Multi-objective capacity planning for distributed energy interconnection system in distributed networks

Since distributed network performs well in improving the energy efficiency and reducing the pollution emission, it is broadly regarded as an efficient approach to solve energy short-age problem and environmental pollution problem. This paper focuses on the optimal capacity planning of distributed heating and cooling (DHC) system integrated with high penetration of renewable energies in distributed network. A model of solar-assisted distributed heating and cooling (DHC) system integrated with energy storage is presented. Then a comprehensive evaluation index which consists of energy, environmental and economic benefit is established using judgment matrix approach. This planning problem is solved by employing the multi-objective particle swarm optimization (MOPSO) algorithm. Taking the separate production (SP) system as reference, the result shows that the proposed DHC system is feasible in investment and has good performance in reducing pollution emission. Moreover, the proposed two-stage optimization method is also proved to be better than the traditional optimization method. The occasion in which the concentrating solar power plant is economical is analyzed as well.