Hybrid energy storage systems (HESS), combining batteries and supercapacitors, offer an effective solution for simultaneously addressing energy and power demands in modern energy systems. However, the performance and lifetime of these systems are strongly dependent on the employed energy management strategy, which must balance fast power regulation, safety enforcement, and long-term battery health. Existing reinforcement learning-based approaches often adopt flat control architectures that entangle short-term decision-making with long-term aging effects, leading to training instability, limited interpretability, and non‑stationary reward structures. This paper proposes a hierarchical multi‑timescale reinforcement learning framework for energy management of battery-supercapacitor hybrid systems with explicit aging‑aware constraint adaptation. The proposed architecture decomposes the control problem into three coordinated layers: a fast safety‑critical layer that enforces hard operational constraints, an intermediate reinforcement learning layer based on Proximal Policy Optimization (PPO) for real‑time power allocation, and a slow supervisory layer that estimates battery state‑of‑health (SOH) using a Gaussian Process Regression (GPR) aging model. Instead of directly embedding aging costs into the learning objective, battery degradation is incorporated through SOH‑dependent adaptive current limits, ensuring quasi‑stationary learning dynamics and safe long‑term operation. Comprehensive simulation studies are conducted under nominal, progressive aging, and safety‑critical operating conditions. Under nominal SOH, the proposed framework maintains battery and supercapacitor SOC deviations within ± 3% of their reference values while keeping battery current below the nominal 125 A limit. During aging progression, the method achieves a lower time‑averaged squared battery current and reduced cumulative energy throughput compared to reward‑based SAC and flat RL baselines, resulting in a measurably slower SOH decline for equivalent stress levels. Moreover, under aggressive pulsed loads at reduced SOH, the proposed approach yields the lowest constraint activation frequency, demonstrating superior intrinsic safety and robustness. Overall, the results confirm that hierarchical time‑scale separation enables stable, aging‑aware, and physically consistent energy management without compromising learning performance.

This paper presents the functional testing framework and capability requirements for grid-forming energy storage resources in the Southwest Power Pool. Grid-forming controls enhance system stability by maintaining voltage and frequency under transient and weak-grid conditions, outperforming conventional grid-following controls. Southwest Power Pool is implementing a phased approach for the adoption of grid-forming energy storage resource requirements. Southwest Power Pool’s Phase 1 adoption focuses on system strength capability, detailing model requirements, simulation procedures, and data exchange. Functional tests— including loss of the last synchronous machine, rate of change of frequency, phase jump, and short circuit ratio ramp-down with fault—are described with success criteria, alongside targeted modifications and exemptions to IEEE Std 2800-2022.

This paper presents a novel fault detection method for VSC-HVDC networks, which integrates fuzzy logic and Support Vector Regression (SVR). Fuzzy logic generates a composite signal from voltage, current, power, and impedance features, enhancing the line representation. A 200-2500 Hz bandpass filter is utilized to extract the main fault components, and their root mean square (RMS) values are calculated. The processed signal then trains an SVR model that estimates the received signal. A fault is declared when the difference between the estimated and original signals exceeds a predefined threshold. The method achieves fast and accurate fault detection with reduced computational complexity and utilizes hybrid features. Unlike wavelet or traveling wave-based methods, it operates at a sampling rate of 4 kHz. The method, tested on a VSC-HVDC network under various line distances, impedances, noise, and communication link disturbances, demonstrates robust performance. Comparisons with similar studies and other machine learning techniques confirm its superiority in accuracy and efficiency.

