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Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters’ capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid’s efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas reduction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to the increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy; however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In the future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

The integration of renewable energy sources (RESs) is rapidly increasing within energy systems worldwide. However, this shift introduces intermittency and uncertainty on the supply side. To hedge against RES intermittency, demand-side flexibility introduces a practical solution. Therefore, further studies are required to unleash demand-side flexibility in power systems. This flexibility is relevant across various sectors of power systems, including residential, industrial, commercial, and agricultural sectors. This paper reviews the key aspects of demand-side flexibility within the residential sector. To achieve this objective, a general introduction to demand flexibility across the four sectors is provided. As a contribution of this paper, and in comparison with previous studies, household appliances are classified based on their flexibility and controllability. The flexibility potential of key residential demands, including heat pumps, district heating, electric vehicles, and battery systems, is then reviewed. Another contribution of this paper is the exploration of demand-side flexibility scheduling under uncertainty, examining three approaches: stochastic programming, robust optimization, and information-gap decision theory. Additionally, the integration of demand flexibility into short-term electricity markets with high-RES penetration is discussed. Finally, the key objective functions and simulation software used in the study of demand-side flexibility are reviewed.

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions.

The significant expansion of renewable energies has led to an increased importance of storage systems. Decentralized storage solutions, including Home Battery Energy Storage Systems (HBESSs) and District Battery Energy Storage Systems (DBESSs), play a crucial role in this context. This study compares individual HBESSs with a community-used DBESS regarding the grade of autarky and self-consumption, specifically focusing on a planned residential area consisting of 36 single-family houses. A simulation tool was developed to conduct load flow simulations based on household electricity consumption, wallbox profiles for electric vehicle charging, and photovoltaic generation data across various battery capacities and system boundaries. The results demonstrate that the DBESS, compared to individual HBESSs with equivalent cumulative battery capacities, can achieve a maximum increase in the grade of autarky of up to 11.6%, alongside an 8.0% increase in the grade of self-consumption for the given use case. In terms of capacity, the DBESS allows for a saving of up to 68% compared to HBESS to achieve similar results for the studied neighborhood.

This work quantifies the techno-economic performance of AC and DC residential building distribution. Two methods, utilising software and hardware configurations, are showcased to improve DC distribution: (i) a novel rule-based battery dual-objective operation (DOO) and (ii) a modular Master/Slave design of the grid-tied converter (GC). Both methods use the GC’s load-dependent efficiency characteristic, eliminating partial-load operation and enhancing energy efficiency. The work uses measured annual PV and load data to evaluate the performance of the methods compared to AC and DC references. The techno-economic analysis includes the annual net electricity bill and monetised battery degradation. The results show that the DOO eliminates GC partial-load operation at the cost of increased battery usage, resulting in marginal net savings. In contrast, the modular converter design significantly reduces losses: −157 kWh/a (−31%) and −121 kWh/a (−26%), respectively, relative to the DC and AC references. For a parametric sweep of electricity price and discount rate, the Lifetime Operating Cost (LOC) comparison shows savings from DC of up to USD 575 compared to AC.

In the context of the German Energiewende, the current government intends to install six million heat pumps by 2030. Replacing gas heating by power has significant implications on the infrastructure. One of the biggest advantages of using gas is the existing storage portfolio. It has not been clarified yet how power demand should be structured on an annual level—especially since power storage is already a problem and solar power is widely promoted to fuel heat pumps, despite having an inverse profile. In this article, three different solutions, namely, hydrogen, batteries, and carbon capture and storage, are discussed with respect to resources, energy, and financial demand. It shows that relying solely on batteries or hydrogen is not solving the structuring problem. A combination of all existing technologies (including fossil fuels) is required to structure the newly generated electricity demand.

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators.

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees’ increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building’s micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building’s electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset’s rating, the electrolyzer units’ capacity and the tilt angle of the rooftop’s photovoltaic panels, which minimize the building’s interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions.

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.

E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.

