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Öğe A Cyber-Secure generalized supermodel for wind power forecasting based on deep federated learning and image processing(Elsevier Ltd, 2022) Moayyed, Hamed; Moradzadeh, Arash; Mohammadi-Ivatloo, Behnam; Aguiar, A. Pedro; Ghorbani, RezaAccurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran.Öğe Deep learning-based cyber resilient dynamic line rating forecasting(Elsevier, 2022) Moradzadeh, Arash; Mohammadpourfard, Mostafa; Genc, Istemihan; Şeker, Şahin Serhat; Mohammadi-Ivatloo, BehnamIncreased integration of renewable energy resources into the grid may create new difficulties for ensuring a sustainable power grid which drives electric utilities to use a number of cost-effective techniques such as Dynamic line rating (DLR) that enable them to run power networks more efficiently and reliably. DLR forecasting is a technique devised to accurately forecast the maximum current carrying capacity of overhead transmission lines. DLR offers many advantages, including increased renewable energy penetration without system reinforcement, improved grid dependability, and lower congestion costs. So far, many solutions have been proposed for DLR forecasting, which, despite estimating the exact capacity of the DLR, have some problems, such as installing multiple sensors and measurement devices and communication networks with precise calibration, and also neglect cyberattacks which may lead to operators making inappropriate operational choices. To address these issues, in this paper, a novel hybrid deep learning-based DLR forecasting approach called the autoencoder bidirectional long short-term memory (AE-BiLSTM) is efficiently and precisely developed. Several scenarios were developed to test the robustness and accuracy of the proposed methodology using real-world data with and without cyber-attacks. Detection of cyber-attack is done based on the increase in the least square errors of forecasting models. Then, the carefully designed hybrid AE-BiLSTM method reconstructs the falsified measurement data and provides reliable DLR forecasting. Also, a comparative study is carried out. The numerical results demonstrated that the proposed hybrid approach can significantly provide acceptable performance even under cyber-attacks and forecast DLR values with the least possible error, outperforming the existing conventional and deep learning-based techniques.Öğe Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles(Elsevier Ltd, 2022) Seyfi, Mohammad; Mehdinejad, Mehdi; Mohammadi-Ivatloo, Behnam; Shayanfar, HeidaraliThe virtual energy hub (VEH), a combination of virtual power plant and energy hub concepts, faces many uncertainties due to its constituent distributed energy resources. This paper presents the deep learning-based scheduling of VEH for participation in electrical and thermal markets using bidirectional long short-term memory (BLSTM) network, which offers excellent accuracy in forecasting uncertain parameters by concurrent using past and future dependencies. In addition to applying learning methods, energy storage systems can also influence the optimal management of uncertainties. To provide the required electrical storage equipment, the VEH employs plug-in hybrid CNG-electric vehicles (PHGEVs) that can use both electrical energy and compressed natural gas (CNG) to fulfill their energy needs. The alternative fuel can tackle the limitations of prolonged charging of electric vehicles and excess load caused by these vehicles at peak hours. To supply the secondary fuel of PHGEVs, the modeled VEH includes a CNG station, which compresses the natural gas imported from the natural gas grid before delivering it to the vehicles. Furthermore, phase change material-based thermal energy storage (PCMTES) is considered in the VEH configuration, which unlike other common thermal energy storage systems, operates at a constant temperature during the charging and discharging period. Lastly, the simulation of the developed system illustrates that PHGEVs can reduce the imposed cost in unforeseen situations by up to 26 percent and increase the system's flexibility.Öğe Designing a robust decentralized energy transactions framework for active prosumers in peer-to-peer local electricity markets(Institute of Electrical and Electronics Engineers Inc., 2022) Mehdinejad, Mehdi; Shayanfar, Heidar Ali; Mohammadi-Ivatloo, Behnam; Nafisi, HamedIn this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution's existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players' private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method.