Heuristic Optimization Algorithms for Power System Scheduling Applications

Power system scheduling is one of the most complex multi-objective scheduling problems, and a heuristic optimization method is designed for finding the OPF solution. Stochastic weight trade-off chaotic mutation-based non-dominated sorting particle swarm optimization algorithm can improve solution-search-capability by balancing between global best exploration and local best utilization through the stochastic weight and dynamic coefficient trade-off methods. This algorithm with chaotic mutation enhances diversity and search-capability, preventing premature convergence. Non-dominated sorting and crowding distance techniques efficiently provide the optimal Pareto front. Fuzzy function is used to select the local best compromise. Using a two-stage approach, the global best solution is selected from many local trials. The discussed approach can schedule the generators in the systems effectively, leading to savings in fuel cost, reduction in active power loss and betterment in voltage stability.

A Multi-Objective Risk Scheduling Model of an Electrical Power System-Containing Wind Power Station with Wind and Energy Storage Integration

The integrated operation of wind storage is a developmental trend for future wind power stations. Compared with energy storage and wind power system scheduling, the utilization ratio of wind power is improved. This paper analyzes the power system scheduling risks that are brought about by wind power stations with wind and energy storage integration and puts forward the corresponding risks indexes, which are based on the physical structure and the long-operation features of the battery energy storage system. This paper also proposes the multi-objective optimization scheduling model, considering the economy of optimization, risk of load-shedding, and wind power curtailment that is caused by the full failure and partial failure of energy storage and wind turbines, and the uncertainty of the wind power output. Example results have shown that the optimization scheduling model can apply to various control strategies and different risk levels of the system, and reduce the risk of an electric power system containing a wind power station with wind and energy storage integration. In the meantime, it can also improve the economical efficiency and utilization rate of the wind power system.