Mirzaei, Mohammad Amin, Mehrjerdi, Hassan, and Mansour Saatloo, Amin, 2023, Look-ahead scheduling of energy-water nexus integrated with Power2X conversion technologies under multiple uncertainties, Sustainable Cities and Society, vol. 99, Dec. 2023, Article number 104902. Abstract.
Sahar Seyyedeh-Barhagh, Mehdi Abapour, Behnam Mohammadi-Ivatloo and Miadreza Shafie-khah, 2023, Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles, Sustainable Cities and Society, vol. 99, December 2023, Article number 104948. Abstract.
Tang, Zao; Liu, Jia; Lin, Mohan; Tang, Yi; Zhao, Jianfeng; and Zeng, Pingliang; 2023, Info-gap decision theory based wind-storage system day-ahead bidding strategy, Lecture Notes in Electrical Engineering, Volume 1030 LNEE, Pages 47–56. 37th Annual Conference on Power System and Automation in Chinese Universities, CUS-EPSA 2022, Hangzhou, 23–25 October 2022, Code 293869. Abstract.
Yan, Yufei; Tang, Zao; Liu, Youbo; Wang, Miao; Xiang, Yue; Gao, Hongjun; and Liu, Junyong, 2023, Day-ahead bidding strategy of combined wind storage system considering risk preference under dual uncertainties, Dianwang Jishu/Power System Technology, Volume 47, Issue 3, Pages 1078–10875, March 2023. Abstract.
Dong, J.; Dou, X.; Liu, D.; Bao, A.; Wang, D.; Zhang, Y., 2023, Energy trading strategy of distributed energy resources aggregator in day-ahead market considering risk preference behaviors, Energies, 2023, vol.16, \#4, 1629. https://doi.org/10.3390/en16041629. Abstract.
Dong J, Dou X, Liu D, Bao A, Wang D, Zhang Y and Jiang P (2023), Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multistakeholder risk preference behaviors, Frontiers in Energy Research, 11:1173981. doi: 10.3389/fenrg.2023.1173981, Abstract.
Ehsan Hooshmand and Abbas Rabiee, 2019, Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory, IET Generation, Transmission & Distribution, vol. 13, #4. https://doi.org/10.1049/iet-gtd.2018.5671. Abstract.
Danial Masihabadi, Mohsen Kalantar, Zahra Majd, Seyed Vahid Sabzpoosh Saravi, 2023, A novel information gap decision theory-based demand response scheduling for a smart residential community considering deep uncertainties, IET Generation, Transmission & Distribution, https://doi.org/10.1049/gtd2.12744. Abstract.
Sobhan Dorahaki, Masoud Rashidinejad, Seyed Farshad Fatemi Ardestani, Amir Abdollahi and Mohammad Reza Salehizadeh, 2023, Probabilistic/information gap decision theory-based bilevel optimal management for multi-carrier network by aggregating energy communities, Renewable Power Generation, IET,
DOI: 10.1049/rpg2.12685. Abstract.
Michal Jasinski, Arsalan Najafi, Omid Homaee, Mostafa Kermani, Georgios Tsaousoglou, Zbigniew Leonowicz and Tomas Novak, 2023, Operation and planning of energy hubs under uncertainty – A review of mathematical optimization approaches, IEEE Access, to appear. Abstract.
Sandeep Chawda, Parul Mathuria & Rohit Bhakar, 2023, Load serving entity’s profit maximization framework for correlated demand and pool price uncertainties, Technology and Economics of Smart Grids and Sustainable Energy, volume 8, Article number: 2 (2023). Abstract.
Vahid-Ghavidel, Morteza; Shafie-khah, Miadrez; Javadi, Mohammad S.; Santos, Sérgio F.; Gough, Matthew; Quijano, Darwin A. and Catalao, Joao P.S., 2023, Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system, Energy, 265: 126289. DOI 10.1016/j.energy.2022.126289. Abstract.
Abdolaziz Mallahi, Amir Abdollahi, Masoud Rashidinejad, Ehsan Heydarian-Forushani, And Ameena Saad Al-Sumaiti, 2022, An Investigation on the Impacts of Low Probability and High Intensity Events on Wind Power Generator’s Market Participation, IEEE Access, 18 February 2022. Abstract.
Mojtaba Mohseni, Ali Sedaghatkerdar, Mahdi Mohseni, Ehsan Heydarian-Forushani, Sitki Guner, Aydogan Ozdemir, 2022, Bidding strategy of a microgrid in joint energy and reserve markets: An IGDT-based approach, August 2022, 57th International Universities Power Engineering Conference (UPEC). Abstract.DOI: 10.1109/UPEC55022.2022.9917702
Liying Wang, Houqi Dong, Jialin Lin, and Ming Zeng, 2022, Multi-objective optimal scheduling model with IGDT method of integrated energy system considering ladder-type carbon trading mechanism, Intl. J. of Electrical Power & Energy Systems, vol. 143, December 2022, 108386. Abstract.