Hydrogen energy is regarded as a key path to combat climate change and promote sustainable economic and social development. The fluctuation of renewable energy leads to frequent start/stop cycles in hydrogen electrolysis equipment. However, electrochemical energy storage, with its fast response characteristics, helps regulate the power of hydrogen electrolysis, enabling smooth operation. In this study, a multi-objective constrained operation optimization model for a wind/battery storage/alkaline electrolyzer system is constructed. Both profit maximization and power abandonment rate minimization are considered. In addition, some constraints, such as minimum start/stop times, upper and lower power limits, and input fluctuation limits, are also taken into account. Then, the non-dominated sorting genetic algorithm II (NSGA-II) algorithm and the entropy method are used to optimize the operation strategy of the hybrid energy system by considering dynamic hydrogen production efficiency, and through optimization to obtain the best hydrogen production power of the system under the two objectives. The change in dynamic hydrogen production efficiency is mainly related to the change in electrolyzer power, and the system can be better adjusted according to the actual supply of renewable energy to avoid the waste of renewable energy. Our results show that the distribution of Pareto solutions is uniform, which indicates the suitability of the NSGA-II algorithm. In addition, the optimal solution indicates that the battery storage and alkaline electrolyzer can complement each other in operation and achieve the absorption of wind power. The dynamic hydrogen production efficiency can make the electrolyzer operate more efficiently, which paves the way for system optimization. A sensitivity analysis reveals that the profit is sensitive to the price of hydrogen energy.

The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.

Energy communities (ECs) can become a potential alternative to promote the fight against climate change. Technological progress and price reductions in recent years have made renewable energy-generation systems increasingly affordable and have generated economic benefits by reducing the value of electricity bills for community members, as well as reducing the growing environmental impact. In this context, the authors have taken Tolosa as a case study and conducted a technical and economic analysis of different possible structures of ECs (physical, virtual, with or without storage, participants with different types of consumption, etc.) by comparing them with each other. The generation capacity of the community and the optimal energy-management algorithms have been illustrated, from which the economic benefits for each member are extracted. A dynamic distribution factor is established as the basis of the algorithms, making the benefits fairer. The results obtained from this work, in addition to illustrating the economic benefits that each type of participant can receive, help to define the most appropriate community structure for each participant while highlighting the social and climate benefits that ECs can provide.

The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect.

The rapid electrification of vehicles has led to a great increase in numbers of charging stations and a growing appetite for charging power, with stochastic charging behaviours heavily loading the electricity grid. The upcoming difficulties and increasing costs associated with electricity production will require a rapid development of smart grids and city networks. Smart micro-grids established in nearly zero-energy buildings (nZEB) are a promising strategy to support grid stability and resilience at a reduced cost. A significant amount of electricity storage capacity is necessary for optimal dispatch of the self-produced photovoltaic electricity. For office buildings, this capacity can be provided by the aggregate battery storage of the employees’ electric vehicles, which connect to the smart grid during working hours for charging and, if allowed, for discharging. An additional, fully controllable electricity source that is necessary to support an optimal micro-grid is the internal combustion engine-powered generator that is present in every commercial and office building as an emergency power supply. In the current study, a preliminary investigation of a smart micro-grid in a near zero-energy office building with a 218 kWp rooftop photovoltaic installation is carried out. The required electricity storage capacity is supplied by the employees’ electric vehicles, which stay connected to the building’s in-house chargers during working hours. The optimal rating of the natural gas-fuelled GenSet is determined based on a system’s operation and control study. Optimal dispatch of the different power sources to support the building’s autonomy and seasonal timing of electricity export to the grid is studied versus the electricity demand profiles of the electricity grid.

