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.
A reliability-based optimal μ-PMU (micro-phasor measurement unit) placement scheme is suggested for efficient observability enhancement of smart distribution grids at steady-state and contingencies conditions. This article introduces a unique method for the μ-PMU allocation in reconfigurable smart distribution grids in which communication system requirements and zero injection nodes (ZINs) are considered. The original objective function and limitations are proposed aiming at minimizing the capital cost, including communication links and installation costs of μ-PMU, optical power ground wire cost, power losses cost, and reliability cost as well as obtaining the maximum number of measurement redundancy constrained to full system observability in the presence of ZINs and tie switches. The suggested method is formulated as a mixed-integer linear programming problem applied to find optimal μ-PMU locations considering the cost of communication infrastructure and co-optimize the system switching plan simultaneously. In this regard, CPLEX-a high-performance mathematical solver-is used to solve the proposed mixed-integer linear optimization problem to reach the global optimality. The simulations are performed on 33, 69, and 85-bus radial distribution networks, and comprehensive simulation studies show the effectiveness of the suggested method.
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