Search

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS. [ABSTRACT FROM AUTHOR]

System strength is both supplied and demanded in a power system during normal operations and in the presence of disturbances. This is characterised by stable voltage and frequency, supporting renewable generation such as wind and solar. Because the retirement of synchronous generators reduces system strength supply, and the connection of new inverter-based resource (IBR) generators increases demand, there is an urgent need for new sources of system strength. This paper provides an overview of the challenges brought about by grid modernisation. It highlights tangible solutions provided by synchronous condensers (SCs) to bolster grid strength, stability, and reliability while accommodating the rising influx of renewable energy sources (RESs). Furthermore, this paper examines the role of SCs in improving weak grids, voltage control, power quality, short-circuit levels, and inertia management. It introduces the role of innovative hybrid synchronous condenser (HSC) systems to boost grid reliability and resilience. It also elaborates on the optimisation strategies for SC sizing, placement, and control and outlines economic aspects of their deployment. The review also highlights future directions and challenges in SC technology, emphasising the need for ongoing research and development to enhance system design and operation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack's lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. [ABSTRACT FROM AUTHOR]

Renewable energy communities foster the users' engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources (RESs) and the growing demand for sustainable power solutions have necessitated the widespread deployment of energy storage systems. Among these systems, battery energy storage systems (BESSs) have emerged as a promising technology due to their flexibility, scalability, and cost-effectiveness. This paper aims to provide a comprehensive review of the diffusion and deployment of BESSs across various applications, analyzing their impact on grid stability, renewable energy integration, and the overall energy transition. The paper examines the key drivers and challenges associated with BESS adoption, as well as market trends influencing their proliferation. Through an analysis of empirical data, this study aims to shed light on the current state of BESS diffusion. Finally, this research contributes to the knowledge base surrounding battery storage technology and provides insights into its role in achieving a sustainable and reliable energy future. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

This study describes a laboratory model of a battery energy storage system (BESS) designed for testing algorithms aimed at reducing peak power consumption in railway traction substations. The system comprises a DC/DC converter and battery energy storage. This article details a laboratory model of a bidirectional buck-boost DC/DC converter, which is used to transfer energy between the battery energy storage and a DC line. It presents an analysis of DC/DC converter systems along with simulation studies. Furthermore, the results of laboratory tests on the DC/DC converter model are also provided. The control algorithm of the system in the traction substation is focused on reducing peak power, offering benefits such as lower charges for the railway operator due to the possibility of reducing contracted power requirements. From the perspective of the power grid, the reduction in power fluctuations and, consequently, voltage sags, is advantageous. This paper includes a description of a hardware simulator for verifying the system's control algorithms. The verification of the control algorithms was performed through experimental tests conducted on a laboratory model (a hardware simulator) of the system for dynamic load reduction in traction substations, on a power scale of 1:1000 (5.5 kW). The experimental tests on the laboratory model (hardware simulator) demonstrated the effectiveness of the algorithm in reducing the peak power drawn from the power source. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea's fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations. [ABSTRACT FROM AUTHOR]

Global society is significantly speeding up the adoption of renewable energy sources and their integration into the current existing grid in order to counteract growing environmental problems, particularly the increased carbon dioxide emission of the last century. Renewable energy sources have a tremendous potential to reduce carbon dioxide emissions because they practically never produce any carbon dioxide or other pollutants. On the other hand, these energy sources are usually influenced by geographical location, weather, and other factors that are of stochastic nature. The battery energy storage system can be applied to store the energy produced by RESs and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources. The main purpose of the review paper is to present the current state of the art of battery energy storage systems and identify their advantages and disadvantages. At the same time, this helps researchers and engineers in the field to find out the most appropriate configuration for a particular application. This study offers a thorough analysis of the battery energy storage system with regard to battery chemistries, power electronics, and management approaches. This paper also offers a detailed analysis of battery energy storage system applications and investigates the shortcomings of the current best battery energy storage system architectures to pinpoint areas that require further study. [ABSTRACT FROM AUTHOR]