In electric vehicles, currents with high-frequency ripples flow in the power cabling system due to the switching operation of power converters. Inside the cables, a strong coupling between the thermal and electromagnetic phenomena exists, since the temperature and Alternating Current (AC) density distributions in the strands affect each other. Due to the different time scales of magnetic and heat flow problems, the computational cost of Finite Element Method (FEM) numeric solvers can be excessive. This paper derives a simple analytical model to calculate the total losses of a multi-stranded cable carrying a Direct Current (DC) affected by a high-frequency ripple. The expression of the equivalent AC cable resistance at a generic frequency and temperature is derived from the general treatment of multi-stranded multi-layer windings. When employed to predict the temperature evolution in the cable, the analytical model prevents the use of complex FEM models in which multiple heat flow and magnetic simulations have to be run iteratively. The results obtained for the heating curve of a 35 mm2 stranded cable show that the derived model matches the output of the coupled FEM simulation with an error below 1%, whereas the simple DC loss model of the cable gives an error of 2.4%. While yielding high accuracy, the proposed model significantly reduces the computational burden of the thermal simulation by a factor of four with respect to the complete FEM routine.

Today, increasing numbers of batteries are installed in residential and commercial buildings; by coordinating their operation, it is possible to favor both the exploitation of renewable sources and the safe operation of electricity grids. However, how can this multitude of battery storage systems be coordinated? Using the Application Programming Interfaces of the storage systems’ manufacturers is a feasible solution, but it has a huge limitation: communication to and from storage systems must necessarily pass through the manufacturers’ cloud infrastructure. Therefore, this article presents an IoT-based solution which allows monitoring/controlling battery storage systems, independently from the manufacturers’ cloud infrastructure. More specifically, a home gateway locally controls the battery storage using local APIs via Wi-Fi on the condition that the manufacturer enables them. If not, an auxiliary device allows the home gateway to establish a wired communication with the battery storage via the SunSpec protocol. Validations tests demonstrate the effectiveness of the proposed IoT solution in monitoring and controlling ABB, Sonnen and SolarEdge storage systems.

This work presents a comparison of alternating current (AC) and direct current (DC) distribution systems for a residential building equipped with solar photovoltaic (PV) generation and battery storage. Using measured PV and load data from a residential building in Sweden, the study evaluated the annual losses, PV utilization, and energy savings of the two topologies. The analysis considered the load-dependent efficiency characteristics of power electronic converters (PECs) and battery storage to account for variations in operating conditions. The results show that DC distribution, coupled with PV generation and battery storage, offered significant loss savings due to lower conversion losses than the AC case. Assuming fixed efficiency for conversion gave a 34% yearly loss discrepancy compared with the case of implementing load-dependent losses. The results also highlight the effect on annual system losses of adding PV and battery storage of varying sizes. A yearly loss reduction of 15.8% was achieved with DC operation for the studied residential building when adding PV and battery storage. Additionally, the analysis of daily and seasonal variations in performance revealed under what circumstances DC could outperform AC and how the magnitude of the savings could vary with time.

PV and battery systems have been widely deployed in residential applications due to increasing environmental concerns and fossil energy prices. Energy management strategies play an important role in reducing energy bills and maximize profits. This paper first reviews the state of energy management problems, including commonly used objectives, constraints, and solutions for PV and battery applications. Then, a comparative study of different energy management strategies is conducted based on a real applied product and household profile. Moreover, results are discussed, and suggestions are given for different scenarios. Finally, conclusions and insights into future directions are also provided.

In this paper, a techno-economic investigation of a small-scale solar water pumping system combined with power generation is conducted numerically. Irrigation and power production for a typical small-size citrus farm located in southern Iran is simulated. The system consists of monocrystalline photovoltaic panels (CS3K-305MS, 305 W), absorbent glass material batteries (8A31DT-DEKA, 104 Wh), inverters (SMA Sunny Boy 2.0, 2000 W), and a pumping storage system. The key concepts of water shortage probability (WSP) and loss of power supply probability (LPSP) are used in conjunction with users’ tolerances and sizing of the system. A genuine MATLAB code was developed and validated before the simulations. A specific electricity consumption pattern for a rural home and a variable irrigation water profile were considered. The main objective of the study is to size a system that provides both electricity for domestic use of a home as well as the energy required for running the irrigation pumps with respect to investment cost, LCOE, WSP, and LPSP. The main findings of the research are that LPSP and WSP threshold tolerances can have a preponderant effect on the cost and sizing of the system. Interestingly, results reveal that there is a minimum variation of the capital expenditure (CAPEX) versus the number of PV panels. For the optimal configuration, the study indicates that shifting from an LPSP of 0% to 3% (or about ten days of potential yearly shortage) makes the LCOE drop by about 55%, while the WSP decreases by about 36%.

