Robust model predictive control based voltage regulation method for a distribution system with renewable energy sources and energy storage systems

Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Microgrids (MGs) are a critical component in modern power systems, comprising interconnected loads and distributed energy resources (DERs) that can operate autonomously or in conjunction with the main grid. This review examines the role of MGs in advancing smart grid technologies, emphasizing the optimal integration of renewable energy sources (RES) and energy storage systems (ESS) to enhance their resilience and efficiency. It provides a detailed analysis of various MG architectures, including AC, DC, and hybrid topologies, alongside their operational modes. The paper highlights the pivotal role of control systems in optimizing energy management within MGs. Additionally, the challenges associated with MG integration, such as harmonic distortion, system stability, and regulatory constraints, are discussed. The review further explores the application of intelligent control strategies, including Fuzzy Logic Controllers (FLCs) and Genetic Algorithms (GAs), to optimize battery energy storage systems (BESS) and improve overall MG performance. The findings emphasize the need for innovative optimization techniques and intelligent control systems to overcome current limitations, thus enhancing the potential of MGs in future energy systems

Island communities require in-detail mapping of resources available for exploitation for energy purposes, since infrastructure and connections to the mainland present, in most cases, a weak point of the island energy supply. As the present energy supply on Croatian islands relies mostly on fossil fuels and electricity from the mainland, it becomes obvious that exploitation of renewable energy sources is the only solution that leads towards self-sufficiency and sustainable development. In order to design a self-sufficient and sustainable island, three major technological changes are needed: integration of renewable energy sources alongside with energy savings and improvements in energy efficiency in the energy production. Analyses for several other Croatian islands have been performed using Renewislands/ADEG methodology in order to assess all possible outcomes. The scenarios in these cases have shown that islands can become self-sufficient through combining renewable technologies and energy storage systems. Energy storage systems will be crucial for achieving desired objectives in terms of energy independence from the mainland and in general import of fossil fuels. The analysis conducted for the island of Hvar will result in creation of several scenarios which will clearly point out the favorable solutions for improvement of both security of energy supply and covering the majority of energy demand with renewable energy sources and storage technologies. Also, when talking about implementation of renewable technologies on island of Hvar, an optimal mix of technologies must be applied in order to avoid excess costs and to achieve minimal impact on environment in terms of visual pollution.

The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s.

This paper presents a two-stage approach for solving the optimal voltage regulation problem in unbalanced radial distribution system in the presence of photovoltaic (PV) generation. The on-load tap changer (OLTC) and static VAr compensator (SVC) have been considered as the voltage control devices in this work. The formulated voltage control problem is a mixed-integer nonlinear programming problem which remains unsolved even after 8 h due to its computational burden. However, the proposed two-stage approach can solve this problem in less than 10 min. The feasibility of the proposed approach has been demonstrated on a modified IEEE 123-bus radial distribution system.

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

This research explores the resurgence of DC applications in power systems, focusing on the integration of DC microgrids and the effectiveness of control strategies in managing such systems. The research aims to investigate how artificial intelligence (AI) can enhance the efficiency of DC microgrids, especially in the context of renewable energy sources (RES) integration. The methodology involves a comprehensive review of existing literature on the evolution of DC and AC power systems, with a particular emphasis on voltage regulation. The research examines various DC microgrid control methods, including centralized, decentralized, distributed, and hierarchical strategies. Special attention is given to the role of AI, machine learning, and the Internet of Things (IoT) in the tertiary level of hierarchical control, which aims to optimize system performance. The findings highlight the advantages of DC microgrids, particularly their compatibility with RES like Solar Photovoltaics (PV) and energy storage systems, as well as modern electrical appliances. Hierarchical control, with its primary, secondary, and tertiary levels, effectively manages voltage, power sharing, and operational efficiency. The tertiary level of control, when combined with AI technologies, significantly enhances the microgrid's performance through real-time data analysis and predictive decision-making, improving responsiveness and the integration of renewable energy. The research concludes that hierarchical control methods, augmented by AI, enable reliable, stable, and efficient operation of DC microgrids. These strategies improve the coordination of distributed generation, reduce operational costs, and enable participation in energy markets, promoting the sustainability of power systems. Future research should focus on advanced AI algorithms for optimization, exploring scalable control methods for larger microgrids and hybrid AC-DC systems to enhance energy transfer and storage capabilities.

The battery energy storage system has become an indispensable part of the current electricity network due to the vast integration of renewable energy sources (RESs). This paper proposes an optimal charging method of a vanadium redox flow battery (VRB)-based energy storage system, which ensures the maximum harvesting of the free energy from RESs by maintaining safe operations of the battery. The VRB has a deep discharging capability, long cycle life, and high energy efficiency with no issues of cell-balancing, which make it suitable for large-scale energy storage systems. The proposed approach determines the appropriate charging current and the optimal electrolyte flow rate based on the available time-varying input power. Moreover, the charging current is bounded by the limiting current, which prevents the gassing side-reactions and protects the VRB from overcharging. The proposed optimal charging method is investigated by simulation studies using MATLAB/Simulink.

