Fast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods.

Excessive penetration of renewable energy resources into the distribution grid without additional preventive measures has led to several operational problems. However, most strategies developed to accommodate more renewable energy units suffered from other operational problems. Therefore, further efforts are needed to address the other key vulnerabilities of the grid in addition to maximizing the hosting capacity. In this regard, this study is devoted to a new multi-objective formulation to maximize the hosting capacity and minimize the total energy losses while satisfying the operational constraints and maximizing the energy transferred to off-peak hours. The Multi-Objective Advanced Gray Wolf Optimization (MOAGWO) algorithm is used as a solution tool. The proposed formulation and solution algorithm are tested on IEEE-33-bus and 69-bus medium voltage test systems. The impacts of energy storage systems, voltage regulators, and static var compensators on the hosting capacity and the objective functions are identified using several scenarios. The results showed that the optimal device type and locations depend on the level of DG penetration. Finally, a comparison according to two popular multi-objective performance indices showed that the quality of the Pareto front distribution obtained by MOAGWO was better than the ones obtained with the two other popular heuristic methods.

This paper proposes an optimal coordination strategy for electric vehicles and energy storage devices in distribution grids besides the optimal allocation problem of renewable distributed generation (RDGs) and energy storage devices (ESDs). By finding the optimal number, size, and site of the RDGs and ESDs, together with the operation strategy of the ESDs and smart charging of a large number of EVs, the performance of the distribution grids will be improved. An advanced grey wolf optimization (AGWO) algorithm is used to minimize energy losses and voltage violations simultaneously in the test systems. Simulations are tested on IEEE 33 and 69 bus networks to find near-optimal solutions for the optimization problem. Based on the simulation results, the proposed optimization framework reduced the systems' losses while minimizing the voltage violations by finding the optimal control parameters of the devices.

This paper presents a novel two-stage optimization approach for the optimal sizing of static var compensators and their operational management to maximize the photovoltaic integration capacity of distribution systems. In the first stage, the optimal locations for fixed size photovoltaic (PV) systems are determined to minimize the sum of total voltage violations. In the second phase, the size of the PV units are resized and the optimal size, number, location, and operating strategy of the SVC units are determined to maximize PV hosting capacity. In both phases, the Marine Predators algorithm is used for the solution optimization equations. The performance of the proposed approach and solution method is validated on modified 33-node and 141-node radial distribution networks. The results are discussed from the point of view of the maximum hosting capacity and compared with the Grey Wolf optimization and Whale Optimization algorithms in terms of computational performance.

The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors.

Weather-dependent distributed renewable energy sources such as photovoltaics (PVs) and wind turbines (WT) are increasingly being connected to distribution networks (DNs). Increased penetration of these intermittent sources brought the necessity of using energy storage systems (ESSs) to achieve the intended benefits. This study presents an optimization process to determine optimal numbers, sizes, locations and distributed energy resources (DERs) as well as to determine the optimal operating strategy of ESSs in a distribution network. The objective is to improve the voltage profile and to minimize the installation costs. The proposed multi-objective formulation problem is solved by using ant lion multi-objective optimization algorithm. At the second part of the study, optimal values are tested with monthly extreme distributions and the impacts of load and distributed generation volatilies on the voltage profiles which were determined by Pareto-optimal solution candidates are analysed. Simulations were performed on 33 bus radial distribution system using Matlab. Finally the benefits obtained by the optimal solutions with less risk are compared.