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals.

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as ‘green’ storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives.

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs.

The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-min data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system.

Near zero energy buildings are increasing worldwide, exploiting low-carbon technologies in heating and electricity self-production. Commercial buildings are increasingly considered as candidates for the installation of smart micro-grids, which may profit from the added storage capacity of the batteries of employees electric vehicles, stationed during daytime in their charging lots. Smart exploitation of the interaction of these electricity sources and sinks may prove essential to address the complex electricity network demand patterns in today’s fast changing energy mixture. The interaction of an efficient office building’s energy system with a big rooftop photovoltaic installation and the aggregate storage capacity of 40 electric cars that are connected in the building’s charging lots is studied by means of transient simulation in TRNSYS environment. The 18-zone building’s heating, ventilation, and air conditioning system, the cars’ batteries, and photovoltaic systems’ interactions are analyzed on a monthly, seasonal, and hourly basis, against the respective demand curves of the Greek network. The results suggest that the specific system’s size may profitably support the operation of a smart micro-grid. The total annual electricity consumption of the building is computed to reach 112,000 kWh, or 20 kWh/m2y. The annual electricity needs of the 40 electric cars, amounting to 101,000 kWh, can be fully met with 30% of the photovoltaic electricity production. Thus, the building becomes a net exporter of electricity to the network, with maximum exported electricity occurring daily between 12:00 and 14:00, which is favorable to meeting the demand curve. Thus, the establishment of smart micro-grids in commercial buildings with large rooftop photovoltaic panels’ capacity and a significant number of electric cars in the employees’ car fleet is quite effective in this direction.

Energy storage makes energy continuously available, programmable, and at power levels different from the original intensity. This study investigates the feasibility of compressed-air energy storage (CAES) systems on a small scale. In addition to the CAES systems, there are two TES (thermal energy storage) systems for the recovery of calories and frigories. The micro-CAES + TES system is designed for a single-family residential building equipped with a photovoltaic system with a nominal power of 3 kW. The system is optimized as a potential alternative to battery storage for a typical domestic photovoltaic system. The multi-objective optimization analysis is carried out with the modeFRONTIER software. Once the best configuration of the micro-CAES + TES system is identified, it is compared with electrochemical storage systems, considering costs, durability, and performance. The efficiency of CAES (8.4%) is almost one-tenth of the efficiency of the most efficient batteries on the market (70–90%). Its discharge times are also extremely short. It is shown that the advantages offered by the application of mechanical accumulation on a small scale are mainly related to the exploitation of the thermal waste of the process and the estimated useful life compared to the batteries currently on the market. The studied system proves to be non-competitive compared to batteries because of its minimal efficiency and high cost.

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.

Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics-based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.

In this paper, the authors propose to use batteries to improve the performance of grid-connected photovoltaic plants when their photovoltaic fields are subject to partial shading phenomena. Particular attention is devoted to predictable and repetitive partial shadings, such as those that often appear in urban residential environments. Firstly, battery packs with proper nominal voltage and capacity are connected in parallel to partially shaded photovoltaic submodules. Then, the shaded photovoltaic submodules are properly disconnected and connected to the respective photovoltaic string by using a “battery control unit”, which is operated by taking into account characteristics of the specific partial shading phenomenon to cope with. To demonstrate the effectiveness of the proposed technique, an experimental study is performed to compare the performances of two identical prototypal grid-connected photovoltaic generators subject to identical artificial and repetitive partial shadings. Only one of the photovoltaic generators is equipped with batteries together with their respective battery control unit, while the second one is simply equipped with conventional bypass diodes. The main advantages of the proposed technique are a greatly improved whole power generation together with the elimination of hotspot phenomena.

