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Mostafa Vahedipour-Dahraie
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

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Journal article
Published: 19 May 2021 in International Journal of Electrical Power & Energy Systems
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Homa 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.

Journal article
Published: 01 March 2021 in IEEE Transactions on Smart Grid
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ACS Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Miadreza Shafie-Khah; Joao P. S. Catalao. Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs. IEEE Transactions on Smart Grid 2021, 12, 1405 -1415.

AMA Style

Mostafa Vahedipour-Dahraie, Homa Rashidizadeh-Kermani, Miadreza Shafie-Khah, Joao P. S. Catalao. Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs. IEEE Transactions on Smart Grid. 2021; 12 (2):1405-1415.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Miadreza Shafie-Khah; Joao P. S. Catalao. 2021. "Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs." IEEE Transactions on Smart Grid 12, no. 2: 1405-1415.

Journal article
Published: 05 January 2021 in IEEE Systems Journal
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This article proposes a peer-to-peer (P2P) energy trading framework for wind power producer (WPP) in the retailing layer to increase its revenue and to promote wind power utilization. In this framework, the WPP can provide energy consumption of demand response providers (DRPs) in a competitive environment through both the main grid and rival load-serving entities. Also, based on a P2P pricing mechanism, the WPP can choose if to trade energy in a P2P platform or not. The proposed problem is formulated as a stochastic bilevel optimization model, in which in the upper level, the WPP profit is maximized and in the lower level, the DRPs participate in DR programs and tend to choose the fairest supplier among the WPP and LSEs to minimize their energy procurement costs. The proposed method is applied to a realistic case study and the results demonstrate that with P2P, the WPP can schedule the energy transaction with peers to offset part of the energy deviations. Moreover, different values of P2P prices lead to different values of energy transactions due to the relatively diversity pattern of local generation of rival LSEs and their offering prices and demand and generation of the WPP.

ACS Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer. IEEE Systems Journal 2021, PP, 1 -10.

AMA Style

Homa Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Miadreza Shafie-Khah, Pierluigi Siano. A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer. IEEE Systems Journal. 2021; PP (99):1-10.

Chicago/Turabian Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. 2021. "A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer." IEEE Systems Journal PP, no. 99: 1-10.

Journal article
Published: 19 October 2020 in IEEE Systems Journal
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In this article, a risk-constrained stochastic framework is presented for joint energy and reserve scheduling of a resilient microgrid considering demand side management. The optimization problem is formulated to schedule the system operation in both normal and islanding modes by addressing the prevailing uncertainties of islanding duration as well as prediction errors of loads, renewable power generation, and electricity price. In a normal operation mode, where the grid-connection is available, the energy and reserve of local resources and energy trading with the main grid is scheduled to maximize the operator's profit considering feasible islanding. In a resilient operating mode, which is triggered by a disturbance in the main grid, the local resources should be scheduled to supply loads with the lowest emergency load shedding. To balance the economy and security requirements under uncertainties, the optimal scheduling is done properly through a security-constrained power flow method by considering system's objectives and constraints. Moreover, to properly handle the uncertainties of the problem, conditional value-at-risk metric is incorporated with the optimization model to control the risk of profit variability. The proposed scheme is implemented on a test microgrid and various case studies are presented to verify its effectiveness in normal and resiliency operating conditions.

ACS Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam. Risk-Based Stochastic Scheduling of Resilient Microgrids Considering Demand Response Programs. IEEE Systems Journal 2020, 15, 971 -980.

AMA Style

Mostafa Vahedipour-Dahraie, Homa Rashidizadeh-Kermani, Amjad Anvari-Moghaddam. Risk-Based Stochastic Scheduling of Resilient Microgrids Considering Demand Response Programs. IEEE Systems Journal. 2020; 15 (1):971-980.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam. 2020. "Risk-Based Stochastic Scheduling of Resilient Microgrids Considering Demand Response Programs." IEEE Systems Journal 15, no. 1: 971-980.

Journal article
Published: 13 October 2020 in IEEE Systems Journal
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This article proposes a risk constrained decision-making problem for wind power producers (WPPs) in a competitive environment. In this problem, the WPP opts to maximize its likely profit whereas aggregators want to minimize their payments. So, this bilevel problem is converted to a single level one. Then, the WPP offers proper prices to the aggregators to attract them to supply their demand. Also, these aggregators can procure reserve for the WPP to compensate its uncertainties. Therefore, through a peer-to-peer (P2P) trading mechanism, the WPP requests the aggregators to allocate reserve to cover the uncertainties of the wind generation. Also, due to the presence of uncertain resources of the problem, a risk measurement tool is applied to the problem to control the uncertainties. The effectiveness of the model is assessed on realistic data from the Nordpool market and the results show that as the loads become responsive, more loads are allowed to choose their WPP to supply their load. Also, the reserve that is provided by these responsive loads to the WPP increases.

