Journal
ENERGY
Volume 198, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.117347
Keywords
Integrated electricity and heat system; Real-time optimization; Risk-averse optimization; Stochastic optimization; Approximate dynamic programming
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Funding
- National Natural Science Foundation of China [51777078, 61963020]
- Fundamental Research Funds for the Central Universities [D2172920]
- Key Projects of Basic Research and Applied Basic Research in Universities of Guangdong Province [2018KZDXM001]
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Coordinated operation of integrated electricity and heat system can improve operation flexibility and reduce cost. However, multiple uncertainties challenge its optimal operation. This paper aims at developing a risk-averse and computationally efficient policy for real-time stochastic dispatch of integrated electricity and heat system, which improves the economy as well as avoiding the risk of high costs in critical scenarios. First, real-time dispatch of integrated electricity and heat system is formulated as a multistage risk-averse stochastic sequential optimization problem with dynamic risk measure, where combined heat and power unit, energy storage, flexible electricity and heat load are jointly utilized to minimize the risk-adjusted total costs. Next, a risk-averse dynamic programming formulation of the original problem is presented, upon which a data-driven risk-averse approximate dynamic programming is employed to address computational challenge, and develop almost optimal and computationally efficient policy. By exploiting information from training samples in off-line learning, the proposed algorithm can efficiently responses to the stochastic exogenous information. Comparative simulations with different risk-aversion preferences and different methods verify the effectiveness of the proposed algorithm. (C) 2020 Elsevier Ltd. All rights reserved.
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