Strategic capacity withholding by energy storage in electricity markets

Ye, Yujian; Papadaskalopoulos, Dimitrios; Moreira, Roberto; Štrbac, Goran

doi:10.1109/ptc.2017.7981200

Cited by 12 publications

(19 citation statements)

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“…A large number of previous papers [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] have modelled price-making ESSs and quantitatively analysed their ability to exercise market power. However, the modelling approaches adopted in these works exhibit certain limitations.…”

Section: Background and Motivationmentioning

confidence: 99%

“…However, the parameters of this function are determined through exogenous data and therefore cannot accurately capture the impacts of other market participants' characteristics on price formation. To address this limitation, the authors of [21][22][23][24][25][26][27][28][29][30] model the decision making of a single price-maker ESS through bi-level optimisation approaches, which endogenously represent the market clearing and price formation process. Nevertheless, all these papers assume that only the single examined ESS acts strategically and electricity producers are price-takers (i.e.…”

Section: Background and Motivationmentioning

confidence: 99%

“…• A multi-period equilibrium programming electricity market model is developed, capturing the time-coupling operational characteristics of ESSs as well as network constraints. In contrast to single bi-level/MPEC models employed in [13,[21][22][23][24][25][26][27][28][29][30] and representing an unrealistic monopoly setting, the proposed model captures more accurately the reality, where the market outcome is determined by the interactions of multiple independent and strategic electricity producers and ESSs. • Quantitative analysis with the developed model demonstrates that the price-taking ESSs mitigate market power exercise by strategic producers during peak periods and enhance it during off-peak periods, with the former mitigation significantly dominating the latter enhancement and resulting in an overall positive impact in terms of market efficiency.…”

Section: Scope and Contributionsmentioning

confidence: 99%

“…, producer i behaves as a price-maker and submits an offer price higher than its actual marginal costs (v i, t λ i, b G , ∀b) to the market at t. v. Each demand participant j submits a decreasing stepwise bid curve (capturing the effect of demand's price elasticity [17,19,20,27,34]) to the market at every period t, consisting of a number of blocks c. The price/quantity bids are time-specific and location-specific parameters, capturing the differentiated preferences of consumers across different time periods and the differentiated geographical conditions. Demand participants are assumed price-takers, bidding based on their actual technoeconomic parameters.…”

Section: Modelling Assumptionsmentioning

confidence: 99%

“…Regarding the economic properties of the ESS, following the model employed in [14,15,21,25,27,30], the ESSs do not submit a price offer or bid to the market, since they do not exhibit inherent cost or benefit components (as electricity producers and demands, respectively) apart from their negligible operation and maintenance costs. However, ESS k can exercise market power through capacity withholding (i.e.…”

Section: Modelling Assumptionsmentioning

confidence: 99%

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Investigating the impacts of price‐taking and price‐making energy storage in electricity markets through an equilibrium programming model

Papadaskalopoulos

Moreira

et al. 2018

IET Generation, Transmission & Distribution

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The envisaged decarbonisation of electricity systems has attracted significant interest around the role and value of energy storage systems (ESSs). In the deregulated electricity market, there is a need to investigate the complex impacts of ESSs, considering the potential exercise of market power by strategic players. This study aims at comprehensively analysing the impacts of both price-taking and price-making storage behaviours on energy market efficiency, corresponding to potential settings with small and large storage players, respectively. In order to achieve this and in contrast to previous papers, this work develops a multi-period equilibrium programming market model to determine market equilibrium stemming from the interactions of independent strategic producers and ESSs, while capturing the time-coupling operational constraints of ESSs as well as network constraints. The results of case studies on a test market capturing the general conditions of the GB electricity system demonstrate that the introduction of ESSs mitigates market power exercise and improves market efficiency, with this beneficial impact being higher when ESSs act as price takers. When the electricity network is congested, the location of ESSs also affects the market outcome, with their beneficial impact on market efficiency being higher when they are located in higher-priced areas. − , χ n, m, t + dual variables of constraints (13) (£/MW) ψ n, t − , ψ n, t + dual variables of constraints (14) (£/rad) δ t dual variables of constraints (15) (£/rad) IET Gener. Transm. Distrib.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Scope and Contributionsmentioning