The highly variable power generated from a battery energy storage system (BESS)–photovoltaic distributed generation (PVDG) causes harmonic distortions in distribution systems (DSs) due to the intermittent nature of solar energy and high voltage rises or falls in the BESS. Harmonic distortions are major concerns in the DS, especially when the sizes and locations of these resources are sub-optimal. As a result, many studies are being performed on the optimal allocation of BESS/PVDG systems in distribution network systems. In this regard, this paper seeks to review the existing planning models, optimization methods and renewable energy resources that uncertainty models have employed in solving BESS/PVDGs allocation problems in terms of obtaining optimal solutions/allocations and curtailing the harmonic contents of the DSs. However, studies on optimal allocation planning of BESS/PVDGs have achieved minimum cost but were not able to meet the standard harmonic level of the DSs. The results identified GA, PSO and AIS as some of the most used methodologies while LP, MILP and different configurations of NLP were used in the model formulations of BESS/PVDGs problems. The results also revealed DC-link voltage and switching and grid voltage harmonics as the notable causes and sources of harmonic distortions in BESS/PVDG systems. The current allocation models presented in the recent literature for the planning of BESS/PVDGs do not include the variables necessary for curtailing the harmonic contents in their planning formulations. This paper, therefore, recommends an improved and all-encompassing planning model with an efficient intelligent search algorithm capable of obtaining a global optimum solution and curtailing harmonic distortions from the BESS/PVDG-connected DSs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery management algorithm to optimize the lifetimes of retired lithium batteries with heterogeneous states of health in a battery energy storage system under dynamic power demand. A battery energy storage system allows for the use of retired lithium batteries for applications such as backup power in homes, data centers, etc. In a battery energy storage system, a battery pack consists of several retired batteries connected in parallel or in series to fulfill the required power demand. Owing to the retired batteries' different capacity levels, i.e., states of health, a scheduling strategy is required to turn battery cells inside the battery pack on and off such that the secondary lifetimes of the retired batteries are extended. To establish the optimal scheduling policy, it is necessary to determine the correct states of each battery cell, including the state of charge and the state of health. To that end, the proposed algorithm first estimates the state of charge and state of health for all cells based on data measured using an extended Kalman filter. Then, a deep reinforcement learning scheduling algorithm is implemented to connect/disconnect the battery cells to/from the battery pack based on their states. Via simulation, we show that the proposed algorithm estimates the state of charge and state of health of each battery cell with low error and extends the lifetime of battery packs by 20.6%, compared to methods proposed in previous works. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

Ancillary frequency control schemes (e.g., droop control) are used in wind farms to improve frequency regulation in grids with substantial renewable energy penetration; however, droop controllers can have negative impacts on the damping of wind turbine torsional mode, thereby reducing the lifespan of the turbine gearbox. This paper presents a battery energy storage system (BESS) damper to improve the damping of torsional vibrations when using doubly fed induction generators (DFIGs) for frequency regulation in a microgrid. We formulated a linearized model comprising diesel generators, a wind turbine with five-mass drivetrain, and BESS. We also designed a feedforward compensator to deal with phase lag between the BESS damper signal and DFIG torque. The proposed BESS damper was shown to improve the torsional mode damping by moving the eigenvalues for torsional mode leftward to desirable locations on the complex plane. Dynamic simulations performed using MATLAB/SIMULINK demonstrated the efficacy of the proposed BESS torsional mode damping scheme in terms of torsional mode 1 damping performance and frequency response. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today's modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. [ABSTRACT FROM AUTHOR]

