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Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.
Mohammadali Norouzi; Jamshid Aghaei; Sasan Pirouzi; Taher Niknam; Mahmud Fotuhi-Firuzabad; Miadreza Shafie-Khah. Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles. Applied Energy 2021, 300, 117395 .
AMA StyleMohammadali Norouzi, Jamshid Aghaei, Sasan Pirouzi, Taher Niknam, Mahmud Fotuhi-Firuzabad, Miadreza Shafie-Khah. Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles. Applied Energy. 2021; 300 ():117395.
Chicago/Turabian StyleMohammadali Norouzi; Jamshid Aghaei; Sasan Pirouzi; Taher Niknam; Mahmud Fotuhi-Firuzabad; Miadreza Shafie-Khah. 2021. "Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles." Applied Energy 300, no. : 117395.
Geomagnetically induced currents (GICs) are referred to the quasi-DC current flows in power networks, driven by complex space weather-related phenomena. Such currents are a potential threat to the power delivery capability of electrical grids. To mitigate the detrimental impacts of GICs on critical infrastructures, the GICs should be monitored in power systems. Being inherently DC from the power frequency point of view, the components of GICs are, however, challenging and costly to monitor in AC power grids. This paper puts forward a novel methodology for the real-time estimation of GICs in power transformers. Such aim is attained by means of an extended Kalman filter (EKF)-based approach, mounted on the nonlinear state-space model of the transformer, whose parameters can be derived from standard tests. The proposed EKF-based algorithm employs the available measurements for the transformer differential protection. The proposed approach, relying on the differential current, can properly deal with the external sources of interference like harmonic excitation and loading. The EKF-based estimator presented is validated by simulation and experimental data. The results verify the ability of the proposed approach to robustly estimate the GIC level during various operating conditions.
Behzad Behdani; Mohsen Tajdinian; Mehdi Allahbakhshi; Marjan Popov; Miadreza Shafie-Khah; Joao P. S. Catalao. Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleBehzad Behdani, Mohsen Tajdinian, Mehdi Allahbakhshi, Marjan Popov, Miadreza Shafie-Khah, Joao P. S. Catalao. Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleBehzad Behdani; Mohsen Tajdinian; Mehdi Allahbakhshi; Marjan Popov; Miadreza Shafie-Khah; Joao P. S. Catalao. 2021. "Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
The performance of electric vehicles and their abilities to reduce fossil fuel consumption and air pollution on one hand and the use of photovoltaic (PV) panels in energy production, on the other hand, has encouraged parking lot operators (PLO) to participate in the energy market to gain more profit. However, there are several challenges such as different technologies of photovoltaic panels that make the problem complex in terms of installation cost, efficiency, available output power and dependency on environmental temperature. Therefore, the aim of this study is to maximize the PLO’s operational profit under the time of use energy pricing scheme by investigating the effects of different PV panel technologies on energy production and finding the best strategy for optimal operation of PVs and electric vehicle (EV) parking lots which is achieved by means of market and EV owners’ interaction. For the accurate investigation, four different PV panel technologies are considered in different seasons, with significant differences in daylight times, in Helsinki, Finland.
Mahsa Farahmand; Sara Javadi; Sayyed Sadati; Hannu Laaksonen; Miadreza Shafie-Khah. Optimal Operation of Solar Powered Electric Vehicle Parking Lots Considering Different Photovoltaic Technologies. Clean Technologies 2021, 3, 503 -518.
AMA StyleMahsa Farahmand, Sara Javadi, Sayyed Sadati, Hannu Laaksonen, Miadreza Shafie-Khah. Optimal Operation of Solar Powered Electric Vehicle Parking Lots Considering Different Photovoltaic Technologies. Clean Technologies. 2021; 3 (2):503-518.
Chicago/Turabian StyleMahsa Farahmand; Sara Javadi; Sayyed Sadati; Hannu Laaksonen; Miadreza Shafie-Khah. 2021. "Optimal Operation of Solar Powered Electric Vehicle Parking Lots Considering Different Photovoltaic Technologies." Clean Technologies 3, no. 2: 503-518.
