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Co-Optimization of Behind-the-Meter and Front-of-Meter value Streams in Community Batteries

Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 334-345页

作者： Carmen Bas Domenech Antonella Maria De Corato Pierluigi Mancarella University of Melbourne Parkville 2010VictoriaAustralia Department of Electrical and Electronic Engineering The University of ManchesterManchesterU.K. IEEE

Community batteries(CBs)are emerging to support and even enable energy communities and generally help consumers,especially space-constrained ones,to access potential techno-economic benefits from storage and support local grid decarbonization.However,the economic viability of CB projects is often uncertain.In this regard,typical feasibility studies assess CB value for behind-the-meter(BTM)operation or whole-sale market participation,i.e.,front-of-meter(FOM).This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits.A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.

关键词： Behind-the-meter community battery distributed energy resource(DER) energy community front-of-meter value stacking

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Deep reinforcement learning for the optimized operation of large amounts of distributed renewable energy assets

能源与人工智能(英文)

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能源与人工智能(英文) 2023年第1期11卷 106-117页

作者： Jan Martin Specht Reinhard Madlener Institute for Future Energy Consumer Needs and Behavior(FCN) School of Business and Economics/E.ON Energy Research CenterRWTH Aachen UniversityMathieustraße 1052074AachenGermany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology(NTNU)Sentralbygg 17491GløshaugenTrondheimNorway

This study utilizes machine learning and,more specifically,reinforcement learning(RL)to allow for an optimized,real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain.The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming.For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households.In detail,the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization,but at less than a thousandth of the simulation time.Thus,RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.

关键词： Reinforcement learning Virtual power plant Aggregation of energy value stacking Flexibility of decentral energy assets

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