Öğe Electric load forecasting under False Data Injection Attacks using deep learning(Elsevier Ltd, 2022) Moradzadeh, Arash; Mohammadpourfard, Mostafa; Konstantinou, Charalambos; Genc, Istemihan; Kim, Taesic; Mohammadi-Ivatloo, BehnamPrecise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generators scheduling schemes. The accuracy of the existing electric load forecasting methods relies on data quality due to noisy real-world environments, and data integrity due to malicious cyber-attacks. This paper proposes a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week. The proposed deep learning framework systematically integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models (AE-CLSTM). The feasibility of the proposed solution is validated by using realistic grid data acquired from the distribution network of Tabriz, Iran. Compared to other load forecasting methods, the proposed method shows the highest accuracy in both a normal case with real-world noise and a stealthy False Data Injection Attack (FDIA). The proposed load forecasting method is practical and suitable for mitigating noise in real-world data and integrity attacks.Öğe Exploring potential gains of mobile sector-coupling energy systems in heavily constrained networks(Institute of Electrical and Electronics Engineers Inc., 2022) Habibi, Mahdi; Vahidinasab, Vahid; Mohammadi-Ivatloo, Behnam; Aghaei, Jamshid; Taylor, PhilThe coincidence of high levels of variable, non-dispatchable generation from renewable energy sources (RESs) and congested electricity networks imposes significant constraint payments (CP) on electricity system operators (ESOs) which ultimately is charged to the customers. This paper is inspired by this challenge and proposes an integrated electricity, gas, and transportation energy system taking advantage of power-to-gas (P2G) facilities and electricity/gas storage devices to enhance operational efficiency. It proposes mobile gas storage systems (MGSs) that can store and carry liquid hydrogen or liquefied natural gas (LNG) to the load points or remote locations without access to the gas network. So, the green energy of RESs in the form of gases can be injected, transported, and reutilized in the natural gas network or stored in MGS facilities. Besides, the mobile electricity storage system (MES) can directly store the redundant electricity produced by RESs, and the railway transportation system carries both the MESs and MGSs to the load point of electrical and gas systems. The proposed model reflects CP to wind in the marketing phase and considers incentives for the hydrogen-burning generators. Also, a stochastic platform is employed to capture the inherent uncertainties in the predicted values of the load and RESs' generation. The model is formulated as a mixed-integer second-order cone programming problem and tested on an IEEE 118-bus system integrated with a 14-node gas network and a railway system. The result shows that employing the multi-vector energy system (MVES) elements reduces the total operational cost by 47%, and the CP to wind is reduced by 99.8% by absorbing almost the whole green energy of wind farms while relieving congestion in the electrical grid.Öğe A Fair Risk-Averse Stochastic Transactive Energy Model for 100% Renewable Multi-Microgrids in the Modern Power and Gas Incorporated Network(Institute of Electrical and Electronics Engineers, 2023) Daneshvar, Mohammadreza; Mohammadi-Ivatloo, Behnam; Zare, KazemThe ever-growing multi-vector systems along with the penetration of renewable energy sources (RESs) entail significant changes for shifting from centralized, isolated, and passive structures to potentially distributed, hybrid, and autonomous modern multi-vector energy grids (MVEGs). This paper proposes a transactive energy (TE) solution for the techno-environmental-economic operation of multi-carrier multi-microgrids (MCMs) with 100% RESs by co-optimizing power and gas grids. Indeed, TE is advanced for making a fair economic model by developing the free multi-energy sharing area (MESA) for MCMs to allow them to exchange energy with the aim of pursuing their technical, environmental, and economic goals. As 100% RESs bring severe uncertainties in the energy production sector, appropriately modeling such stochastic variations is a necessary step for obtaining realistic results in exploring the overall system. Thereby, the stochastic conditional value at risk (CVaR) technique is developed to model the risk of MCMs presence in energy interactions, in which scenario generation and reduction are performed by applying the seasonal autoregressive integrated moving average and fast forward selection methods. The problem is cast into a tractable mixed-integer linear programming by properly linearizing AC power flow and nonlinear gas equations that allows the system to extract confident results. The coupled structure of the modified IEEE 33-bus and 14-node gas systems is used as the test system for verifying the effectiveness of the proposed model. The results show the applicability of the proposed model in reliably integrating 100% RESs as well as procuring a fair condition for MCMs in the hybrid structure of the energy grid.