Proposed Energy Management System Research Articles

Novel Hierarchical Energy Management System for Enhanced Black Start Capabilities at Distribution and Transmission Networks

A novel energy management system featuring a unique framework involving multiple hierarchical controllers at the distribution and transmission network levels is proposed. The unique objective function of this energy management system is designed to enhance system inertia during black start and optimise load shedding. The objective function further aims to increase reliance on renewable energy sources, prioritising solar power along with battery and fuel cell technologies. This work delves deeply into the dynamics of multi-area power networks, where some areas possess black start capabilities (BSAs) while others do not (NBSAs). The proposed energy management system specifically explores the complex interplay between these black start capabilities and the hierarchical load restoration order. During grid blackouts, the systems located in BSA areas are tasked with first restoring essential loads in their own regions before extending aid to the adjacent NBSA areas, taking into account factors such as their available reserved power and geographical proximity. This work is extended to analyse complex multi-area power network architectures. This extended analysis provides invaluable insights for enhancing power restoration processes and facilitating the large-scale integration of sustainable energy solutions in complex systems. The proposed energy management system is validated using the IEEE 39-Bus network, which consists of ten distinct areas, each differing in their black start capabilities. The results demonstrate the superiority of the proposed system.

Lead-Acid Battery and Supercapacitor Based Hybrid Energy Storage Systems in Microgrid for Energy Control System

Lead-acid batteries are a common energy storage option in modern microgrid applications. This study suggests installing an Energy Management System (EMS) that is managed by a hybrid energy storage system (HESS) consisting of lead-acid batteries and supercapacitors (SCs). Lower operating costs and longer battery life are the goals. Lead acid battery state of charge (SOC) depletion and daily cycle stress are reduced by SCs, which control and supply the high-frequency, power-dense section of the load cycle. But it's crucial to size the HESS correctly, taking into account load demand, available power sources, and budgetary restrictions. This article explains an algorithm that outlines the HESS's energy management control. One of the main advantages of using HESS versus passive systems is that it reduces the overall size of the system. By estimating future load dynamics using load current, the proposed energy management system primes the system for impending changes.

Optimal Adaptive Fractional Order Integral Sliding Mode Controller-Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

This research presents an optimal energy management system (EMS) for a lithium-ion battery-supercapacitor hybrid storage system used to power an electric vehicle. The storage systems are connected in parallel to the DC bus by bidirectional DC-DC converters and feed a synchronous reluctance motor through an inverter. The proposed energy management strategy is built on the idea to take full benefits of two combined methods: the bald eagle search algorithm and fractional order integral sliding mode control. To evaluate the effectiveness of the suggested optimal energy management strategy, an urban dynamometer driving schedule (UDDS) driving cycle is considered. The obtained results are compared to a classical fractional order integral sliding mode control-based energy management strategy in terms of voltage ripples, overshoots, and battery final state of charge. The ultimate results approve the ability of the proposed energy management system to enhance the power quality and enhance battery power consumption at the same time. Comprehensive processor-in-the-loop (PIL) cosimulations were conducted on the electric vehicle using the C2000 launchxl-f28379d digital signal processing (DSP) board to assess the practicability and effectiveness of the proposed EMS.

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid—integrating multiple Microgrids with a battery management system—can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results, Voltage variation of PI, Fuzzy, and LSTM-ANN is 370V,180V,and 70V . The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

DSP‐based energy optimization management of household single‐phase electric energy router with historical and real‐time data

AbstractThe development and use of single‐phase electric energy router (SPEER) makes the household power system operate more reliably and bring economic income at the same time. This paper presents an economic energy optimization management method of SPEER with historical data of previous day and real‐time data in the grid‐connected mode of SPEER, which can be realized by DSP. First, based on the exchange power between the SPEER and the grid, the optimal charging and discharging power of the battery to make up for the energy difference between the previous day and the real time is obtained through the particle swarm intelligence algorithm, and the optimal economic model of SPEER operation is established. Then, on the hardware experimental platform, DSP is used to realize the real‐time optimization, and the optimal economic optimization algorithm proposed in this paper is implemented and verified on hardware experimental platform. The contradiction between the complexity of optimization algorithm and the limitation of hardware resources is solved in the proposed energy management system.

