Abstract
A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.
Highlights
Traditional power systems (PSs) are designed to accommodate power flow in one direction
Despite the numerous economic and technical benefits derived from renewable energy based distributed generation (RDG), if they are poorly designed and/or placed, the system will be vulnerable to instability issues which jeopardizes the quality like increases losses, injects harmonic currents and deteriorates voltage profile (VP)
A hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for distribution networks (DN) reconfiguration and optimal allocation of three RDG units in primary DNs simultaneously. e objectives of the proposed technique are to minimize real power loss, improvement of voltage profile and feeder load balancing
Summary
Traditional power systems (PSs) are designed to accommodate power flow in one direction. In dynamic DNR, the DN operators can restructure the topology of DNs in real time by switching the remotely controlled switches on or off [14] Both RDG placement/sizing and DNR are highly non-linear, multi-objective, constrained and combinatorial optimization problems. Ant-lion optimization technique is applied in [33] to determine the optimal size and location of RDG with the aim of reducing the purchased energy cost from upstream, reliability improvement, losses and voltage deviation reduction. Due to high combinations of the solution space, search space reduction technique is applied to phase out some solutions In this proposed study, a hybrid PSO and real-coded Genetic Algorithm (HPSOGA) technique is employed for optimal DNR and multiple RDG units placement and sizing in primary DNs simultaneously.
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