This paper introduces a decentralized, risk-averse operational framework for interconnected, unbalanced microgrids and the distribution system. Distribution system operation is modeled as a risk-averse optimization problem using conditional value-at-risk measures to enhance robustness against uncertainties in price-responsive demand bids. Microgrid operations are structured as scenario-based robust optimization problems, capturing the worst-case scenarios for demand and solar PV generation. Non-convex energy management problems within the distribution system and microgrids are reformulated as convex optimization problems using the moment relaxation technique. A decentralized scheme addresses heterogeneous uncertainties in microgrids and the distribution system. The framework is applied to two case studies: a distribution system connected to two microgrids and a modified IEEE 123-bus distribution system connected to four microgrids. The risk-averse optimization solution is compared to that of a stochastic programming approach. For the IEEE 123-bus system, the results show that introducing risk aversion decreases social welfare by 44.63% compared to the risk-neutral solution but improves the conditional value-at-risk by 44.04%. Additionally, the impacts of dispatchable resources, including energy storage and distributed generation, on operation cost, phase balancing, and uncertainty control at the main feeder are examined. It is shown that incorporating a statistical distance metric to regulate power flow at the main feeder decreases social welfare by 6.48%.

Researchers are increasingly interested in Non-intrusive Load Monitoring (NILM) as a cost-effective alternative to Intrusive Load Monitoring (ILM) for smart home energy monitoring. However, most NILM studies assume a fixed number of appliances and adjust classification models accordingly. This approach disregards the reality of varying appliance numbers in households, which can significantly impact model accuracy. To address this limitation, in this study, a comprehensive comparison between the proposed model and existing methods is conducted. The results show that the proposed model outperforms the existing methods on the UK-DALE and EMBED databases with an accuracy increase of 9.492% and 8.386%, respectively. Also, the accuracy of the proposed model has reached 99.74% compared to conventional methods, which shows its significant superiority over the lower accuracies of other models, including 95.11%, 92.09%, 88.70%, and 75.38%. This innovation combines a two-stage feature extraction method based on Soft Sifting Stopping Criterion Empirical Mode Decomposition (SSSC-EMD) and an adaptive deep learning model with architecture tuning capabilities, in order to improve the performance and accuracy of home appliance detection under different conditions.

In the present study, a hybrid method based on the deep learning model BiGRU-BiLSTM along with the VMD-EMD feature extraction method is presented for use in non-intrusive load monitoring applications. By utilizing the strengths of the deep learning model and the features of the combined signal processing model, the proposed method has been able to very effectively estimate the complex patterns of household appliance signals with high accuracy. To analyse how well the proposed technique performs, the open-access UK-DALE dataset has been used. According to the simulation results, the developed method shows a high capability in estimating the power signal generated by household appliances. This paper, by presenting an approach based on deep learning and combined feature analysis, is considered a significant advancement in the field of non-intrusive load monitoring.

This work presents a distributed robust operation framework for the microgrids within the power distribution network. It addresses the uncertainties in photovoltaic generation, as well as active, and reactive loads in the network, by modeling them using polyhedral sets. The energy management problems are formulated as two-stage optimization problems in microgrids and power distribution networks for which Benders decomposition is used to solve the problems. The power flow constraints are relaxed using second-order cone relaxation and moment relaxation techniques in the power distribution network and microgrid energy management problems, respectively. The interaction between the microgrids and the power distribution network is represented using the price signal and the exchanged active and reactive power at the microgrids’ points of common coupling. The effectiveness of the proposed framework is shown using two case studies on IEEE 37-bus and IEEE 123-bus systems. The effect of the energy storage system on the operational cost of the system is assessed. It is shown that total operation cost is decreased by 9.69% when the energy storage system is integrated into the power distribution network. Additionally, the solution derived from the robust problem is compared with that of the deterministic problem.