Bi-objective optimal active and reactive power flow management in grid-connected AC/DC hybrid microgrids using metaheuristic–PSO

Abstract In the context of evolving energy needs and environmental concerns, efficient management of distributed energy resources within microgrids has gained prominence. This paper addresses the optimization of power flow management in a hybrid AC/DC microgrid through an energy management system driven by particle swarm optimization. Unlike traditional approaches that focus solely on active power distribution, our energy management system optimizes both active and reactive power allocation among sources. By leveraging 24-hour-ahead forecasting data encompassing load predictions, tariff rates and weather conditions, our strategy ensures an economically and environmentally optimized microgrid operation. Our proposed energy management system has dual objectives: minimizing costs and reducing greenhouse gas emissions. Through optimized operation of polluting sources and efficient utilization of the energy storage system, our approach achieved significant cost savings of ~15% compared with the genetic algorithm counterpart. This was largely attributed to the streamlined operation of the gas turbine system, which reduced fuel consumption and associated expenses. Moreover, particle swarm optimization maintained the efficiency of the gas turbine by operating at ~80% of its nominal power, effectively lowering greenhouse gas emissions. The effectiveness of our proposed strategy is validated through simulations conducted using the MATLAB® software environment.

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.

Energy Management System for Smart Grid in the Presence of Energy Storage and Photovoltaic Systems

Today, the desire to use renewable energy as a source of clean and available energy in the grid has increased. Due to the unpredictable behavior of renewable resources, it is necessary to use energy storage resources in the microgrid structure. The power generation source and the storage source in microgrids should be selected in such a way that it has the ability to respond to the maximum demand in the state connected to the grid and operate independently. In this article, the optimal capacity and economic performance of a microgrid based on photovoltaic and battery system have been investigated. In this way, first, using the iterative optimization method, the optimal microgrid capacity has been obtained. Then, the dynamic planning method has been used for optimal microgrid energy management. The simulation results show the accuracy and efficiency of the proposed solutions. The proposed controller, while automatically and dynamically adapting to the solar cell output changes, is capable of responding to external requests, such as price signals or satisfying power system constraints or operator requests. In addition, the results indicate that by using the proposed energy management system, the microgrid system can regain stability during one to two cycles, during the occurrence of PV system radiation changes as well as ESS charge changes. And also, according to the ESS charge changes, the voltage changes should be within the defined permissible range between 0.95 and 1.05 pu, which is the result of the unique efficiency of the proposed energy management system.

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Peer-to-Peer Energy Trading Case Study Using an AI-Powered Community Energy Management System

The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.

Interoperability of single-controllable clusters: Aggregate response of low-voltage microgrids

Microgrid clusters are complex systems that require the implementation of management strategies to control multiple elements and regulate the offering energy services. This paper proposes a management approach capable of aggregating the operation of clusters of multifunctional microgrids, aiming at improving the resiliency of distribution and transmission systems, as well as enhancing the microgrids’ power quality, under predictable and unpredictable events. Since the approach manages microgrids inserted into cluster to operate as single-controllable entities, a variety of ancillary services can be offered (e.g., energy time shifting, peak shaving, islanded operation, and many others). Consequently, operational goals can be pursued locally (i.e., at the point of common coupling of each microgrid) and systematically (i.e., at specific medium and high-voltage circuit branches). To demonstrate the applicability of the proposed energy management system, simulations were carried out using Matlab/Simulink, considering customized blocks in phasor domain, and taking the CIGRE’s European benchmark as reference model for the low-, medium- and high-voltage power systems. Different from previous studies, the simulations fully emulate the interoperability among microgrid central controllers, microgrid system operators and distribution system operator, demonstrating that a wide range of ancillary services can be put into action by means of the aggregate response of microgrids and based on price signals, without causing electrical overload. It is also demonstrated in the results that the active and reactive power flow in the high voltage grid is flexibly regulated, the power factor is increased up to 0.955 at different circuit branches, and the power quality is improved in the distribution level during absence of power generation at the medium voltage grid.