Renewable energy sources and sustainability have been attracting increased focus and development worldwide. Qatar is no exception, as it has ambitious plans to deploy renewable energy sources on a mass scale. Qatar may also investigate initiating and permitting the deployment of rooftop photovoltaic (PV) systems for residential households. Therefore, a research gap has been introduced regarding the system design, grid compatibility, economic viability, and energy consumption produced from household rooftop PV systems. Additionally, the lack of supporting policies and a feed-in tariff creates further research and development topics. Therefore, using collected data regarding household power consumption and rooftop PV generation, the purposes of this research study are as follows: (1) determining the economic aspects and practicality of using energy storage systems for self-consumption values; and (2) evaluating the economic viability of rooftop PV systems under different policies and electricity rate schemes. The insights of the results of this study can serve as a stepping stone for decisions and policymakers regarding the application of rooftop PV systems in Qatar. This study utilizes empirical evidence and an economic model to evaluate rooftop PV systems in Qatar and can also be applicable in the middle east region. A few studies in the region produce complementary results, which further supports our findings; however, what makes this paper unique is the use of different economic tools and real collected data while investigating multiple economic and energy policy scenarios.

This paper introduces a new formulated control scheme for enhancing the dynamic performance of a wind driven surface permanent magnet synchronous generator. The designed control scheme is based on predictive control theory, in which the shortcomings of previous predictive controllers are avoided. To visualize the effectiveness of the proposed control scheme, the performance of the generator was dynamically evaluated under two different operating regimes: grid connection and standalone operation in which a battery storage system was used to enhance the power delivery to the isolated loads. In addition, a detailed performance comparison between the proposed controller and traditional predictive controllers was carried out. The traditional control topologies used for comparison were the model predictive direct power control, model predictive direct torque control, and model predictive current control. A detailed description of each control scheme is introduced illustrating how it is configured to manage the generator operation. Furthermore, to achieve the optimal exploitation of the wind energy and limit the power in case of exceeding the nominal wind speed, maximum power point tracking and blade pitch angle controls were adopted. A detailed performance comparison effectively outlined the features of each controller, confirming the superiority of the proposed control scheme over other predictive controllers. This fact is illustrated through its simple structure, low ripples, low computation burdens and low current harmonics obtained with the proposed control scheme.

The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.

Nowadays, many aircraft manufacturers are working on new airplanes to reduce the environmental footprint and therefore meet greenhouse gas reduction targets. The concept of more electric aircraft is one of the solutions to achieve this goal. For this aircraft architecture, several electrical devices are used in order to supply propulsive and non-propulsive functions. This paper focuses on the sizing of a direct hybridization system to supply a non-propulsive function in an aircraft. It is composed of a High-Temperature Proton Exchange Membrane Fuel Cell (HT-PEMFC) and a lithium-ion (Li-ion) battery. This sizing is based on a static model of each storage device. The accuracy of these models is compared with dynamic models during a simulation for an aeronautical mission. Static models are implemented in a genetic algorithm to achieve two goals: on the one hand, satisfy the mission profile, and on the other hand, minimize the mass of the system. Other criteria, such as battery and fuel cell aging estimation, are considered. The obtained results show that the direct hybridization system allows protecting the fuel cell against an accelerated aging.

We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct forecasting aims to maximise the daily evening peak reduction and using as much solar photovoltaic energy as possible. For the latter, we combine a Bayesian (MCMC) linear regression model with an average generation distribution. For the former, we introduce a new error metric that allows even a simple weighted average combined with a simple linear regression model to score very well using the competition performance metric.

