Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads

Abstract

This paper presents a novel probabilistic algorithm for optimal reactive power provision in hybrid electricity markets. The proposed algorithm is a six-stage multiobjective nonlinear constrained optimization problem which takes into account load forecasting inaccuracies. Considering a set of probable forecasted loads, a three-component expected total market payment function is suggested being minimized as cost function of the first stage. Besides economic issues, expected voltage security margin, deviation from multilateral and pool based energy transactions, deviation from spinning reserve contracts, having adequate local reactive power reserve in each voltage control area of the system and transmission congestion probability are well thought out in stages 2–5 as technical aspects of the market. Finally, in the last stage, using different weighting factors to compromise between all objects, a probabilistic multiobjective function is presented to find the best reactive power market schedule. The proposed algorithm is applied on IEEE 24-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.