Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game

Liu, Nian; Cheng, Mengchun; Yu, Xinghuo; Zhong, Jiangxia; Lei, Jinyong

doi:10.1109/tie.2018.2793181

Cited by 245 publications

(134 citation statements)

References 22 publications

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“…Let (I, K I , D) denotes a scenario that there are I prosumers, each has K I resources and the required load adjustment for prosumer i is D i . Then, the scenario (I , K I , D ) is an equal partition of (I, K I , D) when there exists an Z ∈ Z + , such that I = ZI and K I = ZK I , the resources one prosumer possesses and required load adjustment is distributed equally to Z prosumers and satisfies, ∀i, ∀z 1 , z 2 ∈ {1, ..., Z} (19) where p * corresponds to the NE under scenario (I, K I , D). The proof can be found in Appendix G. It shows that the system under I = 1 is the most efficient; otherwise, introducing competition by spreading resources benefits social welfare.…”

Section: Impacts Of Competitionmentioning

confidence: 99%

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Chen

Mei

Zhou

et al. 2020

IEEE Trans. Smart Grid

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This paper proposes a novel energy sharing mechanism for prosumers who can produce and consume. Different from most existing works, the role of individual prosumer as a seller or buyer in our model is endogenously determined. Several desirable properties of the proposed mechanism are proved based on a generalized game-theoretic model. We show that the Nash equilibrium exists and is the unique solution of an equivalent convex optimization problem. The sharing price at the Nash equilibrium equals to the average marginal disutility of all prosumers. We also prove that every prosumer has the incentive to participate in the sharing market, and prosumers' total cost decreases with increasing absolute value of price sensitivity. Furthermore, the Nash equilibrium approaches the social optimal as the number of prosumers grows, and competition can improve social welfare.

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Section: Impacts Of Competitionmentioning

confidence: 99%

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Chen

Mei

Zhou

et al. 2020

IEEE Trans. Smart Grid

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“…An energy storage (ES)-equipped ESM was developed in [17] to facilitate the energy sharing of multiple PV prosumers. In this work, the autonomous PV prosumers were formed as an energy-sharing network, and the energy-sharing activities were categorized as direct and buffered sharing.…”

Section: A Related Workmentioning

confidence: 99%

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Latifi

Rastegarnia

Khalili

et al. 2020

IEEE Trans. Ind. Electron.

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Joint energy consumption and trading management is still a major challenge in smart (micro-) grids. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. Here, an inclusive formulation for energy management and trading of a Micro/Nano-grid (M/NG) is proposed. Subsequently, a holistic solution to jointly optimizing the internal energy consumption management and external local energy trading for a smart grid including several M/NGs is provided. As the problem is computationally intractable, the proposed approach involves three hierarchical stages. Firstly, a game-theoretic online stochastic energy management model is provided with a reinforcement learning solution by which the M/NGs can schedule their power consumptions. Secondly, an effective incentive-compatible doubleauction is formulated by which the M/NGs can directly trade with each other. Thirdly, the central controller develops an optimal power allocation program to reduce the power transmission loss and the destructive effects of local energy trading. The simulation results validate the efficiency of the proposed framework.

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“…If the PV generation is insufficient, the MG will buy energy form the MMG. The energy consumption in the MG can create revenue, especially for industrial and commercial users [18,19]. Therefore, the utility model of the MG mainly considers the government subsidy for PV generation, the benefits of energy consumption, the benefits from selling surplus energy to the EMO and the costs of purchasing energy from the EMO.…”

Section: Utility Model Of Mgsmentioning

confidence: 99%

Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

Tao

Wang

et al. 2019

Applied Sciences

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This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

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Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game

Cited by 245 publications

References 22 publications

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

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