Optimal operation of battery storage systems in standalone and grid-connected DC microgrids using parallel metaheuristic optimization algorithms

This study addresses the problem of optimal operation of batteries in standalone and grid-connected Direct Current (DC) Microgrids (MGs) that include photovoltaic (PV) generators operating at maximum power point. For that purpose, a mathematical model was formulated considering three objective functions: (1) the minimization of operating costs, (2) the reduction of energy losses associated with energy transport in DC MGs, and (3) the minimization of the total emissions of CO2 into the atmosphere produced by conventional generators. The model integrates a set of constraints that represent the operation of DC microgrids. Three parallel versions of well-known solution methodologies were used here: Parallel Particle Swarm Optimization (PPSO), the Parallel Vortex Search Algorithm (PVSA), and the Parallel Ant Lion Optimizer (PALO). These methodologies were implemented to optimize the hourly power flow method based on successive approximations and evaluate the objective functions and constraints. To validate the effectiveness of the solution methods proposed in this paper, two test systems were used: a standalone network and a grid-connected network (both in Colombia). Furthermore, to test the effectiveness of the proposed energy management system (regarding solution, repeatability, and processing times) each solution methodology was executed 100 times in each test system using MATLAB software. Based on the results of the simulations, the methodology with the best performance for standalone grids was the PVSA. In turn, the methodology that achieved the best results in the grid-connected network was the PALO. In both scenarios, the best average reductions in energy fixed costs, variable costs, energy losses, and CO2 emissions were 0.0282%, 1.3039%, 9.1655%, and 0.1254%, respectively. Although these were important benefits in technical and environmental aspects, the results of the economic indicator suggest that the operation of electrical networks should consider variable costs in order to improve the impact of energy management systems in the future.

Hybrid DC-AC microgrid energy management system using grey wolf optimized neural network

Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures.

A new rule-based self-reconfigurable energy management control of grid and batteries connected hybrid regenerative solar power system for critical loads

Renewable energy sources are increasing and diversifying day by day. For this reason, new and more efficient controller designs are being developed on diversified renewable energy sources. Thus, renewable energy power systems can vary with the development of both topological, power system element controllers and energy management system controllers in line with the desired demands. In this paper, Hybrid Regenerative Power System (HRPS) can operate at low power with a designing a novel Central Energy Management System (CEMS) that is suitable for the critical AC & DC loads and it is efficient thanks to the regenerative effect. Equipments in this power system were firstly created and they are connected each other to obtain HRPS at MATLAB-Simulink environment. After the HRPS topology emerged, power flow scenarios are automatically operated according to the proposed Energy Management System (EMS) on the HRPS topology with using real irradiance and temperature spectrums. Then, according to the consequences obtained, how efficient the power system is in itself was calculated and when compared to the literature, it was emphasized in which cases it was advantageous compared to other EMS studies.

Model predictive control ofconsensus-based energy management system for DC microgrid.

The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.

Adaptive Mixed-Integer Linear Programming-Based Energy Management System of Fast Charging Station with Nuclear–Renewable Hybrid Energy System

The concept of transportation electrification is proliferating due to its high impact on emission reduction. However, the increased usage of electric vehicles strains the power grid’s charging infrastructure. As a result, to reduce demand on the power grid, lower the emissions, and solve the intermittency problem of Renewable Energy Sources (RESs), a Nuclear–renewable Hybrid Energy System (N-R HES) is proposed in this research to support the load demand of a Fast Charging Station (FCS). Fulfilling the power demand of the FCS while reducing the generation cost and waste of energy is a vital issue, and hence, energy management with optimization is a must for the hybrid energy system. To address this issue, a model reference adaptive control with a mixed-integer linear programming-based energy management method was modelled to accomplish the charging station’s extensive performance. MATLAB/Simulink software has been used to model and simulate the proposed system, and the results are analyzed. The assessment shows that the proposed energy management system offers an optimized performance of the fast charging station integrating with nuclear and renewable energy.

