Energy Trading System in Microgrids With Future Forecasting and Forecasting Errors

Jeong, Gyohun; Park, Sang‐Don; Lee, Joohyung; Hwang, Ganguk

doi:10.1109/access.2018.2861993

Cited by 13 publications

(17 citation statements)

References 29 publications

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“…Energy trading in a prosumer-based community microgrid is presented in Reference [17], where a game-theoretic approach is employed to design pricing mechanisms in different levels. Jeong et al [18] propose an energy trading system for retailers in a microgrid to maximize their revenues based on future forecasting and forecasting errors. A microgrid market is designed in [19] to optimally assign the net power mismatch to microgrids' agents to minimize dependency on the utility and market clearing cost.…”

Section: Literature Surveymentioning

confidence: 99%

Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study

et al. 2020

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Transactive energy is a novel approach for energy management and trading, which can be used in microgrids to facilitate the integration of distributed energy resources (DERs) in existing networks. The key feature in transactive energy is using market-based solutions for energy management. Hence, an appropriate transactive energy market (TEM) framework should be designed to enable and incentivize DER owners to participate in different markets. The efficient implementation of TEM for microgrid energy management encompasses the application of a variety of design principles. In this rapidly developing area, this paper presents a complete proposal of the TEM as a framework for the design, implementation, and deployment of transactive energy solutions for energy management in microgrids. In particular, we outline the requirements to design an effective market mechanism for the TEM. The applicability of this perspective is demonstrated through the introduction of the Monash Microgrid as a real-world implementation of a TEM solution, where a complete hardware and software foundation is presented as a platform to deploy a market-based solution for microgrid energy management. This is further illustrated through an example scenario, where the application of TEM is discussed to demonstrate the impact of considered design choices on achieving desired objectives.

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Section: Literature Surveymentioning

confidence: 99%

Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study

et al. 2020

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“…The prediction method proposed in this model overcomes the limitation of previous models and guarantees more reliable prediction. However, the existence of the equilibrium trading price among prosumers is not proved in [36]. In this paper, we extend and complement the model in [36] to have better properties than before.…”

Section: Introductionmentioning

confidence: 96%

“…From this viewpoint most of the previous models have obvious limitation in practice. To overcome this limitation, a noncooperative energy trading model based on the distributional forecasting is suggested in [36]. In this model, each prosumer splits each period into much shorter time durations and stochastically predict the energy level changes during all time durations.…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

2020

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In this paper, we propose a periodic energy trading system in microgrids based on day-ahead forecasting of energy generation and consumption. In the proposed model, each noncooperative prosumer calculates her reward function under her energy change forecasting based on Gaussian process regression and determines her optimal action. Then, the system establishes the equilibrium trading price when all prosumers execute their optimal actions simultaneously. We prove the existence of the equilibrium trading price and establish an algorithm that leads to the equilibrium. Our numerical example shows that the proposed system outperforms its previous model. INDEX TERMSMicrogrids, smart grids, energy trading system, future forecasting, Gaussian process regression.

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“…As the aggregators of DERs in certain geographical regions, microgrids are important participants in the power market [4]. By implementing internal dispatch, microgrids can provide economic benefits through applying demand response projects and avoiding long distance energy transmission [5].…”

Section: Introductionmentioning

confidence: 99%

A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Wang

Shao

et al. 2019

Energies

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Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids’ profits.

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Energy Trading System in Microgrids With Future Forecasting and Forecasting Errors

Cited by 13 publications

References 29 publications

Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study

Transactive Energy Market for Energy Management in Microgrids: The Monash Microgrid Case Study

Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

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