Two-stage Correction Strategy-based Real-time Dispatch for Economic Operation of Microgrids

Sun, Jinsheng; Hu, Changhong; Liu, Lei; Liu, Jinyuan; Shi, Jianquan

doi:10.23919/cjee.2022.000013

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The price of the diesel is usually set as U p = USD 1.2, η 1 = 0.0096, η 2 = 0.088, θ 1 = 0.08, θ 2 = 0.01 [29]. From Formulas ( 12), ( 14) and ( 17), we can find that both the constraints and the objective function contain quadratic terms.…”

Section: Objective Functionmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching

Chen,

Liu,

Cui

et al. 2024

Applied Sciences

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Optimal energy transmission dispatching of microgrid systems involves complicated transmission energy allocation and battery charging/discharging management and remains a difficult and challenging research problem subject to complex operation conditions and action constraints due to the randomness and volatility of new energy. Traditional microgrid transmission dispatching mainly considers the matching of the demand side and the supply side from a macro perspective, without considering the impact of line loss. Therefore, a Hierarchical Deep Q-network (HDQN) approach for microgrid energy dispatching is proposed to address this issue. The approach takes the power flow of each line and the battery charging/discharging behavior as decision variables to minimize the system operation cost. The proposed approach employs a two-layer agent optimization architecture for simultaneously processing the discrete and continuous variables, with one agent making upper layer decisions on the charging and discharging behavior of the batteries, and the other agent making lower layer decisions on the transmission energy allocation for the line. The experimental results indicate that the proposed approach achieves better performance than existing approaches.

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Section: Objective Functionmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching

Chen,

Liu,

Cui

et al. 2024

Applied Sciences

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“…Reference In [31], a real-time scheduling method was proposed for microgrid economic operation based on a two-stage correction strategy, which maintains the economic efficiency of the daily plan. It should be noted that the above literature has studied the economic dispatch problem of microgrids, but does not consider the control and optimization problems in the case of multi-microgrids interconnections.…”

Section: Introductionmentioning

confidence: 99%

Economic dispatch of multi-microgrids considering flexible load based on distributed consensus algorithm

Wu,

Li,

et al. 2024

Preprint

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This paper investigates the economic dispatch (ED) problem of multi-microgrids considering the flexible loads based on distributed consensus algorithm. Firstly, the structure topology diagram of distributed generator nodes is designed based on the global interconnection of multi-microgrids, then the flexible load is considered as adjustable load and be added into the fixed loads. Next, a multi-microgrids economic dispatch (MMED) model which contained fixed and flexible loads is constructed with the goal of minimizes the operating cost of the entire system. The proposed MMED model is a two-layer coordinated optimization model. The power transmission cost between each microgrid is used as the multi-microgrids layer framework, and the internal operating costs of a single microgrid is the sub-microgrid layer framework. The distributed consensus algorithm is designed to solve the MMED problem with the considering of constraint condition of distributed generators in each microgrid. Finally, some examples illustrate that the proposed method can not only optimize the operation of each single microgrid, but also maintains the stability of multi-microgrids through the adjustment of flexible load, and reduces the total operating cost of the whole multi-microgrids system.

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“…The degree of interactive coupling between energy supply and demand has gradually deepened with the reform of the electricity market and the continual development of the CIES, and has transformed the traditional top-down, integrated structure of the supply and demand for energy into an interactive structure (Sun et al, 2022;Veichtlbauer et al, 2022;Qiu et al, 2023). However, the presence of multiple interest groups in the CIES makes it difficult to accurately describe their interactions by using the traditional, centralized strategy for optimization because each player needs to coordinate its interests when interacting with the other players.…”

Section: Introductionmentioning

confidence: 99%

Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading

Dai,

Jiang,

Chen

et al. 2023

Front. Energy Res.

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To alleviate the challenges posed by high energy consumption, significant carbon emissions, and conflicting interests among multiple parties in a community-level microgrid, the authors of this study propose a master–slave game-based optimal scheduling strategy for a community-integrated energy system (CIES). First, we analyze the decision variables and revenue-related objectives of each stakeholder in the CIES, and use the results to construct a framework of implementation. Second, we develop a model to incentivize peak regulation and a ladder-type carbon trading model that consider the correlation between the load owing to residential consumers, the load on the regional grid, and the sources of carbon emissions. Third, we propose a master–slave game-based mechanism of interaction and a decision-making model for each party to the game, and show that it has a Stackelberg equilibrium solution by combining genetic algorithms and quadratic programming. The results of evaluations showed that compared with an optimization strategy that considers only the master–slave game, the proposed strategy increased the consumption surplus of the user aggregator by 13.65%, the revenue of the community energy operator by 7.95%, increased the revenue of the energy storage operator, reduced CO2 emissions by 6.10%, and adequately responded to peak-cutting and valley-filling by the power grid company.

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Two-stage Correction Strategy-based Real-time Dispatch for Economic Operation of Microgrids

Cited by 8 publications

References 23 publications

A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching

A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching

Economic dispatch of multi-microgrids considering flexible load based on distributed consensus algorithm

Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading

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