In this paper, a bidirectional converter with multi-mode control strategies is proposed for a battery energy storage system (BESS). This proposed converter, which is composed of a half-bridge-type dual-active-bridge (HBDAB) converter and an H-bridge inverter, is able to operate the BESS with different power conditions and achieve the DC–AC function for lower input DC voltage applications. For the HBDAB converter, the variable-frequency control (VFC) and phase-shift control (PSC) are both adopted to achieve zero-voltage switching over a wider power range and the battery module balance control capability for BESS, respectively. In addition, the interleaved configuration is used to reduce the current ripple and increase the overall current rating. For the H-bridge inverter, the unipolar control mode (UCM) and totem-pole control mode (TPCM) are adopted to manage the real and reactive current control under different AC grid conditions. The UCM offers a reduction in current ripple for real and reactive power control. The TPCM is able to eliminate switching losses and achieve higher conversion efficiency for pure real power control. Considering applications for battery energy storage systems, the principle of operation and voltage gain analysis are described. Finally, computer simulations and hardware experimental results from a prototype system are presented to verify the performance of the proposed converter with the different control strategies. [ABSTRACT FROM AUTHOR]

Battery participation in the service of power system frequency regulation is universally recognized as a viable means for counteracting the dramatic impact of the increasing utilization of renewable energy sources. One of the most complex aspects, in both the planning and operation stage, is the adequate characterization of the dynamic variation of the state of charge of the battery in view of lifetime preservation as well as the adequate participation in the regulation task. Since the power system frequency, which is the input of the battery regulation service, is inherently of a stochastic nature, it is easy to argue that the most proper methodology for addressing this complex issue is that of the theory of stochastic processes. In the first part of the paper, a preliminary characterization of the power system frequency is presented by showing that with an optimal degree of approximation it can be regarded as an Ornstein–Uhlenbeck process. Some considerations for guaranteeing desirable performances of the control strategy are performed by assuming that the battery-regulating power depending on the frequency can be described by means of a Wiener process. In the second part of the paper, more realistically, the regulating power due to power system changes is described as an Ornstein–Uhlenbeck or an exponential shot noise process driven by a homogeneous Poisson process depending on the frequency response features requested of the battery. Because of that, the battery state of charge is modeled as the output of a dynamic filter having this exponential shot noise process as input and its characterization constitutes the central role for the correct characterization of the battery life. Numerical simulations are carried out for demonstrating the goodness and the applicability of the proposed probabilistic approach. [ABSTRACT FROM AUTHOR]

The recent increase in the use of carbonless energy systems have resulted in the need for reliable energy storage due to the intermittent nature of renewables. Among the existing energy storage technologies, compressed-air energy storage (CAES) has significant potential to meet techno-economic requirements in different storage domains due to its long lifespan, reasonable cost, and near-zero self-decay. When viewed as a battery system, the key performance metrics of CAES, like energy density (ED), round trip efficiency (RTE), and the depth of discharge (DoD), have poor values when compared with other battery technologies in similar domains. This prevents CAES from transitioning to a state-of-the-art form of energy storage. This paper reviews the transition of CAES concepts from carbonized to carbonless types of CAES, along with different single-objective optimization strategies and their effects on the overall system's performance. It was discovered that competing performance metrics attributes cause single-objective optimization to have trade-offs that worsen at least one other preferred metric. The topology limitations of the generic CAES design were noted to prevent its use in different domains. To ensure that the optimal convergence of subsystem parameters is retained during charging and discharging periods, a suitable topology and subunit combinations for different domains are necessary. Possible options for solving these problems are identified so that the effects of the trade-offs imposed by optimization are either suppressed or eliminated. [ABSTRACT FROM AUTHOR]

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation. [ABSTRACT FROM AUTHOR]

Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically. [ABSTRACT FROM AUTHOR]

In the XFLEX HYDRO Vogelgrun demonstrator, a run-of-river hydropower plant, the hybridization of one turbine-generator unit with a battery energy storage system is being investigated. This paper describes the integration methodology of the hybrid control algorithm without replacing the existing speed governor of the unit. Furthermore, the comparison of the performances of a non-hybrid and hybrid unit is discussed, and first experiences gained during the operation and monitoring of the hybrid operating mode are presented. [ABSTRACT FROM AUTHOR]

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method. [ABSTRACT FROM AUTHOR]