This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system (MRES) including distributed energy resources (DERs). The main objective of the proposed scheduling approach is the minimization of the total costs of an MRES consisting of both electricity and gas energy carriers. The proposed model considers both electrical and natural gas distribution networks, DER technologies including renewable energy resources, energy storage systems (ESSs), and combined heat and power. The uncertainties pertinent to the demand and generated power of renewable resources are modeled using the chance-constrained approach. The proposed model is applied on the IEEE 33-bus distribution system and 14-node gas network, and the results demonstrate the efficacy of the proposed approach in the matters of diminishing the total operation costs and enhancing the reliability of the system.
Reza Habibifar; Hossein Ranjbar; Miadreza Shafie-Khah; Mehdi Ehsan; João P. S. Catalão. Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach. IEEE Access 2021, 9, 1 -1.
AMA StyleReza Habibifar, Hossein Ranjbar, Miadreza Shafie-Khah, Mehdi Ehsan, João P. S. Catalão. Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleReza Habibifar; Hossein Ranjbar; Miadreza Shafie-Khah; Mehdi Ehsan; João P. S. Catalão. 2021. "Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach." IEEE Access 9, no. : 1-1.
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.
Marzieh Mokarram; Miadreza Shafie-Khah; Jamshid Aghaei. Risk-based multi-criteria decision analysis of gas power plants placement in semi-arid regions. Energy Reports 2021, 7, 3362 -3372.
AMA StyleMarzieh Mokarram, Miadreza Shafie-Khah, Jamshid Aghaei. Risk-based multi-criteria decision analysis of gas power plants placement in semi-arid regions. Energy Reports. 2021; 7 ():3362-3372.
Chicago/Turabian StyleMarzieh Mokarram; Miadreza Shafie-Khah; Jamshid Aghaei. 2021. "Risk-based multi-criteria decision analysis of gas power plants placement in semi-arid regions." Energy Reports 7, no. : 3362-3372.
This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.
Seyed Mehdi Hakimi; Arezoo Hasankhani; Miadreza Shafie-Khah; João P.S. Catalão. Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market. Applied Energy 2021, 298, 117215 .
AMA StyleSeyed Mehdi Hakimi, Arezoo Hasankhani, Miadreza Shafie-Khah, João P.S. Catalão. Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market. Applied Energy. 2021; 298 ():117215.
Chicago/Turabian StyleSeyed Mehdi Hakimi; Arezoo Hasankhani; Miadreza Shafie-Khah; João P.S. Catalão. 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market." Applied Energy 298, no. : 117215.
In this paper, a hybrid power generation company consisting of a concentrated solar power unit, wind turbines, a battery system, and a demand response provider is established to take part in electricity markets. The operating strategy of the hybrid power generation company in day-ahead and adjustment (intraday) markets is determined based on their coordinated operation. To tackle the intrinsic uncertainties, for the first time, a mixed stochastic-interval model is proposed which addresses the uncertainty in demand response and solar energy via interval optimization. The examined problem is formulated as a multi-objective optimization problem in which the risk of both stochastic and interval parameters can be involved. On this basis, the proposed operating strategy covers three objective functions, namely, expected radius and midpoint of the hybrid power generation company's profit together with the conditional value-at-risk. Accordingly, the normal boundary intersection and lexicographic optimization techniques are utilized to derive feasible solutions. Lastly, numerical results are presented and the performance of the proposed framework is investigated. The results indicate that the suggested model can be efficiently used to handle the decision-maker's preference over interval and stochastic parameters, and the risk criterion associated with interval parameters becomes larger as the forecasting errors increase.
Hooman Khaloie; Amjad Anvari-Moghaddam; Javier Contreras; Pierluigi Siano. Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model. Energy 2021, 232, 120975 .
AMA StyleHooman Khaloie, Amjad Anvari-Moghaddam, Javier Contreras, Pierluigi Siano. Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model. Energy. 2021; 232 ():120975.
Chicago/Turabian StyleHooman Khaloie; Amjad Anvari-Moghaddam; Javier Contreras; Pierluigi Siano. 2021. "Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model." Energy 232, no. : 120975.
