Coordination of Generation and Transmission Development Through Generation Transmission Charges—A Game Theoretical Approach

Tohidi, Yaser; Olmos, Luis; Rivier, Michel; Hesamzadeh, Mohammad Reza

doi:10.1109/tpwrs.2016.2581218

Cited by 24 publications

(14 citation statements)

References 48 publications

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“…Hence, we pursue applying an efficient method to convert this nonlinear model to a linear one, which can significantly reduce the computational burden of this large-scale optimisation problem, while guarantee the optimality. In this paper, a Big-M centralised approach is presented to linearise (8) [30][31][32][33]. In addition, a novel algorithm is proposed to linearise (12).…”

Section: Linearisation Methodsmentioning

confidence: 99%

An online method for MILP co‐planning model of large‐scale transmission expansion planning and energy storage systems considering N‐1 criterion

Mazaheri

Abbaspour

Fotuhi‐Firuzabad

et al. 2020

IET Generation Trans & Dist

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In recent years, increased integration of renewable energy sources (RES) calls for extensive and costly investments in transmission networks. In response, power system decisionmakers try to apply alternative solutions aimed to decrease the imposed investment costs. In this context, the presence of large-scale energy storage systems (ESSs) in transmission network can be a practical option for deferring investment in expansion plans of transmission lines, alleviating system congestions, and attaining higher flexibility. In this paper, an efficient model is proposed for co-planning expansion studies of compressed air energy storage (CAES) units and transmission networks. The associated optimisation formulation of co-planning problem is expressed as a MILP model, which can be efficiently solved. The proposed model is applied on the Garver as well as RTS test systems and N-1 criterion is considered to address the system reliability performance in expansion studies. The results demonstrate that the proposed co-planning framework has a superior performance in expansion plans of transmission systems. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Linearisation Methodsmentioning

confidence: 99%

An online method for MILP co‐planning model of large‐scale transmission expansion planning and energy storage systems considering N‐1 criterion

Mazaheri

Abbaspour

Fotuhi‐Firuzabad

et al. 2020

IET Generation Trans & Dist

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“…Coordination of transmission and generation investment is not a new problem and was often posed in terms of whether the transmission decision should lead or follow the generation decision [7] and [8]. The concept of investment coordination is further discussed in [9] and [10], whilst [11] and [12] propose game theoretic models for proactive and reactive coordination. In [13] proactive coordination under imperfect competition is assumed, leading to an analysis of investment coordination between transmission networks and different types of energy storage assets.…”

Section: A Modelling Perspective and Background Researchmentioning

confidence: 99%

“…APPENDIX A: NOMENCLATURE 12 Binary Variables a btks Battery storage charge/discharge indicator; z mt ,y mt Transmission investment decision variables; Incidence matrices I (i) n…”

Section: B Performance Of the Proposed Dbd Algorithmmentioning

confidence: 99%

Optimal Transmission Investment With Regulated Incentives Based Upon Forward Considerations of Firm and Intermittent Resources With Batteries

Khastieva

Mohammadi

Hesamzadeh

et al. 2021

IEEE Trans. Power Syst.

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Regulatory support for transmission investment by private investors follows a complex process of dialogue, information sharing, commitments, and incentives. Whilst this process has attracted substantial research, the formulation of efficient solutions still remains an open question and this has become more complex with the emergence of new intermittent and batterystorage resources. We propose an optimal incentive solution in which the private transmission company earns congestion rents and an incentive fee, set by agreement with the regulator. This fee is set in the context of assessing the impact of current and future generation resources (conventional, intermittent, and batteries) on the congestion rents. The welfare-optimizing solution follows from a mixed integer, nonlinear, bilevel optimization problem, which is computationally challenging. We re-cast the problem for tractability and devise a disjunctive-based decomposition algorithm. Its performance has been successfully demonstrated on two standard IEEE case studies.

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“…It is recognized by researchers that the coordinated optimization of generation and transmission system would achieve a more reliable and economical solution [5]- [8]. In addition, various challenges brought by uncertainties of power generation, load and transmission line could be addressed well via robust optimization [9]- [10].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The investment plan considers candidate generating units, transmission lines, CBs, and SVCs. The investment status of a candidate will change to 1 for the remaining years once installed (5)- (8).…”

Section: B Investment Constraintsmentioning

confidence: 99%

Reliability-Constrained AC Power Flow-Based Co-Optimization Planning of Generation and Transmission Systems With Uncertainties

Zhou¹,

Liu

et al. 2020

IEEE Access

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Coordination of Generation and Transmission Development Through Generation Transmission Charges—A Game Theoretical Approach

Cited by 24 publications

References 48 publications

An online method for MILP co‐planning model of large‐scale transmission expansion planning and energy storage systems considering N‐1 criterion

An online method for MILP co‐planning model of large‐scale transmission expansion planning and energy storage systems considering N‐1 criterion

Optimal Transmission Investment With Regulated Incentives Based Upon Forward Considerations of Firm and Intermittent Resources With Batteries

Reliability-Constrained AC Power Flow-Based Co-Optimization Planning of Generation and Transmission Systems With Uncertainties

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