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Abstract
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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 Paretooptimal
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.
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