Stochastic Safety, Economy, and Low‐Carbon Optimisation in Smart Distribution Networks

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Publicado en:IET Smart Grid vol. 8, no. 1 (Jan/Dec 2025)
Autor principal: Liu, Qiran
Otros Autores: Xu, Yueyang, Li, Qionglin, Wang, Ze, Huo, Qunhai, Wei, Tongzhen
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John Wiley & Sons, Inc.
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022 |a 2515-2947 
024 7 |a 10.1049/stg2.70018  |2 doi 
035 |a 3205529157 
045 2 |b d20250101  |b d20251231 
100 1 |a Liu, Qiran  |u Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 
245 1 |a Stochastic Safety, Economy, and Low‐Carbon Optimisation in Smart Distribution Networks 
260 |b John Wiley & Sons, Inc.  |c Jan/Dec 2025 
513 |a Journal Article 
520 3 |a ABSTRACT Under the dual‐carbon target, distributed energy sources often cause power mismatches between supply and load, challenging the stability and safety of distribution networks. This paper proposes a comprehensive evaluation system for active distribution network operation, focusing on safety, economy and low carbon emissions. A cooperative optimal scheduling strategy for multiple agents based on stochastic programming is also introduced. The operating characteristics of energy sources, storage and loads are modelled to quantify their flexible regulation capabilities. A unified multi‐objective evaluation system is constructed with matching constraints designed and linearised. A smart distribution network cooperative optimisation model is proposed, using the improved K‐means algorithm to generate typical scenarios and obtain optimal scheduling schemes through mixed‐integer linear programming (MILP) optimisation. The simulation model is developed on the MATLAB‐YALMIP platform and solved using CPLEX. In representative annual scenarios, the strategy improves the economic index by 10.9%, the low‐carbon index by an average of 10.7% and the overall index by an average of 12.7%. The results show significant enhancement in multi‐dimensional operational metrics, highlighting its practical relevance. 
653 |a Linear programming 
653 |a Deep learning 
653 |a Energy sources 
653 |a Integer programming 
653 |a Emissions trading 
653 |a Simulation models 
653 |a Electricity distribution 
653 |a Electric vehicles 
653 |a Approximation 
653 |a Energy storage 
653 |a Carbon 
653 |a Energy consumption 
653 |a Industrial plant emissions 
653 |a Economic indicators 
653 |a Scheduling 
653 |a Renewable resources 
653 |a Energy management 
653 |a Optimization 
653 |a Flexibility 
653 |a Algorithms 
653 |a Emissions 
653 |a Methods 
653 |a Multiagent systems 
653 |a Alternative energy sources 
653 |a Branch & bound algorithms 
653 |a Demand side management 
653 |a Stochastic programming 
653 |a Operating costs 
700 1 |a Xu, Yueyang  |u Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 
700 1 |a Li, Qionglin  |u Academy of Electric Power Sciences, State Grid Henan Electric Power Company, Zhengzhou, China 
700 1 |a Wang, Ze  |u Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 
700 1 |a Huo, Qunhai  |u Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 
700 1 |a Wei, Tongzhen  |u Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China 
773 0 |t IET Smart Grid  |g vol. 8, no. 1 (Jan/Dec 2025) 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3205529157/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3205529157/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3205529157/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch