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.

Integrating distributed generation into the main grid requires the active participation of both consumers and prosumers, offering technical and financial benefits for all stakeholders. This study proposes a multi-objective optimization approach to enhance peer-to-peer (P2P) market operations within low-voltage distribution grids. It focuses on optimizing the placement of modular PV panels, maximizing prosumer profits, and refining P2P strategies. Key objectives include minimizing the payback periods for PV owners and reducing the average monthly energy costs for consumers. The Multi-Objective Advanced Gray Wolf Optimizer (MOAGWO) is used as the solution method. Operational scenarios are also compared from a consumer reliability perspective as an assessment metric. The methodology is applied to a 55-node European low-voltage test feeder across six scenarios. Results indicate that prosumer payback periods remain around eight years for up to four prosumers but increase with further installations. Average monthly energy costs for customers ranged from 653 to 1079. A quality assessment based on three multi-objective optimization metrics showed that MOAGWO and MOGWO yielded comparable performances.

The growing complexity of micro-grid management and the demand for resilient, sustainable energy systems require solutions that go beyond traditional management strategies. This paper introduces a Multi-Objective Energy Management System (MOEMS) designed for micro-grids and energy communities, emphasizing resilience and sustainability. Unlike conventional Energy Management Systems (EMS), which mainly focus on cost and efficiency, MOEMS takes a user-centered approach. It incorporates a democratic decision-making process that involves all stakeholders, enabling personalized energy management tailored to user preferences and environmental considerations. MOEMS is used to address grid challenges like power congestion and voltage issues while balancing diverse stakeholder goals. The optimization problem is formulated as a mixed-integer non-linear program and adopted to be solved using a free and open-source solver. The proposed framework leverages the results of a multi-objective optimization model, allowing users to define their preferences. By specifying an acceptable solution space, the central controller in the micro-grid can optimize operations while ensuring that the selected solutions align with user expectations. The system is validated through simulations and a real-world micro-grid case study, demonstrating its adaptability to different setups. To evaluate its effectiveness, MOEMS is compared with traditional EMS approaches, including profile steering and methods that prioritize economic and environmental factors. A real-time implementation in the Kezo micro-grid further demonstrates its capability to dynamically manage energy flows, meet user energy demands, and adapt to real-time fluctuations in supply and demand. Significantly, MOEMS achieved up to 22% higher annual electricity cost savings and a 37% reduction in CO2 emissions compared to traditional EMS methods.

This paper presents a decentralized energy management approach based on a Multi-Objective Energy Management System called DMOEMS, designed for Energy Communities (ECs), aiming to create resilient and sustainable energy systems. DMOEMS integrates a multi-objective optimization framework that aggregates conflicting goals–minimizing electricity cost and CO ₂, reducing Photovoltaic (PV) curtailment, and maximizing self-consumption–by converting them into a single objective using user-defined weight factors. Each local controller optimizes the operation of distributed assets based on localized constraints and user preferences, while an EC controller coordinates aggregated power profiles through an iterative feedback mechanism. This coordination dynamically adjusts weight factors and curtailment strategies to resolve grid congestion without compromising individual privacy. Simulation studies on the realistic Aardehuizen EC demonstrate that DMOEMS effectively mitigates overloading scenarios across diverse operating conditions (high EV charging, normal demand, and excess PV generation), enhances user satisfaction, reduces operational costs, and lowers CO ₂ emissions. The proposed framework highlights the potential of a democratic, decentralized approach to energy management in modern ECs. The numerical results for asset management using DMOEMS indicate improvements in different aspects such as reduction of 20 % in CO ₂ emissions, improvement of 4 % in electricity cost savings, and a 30 % reduction in PV curtailment relative to baseline scenarios. Furthermore, the proposed mechanism in the DMOEMS shows improvement in computational cost by converging faster to resolve grid congestion compared to conventional approaches.

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers.

The Dynamic Hunting Leadership (DHL) algorithm is an innovative heuristic technique that draws inspiration from nature to find almost optimal solutions for various optimization problems. It consists of four variants, each highlighting distinct leadership strategies to guide the hunting process. The development of the algorithm was based on the realization that effective leadership during the hunting process can significantly improve its efficacy. The concept behind these methods is to dynamically modify the number of leaders, which can enhance the algorithm's performance. The stability of DHL variants in exploring the unknown area of the search space and exploitation phases is compared, and the advantages of exploration or exploration ability for the different variants of DHL are discussed. Moreover, the results are compared with more than twenty well-known algorithms. The efficacy of the proposed algorithms in discovering near-optimal solutions is tested across several real-world applications, and the outcomes demonstrate that the DHL algorithm outperforms other competing algorithms. The distinctiveness of the DHL algorithm is its ability to identify the global minimum on various benchmark problems and its superior performance in enhancing the objective value for the welded beam design problem and tension–compression spring problem, surpassing the values achieved by other algorithms. The algorithms’ performance is also tested on an optimal allocation problem of distributed generation (DG) and energy storage system (ESS) for balanced electrical distribution systems. The results show that all four variants of DHL obtained the global minimum for the problem. For the optimal control strategy problem for voltage regulators in three-phase unbalanced power systems, the DHL algorithm improved the objective value by 35.7% compared to the best results found by other algorithms. Based on the analysis and comparison of the best objective values and convergence behavior for all benchmarks and problems, the DHL method proves to be an effective and reliable optimization method.