This paper handles the problem of optimal charging control for electric vehicle (EV) aggregators in energy and ancillary service markets under uncertainties. We propose a robust optimal coordinated charging (OCC) model that formulates a robust linear programming (RLP) for the EV aggregator formed on unidirectional vehicle-to-grid (V2G) and coordinates the provision of regulation and spinning reserves. Our model considers many uncertainties in the electricity markets, like ancillary service prices and the placement signals, as well as accurate parameters associated with EV behavior, like EV availability, time of trips, and trip duration. The objective of the optimization is to maximize the aggregator's income from V2G by contributing to the ancillary services markets. The proposed robust OCC model, a robust linear problem (RLP) model, is simulated using the CPLEX solver in GAMS software. We compare our model with the existing deterministic models and show that our model increases the aggregator's revenues and then reduces the deviation between predicted and realized revenues. We propose a new and effective method for EV aggregators to bid into energy and ancillary service markets under uncertainties. Our model shifts the charging time of EVs from peak to off-peak hours, resulting in an improved final state of charge.

The energy crisis poses a significant challenge to modern society, exacerbated by the increasing deployment of renewable energy sources (RES) like wind turbines (WT), photovoltaics (PV), and combined cooling, heat, and power (CCHP) systems. Also, the optimal operation of integrated energy systems (IES) in the presence of energy storage systems (ESS) is imperative. This study proposes optimized energy dispatching for IES incorporating CCHP and RES, i.e., WT and PV, alongside ESS, including electrical energy storage (EES), thermal energy storage (TES), and plug-in hybrid electric vehicles (PHEVs). As natural gas is the primary fuel for CHP units, proper modeling of the natural gas network is necessary, considering its constraints for optimal gas flow. This paper uses the gas shift factor (GSF) matrix in a linear equation to obtain efficient gas flow in pipelines. This linear equation utilizing the GSF matrix facilitates efficient gas flow computation, reducing computational complexity. The study illustrates the economic benefits of modeling the natural gas network in cost reduction. Additionally, it investigates the impact of ESS on energy management within the IES, demonstrating how EES and TES enhance flexibility in meeting electric, heating, and cooling demands, particularly during peak hours, thus reducing operational costs. PHEVs further contribute to electric load supply when not used for daily trips. The objective function minimizes the total operating costs of the proposed IES, which is formulated as a mixed-integer linear programming (MILP) model implemented in the general algebraic modeling system (GAMS) and solved using the CPLEX solver. Simulation results substantiate the effectiveness of the proposed IES in achieving optimal system performance, highlighting its potential in addressing contemporary energy challenges. The simulation results indicate that by modeling the natural gas network and incorporating TES, the total operating cost decreases from $384,098 to $378,430.

Solar-based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)-based coordinated control for photovoltaic (PV)-based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real-time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro-based fuzzy control approach includes forecasting solar-based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real-time hardware-in-loop experimental setup.

Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through due to fault current limiting and voltage restoring ability during the fault, respectively. This paper presents a method for optimal allocation and control of superconducting magnetic energy storage and superconducting fault current limiters in meshed microgrids. For this purpose, the doubly-fed induction generator voltage deviation, the point of common coupling power deviation, the fault current of transmission lines, and superconducting fault current limiter and superconducting magnetic energy storage characteristics were considered as objective functions. In this paper, the optimization is performed in single-step and two-step by particle swarm optimization algorithm, and the system with the optimal superconducting magnetic energy storage and superconducting fault current limiters are analyzed and compared. The results of simulations show superconducting fault current limiter and superconducting magnetic energy storage reduce 85% of voltage drop, decreases 63% of doubly fed induction generator power deviation, and limits the maximum fault current of transmission lines by 9.8 pu. Finally, the status of the studied system variables has been investigated, in two scenarios related to the different fault locations with equipment that the optimal allocated.

The issue of resilience in electrical distribution systems has been proposed increasingly with the frequent occurrence of natural disasters in recent years and the imposition of high costs due to widespread power outages. To date, various resilience indices have been proposed, some of which have been improved upon over time by extensive research, leading to more comprehensive indices. However, a standard index has not yet been approved and presented in this regard by international committees, despite the efforts made. The issue of resilience in electrical networks was examined by curve analysis index in more detail compared to other proposed indices, although it is not yet complete and should be investigated from various aspects and its shortcomings be eliminated. The present study aims to evaluate some fundamental obstacles in the "curve analysis" index and correct it in a new index called "Combining Investment and Reform" (CIR) index. Two issues of "costs in terms of investment and repairs imposed by the event" and "need to separate critical and non-critical loads" are considered simultaneously in the proposed index. Finally, the capabilities of the proposed index are evaluated and compared in a sample electrical network in the face of events with different intensities.

