Probabilistic distribution planning: Including the interactions between chance constraints and renewable energy

Abstract

Abstract Integration of renewable energy resources introduces several uncertainties for planning of distribution networks, requiring the consideration of random variables. This paper proposes a chance-constrained distribution network planning to deal with the long-term uncertainties related to load power, wind power, and solar power using probability density functions under a pseudo-dynamic approach. The model is constructed through linearized load flow equations which are combined with probability density functions using convolution. The optimization problem is then solved by integer genetic algorithm; minimizing the installation and maintenance costs of substations, feeders, and renewable generators and the expected cost for purchased energy from the upper grid. The chance constraints are formulated for voltage limits, feeder currents’ limits, and substation limits in order to control the satisfaction level of power system parameters. The proposed method, which is computationally efficient, is applied to the 24-nodes and 34-nodes test networks to compare the obtained results with Monte Carlo Simulation along with the full AC load flow and the results show the importance of considering chance constraints and penetration level of renewable energy in terms of investment variables through case studies.