Öğe Multi Microgrid Framework for Resilience Enhancement Considering Mobile Energy Storage Systems and Parking Lots(MDPI, 2023) Abdulrazzaq Oraibi, Waleed; Mohammadi-Ivatloo, Behnam; Hosseini, Seyed Hossein; Abapour, MehdiThis paper proposes a practical and effective planning approach that takes advantage of the mobility and flexibility of mobile energy storage systems (MESSs) to increase distribution system resilience against complete area blackouts. MESSs will be very useful for boosting the system’s resilience in places affected by disasters when the transmission lines are damaged. A joint post disaster restoration strategy for MESSs and PEV-PLs is proposed, along with distributed generation and network reconfigurations, to reduce total system costs, which include customer interruption costs, generation costs, and MESS operation and transportation costs. The integrated strategy accounts for the uncertainty of the production of wind- and solar-powered microgrids (MGs) and different forms of load demand. Therefore, this paper assesses the effect of MESSs on distribution system (DS) resilience in respect of MG cost reduction and flexibility. Due to the multiple networks, PEV-PLs, DGs, and MESS limitations, the suggested restoration problem is stated as a mixed-integer linear programming problem. The suggested framework is complemented using a benchmark testing system (i.e., 33-bus DS). To assess the effectiveness of the proposed model, the model’s output is contrasted with results from typical planning and a traditional model. The comparison of the data shows that the suggested model, in addition to MESSs, effectively achieves a large decrease in cost and enhances the DS resilience level.Öğe Network hardening and optimal placement of microgrids to improve transmission system resilience: A two-stage linear program(Elsevier, 2022) Jalilpoor, Kamran; Oshnoei, Arman; Mohammadi-Ivatloo, Behnam; Anvari-Moghaddam, AmjadThis paper aims to develop a linear two-stage optimization problem based on an attacker-defender resilient planning (AD-RP) model to improve the power system’s operational and infrastructural resilience in the face of low-probability high-impact events. In the developed model, attackers are natural phenomena that can cause the most severe damage to system performance, and defenders are actions that minimize system vulnerabilities. In the first stage, a stochastic model depending on Monte-Carlo simulation is developed to present a new index for selecting the most vulnerable transmission system components. This index is designed based on combining the worst possible case of attack, disaster statistical analysis, system structure and fragility curves. In the second stage, as defense operations, the hardening of vulnerable lines and microgrids placement in the proper places are carried out considering investment budget constraints. Minimizing load shedding and ensuring the resilience of the transmission network are the main objectives behind the second stage. In this regard, a comprehensive metric for the evaluation of the transmission system resilience is introduced. Thanks to a mixed-integer programming problem, the effectiveness of the proposed AD-RP model in increasing system resilience is demonstrated in the IEEE 30-bus and 118-bus test systems.Öğe A New False Data Injection Attack Detection Model for Cyberattack Resilient Energy Forecasting(Institute of Electrical and Electronics Engineers, 2023) Ahmadi, Amirhossein; Nabipour, Mojtaba; Taheri, Saman; Mohammadi-Ivatloo, Behnam; Vahidinasab, VahidAs power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, and z-score metric providing accurate detections with low computational cost and high scalability without utilizing either system's models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input-output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.