Open AccessOptimal device capacity planning and strategy determination in an integrated energy system to promoting IES integration considering reliability value
Author(s) -
Gao Xueqian,
Liu Wenxia
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12183
Subject(s) - reliability (semiconductor) , reliability engineering , value (mathematics) , energy (signal processing) , computer science , capacity planning , mathematical optimization , engineering , mathematics , statistics , physics , power (physics) , quantum mechanics , machine learning , operating system
This paper proposes a novel framework for optimal planning of integrated energy system (IES) that takes into account its reliability value, which aims at promoting the power grid and IES to share the responsibility of the user's energy‐supply reliability by market mechanism, thus promoting IES integration. First, an IES grid‐connected electricity purchase price estimation model considering reliability is established, based on price theory and reliability theory, to quantify the reliability incremental value of IES. Then, a two‐layer collaborative planning model, containing optimal equipment capacity configuration and strategy determination in the upper layer and optimal operation simulation and IES incremental reliability value in the lower layer, is established to coordinate the reliability guarantee capability of distribution network and IES while maximize the profit of IES. Among them, a practical approach of IES probabilistic reliability calculation is studied, considering double uncertainty and time‐series matching of generation and demand, aiming at reducing the complexity of electricity price calculation embedded in planning model. Next, the intelligent krill herd algorithm and MILP are implemented to solve the model. Finally, a case is studied to verify the effectiveness of the model, and analyse the impact of load growth rate and user reliability requirements on the planning results.