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Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win-win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.
Salman Soltaniyan; Mohammad Reza Salehizadeh; Akın Taşcıkaraoğlu; Ozan Erdinç; João P.S. Catalão. An interactive multi-criteria decision-making framework between a renewable power plant planner and the independent system operator. Sustainable Energy, Grids and Networks 2021, 26, 100447 .
AMA StyleSalman Soltaniyan, Mohammad Reza Salehizadeh, Akın Taşcıkaraoğlu, Ozan Erdinç, João P.S. Catalão. An interactive multi-criteria decision-making framework between a renewable power plant planner and the independent system operator. Sustainable Energy, Grids and Networks. 2021; 26 ():100447.
Chicago/Turabian StyleSalman Soltaniyan; Mohammad Reza Salehizadeh; Akın Taşcıkaraoğlu; Ozan Erdinç; João P.S. Catalão. 2021. "An interactive multi-criteria decision-making framework between a renewable power plant planner and the independent system operator." Sustainable Energy, Grids and Networks 26, no. : 100447.
As an intermediator between the wholesale electricity market and retail market, a typical load aggregator submits an optimal bid to the system operator to meet the expected demands of its customers. In this regard, the provision of an effective optimal bidding strategy is very crucial for a load aggregator to increase its profit. Within this context, this paper proposes a two-stage artificial neural network based adaptive bidding strategy procedure for an LA by revealing, modelling, and predicting the aggregative behaviour of the competitors in an hourly electricity market. To this end, we develop the concept of decentralized equivalent rival whose behaviour in the electricity market reflects the aggregation of behaviours of all individual competitors. Also, an equivalent market which its outcomes are approximately equal to those of the real market is modelled. The equivalent market's participants are the load aggregator and its corresponding DER. The proposed approach is capable enough to consider transmission constraints. The performance of the proposed approach has been examined on an illustrative example and the IEEE 30-bus test system by considering transmission network constraints. The proposed artificial neural network-based adaptive bidding strategy has compared with a Q –learning-based bidding approach and the results are analysed.
Mohammad Kiannejad; Mohammad Reza Salehizadeh; Majid Oloomi‐Buygi. Two‐stage ANN‐based bidding strategy for a load aggregator using decentralized equivalent rival concept. IET Generation, Transmission & Distribution 2020, 15, 56 -70.
AMA StyleMohammad Kiannejad, Mohammad Reza Salehizadeh, Majid Oloomi‐Buygi. Two‐stage ANN‐based bidding strategy for a load aggregator using decentralized equivalent rival concept. IET Generation, Transmission & Distribution. 2020; 15 (1):56-70.
Chicago/Turabian StyleMohammad Kiannejad; Mohammad Reza Salehizadeh; Majid Oloomi‐Buygi. 2020. "Two‐stage ANN‐based bidding strategy for a load aggregator using decentralized equivalent rival concept." IET Generation, Transmission & Distribution 15, no. 1: 56-70.
This paper proposes a novel approach for modeling and revealing the competitors' behavior from perspective of an intended player (IP). To this end, from perspective of IP, we define an Equivalent Rival (ER) whose behavior in the electricity market reflects the aggregation of behaviors of all individual competitors. It is assumed that IP and its ER participate in an equivalent market which its outcomes are approximately equal to those of the real market. The revealing procedure is designed as a two-stage Artificial Neural Network-based approach to estimate and predict the bids of ER after each run of the real market. Predicted bids of ER are used for the bidding strategy of IP. The proposed approach has been examined on two different case studies. In the first case study the aggregate supply curve of a market with 12 players has been obtained using the proposed approach and the result has been compared with a Bayesian inference approach. In the second case study a six-player electricity market is considered. The competitors' behavior has been revealed from perspective of an intended player using proposed approach and an optimal bidding strategy based on the proposed approach has been constructed.
Mohammad Kiannejad; Mohammad Reza Salehizadeh; Majid Oloomi‐Buygi; Miadreza Shafie‐Khah. Artificial neural network approach for revealing market competitors’ behaviour. IET Generation, Transmission & Distribution 2020, 14, 1292 -1297.
AMA StyleMohammad Kiannejad, Mohammad Reza Salehizadeh, Majid Oloomi‐Buygi, Miadreza Shafie‐Khah. Artificial neural network approach for revealing market competitors’ behaviour. IET Generation, Transmission & Distribution. 2020; 14 (7):1292-1297.
Chicago/Turabian StyleMohammad Kiannejad; Mohammad Reza Salehizadeh; Majid Oloomi‐Buygi; Miadreza Shafie‐Khah. 2020. "Artificial neural network approach for revealing market competitors’ behaviour." IET Generation, Transmission & Distribution 14, no. 7: 1292-1297.
Due to the increased environmental and economic challenges, in recent years, renewable based distribution generation has been developed. More penetrations from the side of consumers caused a new concept called microgrids which are able to stand with or without connection to the bulk power system. Control of microgrids in islanded mode is very crucial for decreasing the amplitude of frequency deviations as well as damping speed. This chapter aims to propose an optimal combination of FOPD and fuzzy pre-compensated FOPI approach for load-frequency control of microgrids in islanded mode. The optimization parameter of the control scheme is designed by the differential evolution (DE) algorithm which has been improved by a fuzzy approach. In the optimization, control effort is considered as a constraint. Due to the robustness and flexibility of the proposed method, the simulation results have been improved substantially. Robust performance of the proposed control method is examined through sensitivity analysis.
Fatemeh Jamshidi; Mohammad Reza Salehizadeh; Fatemeh Gholami; Miadreza Shafie-Khah. An Optimal Approach for Load-Frequency Control of Islanded Microgrids Based on Nonlinear Model. Advances in Intelligent Systems and Computing 2020, 257 -275.