The recent experiences of extreme weather events highlight the significance of boosting the resilience of distribution systems. In this situation, the resilience of distribution systems planning leads to an efficient solution for protecting the system from these events via line hardening and the installation of distributed generators (DGs). For this aim, this study presents a new two-stage stochastic mixed-integer linear programming model (SMILP) to hedge against natural disaster uncertainty. The first stage involves making investment decisions about line hardening and DG installation. Then, in the second stage, the dynamic microgrids are created according to a master-slave concept with the ability of integrating distributed generators to minimize the cost of loss of load in each uncertain outage scenario. In particular, this paper presents an approach to select the line damage scenarios for the SMILP. In addition, the operational strategies such as load control capability, microgrid formation and network reconfiguration are integrated into the distribution system plans for resilience improvement in both planning and emergency response steps. The simulation results for an IEEE 33-bus test system demonstrate the effectiveness of the proposed model in improving disaster-induced the resilience of distribution systems.
Mostafa Ghasemi; Ahad Kazemi; Mohammad Amin Gilani; Miadreza Shafie-Khah. A stochastic planning model for improving resilience of distribution system considering master-slave distributed generators and network reconfiguration. IEEE Access 2021, 9, 1 -1.
AMA StyleMostafa Ghasemi, Ahad Kazemi, Mohammad Amin Gilani, Miadreza Shafie-Khah. A stochastic planning model for improving resilience of distribution system considering master-slave distributed generators and network reconfiguration. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleMostafa Ghasemi; Ahad Kazemi; Mohammad Amin Gilani; Miadreza Shafie-Khah. 2021. "A stochastic planning model for improving resilience of distribution system considering master-slave distributed generators and network reconfiguration." IEEE Access 9, no. : 1-1.
This paper presents a risk-averse stochastic framework for virtual associations (VAs), which are dynamic clusters of prosumers. A VA, as a price taker agent, supports the active participation of prosumers in the day-ahead (DA) electricity market. In this regard, a bi-level optimization model is formulated to optimize the decision-making problem of the VA in the DA market with the main goal of maximizing VA profit and minimizing the total energy costs of prosumers. In this framework, the impacts of peer to peer (P2P) trading among the prosumers and VAs on the offering and bidding strategies of VAs are also considered. In a competition among VAs, the prosumers are able to select the most competitive VA to participate in the DA market. Moreover, due to the uncertainties of market prices, the VA should undertake the risks arising from price volatilities that may cause the VA to suffer from financial loss due to occurrence of some scenarios such as price spikes. To compensate the undesired effects of the occurrence of price spikes, the impacts of demand response (DR) actions and peer to peer (P2P) energy trading among prosumers on the decisions of VA are analyzed. Moreover, an index is defined from which the competitive condition in a retailing layer would be analyzed. Using Nordpool data as a practical test system, the undesired effects of occurrence of price spikes are compensated using demand response (DR) actions and peer to peer (P2P) energy trading among prosumers. Moreover, an index is defined from which the competitive condition in a retailing layer would be analyzed.
Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. Optimal bidding of profit-seeking virtual associations of smart prosumers considering peer to peer energy sharing strategy. International Journal of Electrical Power & Energy Systems 2021, 132, 107175 .
AMA StyleHoma Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Miadreza Shafie-Khah, Pierluigi Siano. Optimal bidding of profit-seeking virtual associations of smart prosumers considering peer to peer energy sharing strategy. International Journal of Electrical Power & Energy Systems. 2021; 132 ():107175.
Chicago/Turabian StyleHoma Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. 2021. "Optimal bidding of profit-seeking virtual associations of smart prosumers considering peer to peer energy sharing strategy." International Journal of Electrical Power & Energy Systems 132, no. : 107175.
Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this paper devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. Firstly, we attempt to establish the customers responsiveness function in relation to different incentives, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, the functional relation is applied to the aggregators decision-making process to formulate the optimal bidding strategy in the day-ahead (DA) market and the optimal scheduling scheme for the energy storage system (ESS) with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.