Ahmad Nikoobakht, Jamshid Aghaei, Miadreza Shafie-khah, João P.S.Catalão, 2022, Risk-averse decision under worst-case continuous and discrete uncertainties in transmission system with the support of active distribution systems, Intl J. of Electrical Power & Energy Systems, vol.138, June 2022. https://doi.org/10.1016/j.ijepes.2021.107913 Abstract.
S. Seyyed Mahdavi, J. Saebi, and A. Ghasemi, 2022, Risk-based approach for self-scheduling of virtual power plants in competitive power markets, J. of Operation and Automation in Energy Engineering, 22 April 2022, DOI
Wachnik, B.; Kłodawski, M.; Kardas-Cinal, E. Reduction of the information gap problem in industry 4.0 projects as a way to reduce energy consumption by the industrial sector, Energies, 2022, 15, 1108. https://doi.org/10.3390/en15031108. Abstract.
S. Chawda, P. Mathuria, and R. Bhakar, 2021, Dynamic sale prices for load serving entity’s risk based profit maximization, Electric Power System Research, vol. 201, p.107544, Dec. 2021, doi: 10.1016/j.epsr.2021.107544. Abstract.
Lijun Geng, Zhigang Lu, Xiaoqiang Guo, Jiangfeng Zhang, Xueping Li, Liangce He, 2021, Coordinated operation of coupled transportation and power distribution systems considering stochastic routing behaviour of electric vehicles and prediction error of travel demand, IET Generation, Transmission & Distribution,
First published: 11 March 2021.https://doi.org/10.1049/gtd2.12161 Abstract.
Morteza Shafiekhani, Abdollah Ahmadi, Omid Homaee, Miadreza Shafie-khah, João P.S.Catalão, 2022, Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads, Energy, Volume 239, Part D, 15 January 2022, 122379. https://doi.org/10.1016/j.energy.2021.122379. Abstract.
Michelle Maceas Henao; Jairo Espinosa Oviedo; Idi Isaac Milan, 2021, Bidding strategy for VPP incorporating price market and solar generation uncertainties using information gap decision theory, 29 June-1 July 2021, 9th International Conference on Smart Grid (icSmartGrid), DOI: 10.1109/icSmartGrid52357.2021.9551261. Abstract.
Milad Eslahi; Behrooz Vahidi; Pierluigi Siano, 2021, A flexible risk-averse strategy considering uncertainties of demand and multiple wind farms in electrical grids, IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2021.3103117. Abstract.
Rahim Fathi , Behrouz Tousi, and Sadjad Galvani, 2021, A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy, Journal of Cleaner Production, 295 (2021) 125984. Abstract.
Xuguang Yu, Gang Li, Yapeng Li, Chuntian Cheng, 2021, Robust short-term scheduling based on information-gap decision theory for cascade reservoirs considering bilateral contract fulfillment and day-ahead market bidding in source systems, February 2021, Environmental Research Letters, https://iopscience.iop.org/article/10.1088/1748-9326/abe6c3. Abstract.
Gang Li, Jia Lu, Rui Yang and Chuntian Cheng, 2021, IGDT-Based Medium-Term Optimal Cascade Hydropower Operation in Multimarket with Hydrologic and Economic Uncertainties, Journal of Water Resources Planning and Management, 147(10).DOI: 10.1061/(ASCE)WR.1943-5452.0001444. Abstract.
Amir Hossein Shojaei, Ali Asghar Ghadimi, Mohammad Reza Miveh, Foad H. Gandoman and Abdollah Ahmadi, 2021, Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory, Renewable Energy, 163: 1427-1443. Abstract.
Pouria Sheikhahmadi, Salah Bahramara, Andrea Mazza, Gianfranco Chicco, João P.S. Catalão, 2021, Bi-level optimization model for the coordination between transmission and distribution systems interacting with local energy markets, International Journal of Electrical Power & Energy Systems, 124: 106392. Abstract.
Sima Aznavi, Poria Fajri, Eric M. Wilcox and Mohammad B. Shadmand, 2020, Risk Assessment of Smart Buildings Equipped with Solar Generation Using Information Gap Decision Theory, 2020 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA, pp. 2142-2147, doi: 10.1109/ECCE44975.2020.9235433. Abstract.
Mohammad Amin Mirzaei, Mohammad Hemmati, Kazem Zare, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Mousa Marzband and Ali Farzamnia, 2020, Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation, IEEE Access, vol. 8, art. no. 9126773, pp.121780-121794. Abstract.
Ramyar Mafakheri, Pouria Sheikhahmadi, Salah Bahramara, 2020, A two-level model for the participation of microgrids in energy and reserve markets using hybrid stochastic-IGDT approach, Electrical Power and Energy Systems, 119 (2020) 105977. Abstract.
