Performance comparison of enhanced PSO and DE variants for dynamic energy/reserve scheduling in multi-zone electricity market

During the last decade, energy regulatory policies all over the globe have been influenced by the introduction of competition. In a multi-area deregulated power market, competitive bidding and allocation of energy and reserve is crucial for maintaining performance and reliability. The increased penetration of intermittent renewable generation requires for sufficient allocation of reserve services to maintain security and reliability. As a result the market operators and generating companies are opting for market models for joint energy and reserve dispatch with a cost minimization/profit maximization goal. The joint dispatch (JD) problem is more complex than the traditional economic dispatch (ED) due to the additional constraints like the reserve limits, transmission limits, area power balance, energy-reserve coupling constraints and separate sectional price offer curves for both, energy and reserve.The present work proposes a model for the joint static/dynamic dispatch of energy and reserve in deregulated market for multi-area operation using enhanced versions of particle swarm optimization (PSO) and differential evolution (DE). A parameter automation strategy is employed in the classical PSO and DE algorithms (i) to enhance their search capability; (ii) to avoid premature convergence; and (iii) to maintain a balance between global and local search. The performance of enhanced PSO and DE variants is compared for single/multi-area power systems for static/dynamic operation, taking both linear and non-smooth cost functions. The proposed approach is validated on two test systems for different demands, reserve requirements, tie-line capacities and generator outages.