Xiaoxing Lu; Xinxin Ge; Kangping Li; Fei Wang; Hongtao Shen; Peng Tao; Junjie Hu; Jingang Lai; Zhao Zhen; Miadreza Shafie-Khah; Joao P. S. P. S. Catalao. Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives. IEEE Transactions on Industry Applications 2021, 57, 3329 -3340.
AMA StyleXiaoxing Lu, Xinxin Ge, Kangping Li, Fei Wang, Hongtao Shen, Peng Tao, Junjie Hu, Jingang Lai, Zhao Zhen, Miadreza Shafie-Khah, Joao P. S. P. S. Catalao. Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives. IEEE Transactions on Industry Applications. 2021; 57 (4):3329-3340.
Chicago/Turabian StyleXiaoxing Lu; Xinxin Ge; Kangping Li; Fei Wang; Hongtao Shen; Peng Tao; Junjie Hu; Jingang Lai; Zhao Zhen; Miadreza Shafie-Khah; Joao P. S. P. S. Catalao. 2021. "Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives." IEEE Transactions on Industry Applications 57, no. 4: 3329-3340.
During the ongoing evolution of energy systems toward increasingly flexible, resilient, and digitalized distribution systems, many issues need to be developed. In general, a holistic multi-level systemic view is required on the future enabling technologies, control and management methods, operation and planning principles, regulation as well as market and business models. Increasing integration of intermittent renewable generation and electric vehicles, as well as industry electrification during the evolution, requires a huge amount of flexibility services at multiple time scales and from different voltage levels, resources, and sectors. Active use of distribution network-connected flexible energy resources for flexibility services provision through new marketplaces will also be needed. Therefore, increased collaboration between system operators in operation and planning of the future power system will also become essential during the evolution. In addition, use of integrated cyber-secure, resilient, cost-efficient, and advanced communication technologies and solutions will be of key importance. This paper describes a potential three-stage evolution path toward fully flexible, resilient, and digitalized electricity distribution networks. A special focus of this paper is the evolution and development of adaptive control and management methods as well as compatible collaborative market schemes that can enable the improved provision of flexibility services by distribution network-connected flexible energy resources for local (distribution system operator) and system-wide (transmission system operator) needs.
Hannu Laaksonen; Hosna Khajeh; Chethan Parthasarathy; Miadreza Shafie-Khah; Nikos Hatziargyriou. Towards Flexible Distribution Systems: Future Adaptive Management Schemes. Applied Sciences 2021, 11, 3709 .
AMA StyleHannu Laaksonen, Hosna Khajeh, Chethan Parthasarathy, Miadreza Shafie-Khah, Nikos Hatziargyriou. Towards Flexible Distribution Systems: Future Adaptive Management Schemes. Applied Sciences. 2021; 11 (8):3709.
Chicago/Turabian StyleHannu Laaksonen; Hosna Khajeh; Chethan Parthasarathy; Miadreza Shafie-Khah; Nikos Hatziargyriou. 2021. "Towards Flexible Distribution Systems: Future Adaptive Management Schemes." Applied Sciences 11, no. 8: 3709.
Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of power grid. However, PV power has great fluctuations due to various meteorological factors, which increases energy prices and cause difficulties in managing the grid. This paper proposes an ultra-short-term PV power forecasting model based on optimal frequency-domain decomposition(FDD) and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency domain analysis. Then the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then convolutional neural network(CNN) is used to forecast the low-frequency and high-frequency components, and final forecasting result is obtained by addition reconstruction. Based on actual PV data in heavy rain days, the MAPE of the proposed forecasting model is decreased by 52.97%, 64.07% and 31.21%, compared with discrete wavelet transform, variational mode decomposition and direct prediction models. In addition, compared with Recurrent neural network and Long-short-term memory model, the MAPE of CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this paper can improve both the forecasting accuracy and time efficiency significantly.
Jichuan Yan; Lin Hu; Zhao Zhen; Fei Wang; Gang Qiu; Yu Li; Liangzhong Yao; Miadreza Shafie-Khah; Joao P. S. P. S. Catalao. Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model. IEEE Transactions on Industry Applications 2021, 57, 3282 -3295.
