Particle swarm optimised fuzzy controller for charging–discharging and scheduling of battery energy storage system in MG applications

© 2020 The Authors Aiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging–discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging–discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging–discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging–discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research

A Review Of Battery Charging - Discharging Management Controller: A Proposed Conceptual Battery Storage Charging – Discharging Centralized Controller

This paper describes the development of a centralized controller to charge or discharge the battery storages that are connected to renewable energy sources. The centralized controller is able to assist, control, and manage the battery storage charging when excessive power is available from renewable energy sources. At the same time, the centralized controller also performs battery storage discharging when the connected load requires a power source, especially when the renewable energy sources are unavailable. Background studies regarding battery storage charging-discharging are presented in the introduction section. Also, generally developed charging discharging methods or techniques were applied at the system level and not specifically to the battery storage system level. Due to the limited study on battery storage system charging discharging, this paper reviews some of the similar studies in order to understand the battery storage charging–discharging characteristics as well as to propose a new conceptual methodology for the proposed centralized controller. The battery storage State-of-Charge (SoC) is used as the criterion to develop the conceptual centralized controller, which is also used as a switching characteristic between charging or discharging when only the battery energy storages are supplying the output power to the connected load. Therefore, this paper mainly focuses on the conceptual methodology as well as explaining the functionality and operationality of the proposed centralized controller. A summarized comparison based on the studied charging– discharging systems with the proposed centralized controller is presented to indicate the validity of the proposed centralized controller

Optimal energy management of a campus microgrid considering financial and economic analysis with demand response strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts

Energy management of virtual power plant considering distributed generation sizing and pricing

UID/EMS/00667/2019The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model.publishersversionpublishe

Voltage and Reactive Power Control in Islanded Microgrids

Previous studies put on view lots of advantages and concerns for islanded microgrids (IMGs), whether it is initiated for emergency, intentionally planned or permanent island system purposes. From the concerns that have not been addressed yet, such as: 1) The ability of the distributed generation (DG) units to maintain equal reactive power sharing in a distribution system; 2) The ability of the DG units to maintain acceptable voltage boundary in the entire IMG; 3) The functionality of the existing voltage and reactive power (Volt/Var) DG, this thesis analyzes the complexity of voltage regulations in droop-controlled IMGs. A new multi-agent algorithm is proposed to satisfy the reactive power sharing and the voltage regulation requirements of IMGs. Also, the operation conflicts between DG units and Volt/Var controllers, such as shunt capacitors (SCs) and load-ratio control transformer (LRT) during the IMG mode of operation, are investigated in this thesis. Further, a new local control scheme for SCs and LRTs has been proposed to mitigate their operational challenges in IMGs

Integration and Control of Distributed Renewable Energy Resources

The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories—regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Компенсація похибок позиціонування роботаманіпулятора в робочому просторі технологічного обладнання

Промислові роботи широко використовують на сучасних підприємствах. Хоча висока повторюваність промислових роботів може задовольнити більшість потреб процесу, їх низька абсолютна точність позиціонування в робочому просторі не може задовольнити вимоги деяких високоточних завдань. Прикладом є те, що дія робота покладається на вибіркові дані в реальному часі, а не на змодельовані дані. Низька абсолютна точність робота до позиціонування в робочому просторі обладнання призводить до того, що робоча продуктивність значно відрізняється від очікуваної. Тож метою даної статті є аналіз розробленої програми підвищення точності позиціонування робота-маніпулятора в реальному часі за умови компіляції з програмами інтеграційних проєктів автоматизації. У статті розглянуто деформацію ланок, що є однією з виробничих похибок робота-маніпулятора. Дія прикладених зусиль на з'єднаннях і ланках може по-різному впливати на деформацію пози робота, а ці зміни складно врахувати за допомогою лінійної алгебри, тому останнім часом зріс попит на "офлайнове програмування", що дозволить змоделювати роботу робота-маніпулятора зі зміною у часі і матиме прийнятну ціну. Тому було розглянуто застосування штучних нейронних мереж на основі алгоритмів оптимізації для компенсації абсолютної похибки. Після аналізу існуючих технічних рішень було обрано генетичний алгоритм, що задовольняє умови простоти реалізації, зрозумілості, відсутність зайвих обчислень, гнучкість до використовуваних типів даних. Також в роботі представлено загальну схему алгоритмів оптимізації параметрів штучних нейронних мереж та ітеративний процес пошуку оптимальних значень гіперпараметрів із використанням генетичного алгоритму. Після вибору методу моделювання та алгоритму оптимізації, протестовано розроблений алгоритм на відкритому наборі даних. Отримані результати показали підвищення точності від 22 до 77 % (точності позиціонування без застосування компенсаційної методики). Отже, в даній статті описано метод підвищення абсолютної точності позиціонування за умови компіляції з програмами інтеграційних проєктів автоматизації. Результатом роботи є програма, що заснована на вищезгаданому методі.Industrial works are widely used in modern enterprises. Although high repeatability of industrial robots can satisfy most process needs, their low absolute positioning accuracy in the workspace cannot meet the requirements of some high-precision tasks. An example, is that the action of a robot relies on real-time sample data rather than simulated data. The low absolute robot’s accuracy to positioning leads to the fact that work productivity is significantly different from what is expected. Therefore, the purpose of this paper is to analyze the developed program to increase the accuracy of robot-manipulator’s positioning in real time under the compilation with the programs of integration automation projects. The paper considers the deformation of the links, which is one of the robot manipulator’s production errors. The effect of applied forces on the connections and links can affect the deformation of the robot posture in different ways, and these changes are difficult to account for using linear algebra, so recently the demand for "offline programming" has increased, which will allow you to simulate the work of a robot manipulator with a change in time and will have a reasonable price. Therefore, the application of artificial neural networks based on optimization algorithms to compensate for absolute error was considered. After analyzing the existing technical solutions, a genetic algorithm was chosen that satisfies the conditions of ease of implementation, clarity, lack of redundant calculations, flexibility to the data types used. The paper also presents a general scheme of algorithms for optimizing the parameters of artificial neural networks and an iterative process of finding optimal values of hyperparameters using a genetic algorithm. After choosing the modelling method and optimization algorithm, the developed algorithm on an open data set was tested. The obtained results showed an increase in accuracy from 22 to 77% (positioning accuracy without the use of compensation methods). Therefore, this article describes a method of increasing the absolute accuracy of positioning under the condition of compilation with programs of integration automation projects. The result is a program based on the above method