Öğe A novel transactive energy trading model for modernizing energy hubs in the coupled heat and electricity network(Elsevier Ltd, 2022) Daneshvar, Mohammadreza; Mohammadi-Ivatloo, Behnam; Zare, KazemA high or full contribution of renewable energy resources (RERs) in future modern grids is inevitable due to a great need for developing an environmentally friendly society. In modernizing renewable-based energy hubs, optimal energy management has been rapidly challenged due to intermittences of RERs in multi-vector energy networks (MVENs) over recent years. This paper proposes an innovative peer-to-peer (P2P) energy trading model for energy management of multi-vector energy hubs to consider the unpredictability challenge of RERs. For this purpose, the transactive energy paradigm is used to advance a new sustainable energy sharing environment for creating time-to-time energy balance by enabling energy hubs to exchange energy with each other freely. The uncertainty quantification was conducted by applying an autoregressive integrated moving average for generating multiple scenarios and the fast forward selection method for reducing them to the plausible number in the stochastic programming process. The coupled IEEE 10-bus and 10-node district heating network was designated as the test system for analyzing the optimal energy management of the distributed hub energies. The results designated the effectiveness of the proposed transactive energy-based P2P energy trading model in providing momentous financial and technical benefits for community energy hubs in MVENs.Öğe Optimal scheduling of a self-healing building using hybrid stochastic-robust optimization approach(Institute of Electrical and Electronics Engineers Inc., 2022) Akbari-Dibavar, Alireza; Mohammadi-Ivatloo, Behnam; Zare, Kazem; Anvari-Moghaddam, AmjadThis article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a twostage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $ 25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices.Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case.Öğe Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management(Elsevier, 2022) Aghdam, Farid Hamzeh; Mudiyanselage, Manthila Wijesooriya; Mohammadi-Ivatloo, Behnam; Marzband, MousaThe virtual energy storage system (VESS) is one of the emerging novel concepts among current energy storage systems (ESSs) due to the high effectiveness and reliability. In fact, VESS could store surplus energy and inject the energy during the shortages, at high power with larger capacities, compared to the conventional ESSs in smart grids. This study investigates the optimal operation of a multi-carrier VESS, including batteries, thermal energy storage (TES) systems, power to hydrogen (P2H) and hydrogen to power (H2P) technologies in hydrogen storage systems (HSS), and electric vehicles (EVs) in dynamic ESS. Further, demand response program (DRP) for electrical and thermal loads has been considered as a tool of VESS due to the similar behavior of physical ESS. In the market, three participants have considered such as electrical, thermal and hydrogen markets. In addition, the price uncertainties were calculated by means of scenarios as in stochastic programming, while the optimization process and the operational constraints were considered to calculate the operational costs in different ESSs. However, congestion in the power systems is often occurred due to the extreme load increments. Hence, this study proposes a bi-level formulation system, where independent system operators (ISO) manage the congestion in the upper level, while VESS operators deal with the financial goals in the lower level. Moreover, four case studies have considered to observe the effectiveness of each storage system and the simulation was modeled in the IEEE 33-bus system with CPLEX in GAMS.Öğe Photovoltaic array reconfiguration under partial shading conditions for maximum power extraction: A state-of-the-art review and new solution method(Elsevier Ltd, 2022) Rezazadeh, Sevda; Moradzadeh, Arash; Pourhossein, Kazem; Akrami, Mohammadreza; Mohammadi-Ivatloo, Behnam; Anvari-Moghaddam, AmjadNon-uniform irradiance due to partial shading conditions (PSCs) reduces the power delivered by the photovoltaic (PV) cell. The output power reduction in the PV arrays directly depends on the shading pattern and type of array configuration which is selected. So far, many dynamic and static reconfiguration methods have been used for maximum power point tracking under PSCs in the PV arrays. However, most conventional methods suffer from some major problems such as the need for additional equipment and sensors, complex wiring, the use of expensive sensors, production of complex switching matrices, high costs, and inability to reconfigure PV arrays with very small, large, and non-square sizes. Accordingly, this paper, after reviewing the dynamic and static PV array reconfiguration methods, presents a novel static-based technique called 8-Queen's for reconfiguring the PV modules corresponding to the Total-Cross-Tied (TCT) inter-connection PV array. The 8-Queen's technique has a great ability to apply on high dimensions and rectangular shapes PV arrays and is based on the movement of 8 queens on the chessboard so that none of the queens can attack the others. The effectiveness of the suggested method is