Research on Demand Response of Electric Vehicle Agents Based on Multi-Layer Machine Learning Algorithm

Lin, Jianping; Dong, Ping; Liu, Mingbo; Huang, Xuewei; Deng, Wenli

doi:10.1109/access.2020.3042235

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…The number of items in a collection will be chosen in such a way that the total weight is less than or equal to the stated limit. (Mantovani et al, 2015;Kulkarni and Thorat, 2019;Zardari et al, 2019;Ardabili et al, 2020;Frequency et al, 2020;Hu et al, 2020;Ji et al, 2020;Lin et al, 2020). In other terms, the problem with knapsack is a problem in which several things with varied weights are presented and the one with not more weight is chosen than a fixed one called W. Two kinds of knapsack problems exist: 0-1 Knapsack issue, fractional knapsack issue (Saharia et al, 2016;Wei and Hu, 2018;Riaz et al, 2019;Vigneswari et al, 2019).…”

Section: Knapsack Problemmentioning

confidence: 99%

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Balouch

Abrar

Muqeet³

et al. 2022

Front. Energy Res.

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The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost scheduling, while the third layer, demand response which is a significant expansion of this domain. The implementation of the proposed architecture in MATLAB/Simulink, with test results. demonstrating the significance of the proposed solution

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Section: Knapsack Problemmentioning

confidence: 99%

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Balouch

Abrar

Muqeet³

et al. 2022

Front. Energy Res.

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show abstract

“…Moreover, EVs mainly participate in demand response to profit (Lin et al, 2020) or charge and discharge intelligently based on time-of-use (TOU) electricity price (Wang et al, 2019b;Wang et al, 2019c). The battery is often used for peak shaving and valley filling as grid-side storage.…”

Section: Introductionmentioning

confidence: 99%

Research on Game Operation of Multi-Stakeholder in Distribution Network in Electricity Market

et al. 2021

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With the development of the electricity market, various stakeholders such as batteries, multi-microgrid (MMG), and electric vehicle (EV) clusters, can trade with either the distribution network or each other to meet their power balance needs and to maximize their profits. This paper proposes a two-level game model based on game theory to study the operation strategy of stakeholders in the distribution network. First, each stakeholder predicts its electricity demand profile. A Markov Decision Process (MDP) model of random variables is established to predict the charging and discharging power of the battery. Then, the two-level game is presented to let multi-stakeholder participate, in which different kinds of stakeholders have different game strategy limits. Additionally, suggestions for battery operation modes under different compensation coefficients are given to participate in the subsequent two-level game. An algorithm is proposed to allow stakeholders to merge or split self-adaptively based on Nondominated Sorting Genetic Algorithm II (NSGA-II) to optimize operation mode. Finally, the proposed model is applied to the PG and E69-bus distribution system and a practical 101-bus distribution system in China. The case studies show that different game strategy limits of the stakeholders will affect the distribution of the Nash equilibrium (NE) solutions. The multi-stakeholder system can better absorb regional unbalanced power through electricity transactions, and further increase the benefits of each stakeholder.

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Bin modelling approach to cluster control the EVs for implementing demand response program

Kumar,

Ghose

2024

Electr Eng

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Research on Demand Response of Electric Vehicle Agents Based on Multi-Layer Machine Learning Algorithm

Cited by 11 publications

References 21 publications

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Research on Game Operation of Multi-Stakeholder in Distribution Network in Electricity Market

Bin modelling approach to cluster control the EVs for implementing demand response program

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