confidence: 99%

Section: Modelling Assumptionsmentioning

confidence: 99%

Section: Modelling Assumptionsmentioning

confidence: 99%

See 3 more Smart Citations

Investigating the impacts of price‐taking and price‐making energy storage in electricity markets through an equilibrium programming model

Papadaskalopoulos

Moreira

et al. 2018

IET Generation, Transmission & Distribution

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Proposing new analytical price‐based market power and coalition indices based on LMP share in double‐sided power market

Saghravanian

Samadi

Hajiabadi

2021

Int Trans Electr Energ Syst

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One of the strategic actions to gain more profit in the electricity market is to exercising market power as a leverage in exclusive situations. The main goal of this article is to assess load serving entities market power via self-action scramble by price-based indices as an application of the structural decomposition approach.In addition, the possibility of forming a beneficial coalition due to the cross interactions between players can be evaluated using the proposed indices. These coalitions may be occurred between two generation units, two load serving entities, or a generation unit and a load serving entity. The cumulative sharing impacts of one load serving entity on the locational marginal prices of all units owned by a GenCo is also defined as a new index. To achieve this goal, the first step is to extract the structural decomposition coefficients of market components consisting of suppliers, inelastic, and elastic loads, which determine the contribution of all players on the electricity price separately. In the second step, new structural indices to determine self/cross-share index of each player in the electricity price based on the contributing factors obtained from the decomposition process. So, it is possible for market operator to prioritize the potential of exercising market power and forming coalition with a price-based index for both-sides players. As well, it is important for market operators to explore the economic justification of relationships between players to prevent market deviation. The proposed approach is evaluated on the IEEE 24-bus system, and the results are discussed.List of Symbols and Abbreviations: SG, set of all generators; D, set of all demands; L, set of all transmission lines; N G :N G , set of upper and lower bounded generators; N G , set of marginal generation units; N D , set of all fully dispatch elastic loads; N D , set of all partial dispatch elastic loads; L cong , set of all congested lines; n, bus number; a i , intercept of bid function for generator i; b i , slope of bid function for unit i; c j , intercept of offer function for load j; d j , slope of offer function for load j; P max l , upper flow limits of line l; P max g , P min g , upper/lower active power limit of generators; P max e , P min e , upper/lower active power limit of LSEs; P inelastic j , the active power of jth inelastic load; P d , total dispatched active power consist of elastic and inelastic load; P frc , total forecasted active power load; P g i , the active power generated by ith unit; P ej , the active power of jth LSE; α l:i , power transmission distribution factor of line l with respect to node ith generator; α l:j , power transmission distribution factor of line l with respect to node jth load; λ, Lagrange multiplier of the equality constraint; μ max i , μ min i , Lagrange multiplier of maximum/minimum generation limits refer to ith unit; Γ max l , Γ min l , Lagrange multiplier of the upper/lower of line flow limits refer to lth transmission line; ω max n , ω min n , Lagrange multiplier o...

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Exploring the Concept of Hosting Capacity from an Electricity Market Perspective

Valenzuela

Moreno

Papadaskalopoulos

et al. 2020

Hosting Capacity for Smart Power Grids

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Strategic capacity withholding by energy storage in electricity markets

Cited by 12 publications

References 10 publications

Investigating the impacts of price‐taking and price‐making energy storage in electricity markets through an equilibrium programming model

Investigating the impacts of price‐taking and price‐making energy storage in electricity markets through an equilibrium programming model

Proposing new analytical price‐based market power and coalition indices based on LMP share in double‐sided power market

Exploring the Concept of Hosting Capacity from an Electricity Market Perspective

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