Cost-Effective Design of Home Energy Management System with PV-Wind and Battery Storage in a Grid-Tied Microgrid

Special issue on “Optimal design and operation of energy systems”

Worldwide, the current energy system increasingly shows economic, social and environmental drawbacks and it does not appear to be sustainable. Among the most pressing problems are climate change due to CO2 emissions, the depletion of fossil resources, the security of energy supply, and threats to economic development. These problems can be addressed by moving towards energy systems that increasingly rely on renewable and sustainable energy sources. At the same time, the rapid progress in renewable energy harvesting and information and telecommunication technologies offer unprecedented opportunities for energy generation, distribution and management, and to more strongly involve people in the process of energy generation and management, which is needed to further enhance the sustainable energy systems. Indeed, renewable and sustainable energy sources are likely to be produced and distributed in a decentralised manner, and require energy management systems that differ greatly from the current centralised system. Hence, the integration of renewable sources has major implications for the design and management of energy systems, relying on innovative ICT solutions and stronger user involvement that facilitate supply and demand matching. Effective energy management requires the active participation and support of end users, who should accept the energy management systems, and adapt their energy use to the available demand as to enhance the efficiency and stability of the energy system. Hence, the design of future smart grids should be based on sound behavioural models, which should be integrated in ICT centred solutions that facilitate supply and demand matching. More specifically, we need to understand how truly complex information on energy production, use and storage can be effectively communicated among relevant actors, and what type of information and incentives should be provided to realise effective supply and demand matching. In addition, we need to understand how to take into account cultural specifics in the design of energy management systems, to enhance effective energy management across the world. The active and aware involvement of the end-user can only be achieved if a scalable and reliable ICT infrastructure is in place, which naturally interfaces the energy flows with the decision autonomy of the people. Hence, the design of effective energy management systems should be take into account key behavioural as well as ICT requirements in an integrated way. Our proposal is thus to investigate (1) ICT based solutions for monitoring and controlling the power grid focusing on the distribution networks (medium-low voltage) and buildings (offices and residential complexes) specific to the Qatar context; (2) behavioural models on effective and acceptable incentives to match energy supply and demand; (3) cultural specificities in the design of energy management systems by comparing results from research in Qatar to similar research in other parts of the world, in particular Europe. Together, these will provide important insights in how to promote efficient and stable renewable energy systems that meet Qatar's sustainability goals.

Energy sustainability has become one of the main issues in power system planning in the Indonesian archipelago system, which has many small, isolated systems. For that purpose, green and sustainable generation expansion planning (GEP) procedures based on local energy resources is required. GEP is a necessary procedure for fulfilling electricity demand, which determines the generating units to be installed within a specified time horizon with minimal total costs as the objective function. This study uses GEP considering the interconnection option among the existing small scattered generation systems in Maluku: the isolated Ambon, Seram, Haruku, and Saparua systems. With interconnection, the utilization of local renewable energy sources would be increased, especially biomass, which has abundant potential in these areas. The GEP was simulated in the PLEXOS environment using mixed-integer linear programming (MILP). For comparison purposes, there were interconnection and isolated system scenarios. The results showed that the interconnection system would have a high share of a renewable energy source (RES) of up to 54% in 2050, most of which is biomass as the primary local energy source. The interconnection system scenario met the LOLP criteria and had a lower reserve margin and total costs than the isolated scenario, with USD 773.7 million.

