Lyapunov Optimization Based Online Energy Flow Control for Multi-energy Community Microgrids

Wang, Guofeng; Cai, Wenhui; Zhang, Youbing; Zhao, Kangli; Xu, Xiangzhi

doi:10.1109/gtdasia.2019.8715917

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…A Long Short Term memory network is proposed in [26] to forecast in day-ahead the energy demand in a whole P2P community. Other researchers try on the other hand to avoid the complexity of forecasting models by developing online optimization methods [27,28]. Finally, some studies leverage data analytics for improving the sizing of the communities, such as [29], in which a load profile generator based on Self Organized Maps (SOMs) is proposed.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

Grève

Bottieau

Vangulick³

et al. 2020

Energies

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Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

Grève

Bottieau

Vangulick³

et al. 2020

Energies

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“…To verify the validity of the proposed energy flow control strategy, four typical ELNs with different structures are chosen for case analysis [30]. e power supply side of each ELN consists of PV, WT, gas turbine, ESS, and the main grid; and the demand side is equipped with basic loads and electric refrigerators.…”

Section: Case Studiesmentioning

confidence: 99%

A Decentralized Energy Flow Control Framework for Regional Energy Internet

et al. 2019

Self Cite

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As a new form of smart grid, the energy transmission mode of the Energy Internet (EI) has changed from one direction to the interconnected form. Centralized scheduling of traditional power grids has the problems of low communication efficiency and low system resilience, which do not contribute to long-term development in the future. Owing to the fact that it is difficult to achieve an optimal operation for centralized control, we propose a decentralized energy flow control framework for regional Energy Internet. Through optimal scheduling of regional EI, large-scale utilization and sharing of distributed renewable energy can be realized, while taking into consideration the uncertainty of both demand side and supply side. Combing the multiagent system with noncooperative game theory, a novel electricity price mechanism is adopted to maximize the profit of the regional EI. We prove that Nash equilibrium of theoretical noncooperative game can realize consensus in the multiagent system. The numerical results of real-world traces show that the regional EI can better absorb the renewable energy under the optimized control strategy, which proves the feasibility and economy of the proposed decentralized energy flow control framework.

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Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading

et al. 2023

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Lyapunov Optimization Based Online Energy Flow Control for Multi-energy Community Microgrids

Cited by 7 publications

References 14 publications

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

A Decentralized Energy Flow Control Framework for Regional Energy Internet

Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading

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