Price impact assessment for large-scale merchant energy storage facilities

Zamani-Dehkordi, Payam; Shafiee, Soroush; Rakai, Logan; Knight, Andrew M.; Zareipour, Hamidreza

doi:10.1016/j.energy.2017.02.107

Cited by 21 publications

(9 citation statements)

References 32 publications

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“…In general, the quantity of ESS transaction is related to the SOC and the spot price [29], the relationship of which can be formulated using fESS(·) as follows:…”

Section: Price-responsive Model Of Essmentioning

confidence: 99%

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Dynamic pricing for decentralized energy trading in micro-grids

et al. 2018

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The fast deployment of distributed energy resources in the electric power system has highlighted the need for an efficient energy trading transactive model, without the need for centralized dispatch. In this field, a particular challenge is the determination of an effective pricing scheme that is able to produce benefits for all participants. In this paper, a novel dynamic pricing methodology is presented, offering a market-oriented means to drive decentralized energy trading and to optimize financial benefits for owners of distributed energy resources. Firstly, a price-responsive model for each type of distributed energy resource is investigated. Particularly, the decoupled State of Charge function is proposed to calculate the value of a single charging/discharging action for energy storage systems. In addition, an adaptable three-tiered framework is designed, including micro-grid balancing, aggregator scheduling, and trading optimization. By launching Tier I, II, and III, the spot prices for participants are iteratively updated and optimized in inner-micro-grid, inner-aggregator, and inter-aggregators level. The framework is able to maximize the financial savings from renewable energy, and meanwhile, provide a dynamic price signal to assist stakeholders in determining response actions and trading strategies. A realistic case is simulated using Java Agent Development framework based multi-agent modeling. The results indicate that the presented methodology enables decentralized energy trading and permits easier marketization of micro-grids with a high share of distributed energy resources.

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“…In general, the quantity of ESS transaction is related to the SOC and the spot price [29], the relationship of which can be formulated using fESS(·) as follows:…”

Section: Price-responsive Model Of Essmentioning

confidence: 99%

“…For simplicity, and without the loss of generality to represent the negative correlated and one-to-one correspondent relationship between optimal SOC and spot price, formula (9) can be expressed as follows, which is derived from [29], [31], [32], [33]. In Fig.…”

Section: Price-responsive Model Of Essmentioning

confidence: 99%

Dynamic pricing for decentralized energy trading in micro-grids

et al. 2018

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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%

“…, 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%

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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“…Real-time pricing, with the utilisation of the automatic metering infrastructure, can help solve the abovementioned problem [17]. The real-time pricing may enhance competitiveness in the market and encourage more bidding activities in the market [18,19]. Furthermore, real-time pricing scheme can consider the participation of new source like the electric vehicle in the bidding process [20,21].…”

Section: Introductionmentioning

confidence: 99%