He Kaiyuan

Sequential control of capacitors and taps of distribution network with distributed generation

Capacitors and taps of MV distribution systems are usually discrete optimization objects, a control sequence is needed to respond to the randomness of renewable distributed generations (DG), and many existing algorithms are trapped into the dimension house. A control strategy is proposed to minimize network losses, switching costs, generation wastes of DGs and voltage deviation, a weighted sum method is used to reflect the preferences on the mutually conflicting control objectives. For the mixed integer nonlinear programming (MINLP) problems of capacitors and taps control, a cooperative particle swarm optimization (Co-PSO) method employing cooperative behavior to significantly improve the optimizing ability is used. Finally, the correctness, convergence and robustness of the proposed algorithm are verified by comparison against existing popular schemes through detailed simulations over 24h on the IEEE 13-bus unbalanced distribution system.

Research on probabilistic reactive power optimization considering the randomness of distribution network

With the rapid development of intelligent distribution network, the uncertainty of the load and the randomness of distributed generation have brought new challenges to distribution network control operation especial in reactive power optimization. This paper uses probabilistic power flow algorithm based on three-point estimate method to solve the uncertainty caused by power flow calculation in the stochastic models of load and wind power so as to propose a method of information entropy principle to measure the voltage fluctuation. On the basis of this method, a model of probabilistic reactive power optimization considering minimum network loss and voltage fluctuation is built. Taking the IEEE 33 nodes system which contains wind power generation as an example and we draw a conclusion that if we add the minimum voltage entropy to multi-objective reactive power optimization objective function, the probability distribution of node voltage is more centralized than that of single objective reactive power optimization. Thus, to optimize reactive power by means of this model could improve the voltage stability of the system and make the voltage distribution near a certain value that within the scope of control in large probability. The proposed multi-objective probabilistic reactive power optimization model is suitable for the actual distribution network reactive voltage control with random properties.