A planning method for siting and sizing of distributed generation based on chance-constrained programming

With the increasing of the new energy's permeability in the regional power network, the inherent characteristics of new energy output bring new challenges to the power network planning. In order to solve the problem that the traditional distribution network planning is too conservative for the new energy planning, a planning method for siting and sizing of distributed generation (DG) based on Chance-constrained programming is proposed in this paper. The method provides an approach to calculate the new energy's maximum installation capability and the optimal installation site for regional power network Basing on the analysis of stochastic models of wind power, solar output and load, the planning model has the objective function of maximizing renewable energy utilization with the considering of several constraint conditions such as node voltage constraint and branch transmission power constraint. The particle swarm optimization (PSO) algorithm and Monte-Carlo simulation method is used to solve the optimization problem. Taking a regional power network as an example, Power System Analysis Software Package (PSASP) is used to carry out transient and steady stability verification for the extreme scenario, and contingency plans for the accidents which is not satisfied with the verification requirement must be worked out to ensure the safe and stable operation of the network.