AMA StyleJichuan Yan, Lin Hu, Zhao Zhen, Fei Wang, Gang Qiu, Yu Li, Liangzhong Yao, Miadreza Shafie-Khah, Joao P. S. P. S. Catalao. Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model. IEEE Transactions on Industry Applications. 2021; 57 (4):3282-3295.
Chicago/Turabian StyleJichuan Yan; Lin Hu; Zhao Zhen; Fei Wang; Gang Qiu; Yu Li; Liangzhong Yao; Miadreza Shafie-Khah; Joao P. S. P. S. Catalao. 2021. "Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model." IEEE Transactions on Industry Applications 57, no. 4: 3282-3295.
The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (OSVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implementation of the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of the paper, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance.
Seyed Hashemipour; Jamshid Aghaei; Abdollah Kavousi-Fard; Niknam Taher; Ladan Salimi; Pedro Crespo del Granado; Miadreza Shafie-Khah; Fei Wang; Joao P. S. Catalao. Optimal Singular Value Decomposition Based Big Data Compression Approach in Smart Grids. IEEE Transactions on Industry Applications 2021, 57, 3296 -3305.
AMA StyleSeyed Hashemipour, Jamshid Aghaei, Abdollah Kavousi-Fard, Niknam Taher, Ladan Salimi, Pedro Crespo del Granado, Miadreza Shafie-Khah, Fei Wang, Joao P. S. Catalao. Optimal Singular Value Decomposition Based Big Data Compression Approach in Smart Grids. IEEE Transactions on Industry Applications. 2021; 57 (4):3296-3305.
Chicago/Turabian StyleSeyed Hashemipour; Jamshid Aghaei; Abdollah Kavousi-Fard; Niknam Taher; Ladan Salimi; Pedro Crespo del Granado; Miadreza Shafie-Khah; Fei Wang; Joao P. S. Catalao. 2021. "Optimal Singular Value Decomposition Based Big Data Compression Approach in Smart Grids." IEEE Transactions on Industry Applications 57, no. 4: 3296-3305.
The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional auto-encoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of 2D CAEs and 3D CAEs. For 2D CAEs and 3D CAEs, 4 architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry (PIV) and Fourier phase correlation theory (FPCT) are introduced to build the benchmark models. Besides, 5 different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.
Yuwei Fu; Hua Chai; Zhao Zhen; Fei Wang; Xunjian Xu; Kangping Li; Miadreza Shafie-Khah; Payman Dehghanian; Joao P. S. P. S. Catalao. Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting. IEEE Transactions on Industry Applications 2021, 57, 3272 -3281.
AMA StyleYuwei Fu, Hua Chai, Zhao Zhen, Fei Wang, Xunjian Xu, Kangping Li, Miadreza Shafie-Khah, Payman Dehghanian, Joao P. S. P. S. Catalao. Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting. IEEE Transactions on Industry Applications. 2021; 57 (4):3272-3281.
Chicago/Turabian StyleYuwei Fu; Hua Chai; Zhao Zhen; Fei Wang; Xunjian Xu; Kangping Li; Miadreza Shafie-Khah; Payman Dehghanian; Joao P. S. P. S. Catalao. 2021. "Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting." IEEE Transactions on Industry Applications 57, no. 4: 3272-3281.
The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.
Morteza Sheikh; Jamshid Aghaei; Hossein Chabok; Mahmoud Roustaei; Taher Niknam; Abdollah Kavousi-Fard; Miadreza Shafie-Khah; Joao P. S. Catalao. Synergies Between Transportation Systems, Energy Hub and the Grid in Smart Cities. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -15.
AMA StyleMorteza Sheikh, Jamshid Aghaei, Hossein Chabok, Mahmoud Roustaei, Taher Niknam, Abdollah Kavousi-Fard, Miadreza Shafie-Khah, Joao P. S. Catalao. Synergies Between Transportation Systems, Energy Hub and the Grid in Smart Cities. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-15.
Chicago/Turabian StyleMorteza Sheikh; Jamshid Aghaei; Hossein Chabok; Mahmoud Roustaei; Taher Niknam; Abdollah Kavousi-Fard; Miadreza Shafie-Khah; Joao P. S. Catalao. 2021. "Synergies Between Transportation Systems, Energy Hub and the Grid in Smart Cities." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-15.