General Background: The rapid advancements in solar-powered unmanned aerial vehicles (UAVs) have increased interest in optimizing their energy management systems (EMS) to enhance performance and flight endurance. Specific Background: Effective EMS in solar UAVs requires advanced strategies for solar energy harvesting, energy storage, and power distribution to maximize operational efficiency under varying environmental conditions. Knowledge Gap: Despite recent progress, challenges remain in energy conversion efficiency, battery storage capacity, and the integration of intelligent predictive control mechanisms, limiting the UAVs’ ability to operate autonomously for extended periods. Aims: This review investigates current EMS optimization strategies for solar-powered UAVs, emphasizing multi-objective optimization techniques, energy management algorithms, and the impact of environmental conditions on UAV performance. It also explores the role of artificial intelligence (AI) and machine learning in improving EMS efficiency. Results: Studies highlight that multi-objective genetic algorithms (MOGAs) effectively balance energy allocation between propulsion, battery storage, and payload, leading to significant endurance improvements. Fuzzy logic-based power management systems enhance energy efficiency by dynamically adjusting power distribution based on real-time UAV energy demands. Adaptive energy management strategies that integrate environmental and operational data improve flight times by up to 30% under extreme conditions. Novelty: This review synthesizes state-of-the-art EMS strategies, identifying key optimization techniques and emerging AI-driven solutions for adaptive and predictive energy management. By consolidating findings from diverse methodologies, it provides a comprehensive assessment of how intelligent control systems enhance UAV autonomy. Implications: The findings underscore the necessity of developing more efficient power conversion technologies, advanced battery storage solutions, and AI-based EMS frameworks to enable long-duration UAV operations. Future research should focus on refining these technologies to improve UAV performance in energy-intensive applications such as surveillance, environmental monitoring, and disaster response. Highlights: Optimization: MOGAs and fuzzy logic improve energy efficiency and endurance. Adaptation: Real-time power adjustments enhance UAV performance in harsh conditions. AI Integration: Machine learning enables predictive, autonomous energy management. Keywords: Solar-powered UAVs, Energy Management Systems, Optimization Algorithms, Adaptive Control, Artificial Intelligence

Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.

In the past few years, a lot of research has been done in the field of Microgrids (MG) and Smart Grids (SG). The goals of them are to make the power infrastructure more reliable, effective and to incorporate Distributed Generations (DG) for example Renewable Energy Sources (RES), Combined Heat and Power (CHP) systems, Batteries in an efficient and cost-effective manner. Nowadays, roof top solar systems and micro CHP have become really popular for home usage. With the installation of solar Photovoltaics (PV), and micro CHP (mCHP) users are already participating in the operation of DC-AC systems. Therefore, it is important to control their usage. This can be done using an Energy Management System (EMS). The whole system (home and power grid) can be controlled on various parameters. These parameters can include power generated from solar PV, mCHP and power grid, state of charge of battery, AC and DC load demand, price of power, selection of renewable or grid power by the user, etc. The purpose of this paper is to develop appropriate control algorithms for the energy management of DG units in an AC/DC Microgrid which mainly incorporated PVs units (PV), mCHP and Batteries. The main focus is on the energy control and management of the mCHP system in order to meet the consumers’ electrical and thermal needs and to minimize the power flow from the main power grid. All algorithms have been implemented with the Matlab program.

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.

The Energy Management System (EMS) used in Hybrid Electric Vehicles (HEVs) with an electric drive system powered by renewable energy sources is thoroughly investigated in this study. The study focuses on the crucial elements of encouraging sustainability and maximizing energy efficiency in transportation. The analysis focuses on the EMS's integration with renewable energy sources like solar, wind, biomass and mechanical vibration. This research is thoroughly reviewed by explaining the efficient management of the power flow between the internal combustion engine, electric motor, and renewable energy inputs, advanced control algorithms and optimization strategies. By incorporating solar panels into the design of a vehicle, the demand on the primary power source is decreased and electricity can be produced to fuel auxiliary systems like air conditioning. It is possible to use wind energy to create electricity for the car's auxiliary systems and electronics. Under various driving conditions and operational scenarios, the study assesses how well the suggested EMS performs, taking into account variables like fuel economy, emissions reduction, and overall system reliability. Testing in real-world scenarios confirms the system's efficacy and offers perceptions into its usefulness. The study explores the effects of fluctuating renewable energy availability and suggests adaptable tactics to strengthen the system's resistance to shifting circumstances. The research will pave the way for the creation of reliable EMS solutions for HEVs and provide environmentally friendly and sustainable mobility. In order to promote a more environmentally friendly and economically viable paradigm for hybrid electric vehicles, the study intends to direct future developments in the integration of renewable energy sources into electric drive systems. Enhanced predictive capabilities can make well-informed decisions about power distribution and consumption by assessing real-time data, weather forecasts, traffic patterns, and driver behavior. This can enhance energy management. The goal of the review is to develop and enhance renewable energy-based energy harvesting technology. These technologies' increased weight reduction and increased efficiency will make it easier to integrate them into electric drive systems.