expressed by implementing it on 7 cases of the TCT PV array in different sizes and various PSCs. In a comparative scenario, the performance and effectiveness of the proposed 8-Queen's technique are evaluated compared to other conventional methods. Indicators of global maximum power point (GMPP), fill factor, power efficiency, and mismatch losses evaluate the results of the employed methods. The evaluation of results represents the effectiveness of the 8-Queen's technique compared to other used methods. In addition, the performance evaluation of the proposed technique in real-world PV arrays is performed by modeling a sample PV array taking into account measurement errors. The results in this step also show that the proposed technique can also provide acceptable performance for solving problems related to maximum power point tracking under PSCs in PV systems.Öğe Protecting Power Transmission Systems against Intelligent Physical Attacks: A Critical Systematic Review(MDPI, 2022) Sadeghian, Omid; Mohammadi-Ivatloo, Behnam; Mohammadi, Fazel; Abdul-Malek, ZulkurnainPower systems are exposed to various physical threats due to extreme events, technical failures, human errors, and deliberate damage. Physical threats are among the most destructive factors to endanger the power systems security by intelligently targeting power systems components, such as Transmission Lines (TLs), to damage/destroy the facilities or disrupt the power systems operation. The aim of physical attacks in disrupting power systems can be power systems instability, load interruptions, unserved energy costs, repair/displacement costs, and even cascading failures and blackouts. Due to dispersing in large geographical areas, power transmission systems are more exposed to physical threats. Power systems operators, as the system defenders, protect power systems in different stages of a physical attack by minimizing the impacts of such destructive attacks. In this regard, many studies have been conducted in the literature. In this paper, an overview of the previous research studies related to power systems protection against physical attacks is conducted. This paper also outlines the main characteristics, such as physical attack adverse impacts, defending actions, optimization methods, understudied systems, uncertainty considerations, expansion planning, and cascading failures. Furthermore, this paper gives some key findings and recommendations to identify the research gap in the literature.Öğe Risk assessment of renewable energy and multi-carrier energy storage integrated distribution systems(John Wiley and Sons Ltd, 2022) Tunçel, Süleyman; Oskouei, Morteza Zare; Şeker, Ayşe Aybike; Gözel, Tuba; Hocaoğlu, Mehmet Hakan; Abapour, Mehdi; Mohammadi-Ivatloo, BehnamHigh-power renewable energy sources (RESs) are recognized as a significant trend for the development of power distribution systems in an eco-friendly manner. Due to the aging of distribution system infrastructure, many existing systems do not have the appropriate strength level to host the high penetration of RESs. Therefore, increasing the hosting capacity of RESs in distribution systems will lead to an increase in operational risks. Hence, distribution system operators are looking for operational solutions to mitigate the adverse effects of using non-dispatchable high-power RESs in existing systems. According to some strong evidence, multi-carrier energy storage systems (ESSs) can provide more operational flexibility for power distribution systems to enhance system strength levels in the presence of a high proportion of renewable power. Motivated by this observation, this paper presents a stochastic risk assessment strategy to comprehensively evaluate the performance of distribution systems considering the high penetration of renewable power generation and multi-carrier ESSs from an economic and technical risks point of view. From the technical standpoint, the branch power flows outside permissible ranges and the bus voltages over-limits are used to assess the operational risk of distribution systems when hosting high-power RESs with/without multi-carrier ESSs. The multi-energy storage systems are equipped with power-to-gas and tri-state compressed air energy storage facilities to exploit economic opportunities from gas networks as well as to mitigate techno-economic risks. In the proposed strategy, the scenario-based stochastic programming approach is used to handle renewable power volatility and demand uncertainty. The presented risk assessment strategy is applied to the 33-bus test system, and the operational risks of the test system are significantly reduced while minimizing the operational costs through the coordination of the multi-type ESSs.