Energy performance contracts are a very promising area for attracting private investment in the renewable energy sector. The concept of energy performance contracting is a well-established mechanism aimed at increasing the energy efficiency of a facility and reducing annual maintenance costs. This paper presents a hierarchical model of a decentralized energy system with renewable energy sources and a battery energy storage system under an energy service agreement. This model reflects the interaction between the client and the performance company. The model includes the main parameters characterizing the energy service contract, such as net present value, contract duration and levelized cost of energy. As an example, a real decentralized power system is considered, which currently only uses diesel generation. In the case of building a photovoltaic system, the optimal equipment composition consists of a 100 kW solar station and storage batteries with a capacity of 240 kW·h. The optimal contract term is 5 years, and diesel fuel savings are 69%. [ABSTRACT FROM AUTHOR]

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. [ABSTRACT FROM AUTHOR]

A power conversion interface for a small-capacity photovoltaic power generation system (PPGS) is proposed in this paper. The proposed power conversion interface is composed of a DC–DC converter (DDC), a battery pack and a buck–boost inverter (BBI). The battery pack is integrated to the power conversion interface to store or release energy for smoothing the power fluctuation of the small-capacity PPGS. The battery pack is only activated when a large power change occurs in the photovoltaic array (PV array). The DC power, no matter whether is from the PV array or the battery pack, needs to be converted into AC power only by the BBI in the proposed power conversion interface. Moreover, the PV array charges the battery pack only through the DDC, which simplifies the power circuit and improves the power conversion efficiency. To verify the feasibility of the proposed power conversion interface, a hardware prototype is established for practical experiments. [ABSTRACT FROM AUTHOR]

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand. [ABSTRACT FROM AUTHOR]

Substation (SS) auxiliary systems (SAux) are facilities responsible for hosting the alternating (AC) and direct current (DC) busbar to serve the equipment and systems that perform the substation's protection, control, and supervision. External and internal power supplies typically ensure the continuity of such a facility. The electricity support will be restricted to diesel emergency generators (DG) if the external power supply is unavailable due to a contingency. The DG present a slower response time and are susceptible to starting failures. Microgrids with Battery Energy Storage Systems (BESS) paired with photovoltaic systems (PV) are presented as an innovative and reliable solution for powering the SAux. In this article, tests were carried out on the microgrid of the Edson Mororó Moura Institute of Technology (ITEMM) in Brazil to support the use of microgrids BESS/PV in the SAux of a transmission SS of the São Francisco Hydroelectric Company (Chesf). Without an external power supply, BESS commands the action of islanded operation, maintaining both voltage and frequency requirements of the microgrid without load shedding. It was possible to observe all operations of the microgrid. The experimental results showed that the solution proposed in the paper implements a dependable self-dispatchable autonomous power supply. [ABSTRACT FROM AUTHOR]

Worldwide energy shortages and the green energy revolution have triggered an increase in the penetration of standalone microgrids. However, they have limited generation capacity and are wasteful when excess generated energy is curtailed. This presents an opportunity for the coordinated operation of multiple prosumer microgrids that absorb this waste to enhance their resiliency and reliability. This paper proposes a reliable prosumer model with an inbuilt energy management system (EMS) simulator that considers the mentioned deficiencies for constructing resilient interconnected prosumer microgrids. The EMS simulator operates in a real-time dynamic environment to coordinate the prosumer components and performs flexible switching for (1) prioritizing critical load by shedding the non-critical load and (2) meeting load demand locally or from other interconnected prosumers. The EMS simulator maintains energy balance by setting limits for the battery energy storage system (BESS) to preserve energy during low generation and performs real-time monitoring. The novelty of this model lies in its simplicity and flexibility, which allows interconnected prosumer microgrids to operate in cooperation without the need for communication. The proposed model is evaluated in a post-disaster off-grid scenario using a single-phase average value model that considers reasonable computation time. The simulation results of the proposed system indicate the preservation of stored energy while maintaining critical resources beyond three days of poor weather conditions. [ABSTRACT FROM AUTHOR]

In recent years, important regulatory changes have been introduced in Spain in the fields of self-consumption and energy tariffs. In addition, electricity prices have risen sharply, reaching record highs in the last year. This evidences the need to conduct new research studies in order to provide an accurate picture of the profitability of battery energy storage systems and photovoltaic systems. This paper proposes a complex simulation tool developed to assist in the optimal design of these kinds of facilities. The tool is used in this study to analyze the benefits of including batteries in PV systems under different self-consumption models, different consumer profiles and different locations across the country. The research results indicate that at current electricity prices, the use of batteries is less profitable than selling excess energy to the grid, unless the price of batteries drops drastically by more than 50% in all the cases analyzed. However, at current battery prices, they become a valuable resource in facilities that do not feed energy surplus into the grid. [ABSTRACT FROM AUTHOR]

In the context of the increasing penetration of intermittent renewable energy resources (RES), one of the significant challenges facing traditional bulk power systems and microgrids is the scheduling generation units problem. Many studies have focused on solving the energy management problem for microgrids integrating RES. To address the intermittency caused by RES, flexible components such as battery energy storage systems (BESS) or demand response (DR) are considered. To clarify the problem of integrating these flexible components, a mixed-integer programming (MIP) approach for the unit commitment (UC) problem for microgrids with BESS and DR is proposed in this paper. An incentive-based demand response model as a negative power source and a detailed model for the vanadium redox battery (VRB) are introduced to improve the efficiency and reliability of microgrids. The objective optimization function, including the costs of generation, emissions, and maintenance, is minimized considering the uncertainty of the load and renewable energy sources. The obtained simulation results are compared with the genetic algorithm (GA) method as the basis for verification in different case studies. The obtained results have clarified the effect of using the BESS model and DR program on system operation. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries. [ABSTRACT FROM AUTHOR]

Large-scale integration of PV generators in distribution grids will impair the voltage stability due to the stochastic and fluctuated PV power generation. To tame the volatile PV power generation, battery energy storage systems (BESS) are deployed as an effective yet expensive power buffering mechanism. In this paper, a dual ascent-based voltage optimization control is proposed to achieve the concurrent regulation of battery State-of-Charge (SoC), nodal voltages, and distribution loss. This control features the limited dependence on the communication network with information interaction between neighboring nodes. Besides, it can achieve the optimal power flow minimizing the distribution loss while maintaining the BESS SoC within a healthy range. The derivation of the control framework is provided, and comparative simulations in the IEEE 37-node distribution system are performed to validate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

To avoid the potential adverse impacts of climate change from global warming, it is suggested that the target of net zero emissions should be reached by this mid-century. Thailand is aiming to achieve carbon neutrality by 2050. Since electricity generation is one of the largest producers of carbon dioxide emission, the associated emissions must be greatly reduced to achieve the targets mentioned above. Thus, new generation expansion plans must be well developed. This paper discusses the development of generation expansion plans considering Thailand's latest policies along with enhancement of the existing multi-period linear programming model, allowing new electricity generation technologies having low emissions, e.g., solar PV with battery and hydrogen blending in natural gas, to be integrated into generation expansion planning. Then, four generation expansion plans with different levels of hydrogen blending in natural gas are proposed and discussed. It is found that Thailand can achieve carbon neutrality by 2050 by promoting more use of renewable energy altogether with trade-off between land for solar PV installation and amount of hydrogen blended in natural gas. The lesson learned from this study provides crucial information about possible pathways to achieve carbon neutrality in the electricity sector for policy makers in other countries. [ABSTRACT FROM AUTHOR]

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered. [ABSTRACT FROM AUTHOR]

This study aims to review the potential benefits of peak load shaving in a microgrid system. The relevance of peak shaving for a microgrid system is presented in this research review at the outset to justify the peak load shaving efficacy. The prospective benefits of peak shaving in microgrid systems, including technological, economic, and environmental advantages, are thoroughly examined. This review study also presents a cost–benefit numerical analysis to illustrate the economic viability of peak load shaving for a microgrid system. Different peak shaving approaches are briefly discussed, as well as the obstacles of putting them into practice. Finally, this review study reveals some potential future trends and possible directions for peak shaving research in microgrid systems. This review paper lays a strong foundation for identifying the potential benefits of peak shaving in microgrid systems and establishing suitable projects for practical effectuation. [ABSTRACT FROM AUTHOR]

Hybrid electric ships powered by diesel generators and batteries are the main configuration for shipboard microgrids (SMGs) in the current maritime industry. Extensive studies have been conducted for the hybrid operation mode, whereas the all-electric operation mode and the switching between the aforementioned two modes in a system with multiple generators and batteries have not been tested. In this paper, a coordinated approach for a hybrid electric ship is proposed, where two operation modes have been simultaneously considered. More specifically, for achieving an efficient operation with reduced generator wear losses, the governor-less diesel-engine-driven generators have been adopted in the study. According to the practical operation conditions, two operation modes, the all-electric and hybrid modes, are preset. Based on these, the coordination of the generators acting as the main power sources and batteries regulating the power flow and improving the generator efficiency is studied. The governor-less diesel generators are regulated to inject the rated power in order to maximize the generator efficiency, while the DC bus voltage is regulated by DC/DC converters. For the benefit of the overall lifespan of battery banks, power sharing during charging and discharging states have been realized by the state of charge (SoC)-based adaptive droop regulator. For the test of two operation modes, as well as the mode switching, a simulation assessment in a 1 kV DC SMG has been conducted. The simulation results show that the DC bus voltage can be controlled well, and that the power sharing among batteries follows the design. Additionally, smooth transients can be observed during mode switching when the proposed control scheme is applied. [ABSTRACT FROM AUTHOR]

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS's remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power's grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS's capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS's ability to smooth wind power fluctuations. [ABSTRACT FROM AUTHOR]

Concentrated solar power (CSP)—photovoltaic (PV) hybrid power plants allow for the generation of cheap electrical energy with a high capacity factor (CF). A deep integration of both technologies offers synergies, using parts of the PV generated electricity for heating the thermal storage tank of the CSP unit. Such configurations have been previously studied for systems coupled by an electric resistance heater (ERH). In this work, the coupling of a CSP and a PV plant using a heat pump (HP) was analyzed due to the higher efficiency of heat pumps. The heat pump is used as a booster to lift the salt temperature in the storage system from 383 to 565 °C in order to reach higher turbine efficiency. A techno-economic analysis of the system was performed using the levelized cost of electricity (LCOE), the capacity factor and nighttime electricity fraction as variables for the representation. The CSP–PV hybrid with a booster heat pump was compared with other technologies such as a CSP–PV hybrid plant coupled by an electric heater, a standalone parabolic trough plant (PT), a photovoltaic system with battery storage (PV–BESS), and a PV thermal power plant (PVTP) consisting of a PV plant with an electric heater, thermal energy storage (TES) and a power block (PB). [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) can overwhelm some of the environmental challenges of a low-carbon power sector through self-consumption with standalone photovoltaic (PV) systems. This solution can be adapted for different applications such as residential, commercial, and industrial uses. Furthermore, the option to employ second-life batteries derived from electric vehicles represents a promising opportunity for preserving the environment and improving the circular economy (CE) development. Nowadays, the industrial sector is progressively applying CE principles in their business strategies, and focusing on the potential positive consequences of CE eco-innovations on climate change mitigation. With the aim to promote the transition to an open-loop circular economy for automotive batteries, this study assesses and quantifies the potential environmental benefits resulting from the integration of a second-life battery-based BESS (SL-BESS) connected to an industrial machine. For this purpose, various scenarios involving the use of BESS, SL-BESS, and a standalone PV system are compared with a base case, where the machine is entirely powered by electricity from the grid. The examination of life cycle stages follows the life cycle assessment (LCA) cradle-to-grave methodology as outlined in ISO 14040:2006 and ISO 14044:2006/Amd 1:2017. Simapro® 9 is utilized as the software platform. Results demonstrate that the combination of the SL-BESS with a standalone photovoltaic (PV) system represents the optimal solution in terms of global warming potential (GWP) reduction, with a saving of up to −74.8%. However, manufacturing and end-of-life stages of PV and batteries contribute to abiotic depletion and human toxicity, resulting from the use of chemicals and the extraction of resources essential for their manufacture. Indeed, when BESS is made of new batteries, it demonstrates the most significant impacts in terms of AD at 1.22 × 10−1 kg Sb eq and human toxicity (HT) at 3.87 × 103 kg 1,4-DB eq, primarily attributable to the manufacturing stages of both BESS and PV systems. The findings represent a significant breakthrough, highlighting the substantial capacity of incorporating SL-BESS alongside renewable energy sources to mitigate GWP resulting from industrial applications, and the criticality of repurposing decommissioned batteries from the automotive industry for secondary use. [ABSTRACT FROM AUTHOR]

The integration of an energy storage system into an integrated energy system (IES) enhances renewable energy penetration while catering to diverse energy loads. In previous studies, the adoption of a battery energy storage (BES) system posed challenges related to installation capacity and capacity loss, impacting the technical and economic performance of the IES. To overcome these challenges, this study introduces a novel design incorporating a compressed CO2 energy storage (CCES) system into an IES. This integration mitigates the capacity loss issues associated with BES systems and offers advantages for configuring large-scale IESs. A mixed integer linear programming problem was formulated to optimize the configuration and operation of the IES. With an energy storage capacity of 267 MWh, the IES integrated with a CCES (IES–CCES) system incurred an investment cost of MUSD 161.9, slightly higher by MUSD 0.5 compared to the IES integrated with a BES (IES–BES) system. When not considering the capacity loss of the BES system, the annual operation cost of the IES–BES system was 0.5 MUSD lower than that of the IES–CCES system, amounting to MUSD 766.6. However, considering the capacity loss of the BES system, this study reveals that the operation cost of the IES–BES system surpassed that of the IES–CCES system beyond the sixth year. Over the 30-year lifespan of the IES, the total cost of the IES–CCES system was MUSD 4.4 lower than the minimum total cost of the IES–BES system. [ABSTRACT FROM AUTHOR]

This comparison of five power plants in West Texas is intended to provide various decision-makers and stakeholders with a holistic picture of the life-cycle environmental impacts associated with these power plants. A key contribution of this analysis is that we assumed all power plants generate the same amount of electricity over a 30-year life, taking a 500 MW combined-cycle gas turbine (CCGT) plant as a benchmark. Also, in two cases, we added battery storage to wind and solar PV facilities to render them nearly as dispatchable as the CCGT. We included the entire supply chain supporting electricity generation, which encompassed raw material sourcing, processing, manufacturing, operations, and product end of life, also called "cradle to grave". We report on 18 environmental impacts using ReCiPe midpoint (H) impact assessment. The supply chains are global, and impacts are felt differently by host communities across the world. The results can help stakeholders identify hotspots across numerous supply chains with the highest environmental impacts. We discuss some remedial measures and challenges to inform future analysis by the research community. [ABSTRACT FROM AUTHOR]

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38 % was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV. [ABSTRACT FROM AUTHOR]

Worldwide, governmental organizations are restructuring energy policies, making them cleaner, encouraging transformation and energy transition by integrating renewable sources, engaging in environmental preservation, and, notably, meeting the growing demand for sustainable energy models, such as solar and wind energy. In the electricity sector, reducing carbon emissions is crucial to facilitating the integration of microgrids (MGs) with renewable sources and Battery Energy Storage Systems (BESSs). This work constitutes a systematic review that thoroughly analyzes the sizing of MGs with BESSs. The unpredictability and variability of renewable sources justify the complexity of this analysis and the loads connected to the system. Additionally, the sizing of a BESS depends primarily on the application, battery technology, and the system's energy demand. This review mapped and identified existing computational and optimization methodologies for structured sizing in technical indicators of an MG with a BESS based on articles published between 2017 and 2021. A protocol was defined in which articles were filtered in multiple stages, undergoing strategic refinements to arrive at the final articles to address the Research Questions (RQs). The final number of articles was 44, and within these, technical indicators related to the RQs were addressed, covering the most relevant works and comparing them technically, including how each explains the objective and result of their work. The rejected articles did not meet the criteria established by the defined protocol, such as exclusion criteria, quality criteria, and RQs. In conclusion, studies employing the integration of machine learning coupled with optimization techniques exhibit a significant contribution to results, as historical data can aid machine learning for data prediction. [ABSTRACT FROM AUTHOR]

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to evaluate the overall performance of a battery energy storage system (BESS) during (I) grid-connected, (II) black start, and (III) islanded operating modes. To do so, firstly, a novel three-mode controller is proposed and developed. The proportional–integral–derivative (PID) controller is implemented, including the following three components: (1) inertia emulation, (2) frequency-active power and voltage-reactive power droops, and (3) secondary frequency and voltage controllers. Secondly, to effectively evaluate the proposed controller performance under various grid operating conditions during both black start and seamless transition to islanded operation, a set of comprehensive dynamic simulations using Matlab/Simulink is carried out. To this end, the sensitivity analyses on numerous grid operating parameters, such as pre-disturbance grid power, total installed BESS capacity, battery state of charge, unbalanced three-phase load flows, implemented power-frequency controller parameters, and distribution network types with various shares of dynamic and static loads, are performed. Thirdly, to practically improve the seamless transition performance enabling the demand response participation, a fast-controlled thermostatic load scheme is implemented. Simulation results show that the BESS unit using the proposed three-mode controller has great potential to successfully control the frequency and voltage within allowable limits during both islanding and black start modes over a wide range of grid operating conditions. [ABSTRACT FROM AUTHOR]

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited. [ABSTRACT FROM AUTHOR]

Currently, the integration of new energy sources into the power system poses a significant challenge to frequency stability. To address the issue of capacity sizing when utilizing storage battery systems to assist the power grid in frequency control, a capacity optimal allocation model is proposed for the primary frequency regulation of energy storage. Due to the requirement of a large number of actual parameters for the optimal allocation model, a simulation model of energy storage capacity is constructed based on the characteristics of primary frequency control to provide the necessary parameters. Subsequently, the primary frequency modulation output model of energy storage is established by considering the basic action output, the action in the frequency modulation dead zone, and a certain capacity margin. The antlion algorithm is employed to solve the capacity optimal allocation model. Finally, three groups of experiments are designed and compared to demonstrate the effectiveness of the proposed method in setting the capacity margin, which can increase profit to a certain extent. [ABSTRACT FROM AUTHOR]

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems. [ABSTRACT FROM AUTHOR]

Photovoltaic generation is one of the key technologies in the production of electricity from renewable sources. However, the intermittent nature of solar radiation poses a challenge to effectively integrate this renewable resource into the electrical power system. The price reduction of battery storage systems in the coming years presents an opportunity for their practical combination with utility-scale photovoltaic plants. The integration of properly sized photovoltaic and battery energy storage systems (PV-BESS) for the delivery of constant power not only guarantees high energy availability, but also enables a possible increase in the number of PV installations and the PV penetration. A massive data analysis with long-term simulations is carried out and indicators of energy unavailability of the combined system are identified to assess the reliability of power production. The proposed indicators allow to determine the appropriate sizing of the battery energy storage system for a utility-scale photovoltaic plant in a planning stage, as well as suggest the recommended operating points made for each month through a set of graphs and indicators. The presence of an inflection zone has been observed, beyond which any increase in storage does not generate significant reductions in the unavailability of energy. This critical zone is considered the sweet spot for the size of the storage, beyond which it is not sensible to increase its size. Identifying the critical point is crucial to determining the optimal storage size. The system is capable of providing reliable supply of constant power in monthly periods while ensuring capacity credit levels above 95%, which increases the penetration of this renewable resource. Despite the fact that the study focuses exclusively on the analysis from an energy perspective, it is important to consider the constraints associated to real storage systems and limit their oversizing. [ABSTRACT FROM AUTHOR]