Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system

Nowadays, dispersed storage systems (DSSs) have an irrefutable role in creating the favourable substrates for optimal management of active distribution networks (ADNs). Actually, they are capable of managing the congestion of ADNs by providing feasible solution that can dramatically improve the system reliability and resiliency against contingencies that threaten the network security. To this end, this paper deals with optimal arbitrage of DSSs in ADNs including the solar/wind/CHP hybrid energy system aiming at finding the optimal trade-off between congestion and economic targets by defining a novel probabilistic risk-based multi-objective model. In particular, the proposed method is fulfilled considering (1) feeders/line congestions, (2) network voltage deviations, (3) power losses, (4) operating cost of distributed generation associated with the cost of DSS charging/discharging, and (5) uncertainty pertaining to renewable generation. The two conflicting objectives consisting of congestion alleviation and procurement cost minimization are optimized simultaneously by multiobjective particle swarm optimization to purvey the Pareto-optimal curve, and subsequently, fuzzy decision-making is executed to extract the best solution from the Pareto curve obtained with respect to defined risk-based strategies. Finally, a case study referring to the modified IEEE 33-bus distribution system is utilized to evidence the efficiency and proficiency of the proposed congestion relief approach. Published by AIP Publishing.