Abstract
With the increasing amount of distributed generation (DG) integrated into electrical distribution networks, various operational problems, such as excessive power losses, over-voltage and thermal overloading issues become gradually remarkable. Innovative approaches for power flow and voltage controls are required to ensure the power quality, as well as to accommodate large DG penetrations. Using power electronic devices is one of the approaches. In this paper, a multi-objective optimization framework was proposed to improve the operation of a distribution network with distributed generation and a soft open point (SOP). An SOP is a distribution-level power electronic device with the capability of real-time and accurate active and reactive power flow control. A novel optimization method that integrates a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and a local search technique – the Taxi-cab method, was proposed to determine the optimal set-points of the SOP, where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives. The local search technique is integrated to fine tune the non-dominated solutions obtained by the global search technique, overcoming the drawback of MOPSO in local optima trapping. Therefore, the search capability of the integrated method is enhanced compared to the conventional MOPSO algorithm. The proposed methodology was applied to a 69-bus distribution network. Results demonstrated that the integrated method effectively solves the multi-objective optimization problem, and obtains better and more diverse solutions than the conventional MOPSO method. With the DG penetration increasing from 0 to 200%, on average, an SOP reduces power losses by 58.4%, reduces the load balance index by 68.3% and reduces the voltage profile index by 62.1%, all compared to the case without an SOP. Comparisons between SOP and network reconfiguration showed the outperformance of SOP in operation optimization.
Highlights
Growing awareness of energy and environment, and the demand for a reliable, secure, and sustainable power grid lead to the continuously expanding deployments of Distributed Generators (DG)
A novel optimization method that integrates a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and a local search technique – the Taxi-cab method, was proposed to determine the optimal set-points of the soft open point (SOP), where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives
With the distributed generation (DG) penetration increasing from 0 to 200%, on average, an SOP reduces power losses by 58.4%, reduces the load balance index by 68.3% and reduces the voltage profile index by 62.1%, all compared to the case without an SOP
Summary
Growing awareness of energy and environment, and the demand for a reliable, secure, and sustainable power grid lead to the continuously expanding deployments of Distributed Generators (DG). SOP is a power electronic device that can be installed in place of a normally open/closed point in distribution networks, with the capability to accurately control active and reactive power flows between the feeders that it is connected to It has the advantages of fast response, frequent actions and continuous control. The new contributions of this work include: (1) investigating the SOP’s capability of bringing benefits to the distribution networks on multiple objectives simultaneously; (2) providing a multi-objective optimization framework to improve the distribution network operation with an SOP; and (3) proposing a novel optimization method integrating both global and local search techniques, which has the capability of obtaining better and more diverse Pareto optimal solutions than the conventional MOPSO method
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