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Abstract The integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Abstract Demand‐side flexibility or demand response (DR) has long been recognized as a potential source of balancing capacity. The charging of electric vehicles (EVs) is a known source of flexible capacity, and the vast amount of charging capacity available can be utilized for valuable applications, including ancillary power markets, by controlling the charging sessions according to the needs of the power system. One identified and unexplored potential market for EV DR capacity is the Fast Frequency Reserve (FFR) market for the Nordic system area. This article proposes and demonstrates that a home charger can participate in this extremely fast reacting ancillary service market with only software modifications made to the charger. The biggest challenge in the FFR market is the response time requirement for capacity activation. When a certain frequency limit is exceeded, the activation must happen within 0.7 s in the worst case. In this study, a laboratory test setup was constructed to test the capabilities of the FFR‐enabled EV charger. The tests were conducted by building a laboratory microgrid capable of changing the grid frequency. Additionally, an economic feasibility study was carried out to evaluate the business potential of the concept in Finland. The economic study included an analysis of the concept's potential with different levels of DR service availability. The laboratory tests demonstrated the FFR capabilities of the charger and as a final outcome of the research, the Norwegian transmission system operator (TSO) approved the tested charger as a type‐prequalified FFR‐providing entity. The economic study revealed that the approach has good business potential, primarily because of the almost non‐existent cost side even if the availability rate decreases significantly.

Abstract The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

Abstract Natural disasters, man‐made attacks, and cyber‐attacks are some of the main hazards of power system which can interrupt the process of continuous power delivery to the end users. In this way, man‐made attacks characteristics’ are fundamentally different from other hazards due to its adaptive strategy that allows attackers to target the most vulnerable parts of the power system. By choosing appropriate time and place; attackers always have advantages over the defender to penetrate the system and launch malicious actions. Therefore, it is quite necessary to take into account the optimal policy for allocating resources in the defender's strategy. This research proposes a four‐level Defence–Defence–Attack–Defence (DDAD) approach to improve the power system resilience against the intentional human attacks. A new defence level, a planning level, is added to the common three‐level Defence–Attack–Defence (DAD) model in power system planning state in order to prevent cut off loads and voltage drop beyond standard with lower cost. In the proposed method, load priority is used to classify the vulnerable sections. The proposed model is performed on IEEE‐30 bus test system and the results indicate the feasibility of the proposed method. As the results indicate, in proposed method less than 10% budget is needed compared with the previous method while all critical loads will be remained. Furthermore, the new method needs no security guard to protect transmission lines for 30 years.

Substantial benefits can be achieved from electricity trade through the interconnection of electricity networks of neighboring countries. In this study, we introduce a regional electricity exchange model to obtain an optimal power trade level between electrically-interconnected countries. In addition, producing clean electricity is one of the primary goals of many countries. Moreover, electricity production can be optimized in some areas using renewable sources and exported to regions with high demand. Thus, the power exchange using the proposed method and framework is investigated. Additionally, a decision model for planning an optimal configuration of hybrid wind/solar power systems and optimal electricity trade is presented. We analyze the economic, engineering, management, and policy issues to facilitate optimization of power generation and trade at the country level and quantify the gains from the increased trade. The proposed decision model is employed to plan electricity trade considering hybrid wind and solar energy resources. Furthermore, the effectiveness of reducing the variability of hourly power supply is evaluated by integrating the wind and solar power generation technologies. The main decision variables are the power trade value and number of installed wind turbines and solar systems. In case studies, the computational results using the proposed model yielded an optimal power trade between Iran and its neighboring countries, including Turkey, Turkmenistan, Afghanistan, Pakistan, Azerbaijan, Armenia, and Iraq. Consequently, Iran can export a maximum power capacity of 5,300 MW to its neighbors, where Iraq receives the highest share of 2,000 MW. When the electricity demand in Iran is high, Turkey, Turkmenistan, Azerbaijan, and Armenia can export 1,200, 1,000, 80, and 100 MW of electricity to Iran, respectively. Finally, various configurations of a hybrid wind/solar power generation system considering various cost and power supply variations are presented to partly supply the load requirements of the traded electricity. Consequently, Iran can supply 20% of the total demand of the electricity trade by wind and solar system installed capacities of 316.5 and 267 MW, respectively.

Since 1993, Devices based on CNTs have applicationsranging from nanoelectronics to optoelectronics. Thechallenging issue in designing these devices is that thenonequilibrium Green's function (NEGF) method has tobe employed to solve the Schrödinger and Poissonequations, which is complex and time consuming. In thepresent study, a novel smart and optimal algorithm ispresented for fast and accurate modeling of CNT fieldeffecttransistors (CNTFETs) based on an artificial neuralnetwork. A new and efficient way is presented forincrementally constructing radial basis function (RBF)networks with optimized neuron radii to obtain theestimator network. An incremental extreme learningmachine (I-ELM) algorithm is used to train the RBFnetwork. To ensure the optimal radii for incrementalneurons, this algorithm utilizes a modified version of anoptimization algorithm known as the Nelder-Meadsimplex algorithm. Results confirm that the proposedapproach reduces the network size for faster errorconvergence while preserving the estimation accuracy.

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA’s exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.