ACS Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Joao P. S. Catalao. Joint Energy and Reserve Scheduling of a Wind Power Producer in a Peer-to-Peer Mechanism. IEEE Systems Journal 2020, 15, 4315 -4324.

AMA Style

Homa Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Miadreza Shafie-Khah, Joao P. S. Catalao. Joint Energy and Reserve Scheduling of a Wind Power Producer in a Peer-to-Peer Mechanism. IEEE Systems Journal. 2020; 15 (3):4315-4324.

Chicago/Turabian Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Joao P. S. Catalao. 2020. "Joint Energy and Reserve Scheduling of a Wind Power Producer in a Peer-to-Peer Mechanism." IEEE Systems Journal 15, no. 3: 4315-4324.

Journal article
Published: 14 May 2020 in International Journal of Electrical Power & Energy Systems
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In this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP’s profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the VPP energy and reserve management and its security indices are investigated.

ACS Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam; Pierluigi Siano. Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties. International Journal of Electrical Power & Energy Systems 2020, 121, 106126 .

AMA Style

Mostafa Vahedipour-Dahraie, Homa Rashidizadeh-Kermani, Amjad Anvari-Moghaddam, Pierluigi Siano. Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties. International Journal of Electrical Power & Energy Systems. 2020; 121 ():106126.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam; Pierluigi Siano. 2020. "Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties." International Journal of Electrical Power & Energy Systems 121, no. : 106126.

Journal article
Published: 23 January 2020 in IEEE Transactions on Smart Grid
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ACS Style

Homa Rashidizadeh Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties. IEEE Transactions on Smart Grid 2020, 11, 3171 -3184.

AMA Style

Homa Rashidizadeh Kermani, Mostafa Vahedipour-Dahraie, Miadreza Shafie-Khah, Pierluigi Siano. A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties. IEEE Transactions on Smart Grid. 2020; 11 (4):3171-3184.

Chicago/Turabian Style

Homa Rashidizadeh Kermani; Mostafa Vahedipour-Dahraie; Miadreza Shafie-Khah; Pierluigi Siano. 2020. "A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties." IEEE Transactions on Smart Grid 11, no. 4: 3171-3184.

Journal article
Published: 16 July 2019 in International Transactions on Electrical Energy Systems
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ACS Style

Homa Rashidizadeh‐Kermani; Mostafa Vahedipour‐Dahraie; Amjad Anvari-Moghaddan; Josep Guerrero. A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets. International Transactions on Electrical Energy Systems 2019, 29, 1 .

AMA Style

Homa Rashidizadeh‐Kermani, Mostafa Vahedipour‐Dahraie, Amjad Anvari-Moghaddan, Josep Guerrero. A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets. International Transactions on Electrical Energy Systems. 2019; 29 (11):1.

Chicago/Turabian Style

Homa Rashidizadeh‐Kermani; Mostafa Vahedipour‐Dahraie; Amjad Anvari-Moghaddan; Josep Guerrero. 2019. "A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets." International Transactions on Electrical Energy Systems 29, no. 11: 1.

Journal article
Published: 25 May 2019 in Electronics
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This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.

ACS Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam; Vahedipour- Dahraie; Rashidizadeh- Kermani; Anvari- Moghaddam. Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation. Electronics 2019, 8, 577 .

AMA Style

Mostafa Vahedipour-Dahraie, Homa Rashidizadeh-Kermani, Amjad Anvari-Moghaddam, Vahedipour- Dahraie, Rashidizadeh- Kermani, Anvari- Moghaddam. Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation. Electronics. 2019; 8 (5):577.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Homa Rashidizadeh-Kermani; Amjad Anvari-Moghaddam; Vahedipour- Dahraie; Rashidizadeh- Kermani; Anvari- Moghaddam. 2019. "Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation." Electronics 8, no. 5: 577.

Research article
Published: 18 September 2018 in International Transactions on Electrical Energy Systems
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This paper presents a risk‐averse stochastic bi‐level programming approach to solve decision‐making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day‐ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/down regulation market prices, customers' demand, and rival retailers' behaviors are tackled through a stochastic programming model. In the proposed model, responsive loads and electric vehicles (EVs) track the real‐time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single‐level program by replacing the lower‐level problem with its Karush‐Kuhn‐Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study, and the results demonstrate the effectiveness of the proposed framework.