Jun Liu, Chong Chen, Zhenling Liu, Kittisak Jermsittiparsert, Noradin Ghadimi, 2020, An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles, Journal of Energy Storage, 27 (2020) 101057. Abstract.
L. Chen, X. Zhao, D. Li, J. Li and Q. Ai, 2020, Optimal Bidding Strategy for Virtual Power Plant Using Information Gap Decision Theory, 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE), Chengdu, China, 4-7 June 2020, pp. 122–128, doi: 10.1109/ACPEE48638.2020.9136240. Abstract.
Ammar H.M. Aldarajee, Seyed H. Hosseinian, Behrooz Vahidi, Shahab Dehghan, 2020, A coordinated planner-disaster-risk-averse-planner investment model for enhancing the resilience of integrated electric power and natural gas networks, Electrical Power and Energy Systems, 119: 105948. Abstract.
Mohammad Jadidbonab, Behnam Mohammadi-ivatloo, Mousa Marzband, Pierluigi Siano, 2020, Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market, IEEE Transactions on Industrial Electronics, to appear. Abstract.
Ramin Nourollahi, Sayyad Nojavan and Kazem Zare, 2020, Risk-based purchasing energy for electricity consumers by retailer using information gap decision theory considering demand response exchange, in Sayyad Nojavan and Kazem Zare, eds., Electricity Markets: New Players and Pricing Uncertainties, Springer, pp.135-168. Abstract.
Parinaz Aliasghari, Behnam Mohammadi-Ivatloo, Mehdi Abapour, 2020, Risk-based scheduling strategy for electric vehicle aggregator using hybrid Stochastic/IGDT approach, Journal of Cleaner Production, vol. 248, 1 March 2020, article 119270. Abstract.
Hamed Pashaei-Didani, Arash Mohammadi, Hamed Ahmadi-Nezamabad and Sayyad Nojavan, 2020, Information gap decision theory – based risk-constrained energy management of DC microgrids, chapter 5 in Risk-Based Energy Management: DC, AC and Hybrid AC-DC Microgrids, Sayyad Nojavan, Mahdi Shafieezadeh and Noradin Ghadimi, eds., Academic Press. Abstract.
Farhad Samadi Gazijahani and Javad Salehi, 2019, IGDT based Complementarity Approach for Dealing with Strategic Decision Making of Price Maker VPP Considering Demand Flexibility, IEEE Transactions on Industrial Informatics. Abstract.
Navid Rezaei, Abdollah Ahmadi, A.H. Khazali and J. Aghaei, 2019, Multiobjective risk-constrained optimal bidding strategy of smart microgrids: An IGDT-based normal boundary intersection approach, IEEE Transactions on Industrial Informatics, Vol. 15 , # 3 , March 2019, pp.1532-1543. Abstract.
Mehdi Shamshirband, Javad Salehi, Farhad Samadi Gazijahani, 2019, Look-ahead risk-averse power scheduling of heterogeneous electric vehicles aggregations enabling V2G and G2V systems based on information gap decision theory, Electric Power Systems Research, 173:56-70, DOI: 10.1016/j.epsr.2019.04.018. Abstract.
Maziar Karimi, Morteza Kheradmandi, and Abolfazl Pirayesh, 2019, Risk-constrained transmission investing of generation companies, IEEE Transactions on Power Systems, Vol. 34 , Issue: 2, pp.1043–1053, March 2019. Abstract.
Farhad Nazari-Heris, Behnam Mohammadi-ivatloo, D. Nazarpour, 2019, Network constrained economic dispatch of renewable energy and CHP based microgrids, Electrical Power and Energy Systems, 110: 144-160. Abstract.
Mohammad Jadidbonab, Hesameddin Mousavi-Sarabi, Behnam Mohammadi-Ivatloo, 2018, Risk-constrained scheduling of solar-based three state compressed air energy storage with waste thermal recovery unit in the thermal energy market environment, IET Renewable Power Generation. Abstract.
Waqas Ahmad Wattoo, Donghan Feng, Muhammad Yousif and Sohaib Tahir, 2018, A promising scheme for portfolio selection to gain pragmatic pool-based electricity market returns under uncertain circumstances, Studies in Informatics and Control, 27(4): 431-442. Abstract.
Seyed-Ehsan Razavi, Ali Esmaeel Nezhad, Hani Mavalizadeh, Fatima Raeisi, Abdollah Ahmadi, 2018, Robust hydrothermal unit commitment: A mixed-integer linear framework, Energy, to appear. Abstract.
Hamid Asadi Bagal, Yashar Nouri Soltanabad, Milad Dadjuo, Karzan Wakil, Noradin Ghadimi, 2018, Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory, Solar Energy, vol. 169, pp.343-352. Abstract.
Kianoush Ghahary, Amir Abdollahi, Masoud Rashidinejad and Mohammad Iman Alizadeh, 2018, Optimal reserve market clearing considering uncertain demand response using information gap decision theory, Intl. J. Electrical Power & Energy Systems, vol. 101, October 2018, pp.213-222. Abstract.