This study investigates Smart Grid Optimised Buildings (SGOBs) which can respond to real-time electricity prices by utilising battery storage systems (BSS). Different building design characteristics are assessed to evaluate the impact on energy use, the interaction with the battery, and potential for peak load shifting. Two extreme cases based on minimum and maximum annual energy consumption were selected for further investigation to assess their capability of utilising BSS to perform arbitrage, under real-time pricing. Three operational dispatch strategies were modelled to allow buildings to provide such services. The most energy-efficient building was capable of shifting a higher percentage of its peak loads and export more electricity, when this is allowed. When using the biggest battery (220 kWh) to only meet the building loads, the energy-efficient building was able to shift 39.68% of its original peak loads in comparison to the 33.95% of the least efficient building. With exports allowed, the shifting percentages went down to 31.76% and 29.46%, respectively, while exports of 18.08 and 16.34 kWh/m2 took place. The formation of a regulatory framework is vital in order to establish proper motives for buildings to undertake an active role in the smart grid.

Distributed generation (DG) systems are growing in number, diversifying in driving technologies and providing substantial energy quantities in covering the energy needs of the interconnected system in an optimal way. This evolution of technologies is a response to the needs of the energy transition to a low carbon economy. A nanogrid is dependent on local resources through appropriate DG, confined within the boundaries of an energy domain not exceeding 100 kW of power. It can be a single building that is equipped with a local electricity generation to fulfil the building’s load consumption requirements, it is electrically interconnected with the external power system and it can optionally be equipped with a storage system. It is, however, mandatory that a nanogrid is equipped with a controller for optimisation of the production/consumption curves. This study presents design consideretions for nanogrids and the design of a nanogrid system consisting of a 40 kWp photovoltaic (PV) system and a 50 kWh battery energy storage system (BESS) managed via a central converter able to perform demand-side management (DSM). The implementation of the nanogrid aims at reducing the CO2 footprint of the confined domain and increase its self-sufficiency.

The drought that occurred in Zimbabwe in 2020 affected the country’s main hydro-power station causing the electricity supply to be less secure and reliable. This challenge resulted in load-shedding, which is not desirable to mining companies that require constant and reliable power for their operations. In that regard, a techno-economic analysis was carried out to assess the potential of integrating concentrated solar power (+thermal storage) and photovoltaics (+battery storage) to supply power at a typical mine in Zimbabwe. Two scenarios were simulated—a base case with no exports to the grid and another case where exports were allowed. The models were evaluated based on the generated renewable energy offsetting the demand from the mine, the energy exported, the grid contribution, the levelised cost of electricity and the net present value. The results show that the addition of a battery storage system to PV improves the percentage of the load offset by the renewable system and the generated energy by the renewable system by almost double. However, the installation cost, required land, LCOE, and simple pay-back also increased by approximately a factor of 2. The addition of a thermal storage system to CSP increased the generated energy, the capacity factor, and the renewable energy contribution by approximately a factor of 2. However, the land required for development and the installation costs also nearly doubled.

The integration of distributed power plants that rely on renewable energy sources (RESs) is a major challenge for system operators (SOs) due to the variable nature of the input energy (e.g., wind and solar irradiation) to these power sources. A key solution to such a challenge is to coordinate and combine the power generation of these sources such that their behavior is closer to a conventional and dispatchable power station, taking into account the limitations imposed by the battery storage system (BESS), so it is seen as a hybrid power plant (HPP) from the SOs’ viewpoint. This paper develops a model of HPP that encompasses two generation technologies, wind and photovoltaic farms, which are assisted by a BESS. The paper proposes a comprehensive control method that can smooth the HPP output with minimized energy rejection whilst enabling the HPP to provide synthetic inertia and primary frequency response, which are grid-code compliant. The proposed control method is validated through various scenarios, which are implemented on a detailed electromechanical test system modeled in MATLAB/Simulink. The results show and quantify the achieved improvement on stabilizing the HPP capacity factor under variable wind speed. The HPP also enhances the system response to frequency events.