ACS Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Amjad Anvari-Moghaddam; Josep M. Guerrero. Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources. International Transactions on Electrical Energy Systems 2018, 29, e2719 .

AMA Style

Homa Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Amjad Anvari-Moghaddam, Josep M. Guerrero. Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources. International Transactions on Electrical Energy Systems. 2018; 29 (2):e2719.

Chicago/Turabian Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Amjad Anvari-Moghaddam; Josep M. Guerrero. 2018. "Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources." International Transactions on Electrical Energy Systems 29, no. 2: e2719.

Journal article
Published: 01 March 2018 in Journal of Renewable and Sustainable Energy
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Low inertia of distributed energy resources (DERs), high penetration levels of renewable energy sources (RESs), and load demand variations put the islanded microgrids' (MGs) security at the risk of instability. This paper proposes a two-stage stochastic model for coordination of DERs and responsive loads in islanded MGs with regard to voltage and frequency security constraints. Based on the proposed model, scheduling of the controllable units in both supply and demand sides is done in a way not only to maximize the expected profit of the MG operator (MGO) but also to minimize the energy payments of customers under the premise of security and stability of MG. An AC optimal power flow procedure is also used to study the operating condition of the system under uncertainties and to guarantee acceptable nodal voltages and system frequency under different scenarios. The proposed stochastic optimization model is then applied to a typical autonomous MG, and its effectiveness is demonstrated through different scenarios under uncertainties in load consumption and renewable energy resource (RES) productions. Simulation results demonstrate that customers' participation in DR programs has a significant effect on the system's performance in terms of voltage and frequency stability. Moreover, optimal coordination of DERs and responsive loads can increase the expected profit of MGO significantly. The effectiveness of the proposed scheduling approach is verified on an islanded MG test system over a 24-h period.

ACS Style

Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. Optimal scheduling of distributed energy resources and responsive loads in islanded microgrids considering voltage and frequency security constraints. Journal of Renewable and Sustainable Energy 2018, 10, 025903 .

AMA Style

Mostafa Vahedipour-Dahraie, Hamid Reza Najafi, Amjad Anvari-Moghaddam, Josep M. Guerrero. Optimal scheduling of distributed energy resources and responsive loads in islanded microgrids considering voltage and frequency security constraints. Journal of Renewable and Sustainable Energy. 2018; 10 (2):025903.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. 2018. "Optimal scheduling of distributed energy resources and responsive loads in islanded microgrids considering voltage and frequency security constraints." Journal of Renewable and Sustainable Energy 10, no. 2: 025903.

Research article
Published: 15 February 2018 in IET Renewable Power Generation
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Uncertainties in renewable energy resources and electricity demand have introduced new challenges to energy and reserve scheduling of microgrids, particularly in autonomous mode. In this study, a risk-constrained stochastic framework is presented to maximise the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade-off between maximising the operator's expected profit and the risk of getting low profits in undesired scenarios is modelled by using the conditional value-at-risk (CVaR) method. The influence of consumers’ participation in demand response (DR) programs and their emergency load shedding for different values of lost load (VOLL) are then investigated on the expected profit of the operator, CVaR, expected energy not served and scheduled reserves of the microgrid. Moreover, the impacts of different VOLL and risk aversion parameters are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages of customers’ participation in the DR program on the expected profit of the microgrid operator and the reliability indices.

ACS Style

Mostafa Vahedipour‐Dahraie; Amjad Anvari‐Moghaddam; Josep M. Guerrero. Evaluation of reliability in risk‐constrained scheduling of autonomous microgrids with demand response and renewable resources. IET Renewable Power Generation 2018, 12, 657 -667.

AMA Style

Mostafa Vahedipour‐Dahraie, Amjad Anvari‐Moghaddam, Josep M. Guerrero. Evaluation of reliability in risk‐constrained scheduling of autonomous microgrids with demand response and renewable resources. IET Renewable Power Generation. 2018; 12 (6):657-667.

Chicago/Turabian Style

Mostafa Vahedipour‐Dahraie; Amjad Anvari‐Moghaddam; Josep M. Guerrero. 2018. "Evaluation of reliability in risk‐constrained scheduling of autonomous microgrids with demand response and renewable resources." IET Renewable Power Generation 12, no. 6: 657-667.

Journal article
Published: 24 October 2017 in Applied Sciences
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This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners’ viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty―including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators―are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush–Kuhn–Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

ACS Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets. Applied Sciences 2017, 7, 1100 .

