Abstract
The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and Marine Predators Algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators.