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• Probabilistic design of battery storage systems in AC/DC microgrids is proposed. • An analytical approach is proposed based on a closed-form method. • Uncertainties of AC and DC loads, and renewable power generation are considered. • The probabilistic approach makes use of the Taguchi arrays. • Comparison with Monte Carlo approach allows demonstrating accuracy and feasibility. The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges for electrical component planning. This paper provides a tool for the probabilistic sizing of a battery energy storage system in a hybrid AC/DC microgrid (i.e., a microgrid including AC and DC subsections with AC and DC loads and renewable power generation). The procedure is based on an analytical formulation to assess the total costs sustained by the microgrid for the inclusion of the battery energy storage system. The total costs depend on the random nature of the load demand, the electrical energy prices, and the renewable power generation, therefore, a probabilistic approach has been used. Furthermore, to account for uncertainties that affect the input data, the Taguchi orthogonal arrays are applied which allow significantly reducing the computational efforts while guaranteeing the desired accuracy of the results. The proposed analytical formulation along with the use of the Taguchi orthogonal arrays allows limiting the computational complexity even in presence of a large number of random inputs and discretization levels. A case study based on an industrial hybrid microgrid is presented to analyse the results obtained in terms of the optimal sizing of the battery energy storage system and to investigate the sensitivity with respect to some inputs. [ABSTRACT FROM AUTHOR]

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H ∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System. • The impact of BESS on the power system inter-area oscillation damping is investigated. • The impact of dynamic control mode interaction between SGs and BESS on the system oscillation modes under various operating conditions is investigated. • A H ∞ mixed sensitivity scheme-based WADC is designed for BESS to alleviate the power system inter-area oscillations. • The H ∞ mixed sensitivity scheme-based WADC is implemented in the RTDS to assess its feasibility in a real-time environment. [ABSTRACT FROM AUTHOR]

This study examines State of Charge (SoC) balancing control in DC microgrids subject to photovoltaic (PV) fluctuations, aiming to optimize power distribution in energy storage systems influenced by PV disturbances. The proposed approach enhances both the lifespan of storage systems and microgrid stability. To mitigate voltage variation due to PV perturbation, the paper introduces an adjustment in droop control offset. Additionally, it presents a novel discrete Sliding Mode Controller (SMC) characterized by reduced parameter sensitivity, thus enhancing control responsiveness. A SoC balancing control strategy employing sliding mode control is developed to equalize SoC levels across Battery Energy Storage Systems during both charge and discharge cycles. The stability of this strategy is substantiated through the construction of a Lyapunov function. Simulations conducted in a distributed DC microgrid environment using Simulink/SimPower Systems demonstrate the efficacy of the discrete SMC and the SoC balancing algorithm, achieving uniform SoC in energy storage nodes during operation, with improved robustness against PV perturbations. • Characterize the PV-based microgrid instability through a non-linear system. • Use a sliding mode controller to improve voltage robustness with weak sensitivity to control parameters in battery energy storage system. • Achieve equilibrium across battery energy storage systems by using a SoC balancing control strategy. [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.

This research focused on the implementation of state-of-the-art system integration, involving a three-phase 540 KVA bidirectional inverter and a lithium-ion battery energy storage system with a capacity of 1.86 MWh, at the Florida Solar Energy Center (FSEC). The system was firstly put into use at the FSEC for load shifting. The second step was to add photovoltaic (PV) panels for peak shaving. The third step was to take the FSEC completely off-grid. The goal of this research was to provide a case study by implementing a power management algorithm, estimating the PV panel size, and presenting the test results and an analysis. Based on the power management algorithm, load shifting was achieved by using 180 kWh energy storage. To acquire peak shaving and the uniformity of power consumption, an 80 kW PV farm was sufficient. The optimal PV panel size was 500 kW for the off-grid scenario. [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]

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario. [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]

Energy sector is being revolutionized with the introduction of digitalization technologies. Digitalization technologies converted conventional energy grids into smart grids. Therefore, the virtual representation of battery energy storage systems, known as a digital twin, has become a highly valuable tool in the energy industry. This technology seamlessly integrates battery energy storage systems into smart grids and facilitates fault detection and prognosis, real-time monitoring, temperature control, optimization, and parameter estimations. In general, the use of digital twin technology improves the efficiency of the battery system after a thorough assessment of the battery performance. Hence, this paper aims to review the advancements of digital twin technology in battery energy storage systems. In particular, this paper focuses on the different functions and architectures of the digital twin for battery energy storage systems. Then, this paper further analyzes the digital twin characteristics using the Formal Concept Analysis (FCA) algorithm. The FCA is run to find trends and gaps between the digital twin functions and architectures in the battery system. Exploring the trends and gaps from previous research associated with the integration of digital twin with battery energy systems is essential to pave the way for further enhancements in this field. • Review the application of digital twin in battery energy storage systems. • Analyze the digital twin functions and architecture for battery energy storage systems. • Association rule mining technique is employed to explore trends and gaps of integrating the digital twin in battery storage systems. [ABSTRACT FROM AUTHOR]

• This paper proposes a new emergency service market in which BESS can compete reasonably, fully considering the SOC of energy storage equipment. • The AEBS market takes dynamic risk as factor. The dynamic demand and transaction pricing of BESS are more accurate and proactive on risk nodes. • This paper introduces a penalty mechanism to curb the occurrence of dishonest behavior in the LMP method. The power systems with a high proportion of clean energy face both greater uncertainty in power generation and the threat of stability brought by low inertia. After a fault, the rapid drop in power grid frequency, large voltage fluctuations, and insufficient reactive power support can lead to new energy generation units being disconnected from the grid. Fast active and reactive support will play an important role in suppressing fault propagation in low-inertia systems and preventing large-scale power outages. The reserve and frequency regulation market is a necessary means to deal with power grid failure events, but many blackout events occurred due to insufficient reserve capacity and ineffective market transaction mechanisms. As a flexible power regulation resource, BESS (battery energy storage system) has been incorporated into the power ancillary service market planning. In some engineering cases, the frequency regulation ancillary service provided by energy storage has also shown its advantages. However, there is a lack of in-depth research on how to gather BESS to take part in AEBS (ancillary emergency backup services) in the early stage after the failure. Therefore, in view of the dynamic change of power system risks and the different response speed of different backup resources, this paper proposes to establish a segmented combined backup mode to realize the combination of emergency backup at the initial stage of failure and frequency regulation backup during the fault recovery process. A related model of AEBS demand assessment and emergency backup service pricing mechanism is established. And considering the capacity, life loss and opportunity costs of energy storage for AEBS, a LMP (locational marginal price) method is used to calculate the electricity price of AEBS provided by BESS. It has proved the effectiveness by constructing a grid AEBS based on BESS used by the IEEE 30 node system. [ABSTRACT FROM AUTHOR]

• Presenting a MILP framework to reduce computation time and achieve the optimal solution. • Using advanced technologies (EVs, BDGs, BESSs, and DRP) simultaneously to enhance SDN resilience. • Utilizing a hybrid stochastic-IGDT method to model uncertainties of RESs, electricity price, EVs, and load. • Providing RA and RT strategies to face the uncertainty of load. Due to climate changes, the occurrence of low-probability but high-impact disasters such as hurricanes, earthquakes, thunders and etc are increasing. So in the occurrence of these disasters, the resilient operation of smart distribution networks (SDNs) needs to be more investigated by researchers. To address this challenge, in this paper, the optimal resilient operation of the SDN in the presence of renewable energy resources (RERs) and intelligent parking lots (IPLs) is investigated. A hybrid stochastic-IGDT approach is developed to handle the uncertainties of the proposed model. So to do this, the stochastic method is used to modeling of RERs generation, electricity price, and EVs (arrival time, departure time, and initial energy) uncertainties. Also, due to the high impact of load uncertainties on the resilient operation of SDN, the information gap decision theory (IGDT) approach is applied to mitigate the uncertainties of load. In this paper, backup diesel generators (BDGs), battery energy storage systems (BESSs), and demand response programs (DRPs) are suggested to improve the resilient operation of SDN. In order to show the efficacy of the proposed model, four case studies are evaluated on a modified IEEE-33 bus test system. The numerical results indicate, in the risk-averse strategy, the SDN is robust against 7.2% increasing active load by having 10% more operation cost, and the resiliency of the system decrease 3.05% for the worst scenario. On the other side, in the risk-taker strategy, with decreasing 7.4% in active load, the operation cost decrease 10%, and the resiliency of the system increase 4.93%. [ABSTRACT FROM AUTHOR]

Nowadays, clean and sustainable energy development is of essential concern, which justifies use of renewable energy sources. Wind energy is an important resource to provide such a demand. Due to the high costs of wind power generation compared to other renewable sources, the wind turbines should be designed in such a way that they usually operate at the highest point of their power. In this case, because of the random and alternating wind speed, the output power of the wind turbine generators and thus the windfarms fluctuate. When the capacity of windfarms is increased, the power injected from the windfarm into the grid can have significant negative effects on the power grid stability. In order to prevent these effects, it is necessary to use BESSs in windfarms. To this end, a waveform with acceptable fluctuations is considered for the windfarm output power. This waveform is compared with the output power waveform and then the difference between the two waveforms is compensated by a BESS. Proper tracking with the minimum delay reduces the required BESS capacity and thus initial investment cost for the windfarms is significantly reduced. In this paper, using real windfarm data, the conventional tracking methods for the windfarm output power waveform are analyzed and compared, first. Then, a novel tracking scheme, namely the MSA, is proposed, which is based on a two-fold master-slave adaptive linear neuron. Acquired results show that compared with the conventional methods, using the MSA scheme can reduce the costs of a 99 MW windfarm by 4.8 million dollars.

The growing presence of power ramps, typically caused by the intermittency of renewable energy sources (RESs), may ultimately threaten the stability and reliability of the power grid. In the context of power smoothing algorithms such as Moving Average, Ramp Rate and First-Order Low-Pass Filter have been widely used in reference generation for Energy Storage Systems (ESSs). In this scenario, this paper analyzes typical metrics used for evaluating power smoothing techniques and comments on their limitations. Validation of this analysis is conducted using PV generation data sourced from the National Renewable Energy Laboratory (NREL). The results highlight the need to develop new metrics for a fairier comparison. Finally, this work also sets a concrete path for the evolution of said metrics.

Home battery energy storage systems (HBESSs) has been experiencing an increasingly popularization and marketing process. This consequentially leads to an information filtering challenge for the residential customer to choose the most suitable HBESS products from the large number of candidates HBESSs in the market. This paper proposes a novel personalized HBESS recommender system to provide decision-making support for residential customers to make HBESS choice. The system makes HBESS recommendation following 2 stages: (1) in the first stage, the system uses a federated learning process to aggregately analyze the customers’ preference tendencies on HBESS products from the datasets owned by different HBESS service providers without having the data actually exchanged; based on the learnt preference trends, the system generates a HBESS shortlist that are likely to fit the target customer’s profile; and (2) in the second stage, the system further filters the shortlisted HBESSs by evaluating the household energy cost they can create for the target customer. Combining the considerations of both personal preference and energy cost, several HBESS products are finally selected from the previously generated shortlist and recommended to the target customer. Extensive simulations are conducted to validate the effectiveness of the proposed system.

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.

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 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.

The need for a more flexible usage of power is increasing due to the electrification of new sectors in society combined with larger amounts of integrated intermittent electricity production in the power system. Among other cities, Uppsala in Sweden is undergoing an accelerated transition of its vehicle fleet from fossil combustion engines to electrical vehicles. To meet the requirements of the transforming mobility infrastructure, Uppsala municipality has, in collaboration with Uppsala University, built a full-scale commercial electrical vehicle parking garage equipped with a battery storage and photovoltaic system. This paper presents the current hardware topology of the parking garage, a neural network for day-ahead predictions of the parking garage’s load profile, and a simulation model in MATLAB using rule-based peak shaving control. The created neural network was trained on data from 2021 and its performance was evaluated using data from 2022. The performance of the rule-based peak shaving control was evaluated using the predicted load demand and photovoltaic data collected for the parking garage. The aim of this paper is to test a prediction model and peak shaving strategy that could be implemented in practice on-site at the parking garage. The created neural network has a linear regression index of 0.61, which proved to yield a satisfying result when used in the rule-based peak shaving control with the parking garage’s 60 kW/137 kWh battery system. The peak shaving model was able to reduce the highest load demand peak of 117 kW by 38.6% using the forecast of a neural network.

Deep decarbonization of power system operations requires the maximal utilization of available renewable resources. At distribution-level operations, however, grid operators can face numerous challenges in integrating renewables at scale owing to the inherent intermittence of renewable energy resources. These include phenomena such as voltage fluctuations, which are typically mitigated through control actuators such as on-load tap changers (OLTC) as well as energy storage devices, such as battery energy storage systems (BESS). On the one hand, high intermittence of the available renewable portfolio may require increasingly aggressive control of actuators, thereby accelerating the probability of equipment failure. On the other hand, integrating BESS operations and having a diverse renewable generation portfolio can typically help stagger power/energy flow to mitigate the aforementioned adverse impacts. In this paper, we employ a Bayesian framework for equipment lifetime estimation to understand the impact of including tidal energy resources and BESS in distribution system operations for feeders having substantial distribution photovoltaic generation. Our results indicate that while tidal energy alone may slightly decrease equipment reliability, the adverse impact on reliability is significantly magnified by a generation portfolio consisting of tidal generation and photovoltaic generation. We also study the tidal and photovoltaic hosting capacity problem with and without energy storage systems using equipment reliability as an added constraint. We conclude that energy storage increases the reliability-constrained hosting capacity of the distribution system. • OpenDSS-based simulation-oriented investigation of renewable energy integration. • Tidal power variability negatively impacts the reliability of on-load tap changers (OLTC). • Equipment reliability concerns can limit the renewable hosting capacity of a distribution system. • Energy storage can lessen the operational stress on OLTCS thus increasing equipment lifetime. • Energy storage can facilitate the integration of high-capacity distributed energy resources. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) are widely utilized in various applications, e.g. electric vehicles, microgrids, and data centres. However, the structure of multiple cell/module/pack BESSs causes a battery imbalance problem that severely affects BESS reliability, capacity utilization, and battery lifespan. The available balance schemes introduce extra equalizers and suffer from slow balance speed due to the equalizer limits. To tackle this issue, a modular reconfigurable BESS (MR-BESS) topology is introduced in this paper, for which a fast battery balance method is proposed. This combination provides reconfiguration flexibility and fault tolerance capability without the need for any extra components, such as equalizers. Experimental results were performed on a scale-down prototype to verify the operation of the proposed reconfigurable BESS topology and the effectiveness of the fast battery balance method. • A novel modular reconfigurable BESS (MR-BESS) topology is proposed. • An equalizer-free active battery balance method for proposed topology is proposed. • A control algorithm for balance procedure that realizes fast balance speed. [ABSTRACT FROM AUTHOR]

The high penetration of photovoltaic (PV) in power grids typically leads to the displacement of traditional synchronous generators (SGs). However, with a high penetration of PV, fewer SGs are running, and the sharing of responsibility to control the system frequency is reduced and easily exacerbates the problem of reduced inertia response in the power system. This can increase frequency deviation when there is a load-generation imbalance or unexpected disturbances. The limited amount of inertial response from the PV generation means that it cannot provide the same frequency support as SGs. Therefore, this paper suggests a fast frequency control (FFC) technique for the battery energy storage system (BESS) to reduce the instantaneous frequency deviation (IFD) in the Ethiopian grid. The authors specifically provide knowledge of the modeling of droop-type controlled BESS, which can provide additional damping, enhance the inertial ability of the system, and reduce IFD. The simulation results indicate the effectiveness of BESS in mitigating the adverse inertial impact of PV and meeting the necessary grid requirement. The proposed method was tested on the Ethiopian power system model through simulations by using DIgSILENT simulation software. To validate the model, transient frequency simulation studies are performed for various fault conditions, and comparisons are made with and without BESS for different levels of PV penetration. • A model of the Ethiopia–Kenya LCC HVDC interconnection is developed. • The study examines the integration of large and small-scale grid-connected PV systems into the power grid at varying penetration levels. • Frequency responses are analyzed for PV penetration levels (15%–45%) with load or generation contingency. • BESS modeled and controlled using droop control with PI-lead and lead–lag algorithms, considering frequency deviation input. [ABSTRACT FROM AUTHOR]

This paper introduces a new adaptive control strategy for power-sharing in a hybrid AC/DC microgrid (HMG). The existing interlink converter (ILC) control methods exhibit limitations under heavy load and contingency conditions. To overcome this limitation, a bidirectional control strategy is proposed for the ILC, which effectively coordinates battery energy storage systems (BESSs) in both sub-grids while mitigating stress on the ILC and BESSs. The control strategy utilises the state of charge (SoC) and instantaneous power of the BESSs to stabilise frequency and DC voltage on the AC and DC sub-grids, respectively, enabling autonomous operation and plug-and-play capability. Stability analysis employing root locus and a stress level relaxation index for BESSs are presented. Performance evaluation based on the moving mean square error criterion validates the effectiveness of the control strategy. Simulation results using MATLAB/SIMULINK demonstrate the superior performance of the proposed strategy in regulating both voltage and frequency within the system over the existing control approaches. Furthermore, the proposed approach significantly reduces stress levels on the BESSs in different operation conditions. • Enhancing the stability of hybrid AC/DC microgrid. • Proposing a new adaptive strategy for ILC control. • Proposing an autonomous control system based on BESS characteristics. • Easing Stress on the BESSs by deploying an adaptive power management scheme. • Assessing stress level relaxation and voltage and frequency regulation deviations. [ABSTRACT FROM AUTHOR]

Integrating a grid-connected battery into a renewable energy community amplifies the collective self-consumption of photovoltaic energy and facilitates energy arbitrage in the electricity markets. However, how much can energy independence really increase? Is it a cost-effective investment? The answer to these questions represents a novelty in the literature due to the innovative nature of the asset under consideration and the market and regulatory framework in which it is evaluated. Employing a net present value assessment, our analysis incorporated aging effects and conducts sensitivity analyses across various parameters: the number of community customers, electricity market prices, battery cost and size, and the decision to engage in energy arbitrage. Each scenario underwent a 20-year hourly simulation using an aging-aware rolling-horizon 24 h-looking-ahead scheduling, optimized with mixed-integer linear programming. Simulations conducted on the Italian market indicate that dedicating a battery solely to collective self-consumption is the most efficient solution for promoting a community's energy independence, but it lacks economic appeal. However, integrating energy arbitrage, despite slight compromises in self-sufficiency and battery longevity, halves the payback period and enhances the attractiveness of larger battery investments. The net present value is contingent upon the battery size, customer number, and market prices. Nevertheless, if the battery cost does not exceed 200 EUR/kWh, the investment becomes cost-effective across all scenarios. [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]

Distributed renewable sources are one of the most promising contributors for DC microgrids to reduce carbon emission and fuel consumption. Although the battery energy storage system (BESS) is widely applied to compensate the power imbalance between distributed generators (DGs) and loads, the impacts of disturbances, DGs, constant power loads (CPLs) and cyber attacks on this system are not simultaneously considered. Based on this, a distributed fuzzy output consensus control strategy is proposed to realize accurate current sharing and operate normally in the presence of denial of service (DoS) attacks and false data injection (FDI) attacks. Firstly, the whole model of the BESS in DC microgrids embedded into disturbance items, DGs, CPLs and resistive loads, is firstly built. This model could be further transformed into standard linear heterogeneous multi-agent systems with disturbance, which lays the foundation for the following control strategy. Then the model of FDI and DoS attacks are built. Meanwhile, the fuzzy logic controller (FLC) is applied to reduce the burden of communication among batteries. Based on these, a distributed output consensus fuzzy control is proposed to realize accurate current sharing among batteries. Moreover, the system under the proposed control in different cases is analyzed. Finally, the feasibility of the proposed control strategy is verified by numerical simulation results and experiment results. [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]

Changes in the global energy landscape have increased the importance of research on energy management methods in the power grid. In particular, Demand Side Management (DSM) at consumers is attracting worldwide attention. Distributed generation in intelligent consumers, such as Smart Houses (SH), contributes to the introduction of new distributed generation from the demand side by determining optimal scheduling. However, Fuel-cells (FC) and Battery Energy Storage Systems (BESS) are expensive, and their installation is a significant burden for the demand side. Therefore, this paper proposes a Smart Apartment Building (SAB) model in which multiple distributed power sources are shared by multiple consumers to reduce operation costs and carbon emissions through the implementation of highly efficient operation methods. An important aspect of such a study is to understand the characteristics of the demand side and to propose an operating method that takes into account the preferences of the demand side. In this paper, to provide a variety of options, a Pareto front is generated through multi-objective optimization to reduce the total cost and carbon dioxide emissions of the model. The optimal component capacity is also considered at the same time. A Model Predictive Control (MPC)-based optimization algorithm is then developed to achieve highly efficient operation, thus contributing to the reduction of the two objectives. As a result, in a compromise between the two conflicting objectives, the MPC algorithm successfully reduces operation costs by 44.4% and carbon dioxide emissions by 54.7% compared to the original case. [ABSTRACT FROM AUTHOR]

Due to the high penetration of renewable energy sources (RES) such as wind units in power systems, the need to check the stability of transmission networks has been given more attention than before. Therefore, in this paper, due to the importance of the topic, the wide area damping controller (WADC) has been used for the battery energy storage system (BESS) connected to the photovoltaic unit and the permanent magnet synchronous generator (PMSG) in the dc link. The WADC design is based on free weight matrices (FWM), which can solve a set of constraints based on the linear matrix inequality (LMI) based on the delay-dependent feedback control theory. The working method is that the constraints related to LMI are considered in such a way that it has the ability to tolerate the maximum amount of time delay. FWM has been used to communicate between LMI constraints and the maximum value of the time delay margin. FWM matrices are also based on an iterative algorithm based on linearization of the conical complement, which tries to search for the most optimal value for the control parameters. To implement the simulation results in MATLAB software, an improved power system of 16 machines has been used, the results of which are clearly analyzed and show the superiority of the proposed method compared to other mentioned methods.

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.

More hotels are significant energy consumers, and optimizing their energy consumption is essential for reducing their energy costs. Additionally, reliability is crucial for hotels, as power outages or disruptions can have significant impacts on guest experience and business operations. This paper proposes a novel regulatory framework called the hotel renewable energy incentive program for optimizing hotel energy consumption through battery energy storage systems integrated with renewable energy sources. The proposed framework and revenue calculations are formulated based on renewable energy generation and excess energy fed back into the grid. Mixed integer nonlinear programming is used to determine the optimal battery capacity, which allows the hotel to generate and store excess energy more efficiently and earn more revenue through the hotel renewable energy incentive program (HREIP). This research compared the results of the proposed framework with those of standard available frameworks such as net metering (NM) and feed in tarif (FIT). The framework offers a sustainable and cost-effective solution for optimizing hotel energy consumption. The proposed HREIP led to a total cost reduction from ${\$}$ 1042.2 to ${\$}$ 944.84, outperforming other policies and achieving a 9.3% cost reduction. Additionally, the contribution of renewable energy sources increased from 30% without any policy to 31%, 41%, and 43% in the HREIP, FiT, and NM schemes, respectively.

Most rooftop solar photovoltaic systems in Australia export excess power to the grid after meeting local demand, leading to overvoltage issues in distribution feeders. Australian standard AS/NZS 4777.2.2020 mandates grid-connected PV inverters to utilise real and reactive power quality response modes to mitigate these issues. These response modes result in the injection or absorption of reactive power and curtailment of active power when voltage exceeds prespecified thresholds. If the voltage exceeds a maximum permissible limit, the PV inverter shutdown to ensure safe operation. This paper proposes a method to reduce active power curtailment and inverter shutdown by utilizing reactive power support from local battery inverters, if available. The battery inverter provides reactive power support to mitigate overvoltage without affecting its active power flow. If the reactive power of the battery inverter is insufficient, the PV inverter response modes are activated. This technique reduces reliance on PV inverters and thus minimizes active power curtailment. The proposed method is tested on an 11-kV distribution feeder in South Australia. Simulation results indicate that by combining the support of battery inverter and PV inverter, overvoltage, active power curtailment, and the associated financial loss can be eliminated for the studied feeder. The impact of selecting the feeder source bus voltage on overvoltage issues is also investigated, and it is found that selecting lower source bus voltages during high solar radiation periods can eliminate overvoltage problems.

This paper investigates the use of a battery energy storage system (BESS) to enhance the frequency response characteristics of a low-inertia power system following a disturbance or active power mismatch. A simple control strategy of the BESS is proposed to improve the inertia response and primary frequency response of the system. The effectiveness of the proposed control strategy is evaluated on two test systems, which have been modified by replacing some fossil fuel-based generators with solar photovoltaic (PV) systems to create low inertia systems. A BESS is then added to provide additional system inertia. The DigSILENT PowerFactory software is used to model these modified systems with the BESS and to generate the corresponding frequency response characteristics under various power imbalanced conditions. Simulation results indicate that the BESS, with the proposed control strategy, can improve the system rate-of-change of frequency (ROCOF), frequency nadir (minimum frequency), and final steady-state frequency. In the 3-machine system, replacing a traditional power plant with a solar PV farm decreases the frequency nadir from 49.34 Hz to 48.69 Hz following a sudden load increase of 60 MW. However, adding a 30 MW BESS improves the frequency nadir to 49.48 Hz, which is better than the original system. In the 4-machine system, replacing two traditional power plants with solar farms increases the initial ROCOF to 0.55 Hz/s, exceeding the acceptable limit of 0.50 Hz/s for a severe disturbance. However, incorporating a BESS reduces the initial ROCOF to 0.44 Hz/s.

As global energy demands surge, the industrial sector, a key player, is undergoing a crucial transition towards sustainable practices while ensuring efficient production. The implementation of electricity peak shaving, a strategy that minimizes consumption during peak demand periods, emerges as a promising solution. This proactive approach delivers both environmental and economic benefits, such as reduced greenhouse gas emissions and lower energy costs. Recent advancements in the integration of solar photovoltaics, battery storage, and demand response programs have made peak shaving even more attractive. This integrated approach, has garnered significant attention due to its potential to optimize energy use without disrupting industrial operations, offers a path towards responsible industrial sustainability. However, widespread adoption of peak shaving in both industrial settings and across Malaysia is facing early adoption challenges. To bridge this gap, this paper presents a comprehensive systematic literature review, that examines the current state-of-the-art and strategies for peak shaving in the industrial sector. This review focuses on identifying key themes, including objectives, employed technologies, and implementation techniques. In addition, a case study on a Malaysian food manufacturing building is presented to demonstrate the versatility of hybrid solar PV and BESS systems. These systems can directly power operations, store solar energy in batteries, feed excess energy into the grid, and seamlessly switch to grid-supplied power when needed. The results of this case study showed a significant 6.9% reduction in energy costs and an 8.6% decrease in CO2 emissions. Leveraging insights from both literature and real-world projects, this study introduces the Industrial Peak Shaving framework to guide future research and implementation. The goal of this framework is to pave the way for broader and more socially and environmentally responsible industrial sustainability.

Owing to its high capital cost, Battery Energy Storage System (BESS) investment risk has received considerable attention in recent years. Currently, day-ahead frequency regulation service is one major revenue source for BESS, and the revenue is exposed to a compound of stochastic market risk and technical risk. On the market risk side, due to a lack of long-term contracts, investors are exposed to “price risk” and “volume (revenue hours) risk” over the entire investment horizon of 5–10 years. On the technical side, performance issues such as equipment degradation and inherent defects can result in reduced or even negative revenue for BESS under the pay-for-performance remuneration structure. Quantifying these risks is important for investors and banks to assess a project’s investability and bankability. However, existing BESS techno-economic literature has mostly focused on developing optimal control strategies to maximize revenue or optimal battery sizing to reduce capital expenditure. To our knowledge, none of the literature to date has addressed the long-term risk perspective of BESS investment. This study aims to fill this gap by developing a long-term probabilistic revenue estimate that considers these risk factors using Monte Carlo simulations. A case study using Taiwan’s newly launched day-ahead market, which has similar grid dynamics and revenue risk factors as in most markets, is also presented in this paper. Simulation result shows that, for the conservative P90 (90% exceedance probability) scenario, expected return of a hypothetical 10MW (half-hour battery) BESS investment is 8.65% and its debt service coverage ratio is 1.189.

This paper presents a framework for the optimal design of a solar and battery assisted electric vehicle (EV) charging station in southern California, with a focus on maximizing long-term profits while addressing operational uncertainties. The problem is conceptualized as an iterative two-stage decision process. In Stage I, the sampled designs of station infrastructure, including the number of chargers, the size of photovoltaic (PV) array, and capacity of the battery energy storage system (BESS), are specified. In Stage II, the EV charging rule is designed and simulated based on the charging request and solar power datasets. A model predictive controller and an empirical rule-based approach with incoming car forecasts are developed and compared for the vehicle charging management. The simulated annual operational profit and infrastructure investment with the consideration of long-term battery degradation is synthesized to build response surface for better design exploration in Stage I. Our results show that the proposed rule-based approach is computationally more efficient and suitable to integrate with response surface methodology (RSM) for design optimization. In addition, RSM is compared with adaptive particle swarm optimization (PSO) with multiple trials to demonstrate its superiority in high-profit design.

Advances in energy storage technology have allowed the application of load shifting in the utility grid for a more efficient power system operation. However, the economy of the Battery Energy Storage System (BESS) application is not linear to the cost reduction obtained from excluding the costly generators. This paper presents the economic analysis of cost-based load shifting implementation and an approach to determine the generation units to be deactivated and replaced by BESS on three large-scale power grids in Indonesia: Sumbagut, Sumbagteng, and Sumbagsel systems. The economic feasibility is evaluated using the Net Present Value (NPV), considering the savings and the BESS investment cost. The result shows that the proposed approach is more robust than the conventional methods, such as removing the most expensive units, units with the highest cost reduction, units with the best economic return contribution, or the maximum number of units that can be replaced.

[Introduction] The application scenarios of peak shaving and valley filling by energy storage connected to the distribution network are studied to clarify the influence of energy storage access on network losses and voltage quality on the distribution network side. [Method] The paper analyzed the change trend of network loss power with the energy storage injection current and access position during peak hours of energy storage discharge, and then analyzed the change trend of network loss power with the energy storage injection current and access position when the energy storage system was connected and charged during valley hours. For the influence of energy storage connected to the distribution network for peak shaving and valley filling on the voltage of the distribution network, the influence of different energy storage injection currents and access positions on the voltage distribution along the distribution network was studied from the perspective of energy storage discharge during peak hours and energy storage charging during valley hours. Finally, considering the total voltage deviation index of the distribution network, the influence on the total voltage deviation index after the distribution network which was connected to energy storage was studied. [Result] Through simulation calculations, the influence trend of energy storage participating in peak shaving and valley filling for the distribution network on network loss power and voltage loss is analyzed when different fixed or continuous values of energy storage current and access position are taken, and it is compared with theoretical analysis. [Conclusion] The study will provide useful references for the coordinated planning and optimized operation of various energy storage facilities in the distribution network, and it has good engineering reference value.

Microgrids (MGs) are gaining popularity due to their ability to provide reliable and resilient power supply, especially when integrated with renewable energy sources (RESs) and battery energy storage systems (BESS). Reliability is a critical factor for MG owners and policy makers. However, existing reliability indices such as loss of load expectation (LOLE) and expected energy not supplied (EENS) may not offer a comprehensive understanding of MG reliability and resiliency. To address this gap, this paper proposes three new indices for MGs to provide supplementary information on the performance of RESs: the Microgrid Resiliency Index (MRI), the Microgrid Renewable Energy Availability Index (MREAI), and the Microgrid Renewable Energy Energy Index (MREEI). The MRI assesses the MG’s ability to recover from interruptions, providing insights into its resiliency. The MREAI and MREEI offer additional information beyond LOLE and EENS, specifically highlighting the contribution of RESs to the availability and energy losses in the MG. These indices enable a more comprehensive assessment of MG reliability. The formulation for calculation of the indices are provided and applied to a MG with integrated solar photovoltaic (PV), wind turbine (WT), and BESS components. To evaluate the effectiveness of the proposed indices in providing supplementary information on resiliency and the contribution of RESs, various scenarios are examined. These scenarios include the impact of using BESS, changes in RES availability, and variations in RES output. The results demonstrate that the proposed indices effectively capture the contribution of RES to MG reliability and offer valuable insights for decision-making related to RES installation. Also, by integrating BESS, the contribution of RESs to outage hours and lost energy is effectively decreased from 89.41% and 87.41% to 32.69% and 28.94% respectively. Additionally, results highlight the substantial enhancement in the resiliency of the MG, which witnessed an impressive 68.43% improvement.

This paper proposes a robust control based on the integral backstepping control (IBC) for power quality enhancement of micro-grid-connected photovoltaic (PV) system with battery energy storage systems (BESS), The DC side consists of a PV system and battery storage. As for the AC side, it consists of three phases of a multi-functional two-level voltage source inverter (MVSI) coupled to the electrical grid via an inductive filter, all feeding a non-linear load. The MVSI is controlled by a nonlinear direct power control (DPC) strategy with space vector modulation (SVM) and the proposed IBC technique. Using the proposed IBC technique to control the voltage of the DC link voltage loop, the active/reactive power loops of the grid, the compensation of the harmonic currents of the network, and the injection of the power of the PV battery into the grid, loop of the maximum power point tracking (MPPT) regardless of the random nature of weather changes and guarantees optimal power management system battery storage. Using simulation under the MATLAB environment, the proposed control system was validated and compared to the conventional strategies (proportional–integral controller and backstepping controller) in terms of success, performance, robustness, and efficiency. In addition to its ability to respond under any irradiation distortion and non-linear load unbalance.

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.