Öğe A robust decentralized peer-to-peer energy trading in community of flexible microgrids(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Saatloo, Amin Mansour; Mirzaei, Mohammad Amin; Mohammadi-Ivatloo, BehnamThis article proposes a novel platform for microgrid (MG) prosumers that can actively trade energy with the power grid and each other directly. This platform provides Peer-to-Peer (P2P) energy trading among MG prosumers to achieve a win–win outcome in the presence of emerging energy resources, such as electric vehicles (EVs), energy storage systems (ESSs), and demand response programs (DRPs). P2P energy trading, from the power system perspective, can facilitate energy balance locally and self-sufficiency. Despite the conventional noncooperative game that players act individually, prosumers can cooperate in exchanging energy and reaping economic benefits in the proposed model. To this end, a bargaining cooperative game is adopted due to MGs autonomy and self-interest. Besides, to avoid prosumers’ private data launching, the fast-alternating direction method of multipliers is introduced to solve MGs’ energy management problem in a decentralized manner. A robust optimization method is also applied to manage electricity market uncertainty, allowing MGs operators to decide how much risk they want to consider by adjusting the uncertainty budget parameter. The numerical results show that MGs can actively trade with each other and achieve economic benefits. Moreover, many of the utilized technologies such as ESS, DRP, and EVs assist MGs in sharing more energy with each other.Öğe Scenario-based robust energy management of CCHP-based virtual energy hub for participating in multiple energy and reserve markets(Elsevier, 2022) Mohammadi-Ivatloo, Behnam; Seyfi, Mohammad; Mehdinejad, Mehdi; Shayanfar, HeidaraliThe multi-energy systems can operate and schedule the distributed energy resources (DERs) locally to supply the multi-type loads and participate in the energy markets by aggregating the output power of DERs. Recently, the virtual energy hub (VEH) concept, derived from the energy hub and virtual power plant concepts, has been proposed for participating in the electrical and thermal markets. In this paper, robust self-scheduling of a VEH for participating in the energy and reserve markets is presented. The thermal reserve market is proposed to maintain the real-time thermal power balance and compensate for the effects of thermal demand uncertainty. Various types of DERs for supplying loads of each energy carrier are considered. Compressed natural gas (CNG) station is discussed and modeled linearly in the developed VEH to provide the fuel needed by Hybrid CNG and plug-in electric vehicles, which used the CNG as their secondary energy resource. A scenario-based robust approach is developed and presented to maximize the VEH profit and control the downside risk without adding surplus constraints. Finally, the proposed model is simulated in three case studies to evaluate its performance and effectiveness.Öğe Short-term electricity demand forecasting via variational autoencoders and batch training-based bidirectional long short-term memory(Elsevier, 2022) Moradzadeh, Arash; Moayyed, Hamed; Zare, Kazem; Mohammadi-Ivatloo, BehnamElectricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.Öğe A two-point estimate approach for energy management of multi-carrier energy systems incorporating demand response programs(Elsevier Ltd, 2022) Ghahramani, Mehrdad; Nazari-Heris, Morteza; Zare, Kazem; Mohammadi-Ivatloo, BehnamGas-based power plants have attracted more attention in providing electrical energy worldwide because of their lower costs and air pollution. In addition, the use of multi-carrier energy systems has several advantages, such as sustainability benefits and improving performance in supplying the energy demand. This study aims to optimize the total operation cost of multi-carrier energy systems considering the uncertain parameters. The storage technology and consumption side assist the operator in achieving lower costs based on conceptions of demand response programs. Therefore, this study presents a comprehensive mathematical model for the coordinated operation of integrated multi-carrier energy systems while the operational constraints of both gas and power networks are considered. Furthermore, this paper utilizes a new uncertainty modeling method based on Hong's two-point estimate method for addressing the uncertainties of load consumption and wind generation. The proposed model is applied to a gas and power multi-carrier energy system through four case studies. The results affirm the high performance of the presented method and investigate the influence of demand response programs in both sides of energy carriers.
