Optimal Spinning Reserve Capacity Determination Using a Chance-constrained Programming Approach

Abstract It is crucial to procure adequate amount of spinning reserve capacity for maintaining secure and stable operation of a power system. In the competitive power market environment, under the premise of well balancing the system reliability requirement and spinning reserve procuring cost, the determinination of the optimal spinning reserve capacity to account for the uncertainties of forced outage of generators as well as load forecasting errors is an important issue to be dealt with. In a chance-constrained programming framework, a mathematical model with the minimization of spinning reserve procurement cost as the objective and the system security requirement as a chance constraint is formulated. The problem is solved by a Monte-Carlo simulation-based genetic algorithm (GA). A test system with 18 generating units is employed to demonstrate the validity of the proposed model and the solution methodology.