AMA Style

Homa Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Hamid Reza Najafi, Amjad Anvari-Moghaddam, Josep M. Guerrero. A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets. Applied Sciences. 2017; 7 (10):1100.

Chicago/Turabian Style

Homa Rashidizadeh-Kermani; Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. 2017. "A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets." Applied Sciences 7, no. 10: 1100.

Research article
Published: 17 October 2017 in IET Renewable Power Generation
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Increasing penetration of intermittent renewable energy sources and the development of advanced information give rise to questions on how responsive loads can be managed to optimise the use of resources and assets. In this context, demand response as a way for modifying the consumption pattern of customers can be effectively applied to balance the demand and supply in electricity networks. This study presents a novel stochastic model from a microgrid (MG) operator perspective for energy and reserve scheduling considering risk management strategy. It is assumed that the MG operator can procure energy from various sources, including local generating units and demand-side resources to serve the customers. The operator sells electricity to customers under real-time pricing scheme and the customers response to electricity prices by adjusting their loads to reduce consumption costs. The objective is to determine the optimal scheduling with considering risk aversion and system frequency security to maximise the expected profit of operator. To deal with various uncertainties, a risk-constrained two-stage stochastic programming model is proposed where the risk aversion of MG operator is modelled using conditional value at risk method. Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.

ACS Style

Mostafa Vahedipour‐Dahraie; Homa Rashidizadeh‐Kermani; Hamid Reza Najafi; Amjad Anvari‐Moghaddam; Josep M. Guerrero. Stochastic security and risk‐constrained scheduling for an autonomous microgrid with demand response and renewable energy resources. IET Renewable Power Generation 2017, 11, 1812 -1821.

AMA Style

Mostafa Vahedipour‐Dahraie, Homa Rashidizadeh‐Kermani, Hamid Reza Najafi, Amjad Anvari‐Moghaddam, Josep M. Guerrero. Stochastic security and risk‐constrained scheduling for an autonomous microgrid with demand response and renewable energy resources. IET Renewable Power Generation. 2017; 11 (14):1812-1821.

Chicago/Turabian Style

Mostafa Vahedipour‐Dahraie; Homa Rashidizadeh‐Kermani; Hamid Reza Najafi; Amjad Anvari‐Moghaddam; Josep M. Guerrero. 2017. "Stochastic security and risk‐constrained scheduling for an autonomous microgrid with demand response and renewable energy resources." IET Renewable Power Generation 11, no. 14: 1812-1821.

Journal article
Published: 24 May 2017 in Applied Sciences
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Increasing the penetration levels of renewable energy sources (RESs) in microgrids (MGs) may lead to frequency instability issues due to intermittent nature of RESs and low inertia of MG generating units. On the other hand, presence of electric vehicles (EVs), as new high-electricity- consuming appliances, can be a good opportunity to contribute in mitigating the frequency deviations and help the system stability. This paper proposes an optimal charging/discharging scheduling of EVs with the goal of improving frequency stability of MG during autonomous operating condition. To this end, an efficient approach is applied to reschedule the generating units considering the EVs owners’ behaviors. An EV power controller (EVPC) is also designed to determine charge and discharge process of EVs based on the forecasted day-ahead load and renewable generation profiles. The performance of the proposed strategy is tested in different operating scenarios and compared to those from non-optimized methodologies. Numerical simulations indicate that the MG performance improves considerably in terms of economy and stability using the proposed strategy.

ACS Style

Mostafa Vahedipour-Dahraie; Homa Rashidizaheh-Kermani; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids. Applied Sciences 2017, 7, 539 .

AMA Style

Mostafa Vahedipour-Dahraie, Homa Rashidizaheh-Kermani, Hamid Reza Najafi, Amjad Anvari-Moghaddam, Josep M. Guerrero. Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids. Applied Sciences. 2017; 7 (6):539.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Homa Rashidizaheh-Kermani; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. 2017. "Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids." Applied Sciences 7, no. 6: 539.

Journal article
Published: 11 April 2017 in Applied Sciences
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In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.

ACS Style

Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids. Applied Sciences 2017, 7, 378 .

AMA Style

Mostafa Vahedipour-Dahraie, Hamid Reza Najafi, Amjad Anvari-Moghaddam, Josep M. Guerrero. Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids. Applied Sciences. 2017; 7 (4):378.

Chicago/Turabian Style

Mostafa Vahedipour-Dahraie; Hamid Reza Najafi; Amjad Anvari-Moghaddam; Josep M. Guerrero. 2017. "Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids." Applied Sciences 7, no. 4: 378.