A large amount of renewable energy sources and electric vehicles will be integrated into future electricity distribution and transmission systems. New flexibility services from distribution network are needed to manage the related challenges. This paper proposes a local flexible capacity market (LFCM) in the distribution network providing system-wide and local flexibility services for transmission (TSO) and distribution system operators (DSO). The TSO and the DSO play the role of buyers, whereas prosumers connected to the distribution network are the sellers. The LFCM consists of three stages. At the first stage, the offers of flexibility sellers are matched with the bids of flexibility buyers aiming to maximize the social welfare of all participants. At the second stage, the accepted flexible capacities are checked by the DSO not to violate the constraints of the local network. The third stage accepts the offers of the sellers based on the results of the previous stage. The results related to the chosen case study demonstrate that the local flexible resources can help the DSO control the voltage and manage periods of congestion. Besides, the owners of the resources can obtain revenues by selling flexibility services while improving electricity supply reliability.
Hosna Khajeh; Hooman Firoozi; Mohammad Reza Hesamzadeh; Hannu Laaksonen; Miadreza Shafie-Khah. A Local Capacity Market Providing Local and System-Wide Flexibility Services. IEEE Access 2021, 9, 52336 -52351.
AMA StyleHosna Khajeh, Hooman Firoozi, Mohammad Reza Hesamzadeh, Hannu Laaksonen, Miadreza Shafie-Khah. A Local Capacity Market Providing Local and System-Wide Flexibility Services. IEEE Access. 2021; 9 (99):52336-52351.
Chicago/Turabian StyleHosna Khajeh; Hooman Firoozi; Mohammad Reza Hesamzadeh; Hannu Laaksonen; Miadreza Shafie-Khah. 2021. "A Local Capacity Market Providing Local and System-Wide Flexibility Services." IEEE Access 9, no. 99: 52336-52351.
Distribution network connected distributed energy resources (DER) are able to provide various flexibility services for distribution system operators (DSOs) and transmission system operators (TSOs). These local and system-wide flexibility services offered by DER can support the frequency (f) and voltage (U) management of a future power system with large amounts of weather-dependent renewable generation and electric vehicles. Depending on the magnitude of frequency deviation, other active network management-based frequency control services for TSOs could also be provided by DSOs in coordination with adaptive control of DER. This paper proposes utilisation of demand response based on frequency-dependent HV/MV transformer on-load tap-changer (OLTC) operation in case of larger frequency deviations. The main principle underlying the proposed scheme lies in the voltage dependency of the distribution network connected loads. In this paper, it is also proposed to, simultaneously with frequency-dependent OLTC control, utilise reverse reactive power -voltage (QU) - and adaptive active power -voltage (PU) -droops with distribution network connected DER units during these larger frequency deviations, in order to enable better frequency support service for TSOs from DSO networks. The effectivity and potential of the proposed schemes are shown through PSCAD simulations. In addition, this paper also presents a holistic and collaborative view of potential future frequency control services which are provided by DSO network-connected resources for TSOs at different frequency deviation levels.
Hannu Laaksonen; Chethan Parthasarathy; Hosna Khajeh; Miadreza Shafie-Khah; Nikos Hatziargyriou. Flexibility Services Provision by Frequency-Dependent Control of On-Load Tap-Changer and Distributed Energy Resources. IEEE Access 2021, 9, 45587 -45599.
AMA StyleHannu Laaksonen, Chethan Parthasarathy, Hosna Khajeh, Miadreza Shafie-Khah, Nikos Hatziargyriou. Flexibility Services Provision by Frequency-Dependent Control of On-Load Tap-Changer and Distributed Energy Resources. IEEE Access. 2021; 9 (99):45587-45599.
Chicago/Turabian StyleHannu Laaksonen; Chethan Parthasarathy; Hosna Khajeh; Miadreza Shafie-Khah; Nikos Hatziargyriou. 2021. "Flexibility Services Provision by Frequency-Dependent Control of On-Load Tap-Changer and Distributed Energy Resources." IEEE Access 9, no. 99: 45587-45599.