In recent years, solar price drops and regulations have helped residential users to invest in grid-connected photovoltaic (PV) facilities. In Spain, a novel law promotes self-consumption by discounting electricity fed into the grid from the utility bill. However, the performance of PV-based facilities depends on diverse factors. The contribution of this paper is to evaluate the techno-economic performance of such installations for different considerations linked to the Spanish law. A simulation model is used to examine different representative cities, load profiles and alternative objectives: maximising profitability and self-sufficiency. For profit maximisation, results show that load profile variations entail PV size changes up to 5 kWp for the same location, together with huge economic and self-sufficiency differences. In contrast, the solar radiation and compensation rate have a more limited influence. For self-sufficiency maximisation, the economic performance drops close to EUR 0, as benefits are used to double the PV size, buy batteries and reach close to 70% self-sufficiency. Finally, a sensitivity analysis shows a limited impact of the utility tariff and the technology cost on the PV size, but a relevant influence on the benefits. These results can help investors and families to quantify the risks and benefits of domestic self-consumption facilities.

In power systems, high renewable energy penetration generally results in conventional synchronous generators being displaced. Hence, the power system inertia reduces, thus causing a larger frequency deviation when an imbalance between load and generation occurs, and thus potential system instability. The problem associated with this increase in the system’s dynamic response can be addressed by various means, for example, flywheels, supercapacitors, and battery energy storage systems (BESSs). This paper investigates the application of BESSs for primary frequency control in power systems with very high penetration of renewable energy, and consequently, low levels of synchronous generation. By re-creating a major Australian power system separation event and then subsequently simulating the event under low inertia conditions but with BESSs providing frequency support, it has been demonstrated that a droop-controlled BESS can greatly improve frequency response, producing both faster reaction and smaller frequency deviation. Furthermore, it is shown via detailed investigation how factors such as available battery capacity and droop coefficient impact the system frequency response characteristics, providing guidance on how best to mitigate the impact of future synchronous generator retirements. It is intended that this analysis could be beneficial in determining the optimal BESS capacity and droop value to manage the potential frequency stability risks for a future power system with high renewable energy penetrations.

Mining operations in remote locations rely heavily on diesel fuel for the electricity, haulage and heating demands. Such significant diesel dependency imposes large carbon footprints to these mines. Consequently, mining companies are looking for better energy strategies to lower their carbon footprints. Renewable energies can relieve this over-reliance on fossil fuels. Yet, in spite of their many advantages, renewable systems deployment on a large scale has been very limited, mainly due to the high battery storage system. Using hydrogen for energy storage purposes due to its relatively cheaper technology can facilitate the application of renewable energies in the mining industry. Such cost-prohibitive issues prevent achieving 100% penetration rate of renewables in mining applications. This paper offers a novel integrated renewable–multi-storage (wind turbine/battery/fuel cell/thermal storage) solution with six different configurations to secure 100% off-grid mining power supply as a stand-alone system. A detailed comparison between the proposed configurations is presented with recommendations for implementation. A parametric study is also performed, identifying the effect of different parameters (i.e., wind speed, battery market price, and fuel cell market price) on economics of the system. The result of the present study reveals that standalone renewable energy deployment in mine settings is technically and economically feasible with the current market prices, depending on the average wind speed at the mine location.

Battery energy storage is becoming an important asset in modern power systems. Considering the market prices and battery storage characteristics, reserve provision is a tempting play fields for such assets. This paper aims at filling the gap by developing a mathematically rigorous model and applying it to the existing and future electricity market design in Europe. The paper presents a bilevel model for optimal battery storage participation in day-ahead energy market as a price taker, and reserve capacity and activation market as a price maker. It uses an accurate battery charging model to reliably represent the behavior of real-life lithium-ion battery storage. The proposed bilevel model is converted into a mixed-integer linear program by using the Karush–Kuhn–Tucker optimality conditions. The case study uses real-life data on reserve capacity and activation costs and quantities in German markets. The reserves activation quantities and activation prices are modeled by a set of credible scenarios in the lower-level problem. Finally, a sensitivity analysis is conducted to comprehend to what extent do battery storage bidding prices affect its overall profit.