AMA StyleFatemeh Jamshidi, Mohammad Reza Salehizadeh, Fatemeh Gholami, Miadreza Shafie-Khah. An Optimal Approach for Load-Frequency Control of Islanded Microgrids Based on Nonlinear Model. Advances in Intelligent Systems and Computing. 2020; ():257-275.
Chicago/Turabian StyleFatemeh Jamshidi; Mohammad Reza Salehizadeh; Fatemeh Gholami; Miadreza Shafie-Khah. 2020. "An Optimal Approach for Load-Frequency Control of Islanded Microgrids Based on Nonlinear Model." Advances in Intelligent Systems and Computing , no. : 257-275.
Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.
Mohammad Reza Salehizadeh; Mahdi Amidi Koohbijari; Hassan Nouri; Akin Tascikaraoglu; Ozan Erdinç; João P. S. Catalão. Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices. Energies 2019, 12, 2601 .
AMA StyleMohammad Reza Salehizadeh, Mahdi Amidi Koohbijari, Hassan Nouri, Akin Tascikaraoglu, Ozan Erdinç, João P. S. Catalão. Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices. Energies. 2019; 12 (13):2601.
Chicago/Turabian StyleMohammad Reza Salehizadeh; Mahdi Amidi Koohbijari; Hassan Nouri; Akin Tascikaraoglu; Ozan Erdinç; João P. S. Catalão. 2019. "Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices." Energies 12, no. 13: 2601.
Since the beginning of power system restructuring and creation of numerous temporal power markets, transmission congestion has become a serious challenge for independent system operators around the globe. On the other hand, in recent years, emission reduction has become a major concern for the electricity industry. As a widely accepted solution, attention has been drawn to renewable power resources promotion. However, penetration of these resources impacts on transmission congestion. In sum, these challenges reinforce the need for new approaches to facilitate interaction between the operator and energy market players defined as the generators (power generation companies) in order to provide proper operational signals for the operator. The main purpose of this chapter is to provide a combination of a leader–follower game theoretical mechanism and multiattribute decision-making for the operator to choose his best strategy by considering congestion-driven and environmental attributes. First the operator (as the leader) chooses K strategies arbitrarily. Each strategy is constituted by emission penalty factors for each generator, the amount of purchased power from renewable power resources, and a bid cap that provides a maximum bid for the price of electrical power for generators who intend to sell their power in the market. For each of the K strategies, the generators (as the followers) determine their optimum bids for selling power in the market. The interaction between generation companies is modeled as Nash-Supply Function equilibrium (SFE) game. Thereafter, for each of the K strategies, the operator performs congestion management and congestion-driven attributes and emission are obtained. The four different attributes are congestion cost, average locational marginal price (LMP) for different system buses, variance of the LMPs, and the generators’ emission. Finally, the operator’s preferred strategy is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed procedure is applied to the IEEE reliability 24-bus test system and the results are analyzed.
Mohammad Reza Salehizadeh; Ashkan Rahimi-Kian; Kjell Hausken. A Leader–Follower Game on Congestion Management in Power Systems. Springer Series in Reliability Engineering 2015, 81 -112.
AMA StyleMohammad Reza Salehizadeh, Ashkan Rahimi-Kian, Kjell Hausken. A Leader–Follower Game on Congestion Management in Power Systems. Springer Series in Reliability Engineering. 2015; ():81-112.
Chicago/Turabian StyleMohammad Reza Salehizadeh; Ashkan Rahimi-Kian; Kjell Hausken. 2015. "A Leader–Follower Game on Congestion Management in Power Systems." Springer Series in Reliability Engineering , no. : 81-112.
Independent System Operator (ISO) needs to draw sufficient attention to transmission congestion management (TCM) for guaranteeing power system security when deciding about a proposed power generation plan (PGP). To this end, this paper proposes a multi‐attribute decision‐making approach. The proposed decision‐making procedure for a considered PGP includes three major stages: (1) Obtaining different attributes of TCM for all considered scenarios, i.e. the normal operating case and contingencies due to outage of power system components before and after implementation of the PGP. (2) Identifying the effective scenarios before and after the implementation of the PGP. For this purpose, two multi‐attribute decision‐making approaches are applied, one of which ISO could adopt based on its managerial point of view: (a) A conjunctive approach in which scenarios meeting minimal predefined thresholds for their obtained TCM attributes are selected. (b) A pessimistic approach based on data envelopment analysis introduced by Charnes, Cooper, and Rhodes (CCR‐DEA) in which the severest scenarios are selected. 3) Calculating each scenario's Degree of Severity (DOS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and comparing sum of the DOS of the effective scenarios before and after the implementation of the PGP. For an acceptable PGP, sum of effective scenarios' DOS after implementation of the PGP should be less than that of before. The proposed procedure is applied to the IEEE Reliability Test System (RTS) and the results are analyzed. Copyright © 2014 John Wiley & Sons, Ltd.
Mohammad Reza Salehizadeh; Ashkan Rahimi-Kian; Majid Oloomi-Buygi. A multi-attribute congestion-driven approach for evaluation of power generation plans. International Transactions on Electrical Energy Systems 2014, 25, 482 -497.
AMA StyleMohammad Reza Salehizadeh, Ashkan Rahimi-Kian, Majid Oloomi-Buygi. A multi-attribute congestion-driven approach for evaluation of power generation plans. International Transactions on Electrical Energy Systems. 2014; 25 (3):482-497.
Chicago/Turabian StyleMohammad Reza Salehizadeh; Ashkan Rahimi-Kian; Majid Oloomi-Buygi. 2014. "A multi-attribute congestion-driven approach for evaluation of power generation plans." International Transactions on Electrical Energy Systems 25, no. 3: 482-497.