A Distributed and Resilient Bargaining Game for Weather-Predictive Microgrid Energy Cooperation

An, Lu; Duan, Jie; Chow, Mo-Yuen; Duel-Hallen, Alexandra

doi:10.1109/tii.2019.2907380

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…where Φ (𝜆) is a nonlinear projection map of power consumption 𝑃 regarding the Lagrange multiplier 𝜆. 𝐽 (𝜆 * ) is the inverse function of 𝐽 (𝑃 * ). The (17) implies that if the optimal Lagrange multiplier 𝜆 * is obtained, then the global optimal power point 𝑃 * can be achieved in a distributed manner.…”

Section: Problem Optimizationmentioning

confidence: 99%

“…Considering the power constraints on loads, let define Γ as a subset of HVACs and BESSs where the power outputs are saturated. By summing (17) to satisfy (10) while considering (2) and ( 5), the optimal multiplier 𝜆 * is calculated as:…”

Section: Problem Optimizationmentioning

confidence: 99%

“…Our consensus algorithm helps to relax topology conditions in [17]. A number of case studies are conducted to demonstrate its dynamics, robustness, and scalability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

2023

IEEE Trans. Ind. Inf.

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This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to cooptimize the HVAC-BESS by taking into account solar energy forecasts. A surplus-based consensus algorithm is proposed to solve the optimization problem, where the local power mismatch is introduced as a surplus term, and the HVAC-BESS can thus be co-scheduled to maximize renewable energy efficiency at the peak generation time. A set of the convex cost functions are formulated to minimize the HVAC's user dissatisfaction degree and alleviate power loss during the BESS operation. The goal is to collectively minimize the total energy cost in a distributed manner, subject to individual load constraints and power balance constraints. It is theoretically proved that a global convergence of the proposed algorithm is achieved provided that the directed network is strongly connected. The results from a number of case studies are promising, demonstrating the effectiveness and robustness of the algorithm under practical scenarios.

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Section: Problem Optimizationmentioning

confidence: 99%

Section: Problem Optimizationmentioning

confidence: 99%

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Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

2023

IEEE Trans. Ind. Inf.

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“…For the former, its main form is that each VPP participates in the overall market trading in the form of an alliance and preferentially consumes the internal surplus electric energy in the form of energy sharing. Reference [8] proposed a cooperative game model based on one-to-many and many-to-many trading, and uses the nucleolus method to realize the redistribution of the cooperative surplus. References [9,10] improved the nucleolus method by clustering and calculating the excess degree, making it suitable for a large number of VPP groups.…”

Section: Introductionmentioning

confidence: 99%

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

Bai¹,

Zhou²,

Xu³

et al. 2023

Energy Engineering

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The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants. Besides, the game relationship between transaction subjects needs to be further explored. This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game. Firstly, a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant, considering the energy consumption characteristics of users. Secondly, the utility functions of multiple virtual power plants are analyzed, and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process. Finally, the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies. Furthermore, the effect of different parameters on the Nash equilibrium point is explored, and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized. According to the obtained results, compared with the central air conditioning set as constant temperature control strategy, the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs. In addition, the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs. When the service quotation decreases gradually, the P2P transaction between VPPs is more likely to occur.

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“…However, the wholesale price is the necessary reference that cannot be obtained in islanded multimicrogrids. In [27], a distributed collaborative energy bargain scheduling algorithm was designed based on a stochastic renewable power forecasting method. The trading behaviors are analyzed with the impacts of cooperation and dishonest behavior on the bargaining outcome.…”

Section: Introductionmentioning

confidence: 99%

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

Zhao

Guo

Luo

et al. 2022

IEEE Trans. Smart Grid

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A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multimicrogrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of singlemicrogrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

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A Distributed and Resilient Bargaining Game for Weather-Predictive Microgrid Energy Cooperation

Cited by 32 publications

References 24 publications

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

Distributed Robust Model Predictive Control-Based Energy Management Strategy for Islanded Multi-Microgrids Considering Uncertainty

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