Electrical energy supply is a vital part of a modern network, so enhancing its resilience to natural hazards is of paramount importance. The present study aims to determine the number and capacity of truck-mounted mobile generators that can be utilized in electric power distribution networks to provide a certain level of resilience, taking into account the costs and revenues associated with their use. To this aim, a "consolidation index" is used, which is based on the "curve analysis index" and considers "cost (either of the type of investment or of the type of repairs imposed by the event)" and "lack of load supply (with critical and non-critical load separation)" simultaneously. Presenting a "decision curve" allows the planner or policymaker to perform the best by applying the amount of capital or budget at his/her disposal to improve a certain percentage of the resilience in the evaluated network. Finally, the effectiveness and efficiency results of the proposed method are shown in the modified IEEE 33-bus network.

Abstract One of the most significant problems of the modern electricity markets is to deal with renewable energy resources (RERs) scheduling. The RER generations face severe stochastic behaviour, such that short‐term scheduling of them is also complicated. To overcome this drawback, using hydro pumped storage units (HPSUs) as a fast response and eco‐friendly technology can help to smooth fluctuations of these types of generations and consequently to appropriately dispatch all generations in the energy and reserve market. This article suggests a stochastic optimisation model to optimally operate thermal power plants as well as HPSUs in the day ahead energy and reserve market. Optimisation aims to minimise operation costs, emissions, and social costs subject to several technical constraints. There is an intrinsic deviation between predicted and actual uncertainty variables in the power system. This article presents a stochastic optimal operation model based on robust optimisation. To improve the flexibility of the proposed market, the curtailed demand as a demand response programme (DRP) is taken into consideration. The CPLEX solver of the GAMS software is used to solve the proposed model which has been formulated as a robust mixed integer linear problem (RMILP). The effectiveness of the proposed model is evaluated by applying the offered model to the 9‐bus test power system.

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

We are witnessing the growth of microgrid technology and the development of electric vehicles (EVs) in the world. These microgrids seek demand response (DR) and energy storage for better management of their resources. In this research, microgrids, including wind turbines, photovoltaics, battery charging/discharging, and compressed air energy storage (CAES), are considered. We will consider two scenarios under uncertainty: (a) planning a microgrid and DR without considering CAES, and (b) planning a microgrid and DR considering CAES. The cost of charging the battery in the second study decreased by $0.66 compared to the first study. The battery is charged with a difference of $0.7 compared to the case of the first study. We will also pay for unsupplied energy and excess energy in this microgrid. Then, we test the scheduling of vehicles to the grid (V2G) in the IEEE 33-bus network. The first framework for increasing network flexibility is the use of EVs as active loads. The scheduling of vehicles in the IEEE 33-bus network is simulated. Every hour, plug-in hybrid electric vehicle (PHEV) charging and discharging, active power loss, and cost will be compared with IHS and PSO algorithms. The difference obtained using the IHS algorithm compared to the PSO algorithm is 1.002 MW and the voltage difference is 9.14 pu.

Buck DC–DC converters are broadly used in DC microgrids to provide a constant dc voltage for generation and storage components. Changing of load condition affects the quality of voltage in the buck DC–DC converters. When constant power loads (CPLs) are used, the stability of these power electronic devices is at risk due to negative impedance characteristics of the CPLs. In such condition, an efficient control method is required to ensure the proper operation of the converter. For this purpose, development of an adaptive control methodology is essential to evaluate the accurate values of controller parameters in the shortest time to damp the ripples quickly. This paper develops a backstepping controller with nonlinear disturbance observer to regulate the output voltage of a dc/dc converter feeding a CPL. An artificial neural network (ANN) methodology is used to estimate the backstepping control parameters of the buck converter. The training ability of the ANN technique prevents the existing controller from depending on the working point of the microgrid. The ANN methodology adapts the controller with various changes and reflections of uncertainties in the microgrid. Case studies are conducted on a dc/dc buck converter in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.