Centralized optimization methods have been widely used to manage the operation of distribution systems. However, these methods have some restrictions, such as the high computational cost, the reliance on a centralized computer system, and the lack of data privacy. To overcome these limitations, decentralized optimization methods have been proposed in recent years. Decentralized methods divide the control variables of the centralized optimization problem over several controllers, and each controller solves its own subproblem independently. This paper presents a multi-objective optimization framework for managing the operation of distribution systems. The primary objective of the proposed method is to optimize the tap positions of voltage regulators and charging and discharging powers for energy storage devices locally through a decentralized coordination process. The used objective functions take into account the voltage profile within the network, the lifetime of the devices, and the energy losses in the systems. Two decentralized methods, based on the Advanced Arithmetic Optimizer algorithm and the Profile Steering approach, are proposed to address the limitations of centralized optimization methods. The decentralized methods aim to improve the reliability and efficiency of the optimization process, while also minimizing communication and computational costs. The proposed methods are evaluated and compared to a centralized approach using the IEEE 33 and 69 bus systems. The results demonstrate that the proposed decentralized methods can effectively resolve voltage problems, minimize energy losses, and find high-quality solutions with improved computational efficiency compared to the centralized approach.

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

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 explores the effects of active energy communities, as defined by EU Directive 2019/944, on the energy system using simulations modelled based on a real-world ecological community. The energy flexibility of this community is optimized towards the objectives peak reduction, cost minimization, and CO2 emission minimization. The resulting effects for different stakeholders, such as network operators and the community itself, are investigated using different performance indicators. The results show that energy imports can be reduced by 44% when peak reduction is applied. Conflicting objectives may lead to peak synchronization however, with the risk of deteriorating business cases if the network operator needs to intervene. This results in a risk where energy communities abstain from participation in energy market and its benefits.

The benefits of renewable energy sources (RES) are undeniable, despite the fact that controlling their output power is complicated due to their intermittent nature. In this paper, a new set of analytical formulations has been proposed for simultaneous integration and control of wind turbine (WT) and battery energy storage system (BESS) considering the time-varying load models, and resources uncertainty. The objective functions of this method include smoothing the output power of the WT unit, balancing demand and generation, increasing WT shares as well as decreasing the automatic generation control (AGC) reserve capacity which is essential in the gird. In addition, the modification of BESS reference current is considered to prolong the BESS effective lifetime and guarantee the prevention of BESS from over-charge and discharge. The results show that simultaneous integration of WT and BESS in the grid will smooth WT output power, balance load and WT generation, thereby reducing AGC required capacity and increasing the hosting capacity of grids effectively.

Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.

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.

This paper proposes a multi-objective optimization technique for scheduling the charging of electric vehicles (EVs) in electrical distribution systems (DSs). A multi-objective advanced grey wolf optimization algorithm (MOAGWO) is developed to find the Pareto optimal solutions that minimize the DS's operational costs, energy losses costs, voltage violations, and the energy not supplied to EV users using several scenarios. A 449-node system with 63% penetration of EVs is used to demonstrate the efficiency of the proposed method. The quality of the non-dominated optimal solutions found by MOAGWO are validated via a comparison analysis with other well-known methods such as the multi-objective grey wolf optimizer (MOGWO) and the multi-objective particle swarm optimization (MOPSO) algorithm, based on domination rate, spacing index, hypervolume index, and computational cost measurements. The Pareto solutions indicate that the smart charging coordination found by MOAGWO makes the techno-economic operation of the DS possible while satisfying energy-based goals of the EV users.

This paper studies a multi-objective optimization problem that optimizes unbalanced distribution networks with high penetration of photovoltaic power generation whereby the aim is to find the optimal tap positions of voltage regulators (VRs) and locally optimized charging and discharging power profiles for energy storage devices (ESDs) based on a decentralized coordination process. The corresponding objective function is based on the voltage profile of the network and the lifetime improvement of the VRs and ESDs. The Advanced Arithmetic optimizer (AAO) algorithm combined with the profile steering approach is used to find a near-optimal solution for the problem. The decentralized process is compared to a centralized approach for the IEEE 13 and 123 bus systems. Results show that the proposed decentralized method is able to resolve all voltage problems and find good quality solutions in lower computational time compared to the centralized approach and other optimization algorithms.

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD.

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.