Morteza Vahid-Ghavidel, Nadali Mahmoudi, and Behnam Mohammadi-ivatloo, 2018, Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory, IEEE Transactions on Smart Grid.Abstract.
Behdad Vatania, Badrul Chowdhurya, Shahab Dehghanb, Nima Amjady, 2018, A critical review of robust self-scheduling for generation companies under electricity price uncertainty, Intl. J. of Electrical Power & Energy Systems, 97: 428-439. Abstract.
Hessam Golmohamadi and Reza Keypour, 2017, A bi-level robust optimization model to determine retail electricity price in presence of a significant number of invisible solar sites, Sustainable Energy, Grids and Networks, online 27.12.2017. Abstract.
Hossein Ranjbar and Seyed Hamid Hosseini, 2016, IGDT-based robust decision making applied to merchant-based transmission expansion planning, International Transactions on Electrical Energy Systems, DOI: 10.1002/etep.2230.
Deregulation in power systems has created new uncertainties and increased the previous ones. The presence of these uncertainties causes the transmission network to remain monopoly and the private investors not being interested in investing in this section. This paper presents a new merchant-based transmission expansion planning (TEP) formulation from the viewpoint of private investors. The information-gap decision theory (IGDT) is used to model the inherent uncertainties associated with the estimated investment cost of candidate lines and the forecasted system load and NSGAII is utilized to solve the multi-objective optimization problem.This algorithm helps private investors to select the best lines for investment in the presence of uncertainties. In order to verify the effectiveness of the proposed method, it has been applied to the IEEE RTS 24-bus system and the simpliﬁed Iranian 400-kV transmission system.
Soroush Shafiee, Hamidreza Zareipour, Andrew M. Knight, Nima Amjady, Behnam Mohammadi-Ivatloo, 2017, Risk-Constrained Bidding and Offering Strategy for a Merchant Compressed Air Energy Storage Plant, IEEE Transactions on Power Systems, 32(2): 946-957. Abstract.
Sayyad Nojavan, Kazem Zare, Behnam Mohammadi-Ivatloo, 2017, Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory, Electrical Power and Energy Systems, 93: 156-170. Abstract.
Manijeh Alipour, Kazem Zare and Behnam Mohammadi-Ivatloo, 2016, Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets, Renewable and Sustainable Energy Reviews, 60: 421-432.
This paper presents an optimal bidding strategy for industrial consumers with cogeneration facilities, power-only and heat-only units to participate in day-ahead electricity market. A information gap decision theory (IGDT) technique is implemented for determining the optimal bidding strategies considering market price uncertainty. IGDT evaluates the robustness/opportunity of optimal bidding strategy under market price uncertainty considering the consumer choice of taking risk-averse or risk-taking decisions. It is confirmed that the risk-averse or risk-taking decisions might affect the expected profit and bidding curve of the consumers. Moreover, demand response (DR) program has been implemented in order to serve the power and heat demands of the consumer with minimum cost. In the proposed DR program, the total power and heat demand of consumer will be supplied without any curtailed load. The responsive load can vary in different time intervals. In addition, it is assumed that the consumer will pay (receive) for increased (reduced) consumption, which is proportional to the day-ahead market price. In this paper, the heatpower dual dependency characteristic in different types of combined heat and power (CHP) units is taken into account and all technical constraints of generation units are satisfied. In addition, a heat buffer tank with the ability of heat storage is incorporated in the proposed framework. The verification of the proposed method is demonstrated using the simulation of a case study.
Combined heat and power (CHP) system, Demand response programs, Feasible operation region of CHP units, CHP optimal bidding strategy, Information gap decision theory (IGDT).
Mohammadreza Mazidi, Hassan Monsef, Pierluigi Siano, 2016, Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks, Energy Conversion and Management, Volume 115, Pages 103-116.
Demand response and real-time pricing of electricity are key factors in a smart grid as they can increase economic efficiency and technical performances of power grids. This paper focuses on incorporating price-responsive customers in day-ahead scheduling of smart distribution networks under a dynamic pricing environment. A novel method is proposed and formulated as a tractable mixed integer linear programming optimization problem whose objective is to find hourly sale prices offered to customers, transactions (purchase/sale) with the wholesale market, commitment of distribution generation units, dispatch of battery energy storage systems and planning of interruptible loads in a way that the profit of the distribution network operator is maximized while customers’ benefit is guaranteed. To hedge distribution network operator against financial risk arising from uncertainty of wholesale market prices, a risk management model based on a bi-level information-gap decision theory is proposed. The proposed bi-level problem is solved by recasting it into its equivalent single-level robust optimization problem using Karush-Kuhn-Tucker optimality conditions. Performance of the proposed model is verified by applying it to a modified version of the IEEE 33-bus distribution test network. Numerical results demonstrate the effectiveness and efficiency of the proposed method.