Abstract Fossil‐fuelled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using massive unexploited capacity of the battery storage in too many EVs makes these challenges to opportunities. This unused capacity can be employed for the grid ancillary services and trading peer‐to‐peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EVs bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid based on blockchain mechanism. Considering the preferences of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimisation problem aiming at optimal management of EV battery state of charge and energy placement of several services considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real‐world data collected from Tehran city are used as input data of simulation. Numerical results demonstrate the efficacy of the presented model. Simulation results show that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy‐planning model such that it could balance the charging cost. Moreover, this advanced user‐based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid.

Electrical load forecasting is crucial to achieving better efficiency, reliability, and power quality in modern power systems. Applying short-term load forecasting, a balance can be preserved between supply and demand; the cost of electricity production will also be decreased. Several methods are proposed for short-term load forecasting in smart grids in recent years and each of them has its own advantages and weaknesses. Among these methods, popularity is increasing in machine learning techniques. This study is to review three common artificial intelligence load forecasting methods, including long short-term memory, group method of data handling, and adaptive neuro-fuzzy inference system that have been used to forecast load in a smart grid consisting of a photovoltaic, wind turbine, battery energy storage system, and electric vehicle charging stations. The performance of these methods is evaluated given accuracy and the system's hardware requirements. The noisy condition of the system is also investigated when these methods are used for load forecasting. The results show that the long short-term memory model is more accurate than the group method of data handling and adaptive neuro-fuzzy inference system models. However, this method requires much better hardware requirements.

Grid integration of renewables and battery energy storage systems and its consequent synchronous machines retirement may drive power systems into low-inertia conditions with high risk of frequency instability which is associated with lack of sufficient inertia and primary frequency response previously supplied by synchronous machines. Therefore, there is a need to figure out alternative solutions to compensate the lack of system frequency dynamic supports in future renewable-dominated power systems. In this context, this paper aims to highlight wind farm capabilities, particularly doubly-fed induction generator (DFIG) and permanent magnet synchronous generator (PMSG), in system primary frequency control, similar to conventional synchronous units, while also investigates the synergy between the wind unit cooperation with large-scale battery storages. First, a novel dynamic model is presented which takes into account large-scale wind farms, as well as utility-scale batteries, equipped with frequency-sensitive active power reference scheme so as to control the system frequency violations following contingencies. The proposed frequency control model also includes powerelectronics interface model, its associated control loops, and a novel Fuzzy-logic based controller. The proposed Fuzzy-logic based controller along with a wash-out filter allows combined wind-battery system to estimate the system active power mismatch, emerged from a contingency/trip, to determine the consequent frequency variations, and therefore to deliver fast frequency response in a robust and reliable way to arrest the mentioned frequency distortions. The proposed Fuzzy-based controller is then designed via optimizing its model parameters, including the membership function parameters, thereby improving its efficiency in frequency control provision as well as active power control. The optimization process is performed via a widely used artificial bee colony (ABC) algorithm while a multi-objective function is considered. The proposed frequency control model is then evaluated in the 14-generation low-inertia Australian test system with 30% wind penetration. This work tries to provide a deep insight on how to utilize wind farms for frequency support and how to wind-battery frequency response may positively interact with nearby converter-based resources, i.e., photovoltaic units. The proposed Fuzzy-based coordination approach for large-scale wind-battery units can be a potential solution to deal with frequency stability problems in future power systems with low-inertia conditions including multiple nearby converter-based resources.