Collaborative Planning of Distributed Wind Power Generation and Distribution Network with Large-scale Heat Pumps

With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in power grid upgrades,which bring opportunities for renewable power generation integration.The combination of heating by distributed renewable energy with the flexible operation of heat pumps is a feasible alternative for dealing with grid reinforcement challenges resulting from heating electrification.In this paper,a mathematical model of the collaborative planning of distributed wind power generation(DWPG)and distribution network with large-scale heat pumps is developed.In this model,the operational flexibility of the heat pump load is fully considered and the requirements of a comfortable indoor temperature are met.By applying this model,the goals of not only increasing the profit of DWPG but also reducing the cost of the power grid upgrade can be achieved.

Hierarchical Multi-objective Planning for Integrated Energy Systems in Smart Parks Considering Operational Characteristics

An integrated energy system(IES)is considered to be an important supporting technology for emission reduction because it can effectively improve the efficiency of energy utilization and promote its sustainable development.Considering the uncertainties and operational conditions,this paper establishes a bilevel multi-objective optimization model for IES for the Smart Park from the standpoint of economy,technology and environment.The upper level with one objective reflects the economic cost composed of investment,operating and maintenance,etc.The lower level constructs three objectives,including pollution emission,operation costs and renewable energy utilization.Simultaneously,various equality and inequality constraints are addressed to satisfy the technical requirements.In addition,an improved MOEA/D-MC-DC algorithm(Multi Objective Evolutionary Algorithm through Decomposition Based on Monte Carlo and Decoupled Coding,MOEA/D-MC-DC)is presented for handling the complex and nonlinear bilevel multi-objective optimization problems with constraints.A genetic algorithm(GA)is used to solve the upper single objective,while MOEA is employed to cope with the multi-objectives of the lower level.Using three typical IESs in the Smart Park as examples,several simulations are carried out to verify the efficiency,applicability and universality of the proposed model and optimization algorithm.The results show that the proposed method can effectively optimize the configuration of an IES in various Smart Parks.