Innovative energy management system for energy storage systems of multiple-type with cascade utilization battery

The proposed system provides an energy management method for various types of an energy storage system including cascade utilization battery. The method is used to receive, store and manage the relevant operating data from the energy storage battery and also randomly determine the energy distribution coefficient of the energy storage battery. According to the adaptive energy distribution method, the power value of the total distributed energy storage power to the cascade utilization energy is calculated and also the energy distribution coefficient of the energy storage battery in real time is adjusted. Finally, the corrected command value of the energy storage battery power is obtained as an output. The system can not only prevent overcharging and over-discharging of the energy storage system, but also maintain the good performance of the energy storage system. To realize the coordinated control and energy management of the battery power plant, we use multiple types, including conventional battery and cascade utilization power battery control purpose. The performance metrics, namely, real-time energy management, computational time and operating cost are employed for the experimental purpose. The simulation results show the superior performance of the proposed energy management system over other state-of-the-art methods.

Single Phase Grid Connected PV-Wind-Diesel-Battery System Operated Three Phase Induction Motor for Aqua-Farms

Sustainable Aqua Farms requires continuous power supply to drive three phase induction motors for maintaining good oxygen levels. Unfortunately many places are suffering from frequent power cuts as well as availability of only single phase supply. Hence, 1-Φ to 3-Φ conversion is essential to operate 3-Φ motors as well as required operation of diesel generators during no supply from grid. However, consumption of heavy diesel causes many problems in aqua culture which is not sustainable. Hence, in order to reduce diesel consumption, and increase eco-friendly environment, photovoltaic (PV) and wind generator system can be integrated to system. A proper energy management system is required among all these sources to achieve cost effective system with best performance. The battery is used for responding in transient time periods. The proposed energy management system can reduce consumption of diesel by operating it at maximum efficiency. In case of any excess power available from wind and PV, the surplus power can be transmitted to single phase grid to reduce electricity bill. In order to maintain stable dc-link voltage, Sliding Mode Controller (SMC) based DC to DC converters are incorporated. The proposed controller reduces the ripples in motor torque to improve the life time of components used in system as well as can reduce power consumption with the addition these renewable energy resources. For reduction of ripples in torque, a TS-Fuzzy based speed sensor less direct torque controller is implemented to run induction motor through three phase inverter.

Energy management and harmonic compensation of micro‐grids via multi‐agent systems based on decentralized control architecture

AbstractHere, a new energy management strategy based on a multi‐agent structure is presented by introducing hybrid control of AC micro‐grid (MG), where current harmonics compensation is also considered. This energy management strategy is based on Mealy machine in a multi‐agent system (MAS), which is implemented by generating coordination signals and sending them to the renewable energy sources (RES). In RES, maximum power point tracking (MPPT) or reverse of Hill‐climbing (RHC) is used based on these received coordination signals to limit the exchange power between the MG and the grid. Also, the multi‐agent structure of shunt active power filters (SAPFs) and passive filters have been studied in power MGs. The comparison of MG current harmonic compensation by these filters by fast Fourier transform (FFT) algorithm has been investigated in the proposed network. The simulation results show that the proposed energy management system (EMS) with proper management of MG different agents has been able to have a suitable performance for hybrid distributed generation (DG) systems. Also, the SAPF with better and more appropriate harmonic compensation than the SC‐RC‐LCL passive filter can eliminate the harmonic pollution of MG and be used with high performance in hybrid DG systems.

A novel two-stage energy management of hybrid AC/DC microgrid considering frequency security constraints

The increasing penetration of renewable energy in the hybrid AC/DC microgrid (MG) reduces the inertia and further threatens the frequency security. Furthermore, the stochastic and intermittent characteristics of the renewable energy challenge the optimal scheduling of MG. To deal with these issues, this paper proposes a novel two-stage energy management strategy to minimize the operating cost of the hybrid AC/DC MG while ensuring frequency security by considering the uncertainties in both the renewable energy resources (RES) and load demand. Firstly, the relation between the maximal frequency deviation and the power reserve is derived by analyzing the dynamic frequency response model. Secondly, scenario generation and scenario reduction based on Monte Carlo simulation (MCS) are carried out to simulate the unbalanced system power. Thirdly, the day-ahead scheduling stage determines the optimal power reservation of the distributed generators (DGs) for primary and secondary frequency regulation. Then, in the intraday scheduling stage, the day-ahead power reservation plan is adjusted to meet the frequency security constraints by using the real-time updated forecasting information. Finally, the proposed energy management system (EMS) is formulated and solved. The simulation results demonstrate that the proposed method can significantly reduce the operating cost, the maximal frequency deviation, and load shedding.