Content area
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.
Details
Deep learning;
Energy sources;
Integer programming;
Emissions trading;
Simulation models;
Electricity distribution;
Electric vehicles;
Approximation;
Energy storage;
Carbon;
Energy consumption;
Industrial plant emissions;
Economic indicators;
Scheduling;
Renewable resources;
Energy management;
Optimization;
Flexibility;
Algorithms;
Emissions;
Methods;
Multiagent systems;
Alternative energy sources;
Branch & bound algorithms;
Demand side management;
Stochastic programming;
Operating costs
; Xu, Yueyang 1 ; Li, Qionglin 2 ; Wang, Ze 1 ; Huo, Qunhai 1
; Wei, Tongzhen 1 1 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
2 Academy of Electric Power Sciences, State Grid Henan Electric Power Company, Zhengzhou, China