This study presents the optimal model of the coordinated flexible energy and self‐healing management (C‐FE&SH‐M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self‐healing method (SHM) is related to multi‐agent system‐based restoration process (MAS‐based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33‐bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.
Leila Bagherzadeh; Hossein Shayeghi; Sasan Pirouzi; Miadreza Shafie‐Khah; João P. S. Catalão. Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network. IET Renewable Power Generation 2021, 15, 1765 -1777.
AMA StyleLeila Bagherzadeh, Hossein Shayeghi, Sasan Pirouzi, Miadreza Shafie‐Khah, João P. S. Catalão. Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network. IET Renewable Power Generation. 2021; 15 (8):1765-1777.
Chicago/Turabian StyleLeila Bagherzadeh; Hossein Shayeghi; Sasan Pirouzi; Miadreza Shafie‐Khah; João P. S. Catalão. 2021. "Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network." IET Renewable Power Generation 15, no. 8: 1765-1777.
The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This paper proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called EGWO. The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from data sets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.
Seyed Mohammad Jafar Jalali; Sajad Ahmadian; Abbas Khosravi; Miadreza Shafie-Khah; Saeid Nahavandi; Joao P. S. Catalao. A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting. IEEE Transactions on Industrial Informatics 2021, 17, 8243 -8253.
AMA StyleSeyed Mohammad Jafar Jalali, Sajad Ahmadian, Abbas Khosravi, Miadreza Shafie-Khah, Saeid Nahavandi, Joao P. S. Catalao. A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting. IEEE Transactions on Industrial Informatics. 2021; 17 (12):8243-8253.
Chicago/Turabian StyleSeyed Mohammad Jafar Jalali; Sajad Ahmadian; Abbas Khosravi; Miadreza Shafie-Khah; Saeid Nahavandi; Joao P. S. Catalao. 2021. "A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting." IEEE Transactions on Industrial Informatics 17, no. 12: 8243-8253.
This paper proposes a data-driven chance-constrained optimal gas-power flow (OGPF) calculation method without any prior assumption on the distribution of uncertainties of wind power generation. The Gaussian mixture model is employed to fit the uncertainty distribution, where the Bayesian nonparametric Dirichlet process is adopted to tune the component number. To facilitate the online application of the proposed methods, an online-offline double-track distribution construction approach is established, where the frequency of training the relatively time-consuming Dirichlet process Gaussian mixture model can be reduced. On account of the quadratic gas consumption expression of gas-fired generators as well as the linear decision rule based uncertainty mitigation mechanism, the chance constraints would become quadratic ones with quadratic terms of uncertainties, which makes the proposed model more intractable. An equivalent linear separable counterpart is then provided for the quadratic chance constraints, after which the intractable chance constraints could be converted into traditional linear ones. The convex-concave procedure is used to crack the nonconvex Weymouth equation in the gas network and the auxiliary quadratic equalities. Simulation results on two test systems validate the effectiveness of the proposed methods.
Jingyao Wang; Cheng Wang; Yile Liang; Tianshu Bi; Miadreza Shafie-Khah; Joao P. S. Catalao. Data-Driven Chance-Constrained Optimal Gas-Power Flow Calculation: A Bayesian Nonparametric Approach. IEEE Transactions on Power Systems 2021, 36, 4683 -4698.
AMA StyleJingyao Wang, Cheng Wang, Yile Liang, Tianshu Bi, Miadreza Shafie-Khah, Joao P. S. Catalao. Data-Driven Chance-Constrained Optimal Gas-Power Flow Calculation: A Bayesian Nonparametric Approach. IEEE Transactions on Power Systems. 2021; 36 (5):4683-4698.
Chicago/Turabian StyleJingyao Wang; Cheng Wang; Yile Liang; Tianshu Bi; Miadreza Shafie-Khah; Joao P. S. Catalao. 2021. "Data-Driven Chance-Constrained Optimal Gas-Power Flow Calculation: A Bayesian Nonparametric Approach." IEEE Transactions on Power Systems 36, no. 5: 4683-4698.