Wind energy as one of the most promising energy alternatives brings a set of serious challenges in the operation of power systems because of the uncertain nature of wind speed. To address this problem, it is essential to establish a framework to forecast a comprehensive form of information about the wind speed. To this end, an ensemble residual regression deep network is designed to understand fully time-variant and spatial features from the historical data including wind speed and corresponding meteorological data. Then, to enhance the accuracy, a modified error-based loss function is proposed. Consequently, to provide a comprehensive form of information, a modified kernel density estimator is proposed to extract a set of probability density functions (PDFs) with a high level of accuracy and reliability. The simulation results and a comparative analysis on an actual dataset in London, U.K. demonstrate the high capability of the proposed probabilistic wind speed approach.

This paper proposes a new framework for the optimal operation of a microgrid aggregator (MGA) that participates in an oligopoly electricity market. This aggregator obtains an optimal bidding (power and price) strategy for a multigrid (MG) system, i.e., a community MG. Consequently, the granted quantity, i.e., power in the electricity market, is deployed to optimally schedule the MG’s resources to meet demand. As such, as per three-level optimization, the independent system operator (ISO) clears the market with the goal of maximizing the social welfare in the first stage and determining the hourly market price as well as players’ credited power. In the second-level optimization process, the players select the optimal coefficient supply function equilibrium according to the power granted from the market. In third-level optimization, an optimal scheduling for MGs’ resources and demand would be obtained according to the won power in the market to maximize the aggregator profit. In addition, a price-taker MGA is simulated for comparison with the price–maker MGA to highlight the advantage of the proposed technique. Furthermore, a bidding strategy based on game theory is proposed to obtain the optimal price and power of the oligopoly market players and maximize all players’ profits. Finally, a test system including three generators is created to evaluate the performance of the devised bidding strategy. The results show that the proposed bidding strategy can optimally calculate the focal point of the Nash equilibrium (NE) in the oligopoly electricity market.

Recently, the number of Battery Energy Systems (BESs) connected to the grid has grown significantly. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with a linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability and decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in the proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with a day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

In today’s transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as imbalance between generation and demand-side (consumption) and thus increase the operation costs of power systems. On the other hand, distributed energy resources (DERs) in distribution networks (DNs) can be a relatively good alternative to the disconnected GUs with their ability to react quickly. Therefore, robust cooperation problem for integrated TN&DN is essential. However, it is hard to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators’ independence, and the information privacy. On the other hand, the robust cooperation of TN&DN based on ACOPF model is a Mixed-Integer Nonlinear Programming (MINLP), which is impractical to solve with existing solvers. Thus, in this paper, a linearized ACOPF model for robust cooperation of TN&DN problem is presented. Also, an efficient hierarchical decentralized solution method is adopted to solve robust cooperation of integrated TN&DN in which the information privacy is maintained for both network operators. In this paper, the numerical simulations are analyzed for standard an IEEE 30-bus transmission and an IEEE 33-bus distribution networks. Finally, the accuracy and computational efficiency of the proposed robust optimization problem and hierarchical decentralized method is evaluated through simulations.

The purpose of this research is to determine the optimum location to construct gas power plants (GPPs) in semi-arid regions. A combination of ordered weight averaging (OWA) and analytic hierarchy process (AHP) method named OWA-AHP is utilized to prepare land suitability maps with different risk scenarios when fuzzy method is to homogenize inputs. In the proposed method, AHP is to weigh each different parameter while OWA considers risk levels. In order to validate the accuracy of the proposed method, the receiver operating characteristics (ROC) curve is investigated. Besides, the self-organizing map (SOM) algorithm and Pearson correlation are used to determine the most important parameters for constructing GPP. According to obtained results, the areas located in the north and parts of the east and south of the selected case study (about 15%) at all risk levels are the optimum areas to be hosted for GPPs. Furthermore, ROC curve shows that the Area Under the Curve (AUC) values are high for both AHP and OWA-AHP methods (AUC Fuzzy-AHP = 94.0%, AUC OWA = 89.0%). The results of the SOM algorithm and Pearson correlation with high accuracy (RModel1: 0.853 and RModel2: 0.940) also depict that distance to the pipeline and road are the most important parameters to identify suitable locations for GPP.

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

This paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run.

Security-Constrained Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power generation. To cope with these concerns, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of load and wind uncertainty. In addition to being robust model to deal with the uncertainty of wind power production, energy storage devices has been considered for further handling of this issue. Compared to the conventional stochastic programming approach, the robust optimization model is more practical because only requires a deterministic uncertainty set , while in stochastic programming to obtain a probability distribution function of parameter uncertainty is very difficult. To resolve the SCUC problem using robust optimization that is very complex and difficult, Benders decomposition algorithm is used to make smaller problems, which makes reduced complexity of the problem, which results in to solve it easier.

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

In this paper a new bidding strategy become modeling to day-ahead markets. The proposed algorithm is related to the point of view of a generation company (Genco) that its end is maximized its benefit as a participant in sale markets of active power and spinning reserve. In this method, hourly forecasted energy price (FEP) and forecasted reserve price (FRP) is used as a reference to model the possible and probable price strategies of Gencos. A bi-level optimization problem That first level, is used to maximize the individual Genco’s payoffs for obtaining the optimal offered quantity of Gencos. The second one, uses the results of the upper sub-problem and minimizes the consumer’s payment with regard to the technical and network constraints, which leads to the awarded generation of the Gencos. In this paper use of the game theory in exist optimization model. The paper proposes a linear programming approach. A six bus system is employed to illustrate the application of the proposed method and to show its high precision and capabilities.