Smart Reconfiguration of Electric Power Distribution Networks for Power Loss Minimization and Voltage Profile Optimization

Author: Alberto Landeros Rojas
Year: 2018
Supervisors: Mohamed Abdelfattah and Slawomir Kozil

Abstract

Distribution network reconfiguration (DNR) can significantly reduce power losses, improve the voltage profile, and increase the power quality. DNR studies require implementation of the power flow analysis and complex optimization procedures capable of handling large combinatorial problems. The size of the distribution network influences the type of the optimization method to be applied. In particular, straightforward approaches can be computationally expensive or even prohibitive whereas heuristic or meta-heuristic approaches can yield acceptable results with less computation cost. In this thesis work, a customized evolutionary algorithm has been introduced and applied to power distribution network reconfiguration. The recombination operators of the algorithm are designed to preserve feasibility of solutions (radial structure of the network) thus considerably reducing the size of the search space. Consequently, an improved repeatability of results as well as lower overall computational complexity of the optimization process have been achieved. The proposed technique is referred to as feasibility-preserving evolutionary optimization (FPEO). Moreover, approach is adopted to solve DNR. The method is based on sequential stochastic optimization that utilizes mechanisms adopted from simulated annealing (to avoid getting stuck in local minima), and customized network modification procedures that aim at improving the cost function while maintaining the radial architecture of the distribution system. The proposed technique is referred to as feasibility-preserving simulated annealing (FPSA). Both, FPEO and FPSA are comprehensively validated using three IEEE test cases, 33-, 69- and 119-bus systems. At last, a novel algorithm for power loss reduction through distribution network reconfiguration (DNR) and optimization-based allocation of distributed generation (DG) sources is reported. Here, DNR is solved simultaneously with DG allocation. The problem at hand is a complex mixed-integer task. A customized evolutionary algorithm has been developed with recombination operators preserving a radial structure of the network, integer-based operators for DG placement, and floating point operators for handling their power output capacities. Comprehensive numerical validation performed on standard IEEE 33- and 69-bus systems indicates that our methodology outperforms state-of-the-art algorithms available in the literature in terms of the obtained power loss reduction. Furthermore, it features good repeatability of results as demonstrated through statistical analysis of multiple algorithm runs. In this thesis work, two customized optimization algorithms based on evolutionary and simulated annealing optimization have been proposed for solving the DNR limited to radial networks. In addition, optimal allocation of DG and DNR is implemented in a collective fashion based on the customized evolutionary algorithm. The major differences between conventional evolutionary algorithms and the proposed one include dedicated data representation and recombination operators embedding the problem specific knowledge. The second approach involves sequential (not population-based) stochastic optimization that adopts some mechanisms from simulated annealing a way to avoid being stuck in local optima. Furthermore, both methods enforce feasibility of solution, in particular, maintaining radial network structure at all stages of the process, consequently, considerable reduction of the search space size has been obtained. Comprehensive numerical studies are applied on the DNR problem using both, evolutionary and simulated annealing based optimization carried out for 33-, 69- and 119- bus systems. The studies reveal superiority of our approach over state-of-the-art meta-heuristic and artificial intelligence algorithms in terms of reliability, solution repeatability, as well as computational complexity. Optimal DG allocation and DNR numerical studies are carried out for 33- and 69-bus test networks where all relevant problem variables (network configuration, DG allocation and sizes) are processed simultaneously. This leads to a faster convergence of the optimization process as well as better quality and improved repeatability of the results. Comprehensive benchmarking is also provided indicating superiority of the proposed technique over the state-of-the-art algorithms reported in the literature.