The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

In the present scenario, it is very important to concentrate on grid energy storage to maximize renewable energy utilization. Nowadays, commercially feasible projects have been developed to store energy in large level by the development of battery storage technology (BST). In power system, when grid power is lost, battery storage system can deal with the renewable intermittency. Because of intermittent nature, the renewable system provides imbalanced frequency in the grid. So energy storage systems are required to balance the frequency variations by the way of charging and discharging and keep the frequency level at desired limit. Under-frequency events are to be seriously considered and when it happens. State-of-charge estimation is a crucial part in energy management system because SOC estimation involves in modeling and optimizing battery performance in terms of extension of life cycle, cost reduction, and safe operation of batteries for various applications including smart grid application. So estimation of SOC provides a key factor for optimized energy management and control system design. In this paper, equivalent circuit model-based SOC estimation is analyzed based on, mapping of the % of SOC directly with the circuit parameters such as open circuit voltage (OCV) and impedance (Z), and the availability the SOC (tk) is calculated by integrating ampere hour integral method with this one. And also, how the estimation of SOC regulates the grid frequency is also analyzed.KeywordsGrid energy storageBattery storage technology (BST)Imbalanced frequencyGrid frequency regulationSOC estimation

Wireless sensor networks (WSNs) play a key role in extending the smart grid implementation towards residential premises and energy management applications. Efficient supply and demand balance, and consequently reducing the electricity expenses and carbon emissions, is an immediate benefit of implementing smart grids. In this paper, design and implementation of an energy management system (EMS) for efficient load management are proposed. The EMS reduces the consumption of the consumers at the peak load hours and thus reduces the carbon emissions of the household. The proposed system consists of two main parts. The first part is an Energy Management Unit (EMU) which has a graphical user interface for runtime monitoring and control. The second part is sensor nodes which measure the power consumption of the different loads and transfer it to the EMU via multi-hop network. The EMU is implemented using NI LABVIEW software and XBee-PRO ZigBee module to communicate with sensor nodes. Hardware model is implemented using Arduino Uno microcontroller, XBee-PRO ZigBee module and the ACS712 current sensor. The EMS is applied to building of Electrical Engineering Department at Assiut University as a case study.

Background : Increasing Electric Vehicle (EV) possession has resulted in an abundance of Charging Stations (CSs), which nurtures load demands and causes grid interruptions in peak hours. By using an effective Energy Management Strategy (EMS), microgrids provide a workable solution to these problems with the electrical distribution infrastructure. DC microgrids powered by renewable energy present a promising alternative, but their efficacy is limited by the fluctuating availability of renewable energy sources (RES) and the erratic demand for EV charging. Therefore, to ensure cost‐effective, reliable, and environmentally sustainable EV charging, an efficient and adaptive EMS is required. Methods : This research proposes an advanced hybrid energy management approach for DC microgrids powered by RES that incorporate EV charging stations. The method optimises power flow among solar systems, fuel cells, battery storage, and EV loads by combining a Dwarf Mongoose–Zebra Optimisation tuned Proportional–Integral controller with Fuzzy Logic Control (DMZO‐PI+Fuzzy). The hybrid Dwarf Mongoose–Zebra Optimisation algorithm is utilised to optimise PI controller gains. The control signal from the fuzzy control and the DMZO Optimised PI controller are combined to enhance the controller performance in the proposed model of EVCS. MATLAB/Simulink simulations are used to validate the proposed DMZO‐PI+Fuzzy method under various operating conditions. Results : The proposed DMZO‐PI+Fuzzy strategy performs significantly better than traditional approaches, according to simulation results. With a minimum tariff of 0.034 USD/kWh during off‐peak hours, charging costs can be lowered by up to 75.56%. On weekdays and weekends, average charging rates drop to 0.086 and 0.088 USD/kWh, respectively, representing cost savings of 45.26% and 56.11%. Also, under dynamic operating conditions, enhanced convergence speed and DC bus voltage stability are observed, and optimal renewable utilisation results in a maximum GHG emission reduction of 55.75%. Conclusion : The proposed DMZO‐PI+Fuzzy energy management framework offers an effective, reliable, and economical feasible EV charging solution for DC microgrids powered by renewable energy. The approach improves both economic and environmental performance by simultaneously optimising charging costs, the use of renewable resources, and efficient power management (PM) in DC MGs.