In this study, new technologies such as dynamic thermal rating (DTR) technology and energy storage system (ESS) are simultaneously used to optimise the integration of renewable energy sources (RESs) and minimise the load shedding. For achieving the mentioned aims, a new method is proposed to select the candidate lines for the implementation of DTR technologies. The DTR technology is responsible for increasing lines limited capacity. Moreover, optimally placed and sized ESSs save RESs generated power in non‐peak‐hours and inject it to the network in peak hours. For validating the performance of the proposed solution, comprehensive simulations are performed in several stages on IEEE RTS‐24‐ and 30‐bus test systems networks. To meet the increased power demand, RESs (wind and solar) are optimally allocated by using the genetic algorithm (GA) in the test systems. Then, ESS devices are optimally sized and placed by using GA. Finally, candidate lines are selected based on the proposed method and DTR devices are added to the systems. Comprehensive comparisons are presented for comparing the previously presented solutions and the proposed one. It is proved that using DTR technology and ESSs along with the proposed line selection method is the superior solution for system operators.

Number of microgrids (MGs) are increasing in distribution systems. They behave more active than previous due to increasing of distributed generations (DG) and active loads. Therefore, it is necessary to consider them in distribution network daily scheduling. Central scheduling of distribution networks does not meet the privacy of MGs customers and it is vulnerable against cyber-attacks. Therefore, it is necessary to develop a decentralized daily scheduling model for distribution-connected resources to preserve the privacy of MGs. Moreover, the scheduling model must coordinate distribution-connected resources with transmission-connected resources. In this paper, a decentralized model for the daily scheduling of smart distribution networks resources is presented to preserve the privacy of MGs. Analytical Target Cascading (ATC) algorithm is used for optimization decomposition. The model considers controllable and non-controllable loads, distributed generations (DG), Renewable Energy Sources (RES) and Energy Storage Systems (ESS). In order to coordinate transmission and distribution connected resources, electricity price at each hour, at the transmission bus, which the distribution network is connected to, is modeled by a price conjecture curve. Base distribution network operator and Microgrids Operators (MGO) jointly compute an optimal schedule for the entire distribution network. The proposed method is applied to a 39-bus test distribution network and simulation results are discussed.

The beginning of the 21st century requires a serious reassessment and analysis of the conventional methods of obtaining and producing electrical energy. The integration of renewable energy sources and energy storage systems into the power grid is of great importance for ensuring energy security in accordance with the concept of sustainable development. This paper analyzes a model of a distribution network that incorporates distributed energy sources: solar power plants, wind turbines, and electricity storage systems. The focus is on examining their operation over a one-year period, analyzing their impact on the medium-voltage network while considering various weather conditions. The research highlights key factors influencing the design of a microgrid to optimize production capacity and ensure operational stability. Based on the conducted analyses, it has been concluded that microgrids can significantly contribute to voltage stability, provide energy reserves, and enable energy trading in the electricity market. Software tools such as Typhoon HIL, which allow precise modeling of the power system under different scenarios, can facilitate faster and more accurate analysis of such electrical energy systems. Real-time simulations were conducted based on actual data, analyzing system behavior under various weather conditions. These simulations contributed to a better understanding of the dynamic behavior of microgrids, the impact of distributed sources, and the importance of proper sizing, as well as the potential for optimizing energy consumption and storage. The real-time simulations serve as a foundation for the implementation and testing of optimization algorithms in microgrids. The paper provides a practical insight into the planning and design phases of microgrids and all their components. The results of real-time simulations contribute to understanding the role of microgrids in the energy transition and the development of a sustainable power system.

High penetration of photo-voltaic panels (PVs) in the distribution networks causes volatile net load profiles. In turn, voltage control devices (switched capacitors, on-load tap changers (OLTCs) and voltage regulators) work more frequently to keep voltage on distribution feeders within limits. This paper employs the most recently developed mixed-integer programming (MIP) modeling methods to model discrete control devices including OLTCs and switching capacitors for volt/var optimization to explicitly count mechanical switching actions. Further, coordination capability is explored to demonstrate that the use of PV VARs can effectively reduce operations of OLTCs and switched capacitors while obtaining a satisfactory voltage profile. Case studies are performed on the IEEE 33-node distribution feeder with 125% PV penetration.

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

Integration of large-scale renewable energy (RE) sources in particular, wind and solar energy into the grid introduces current and voltage harmonics due to power electronics devices as well as inverter connected into the RE sources. Ensuring adequate harmonics in the line currents of RE integrated power system is one of the biggest challenges today. Therefore, this study investigates the potential impacts in particular current and voltage harmonics causes due to large-scale integration of RE into the Berserker Street Feeder, Frenchville Substation under Rockhampton distribution network (DN), Central Queensland, Australia. From the model analyses, it has clearly evident that harmonics across the network increases with the increase of RE integration and causes uncertainties in the feeder as well as in the DN. This study also explores possible mitigation measures and it has seen that optimized STATCOM effectively reducing the adverse harmonic impacts of integrating large-scale RE into the DN.