Proposing a model for incorporating price-responsive customers in day-ahead scheduling of smart distribution networks; this model provides a win–win situation.
Introducing a risk management model based on a bi-level information-gap decision theory and recasting it into its equivalent single-level robust optimization problem using Karush-Kuhn-Tucker optimality conditions.
Utilizing mixed-integer linear programing formulation that is efficiently solved by commercial optimization software.
Mohammadreza Mazidi, Hassan Monsef, Pierluigi Siano, 2016, Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach, Energy, 116: 214-235.
In the context of restructured electricity market and smart grid, uncertainties including renewable generation, load demand, and electricity price would significantly affect the technical and financial aspects of smart distribution networks. This paper presents a risk-averse decision making tool to help distribution network operator (DNO) in short-term operational activities. The objective is to optimize hourly sale prices offered to the customers, transactions (purchase/sale) with the wholesale market, commitment of distributed generation, dispatch of energy storage systems, and planning of interruptible loads in a way that a target profit for the risk-averse DNO is guaranteed. A bi-level information gap decision theory (IGDT) inspired problem is developed to hedge the DNO against risk imposed by the information gap between the forecasted and actual uncertain variables. The bi-level problem is recast into its equivalent single level problem driven by Karush-Kuhn-Tucker optimality conditions. Since the uncertain variables compete with each other to maximize their enveloped-bounds, the augmented ε-constraint method is used to solve the proposed IGDT-inspired multi-objective optimization problem. A Monte Carlo simulation based after-the-fact analysis is conducted to verify the robust performance of the operational decisions. The effectiveness and efficiency of the proposed model are evaluated on the 33-bus and the 118-bus modified test networks.
Augment ε-constraint method; Information gap decision theory; Short-term operation; Smart distribution network; Risk-averse; Uncertainty modelling
A risk-averse decision making tool is proposed to help DNOs in short-term activities.
Wind generation, load demand, and electricity price are considered uncertain.
IGDT-based and augment ε-constraint methods are presented to manage uncertainties.
Demand response programs are incorporated into the proposed model.
The proposed model is a mixed-integer linear programming optimization problem.
P. Mathuria and A. Singh, 2016, Robust self scheduling framework for GenCos with portfolio optimization, presented at the IEEE Power and Energy Society General Meeting, 2016, vol. 2016-November. doi: 10.1109/PESGM.2016.7741439. Abstract.
Soheil Sarhadi and Turaj Amraee, 2015, Robust dynamic network expansion planning considering load uncertainty, Electrical Power and Energy Systems, 71: 140-150.
This paper presents a dynamic transmission expansion planning framework with considering load uncertainty based on Information-Gap Decision Theory. Dynamic transmission planning process is carried out to obtain the minimum total social cost over the planning horizon. Robustness of the decisions against under-estimated load predictions is modeled using a robustness function. Furthermore, an opportunistic model is proposed for risk-seeker decision making. The proposed IGDT-based dynamic network expansion planning is formulated as a stochastic mixed integer non-linear problem and is solved using an improved standard branch and bound technique. The performance of the proposed scheme is verified over two test cases including the 24-bus IEEE RTS system and Iran national 400-kV transmission network.
Saeed Kharrati, Mostafa Kazemi and Mehdi Ehsan, 2015, Medium-term retailer’s planning and participation strategy considering electricity market uncertainties, International Transactions on Electrical Energy Systems, Article first published online: 27 July 2015, DOI: 10.1002/etep.2113.
This paper presents a risk-constrained programming approach to solve a retailer’s medium-term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope-bound information-gap model. Another source of uncertainty in this problem is the clients’ demand, which is considered via a scenario generation method. The proposed method is formulated as a bi-level stochastic programming problem based on the information-gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi-level problem into a single-level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed.
Meysam Khojasteh and Shahram Jadid, 2018, Reliability-constraint energy acquisition strategy for electricity retailers, Intl J. Electrical Power & Energy Systems, vol. 101, October 2018, pp.223-233. Abstract.
Meysam Khojasteh and Shahram Jadid, 2015, Decision-making framework for supplying electricity from distributed generation-owning retailers to price-sensitive customers, Utilities Policy, Available online 31 March 2015.
In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer’s selling price. A higher selling price increases retailers’ profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer’s energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower subproblem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer’s acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed.
Distributed generation, Elasticity, Electricity retailer, Information gap decision theory, Strategic risk management, Optimization
In a competitive market where all producers must participate in the market, WPPs (wind power producers) face two sources of uncertainty: (i) future market prices, and (ii) their production capability in coming hours. In this paper a risk-constrained optimal self-scheduling method for a WPP considering the uncertainty associated with market prices and wind generation is proposed. IGDT (Information Gap Decision Theory) is used to address theses uncertainties in WPP’s self-scheduling. The proposed IGDT-based model is a bilevel programming approach, which is transformed to an equivalent single level bilinear programming model that can be solved using available solvers. Numerical simulations and discussions are provided.
Sayyad Nojavan, Hadi Ghesmati and Kazem Zare, 2016, Robust optimal offering strategy of large consumer using IGDT considering demand response programs, Electric Power Systems Research, 130: 46-58.
In restructured electricity market, the consumers have various procurement strategies to supply their electricity demand from alternative resources. An approach to cost reduction and risk management for a large consumer is participation in demand response programs (DRP). Due to uncertain nature of pool prices and price fluctuations in pool market, uncertainty modeling is inevitable. For evaluation of different procurement strategies of large consumer, a technique based on information gap decision theory (IGDT) is proposed. This paper develops an energy acquisition model for large consumer with multiple procurement options including distributed generation (DG), bilateral contracts and pool market purchase with participating in DRP. This paper is focused to study the effect of DRP on the procurement strategy problem. Also, the time-of-use (TOU) rates of demand response programs have been modeled and consequently its influence on load profile and procurement strategy has been discussed. The proposed method does not minimize the procurement cost, but allows deriving robust decision with respect to price volatility. The robustness of procurement strategies for high procurement costs is optimized and related procurement strategy is proposed. The proposed method either deals with optimizing the opportunities to take advantage from low procurement costs or low pool prices. Finally, a case study is studied to show the advantages of proposed method.
Large consumer; Electricity procurement strategy; Information gap decision theory (IGDT); Demand response programs (DRP)
Two procurement objective functions for a large consumer considering DRP are proposed.
The new load modeling and formulation for DRP has been proposed.
The procurement strategies of large consumer, a technique based on IGDT is proposed.
The proposed method does not minimize the procurement cost but allows deriving robust decision with respect to price volatility.
The proposed method either deals with optimizing the opportunities to take advantage from low pool prices.
Sayyad Nojavan, Kazem Zare and Mohammad Azimi Ashpazi, 2015, A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market,Electric Power and Energy Systems, 69: 335-343.
This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT-MINLP method.
Optimal bidding strategy, Information gap decision theory (IGDT), Modified particle swarm optimization (MPSO), Market price uncertainty
P. Mathuria and R. Bhakar, 2015, GenCo’s integrated trading decision making to manage multimarket uncertainties, IEEE Transactions on Power Systems, vol. 30, no. 3, pp. 1465–1474, 2015, doi:10.1109/TPWRS.2014.2345744. Abstract.
Morteza Taherkhani and Seyed Hamid Hosseini, 2014, IGDT-based multi-stage transmission expansion planning model incorporating optimal wind farm integration, International Transactions on Electrical Energy Systems, DOI: 10.1002/etep.1965,
In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information-gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors’ strategies. The TEP model is formulated for the risk-averse and risk-seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi-stage time horizon. A genetic algorithm approach is employed to solve the bi-level IGDT-based optimization problem. Finally, this IGDT-based model is applied to the simplified Iranian 400-kV system, and the results are discussed.
Mathuria, P. and Bhakar, R., 2014, Info-Gap Approach to Manage GenCo’s Trading Portfolio With Uncertain Market Returns, IEEE Transactions on Power Systems, vol. 29, issue 6, pp.2916-2925.
An independent generation company (GenCo) secures its future trading position by managing its portfolio among multiple trading options. Future returns of these trading options are not known during decision making and are traditionally estimated using probabilistic or fuzzy methods. Quantifying such uncertainty of market returns by conventional methods does not reflect the information gap existing between estimated and actual market returns. Based on quantification of this information gap, the paper proposes GenCo’s portfolio optimization using a non-probabilistic Information Gap Decision Theory (IGDT). This framework comprehensively models GenCo’s behavior in deciding its trading strategy. Considering GenCo’s risk-averse behavior, the framework provides decisions that are robust towards losses, while considering its risk-seeking behavior the framework offers opportunity to capture windfall gains. The proposed approach has been validated through practical case study of PJM market.
Mathuria, P. and Bhakar, R., IGDT based Genco’s trading decision making in multimarket environment, Power and Energy Society General Meeting, 2014, vol.2014-October, doi: 10.1109/PESGM.2014.6939304. Abstract.
Jamshid Aghaei, Vassilios G. Agelidis, Mansour Charwand, Fatima Raeisi, Abdollah Ahmadi, Ali Esmaeel Nezhad, Alireza Heidari, 2017, Optimal Robust Unit Commitment of CHP Plants in Electricity Markets Using Information Gap Decision Theory, IEEE Transactions on Smart Grid, 8(5): 2296-2304. Abstract.
Mansour Charwand and Zeinab Moshavash, 2014, Midterm decision-making framework for an electricity retailer based on Information Gap Decision Theory, International Journal of Electrical Power & Energy Systems,vol.63., pp.185-195.
