Chance-constrained coordinated optimization for urban electricity and heat networks

Abstract

Urban electricity and heat networks (UEHN) consist of the coupling and interactions between electric power systems and district heating systems, in which the geographical and functional features of integrated energy systems are demonstrated. UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources, in which the application of stochastic optimization approaches to UEHN analysis is highly desired. In this paper, we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads, heat loads, and photovoltaic outputs, as well as the correlations between these uncertain sources. A solution strategy, which combines the Latin Hypercube Sampling Monte Carlo Simulation (LHSMCS) approach and a heuristic algorithm, is specifically designed to deal with the proposed chance-constrained coordinated optimization. Finally, test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.