Modified Benders Decomposition for Solving Transmission Investment Game With Risk Measure

Tohidi, Yaser; Hesamzadeh, Mohammad Reza; Regairaz, Francois

doi:10.1109/tpwrs.2017.2743823

Cited by 14 publications

(5 citation statements)

References 37 publications

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“…The tests performed in Sections VI and VII serve the purpose of demonstrating the functionality of the proposed models. While the existing work tests the proposed models with fiveand eight-generator systems, it is desirable to apply these models to a full system [39], [41], [42], [45], [46]. This will be a further extension of the existing work.…”

Section: Further Discussion and Future Workmentioning

confidence: 99%

“…As observed, the computational time for the SIP model is the highest due to the several binary variables introduced for the linearization process 5 . We can reduce the computational time in our MILP models in two ways: first, we can employ decomposition algorithms to break the MILP model into smaller and easierto-solve optimization problems [41], [42]. Second, we can use scenario generation and reduction algorithm to reduce the size of our MILP models [43], [44].…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Therefore, the choice of five-and eightgenerator systems was for the ease of demonstration. As all our proposed models have been linearized, decomposition methods can be implemented to study a real system[39],[41],[42],[45],[46] 7. Out of the eight generators, three of them are located in Sweden, one in Denmark and four in Norway.…”

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confidence: 99%

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Optimal Dispatch in a Balancing Market With Intermittent Renewable Generation

Shinde

Hesamzadeh

Date

et al. 2021

IEEE Trans. Power Syst.

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In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM). The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up-or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenariogeneration algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient.

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Section: Further Discussion and Future Workmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

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Optimal Dispatch in a Balancing Market With Intermittent Renewable Generation

Shinde

Hesamzadeh

Date

et al. 2021

IEEE Trans. Power Syst.

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“…Finally, the proposed robust co-optimisation model can be transferred to a MILP formulation who can be efficiently solved [21][22][23]. The MILP problem can be solved by branch and bound algorithm, benders decomposition algorithm, heuristic algorithm and commercial software packages such as Cplex [24][25][26]. In this paper, the CESS model is solved by the Cplex solver in the GAMS.…”

Section: Price Uncertaintymentioning

confidence: 99%

Co‐optimisation model for the long‐term design and decision making in community level cloud energy storage system

Chen

Zhang

2020

IET Renewable Power Generation

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Deploying the cloud energy storage system (CESS) is an economic and efficient way to store excess photovoltaic generation and participate in demand response without personal investment on pricy energy storage equipment. It is a shared battery energy storage system (BESS) for local residential and small commercial consumers, which is designed and controlled by the CESS operator. Based on the profit purpose, the CESS operator not only pursues the most economic operating strategy, but also tries to minimize the total investment on the design stage. This paper considers the investment on the batteries, power conversion system, reactive power compensation equipment and the cost including battery degradation cost and operation cost. The electricity price uncertainty and the voltage deviation of the CESS node caused by power exchange are also considered. Moreover, the cases of a largely centralized energy storage system and multiple distributed energy storage systems are all modelled. Finally, an original robust cooptimization model is transferred to a mixed integer linear programming model (MILP) and solved in GAMS. Numerical results based on historical data from 300 residential consumers in Australia present that the battery degradation cost and price uncertainty can't be neglected.

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“…While, in the non-cooperative approach, each transmission planner seeks to maximise its social welfare considering the planning decisions of other planners [11]. In the non-cooperative approach, the social welfare of the whole interconnected system might not be obtained due to competition between different transmission planners [12]. According to these terminologies, the proposed algorithm in this paper, which aims at maximising the overall social welfare of the whole system, falls in the cooperative TEP category.…”

Section: Introductionmentioning

confidence: 99%