Fossil fuel gencos are subject to influence of multiple uncertain but interactive energy and emission markets. It procures production resources from fuel and emission market and sells its generation through multiple contracts in electricity market. With increasing volatility and unpredictability in energy markets, a genco needs to make prudent decision to manage its trading in all involved markets, to guarantee minimum profit. Considering the existing market uncertainties and associated information gap, this paper proposes a robust decision making approach for gencos trading portfolio selection in all three involved markets, based on Information Gap Decision Theory (IGDT). Results from a realistic case study provides a range of decisions for a risk averse genco, appropriate to its nature, and based on the trade-off existing between robustness and targeted profit.
Mansour Charwand, Abdollah Ahmadi, Adel M. Sharaf, Mohsen Gitizadeh and Ali Esmaeel Nezhad, 2015, Robust hydrothermal scheduling under load uncertainty using information gap decision theory, International Transactions on Electrical Energy Systems, DOI: 10.1002/etep.2082.
In midterm planning, the objective of an electricity retailer is to manage a portfolio of different contracts and to determine the selling price offered to its clients. This paper provides a novel technique based on Information Gap Decision Theory (IGDT) to assess different strategies for a retailer under unstructured pool price uncertainty. This method can be used as a tool for assessing the risk levels, considering whether a retailer is risk-taking or risk-averse regarding its midterm strategies. Supply sources include forward contracts, a limited self-generating facility, and the pool. It is shown that in robust strategy, procurement from sources with uncertain prices decreases. Also, the selling price offered to the consumers rises, decreasing the actual demand of the retailer, and consequently the expected profit is decreased. A case study is used to illustrate the proposed technique.
Retailer; Risk; Information Gap Decision Theory; Energy procurement; Selling price
A midterm decision model is introduced for an electricity retailer using IGDT.
This tool is used to assess the risk levels under unstructured pool price uncertainty.
The proposed method provides a robust procurement/selling price strategy.
The method optimizes the robustness function to gain minimum acceptable profit.
The proposed method optimizes the opportunities to take advantage of high profits.
Behnam Mohammadi-Ivatloo, Hamidreza Zareipour, Nima Amjady, and Mehdi Ehsan, Application of Information-Gap Decision Theory to Risk-Constrained Self-Scheduling of GenCos, IEEE Transactions on Power Systems, Vol. 28, No. 2, May 2013, pp1093-1102.
Kazem Zare, Mohsen Parsa Moghaddam and Mohammad Kazem Sheikh El Eslami, 2010, Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology, Energy, vol.35, pp.2999-3007.
This paper provides a technique to derive the bidding strategy in the day-ahead market for a large consumer that procures its electricity demand in both day-ahead market and a subsequent adjustment market. It is considered that hourly market prices are normally distributed and this correlation is modeled by variance-covariance matrix. The uncertainty of procurement cost is modeled using concepts derived from information gap decision theory which allows deriving robust bidding strategies with respect to price volatility. First Order Reliability Method is applied to construct the robust bidding curve. The proposed technique is illustrated through a realistic case study.
Bidding strategy; First order reliability method; Information gap decision theory; Large consumer
Kazem Zare, Antonio J. Conejo, Miguel Carrion, and Mohsen Parsa Moghaddam, 2010, Multi-market energy procurement for a large consumer using a risk-aversion procedure, Electric Power Systems Research, vol.80, pp.63-70.
This paper provides a technique to derive the bidding strategy in the day-ahead market of a large consumer that procures its electricity demand in both the day-ahead market and a subsequent adjustment market. Price uncertainty is modeled using concepts derived from information gap decision theory, which allows deriving robust decisions with respect to price volatility. Risk aversion is built implicitly within the proposed model. Correlations among prices in the day-ahead and the adjustment markets are properly modeled. The proposed technique is illustrated through a realistic case study.
Kazem Zare, Mohsen Parsa Moghaddam, and Mohammad Kazem Sheikh El Eslami, 2010, Electricity procurement for large consumers based on Information Gap Decision Theory, Energy Policy, vol. 38, pp.234-242. Abstract.
Kazemi, M., Mohammadi-Ivatloo, B. and Ehsan, M, 2013, IGDT based risk-constrained strategic bidding of GenCos considering bilateral contracts, 21st Iranian Conference on Electrical Engineering, ICEE 2013, Mashhad, Iran; 14-16 May 2013.
Bi-level optimization; Bilateral contracts; Construction method; Day ahead market; Day-ahead energy markets; Information gap; Strategic bidding; Thermal units; Commerce; Electrical engineering; Uncertainty analysis; Risk assessment.
Kazemi, M., Mohammadi-Ivatloo, B. and Ehsan, M, 2014, Risk-based bidding of large electric utilities using Information Gap Decision Theory considering demand response, Electric Power Systems Research, Vol.114, pp.86-92.
