# 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. distribution network; optimization toolbox; distributed generation; battery energy storage systems +
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of devices to be installed in the system by solving objectives such as cost minimization, profit maximization, load maximization, and efficiency maximization. Optimization problems can be solved using either numerical or analytical methods. Numerical methods are classified into derivative-based and non-derivative methods. Of these, non-derivative methods have gained popularity due to their simplicity and ability to produce near-optimal results. Moreover, non-derivative heuristic algorithms are more successful than numerical algorithms in solving complex problems, especially when the derivative information is not easy to obtain due to the complexity of the functions encountered. We have used four heuristic methods to solve the operation problem in distribution networks. We chose the particle swarm optimization (PSO) method [1] because of its proven record in solving optimization problems since 1995. Three recently developed promising algorithms, Gray Wolf Optimization Algorithm (GWO) [2], Whale Optimization Algorithm (WOA) [3], and Ant Lion Optimization algorithm (ALO) [4], were also used. The main advantages of GWO are as follows. GWO does not require initialization of parameters [5], it is easy to implement due to its simple structure, it has lower memory and computational requirements with the ability to escape from local minima, and it has faster convergence capability, since it reduces the search space during the optimization process by using a smaller number of decision variables [6]. WOA proved to be competitive with several well-known heuristic algorithms using benchmark optimization problems thanks to the small number of parameters and escaping ability from local optimum points [3]. Some educational tools and software packages are used to perform power system analysis, management, and simulation in the literature. These are Power System Toolbox (PST) [7], Power Analysis Toolbox (PAT) [8], Educational Simulation Tool (EST) [9], Power System Analysis Toolbox (PSAT) [10], MatDyn [11], Power System GUI (PowSysGUI) [12], Torrit [13], Internet Technology-based Power System Simulator (InterPSS) [14], and Gym-ANM [15]. Of these, PST, PAT, EST, PSAT, MatDyn, and PowSysGUI run on MATLAB. However, Torrit and Gym-ANM, which are open source and easy to use GUIs, run on Python. The InterPSS project runs in a cloud-based environment, while other projects work offline. There are many research papers that deal with solving optimization problems in smart power grids.