ABSTRACTDemand for effective and cost‐effective energy management solutions has increased due to residential settings' raising reliance on energy storage systems and renewable energy sources; however, integrating these systems seamlessly while preserving balanced grid interaction and financial benefits is a major challenge. This paper proposes an optimal integration strategy for renewable energy and energy storage in Home Energy Management Systems (HEMS) to enhance grid interaction and maximize economic benefits. The proposed approach uses Hiking Optimization (HO) to improve the Peak‐to‐Average Ratio (PAR) and minimize energy expenditures by integrating renewable energy sources and sophisticated optimization techniques into the HEMS. The HO method is employed to optimize the HEMS by minimizing daily energy costs and reducing the PAR through efficient utilization of energy storage systems and renewable energy sources. The proposed method is implemented on the MATLAB platform and contrasted with existing methods, including Particle Swarm Optimization (PSO), Genetic Flower Pollination Algorithm (GFPA), and Deep Neural Network (DNN). The comparison demonstrates the proposed method's improved performance, which achieves a cost of 440 cents. In contrast, the PSO approach yields 550 cents, the GFPA method achieves 666 cents, and the DNN method reaches 688 cents. This comparison illustrates the performance of the proposed strategy in optimizing HEMS performance for cost reduction in residential applications, outperforming traditional energy management techniques.

Microgrids (MGs) play a vital role in the era of deregulated power systems. The number of MGs connected to the power grid increases the complexity of energy management among the main grid and MGs. A multi-microgrid (MMG) system utilizes all available energy sources (renewable and non-renewable) and energy storage systems to manage power exchange efficiently within the MGs and the main grid, enhancing system reliability, stability, and efficiency. With the integration of renewable and non-renewable energy sources, the system can adapt to different energy availability scenarios. This paper presents an optimized energy management scheme for MMG, implemented using multiple integer nonlinear programming in GAMS software. The objective is to minimize the overall operational cost of MMG and reduce emission costs. The dynamic nature of renewable energy sources, the fluctuating demand patterns, and the operational constraints of both the MGs and the main grid. The proposed approach is applied to an MMG system interconnected with the main grid, incorporating both renewable and non-renewable energy sources. The simulation results demonstrate the effectiveness of the presented energy management strategy in lowering system costs for two operating case-I and case-II. The impact of integrating energy storage systems has also been evaluated on the overall cost of the system, revealing their potential to enhance the overall efficiency and cost-effectiveness of the energy management system (EMS). The results show that overall operational cost has been reduced from $1163 in case-I to $1096 in case-II and MMG becomes more independent in case-II as power demand from the main grid is reduced from 10259 kW(case-I) to 9171kW (case-II).

Effective energy management is essential for minimizing operational costs in grid-connected microgrids (MGs), particularly as renewable energy sources such as solar photovoltaics and wind turbines are increasingly integrated into modern power systems. This paper presents a two-stage energy management strategy aimed at minimizing the total cost of a grid-connected MG. In the first stage, day-ahead scheduling, energy dispatch is optimized using stochastic optimization techniques while accounting for uncertainties in renewable generation and load demand. A Monte Carlo simulation generates multiple scenarios to assess future states, facilitating precise decision-making for grid interaction and local generation. As a result, the total operational cost is reduced from Rs. 12,521 to Rs. 12,390, and the total cost is reduced from Rs. 158,090 to Rs. 14,998. The second stage, real-time scheduling, refines the day-ahead plan by adjusting for real-time fluctuations in demand and generation, ensuring system balance and reliability. By integrating metaheuristic algorithms with real-time control, the proposed strategy minimizes energy exchange costs with the grid, reduces operational expenses of conventional generators, and maximizes the utilization of renewable energy. Case studies validate the effectiveness of the proposed methodology in reducing overall costs, maintaining grid stability, and enhancing renewable energy penetration. The method is adaptable to various MG configurations, offering a robust and cost-efficient solution for energy management in grid-connected systems