The present study presents a new risk-constrained bidding strategy formulation of large electric utilities in, presence of demand response programs. The considered electric utility consists of generation facilities, along with a retailer part, which is responsible for supplying associated demands. The total profit of utility comes from participating in day-ahead energy markets and selling energy to corresponding consumers via retailer part. Different uncertainties, such as market price, affect the profit of the utility. Therefore, here, attempts are made to make use of Information Gap Decision Theory (IGDT) to obtain a robust scheduling method against the unfavorable deviations of the market prices. Implementing demand response programs sounds attractive for the consumers through providing some incentives in one hand, and it improves the risk hedging capability of the utility on the other hand. The proposed method is applied to a test system and effect of demand response programs is investigated on the total profit of the utility.
Bidding strategy; Risk management; Information Gap Decision Theory (IGDT); Demand response; Electric utility.
We investigate the effect of demand response on GenCo’s bidding strategy.
A proposed IGDT method is presented for risk management.
The bidding formulation of a company consisted of generation facility along with a retailer part is presented.
A linear programming format of IGDT method is presented.
Soroudi, A. and Ehsan, M., 2013, IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty, IEEE Transactions on Smart Grid, 4(2): 886-895.
This paper presents the application of information gap decision theory (IGDT) to help the distribution network operators (DNOs) in choosing the supplying resources for meeting the demand of their customers. The three main energy resources are pool market, distributed generations (DGs), and the bilateral contracts. In deregulated environment, the DNO is faced with many uncertainties associated to the mentioned resources which may not have enough information about their nature and behaviors. In such cases, the classical methods like probabilistic methods or fuzzy methods are not applicable for uncertainty modeling because they need some information about the uncertainty behaviors like probability distribution function (PDF) or their membership functions. In this paper, a decision making framework is proposed based on IGDT model to solve this problem. The uncertain parameters considered here, are as follows: price of electricity in pool market and demand of each bus. The robust strategy of DNO is determined to hedge him against the risk of increasing the total cost beyond what it is willing to pay. The effectiveness of the proposed tool is assessed and demonstrated by applying it on a large distribution network.
Bilateral contracts; distributed generation; information gap decision theory; risk; uncertainty
Sayyad Nojavan and Kazem Zare, 2013, Risk-based optimal bidding strategy of generation company in day-ahead electricity market using information gap decision theory, Electric Power and Energy Systems, vol. 48, pp.83-92.
This paper considers a price-taker generation company to participate in day-ahead electricity energy market. While making optimal bidding strategy for producer, factors such as the characteristics of generator and the market price uncertainty need to be considered because of having direct impact on the expected profit and bidding curve. The market price considered an uncertain variable and it is assumed that the generation company forecasted the market prices. In this study, the uncertainty model of market price is considered based on the concept of weighted average squared error using a variance–covariance matrix. Information gap decision theory is used to develop the bidding strategy of a generation company. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while producer considers whether a decision risk-averse or risk-taking. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. A case study is used to illustrate the proposed approach.
Optimal bidding strategy, Information gap decision theory, Risk management
A new model for optimal bidding strategy of generation companies is proposed based on IGDT.
This methodology is not based on profit maximization; however, it results in a risk-averse and risk-taking issue.
The generating company can maximize the robustness of its bidding strategy against low market prices.
Also it can be gained from the opportunity of increasing the market prices.
Sayyad Nojavan, Kazem Zare and Mohammad Reza Feyzi, 2013, Optimal bidding strategy of generation station in power market using information gap decision theory (IGDT), Electric Power Systems Research,Volume 96, March 2013, Pages 56-63.
This paper considers a profit-maximizing thermal unit producer that participates in a day-ahead market. The producer behaves as a price-taker in the day-ahead electricity market. This paper provides the information gap decision theory for determining the optimal bidding strategies for the day-ahead market. While making bidding strategy, factors such as characteristics of the generator and market price uncertainty need to be considered as they have direct impact on the expected profit and bidding curve. In this paper, a method of building an optimal bidding strategy is presented under market price uncertainty using information gap decision theory (IGDT). Information gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportunity to windfall – under severe uncertainty. It is shown that risk-aversion and risk-taker may influence the expected profit and bidding curve of a producer. The proposed method is illustrated through a realistic case study.
Optimal bidding strategy; Information gap decision theory (IGDT); Uncertainty
A new model for optimal bidding strategy of generation companies is proposed based on IGDT.
This methodology is not based on profit maximization; however, it results in a risk-averse and risk-taking issue.
The generating company can maximize the robustness of its bidding strategy against low market prices.
Also it can be gained from the opportunity of increasing the market prices.
Kazem Zare, Mohsen Parsa Moghaddam, and Mohammad Kazem Sheikh-El-Eslami, 2011, Risk-Based Electricity Procurement for Large Consumers, IEEE Transactions on Power Systems, vol. 26, no. 4, November 2011. Abstract.
M.-P. Cheong, D. Berleant, and G. B. Shebl’e,Information Gap Decision Theory as a tool for Strategic Bidding in Competitive Electricity Markets, 8th International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, lowa, September 12-16,2004.