Pareto-Frontier Differential Evolution based Financial Approach for Multi-objective Congestion Management using Customer Participation and On-site Generation

Abstract

Day by day increasing demand has increased the loading on the transmission network and in turn has become a cause of increased environmental pollution. Thus Congestion Management (CM) with controlled emission is a crucial issue in contemporary power market. In the present paper, issue of CM has been solved with inclusion of a renewable Wind Energy Source (WES) along with Customer Participation (CP) to create elasticity in their demands. Thus the matter of mitigating emission has been resolved with double efforts i.e. by introducing renewable energy source and by modifying customers demand. CM has been dealt with as a Multi-Objective Optimization Problem (MOOP). Three most relevant objectives have been taken to be solved simultaneously for this MOOP, which are minimization of operational cost, CO2 emission and loading on transmission lines. Moreover, the amount of customer participation and the size and site of renewable WES have also been optimized in the present research work. A promising evolutionary algorithm i.e. Pareto-Frontier Differential Evolution has been proposed for addressing this multi-objective CM problem. The proposed algorithm has also been compared with a well-established multi-objective genetic algorithm on the basis of the numerical results obtained and a statistical analysis to conclude the merit of performance. The present approach has been examined on IEEE 30-bus system and IEEE 118-bus system. Results obtained have been compared with those reported in the literature for the same power systems and for same line thermal limits i.e. for same level of congestion.