Common property (CP) is a significant consumer of electricity in apartment buildings. Although some apartments in Australia have adopted shared microgrid configurations to offset grid consumption, the characteristics and load patterns of CP are rarely discussed due to lack of available data. As common areas normally constitute part of owner corporations, energy distribution in these premises requires attention. This paper presents empirical analysis of the CP load connected to shared solar and battery storage for three apartment complexes located in Perth Australia. Load patterns for CP over a defined dataset period were analyzed, and grid usage reduction was examined by implementing and comparing three energy allocation strategies based on surplus energy utilization. The findings indicated significant grid usage reduction for CP load in different apartments after implementation of three strategies. Instantaneous consumption decreased 72%, and surplus allocation strategy reduced 91%, while consumption-based allocation reduced 76%, of grid electricity. Moreover, consumption-based allocation offered improved cost benefits compared to the other two strategies. The results further revealed the usefulness of energy allocation and effectiveness of surplus energy utilization. Based on outcomes, the strategies provide consolidation with conventional energy trading mechanisms and broadly link to the virtual power plant concept for coordinating energy flows between multiple generators.

More than 2 million houses in Australia have installed solar photovoltaic (PV) systems; however, apartment buildings have adopted a low percentage of solar PV and battery storage installations. Given that grid usage reduction through PV and battery storage is a primary objective in most residential buildings, apartments have not yet fully benefited from installations of such systems. This research presents shared microgrid configurations for three apartment buildings with PV and battery storage and evaluates the reduction in grid electricity usage by analyzing self-sufficiency. The results reveal that the three studied sites at White Gum Valley achieved an overall self-sufficiency of more than 60%. Owing to the infancy of the shared solar and battery storage market for apartment complexes and lack of available data, this study fills the research gap by presenting preliminary quantitative findings from implementation in apartment buildings.

The number of large energy storage units installed in the power system has increased over the last few years. This fact remains closely linked to the increase in the share of renewable energy in electricity generation. This is necessary to maintain the stability of the grid, which is becoming increasingly difficult to maintain due to the growing number of renewable energy sources (RES). Energy production from these sources is difficult to estimate, and possible unplanned shortages and surpluses in production are the cause of voltage and frequency fluctuations, which is an undesirable state. Consequently, the use of energy storage not only contributes to the regulation of grid operation but can also, under appropriate conditions, constitute an additional load if too much energy is generated by RES, or the source when the generation from RES is insufficient. The main contributions of this paper are as follows: A presentation of practical results achieved by implementing two optimal control strategies for a 1 MW (0.5 MWh) battery energy storage (BES) cooperating with a large 144 MW photovoltaic farm. In the first case, the BES was used to generate curtailment at photovoltaic farm to avoid power grid overload. The second case focuses on maximizing profits from selling the energy produced in periods when the unit price for energy was the highest according to energy market forecasts. In both cases, the storage was used simultaneously to cover the producer’s own demand, which eliminated the costs associated with the purchase of energy from the operator, especially during the night supply. A technical and economic evaluation was prepared for both cases, considering the real profits from the investment. The potential of using the BES to increase the functionality of photovoltaic energy sources was determined and discussed in the paper.

The United States Air Force has implemented a dispersed air base strategy to enhance mission effectiveness for near-peer conflicts. Asset dispersal places many smaller bases across a wide geographic area, which increases resupply requirements and logistical complexity. Hybrid energy systems reduce resupply requirements through sustainable, off-grid energy production. This paper presents a novel hybrid energy renewable delivery system (HERDS) model capable of (1) selecting the optimal hybrid energy system design that meets demand at the lowest net present cost and (2) optimizing the delivery of the selected system using existing Air Force cargo aircraft. The novelty of the model’s capabilities is displayed using Clark Air Base, Philippines as a case study. The HERDS model selected an optimal configuration consisting of a 676-kW photovoltaic array, an 1846-kWh battery system, and a 200-kW generator. This hybrid energy system predicts a 54% reduction in cost and an 88% reduction in fuel usage, as compared to the baseline Air Force system. The HERDS model is expected to support planners in their ongoing efforts to construct cost-effective sites that minimize the transport and logistic requirements associated with remote installations. Additionally, the results of this paper may be appropriate for broader civilian applications.