A Heuristic Methods-Based Power Distribution System Optimization Toolbox

Abstract

This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

“…A toolbox designed for incorporating RESs into distribution networks [121] employs heuristic optimization to simulate energy storage and optimize power output, boosting operational efficiency. Additionally, a load power and energy management system [27] utilizes Proteus Visual Design software and Arduino Mega 2560 for advanced metering and load control that is suitable for various applications.…”

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

“…Heuristic algorithms work together with BBA to find a solution of the same quantity and quality in a minimal running time [25,26]. Furthermore, transforming the binaryinteger programming into a nonlinear polynomial model shows the robustness required to find a proper number of PMUs and their placement sites for a fixed number of channels [13][14][15][16][17][18][19][20][21][22][23][24].…”

“…The 0 − 1 integer program can be solved utilizing genetic algorithms and binary particle swarm optimization [18,51]. They follow a probabilistic search of the feasible region due to a random selection of the initial population, searching and finding an optimum solution [25,26,51]. Strategies such as special creation, crossover, and mutation functions are utilized to enforce the variables to be declared in the integer problem solving [72].…”

“…Strategies such as special creation, crossover, and mutation functions are utilized to enforce the variables to be declared in the integer problem solving [72]. GA tries to minimize a penalty function, not necessarily the fitness one, and a penalty term is added to consider any infeasibility while the feasible region is explored [25][26][27]50,51]. The penalty function is performed with a binary tournament choice to find individuals for subsequent generations [25,26,50,51].…”

Special Issue on Algorithms in Planning and Operation of Power Systems

“…Similarly, conventional grids for power transmission and power distribution are also transforming their paradigms. Now, due to the rising power of the digital age and an era of sustainable development, renewable sources have taken the place of fossil fuels, and smart digital grids are also being used [1][2][3].…”