the power retailer needs to analyze the users' needs and predict the user's load demand based on the users' historical electricity consumption data, considering industry, meteorology, regional economy, related industries, and other factors, so that this can guide the retailer to develop electricity purchase. In recent years, numerous scholars have put forward many effective load forecasting methods. Vilar, J. et al. provided two procedures to obtain predi...

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“…All of the works above, deal only with supply side management (SSM) in conventional grid systems considering centralized system operation. In contrast, a smart grid (SG) involves DGs and demand side management (DSM) . DSM refers to the steps taken to influence the energy consumption of the end users in any manner.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, a smart grid (SG) involves DGs and demand side management (DSM). 12 DSM refers to the steps taken to influence the energy consumption of the end users in any manner. The objective is to improve the balance between demand and supply of the energy in order to enhance the sustainability of the system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and operation optimization of RE integrated microgrids considering economic, energy, and environmental aspects

et al. 2019

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Summary Microgrids provide promising solution for integration of renewable energy sources in the electrical grid. To exploit the key benefits, achieving the economical operation of renewable aided microgrids has become necessary and is a challenging task. This paper presents an efficient optimization model to minimize the operational cost of a solar integrated microgrid. We formulate a joint optimization mixed integer problem for appropriate modeling of the system under various practical constraints. An efficient solution is obtained with a distributed approach such that the original problem is solved in two stages. Dual decomposition approach is adopted for cost, emissions, and solar share optimization. Lagrange relaxation, Lambda iteration method, and binary integer programming are employed to obtain the joint optimization solution. Finally, the performance of the proposed model is validated through simulations that show that an overall cost reduction of 4.2070e+04 $ and emission reduction of 7.2001e+03 kg are achieved with the proposed model.

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A review of demand-side management: Reconsidering theoretical framework

Cited by 178 publications

References 195 publications

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

Modeling and operation optimization of RE integrated microgrids considering economic, energy, and environmental aspects

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