Economic assessment of a price-maker energy storage facility in the Alberta electricity market

Shafiee, Soroush; Zamani-Dehkordi, Payam; Zareipour, Hamidreza; Knight, A.M.

doi:10.1016/j.energy.2016.05.086

Cited by 62 publications

(23 citation statements)

References 26 publications

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“…It is worth emphasizing that this is in sharp contrast with the approach presented in [7] and [4], which both assume that the supply curve for every hour of the day is exactly known in advance, or equivalently that the operator is insensitive to estimation errors. Furthermore, our approach will model the supply curve as a piecewise linear function which better captures the increasing marginal impact of supply on prices during high demand periods compared to the piecewise constant model employed in [4].…”

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confidence: 98%

“…An economic bid does include a price component: it indicates that the participant is willing to buy/sell electricity provided that the cleared market price is at most/at least the submitted price bid. In the case of energy storage, the self-scheduling formulation is generally preferred [7,4,8], because the hourly bids are interdependent. For instance, the storage operator needs to be certain that his purchase bids have been accepted so that he can sell this electricity later.…”

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confidence: 99%

“…A few studies have addressed the self-scheduling of a large-scale energy storage facility [7,4,13,8]. The general idea is to describe, at each time step, the market price p t as a non-increasing function of the quantity q t submitted by the energy storage on the market, i.e.…”

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confidence: 99%

“…Since the storage is self-scheduling, its operation is indeed equivalent to an increase (when charging) or a decrease (when discharging) in the demand. [4] and [7] exploit respectively the supply curve of the Alberta and PJM electricity markets to formulate the self-scheduling problem of a price-maker energy storage. In [4], a linear supply curve is constructed for each month based on historical data of prices and quantities.…”

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confidence: 99%

“…In [4], a linear supply curve is constructed for each month based on historical data of prices and quantities. In [7], actual supply curves from the Alberta market are used for years 2010 to 2014. For each hour, based on the supply curve and the demand, generation price quota curves (GPQC) and demand price quota curves (DPQC) are constructed to model respectively the price impacts of discharging and charging.…”

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confidence: 99%

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Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

et al. 2019

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Recent progress in energy storage raises the possibility of creating large-scale storage facilities at lower costs. This may bring economic opportunities for storage operators, especially via energy arbitrage. However, storage operation in the market could have a noticeable impact on electricity prices. This work aims at evaluating jointly the potential operating profit for a price-maker storage facility and its impact on the electricity prices in the New York state market. Based on historical data, lower and upper bounds on the supply curve of the market are constructed. These bounds are used as an input for the robust self-scheduling problem of a price-maker storage facility. Our computational experiments show that the robust strategies thus obtained allow to reduce significantly the loss exposure while maintaining reasonably high expected profits.

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Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

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Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

Aghajani

Kazemzadeh

Ebrahimi

2018

Neural Comput & Applic

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5.12 Energy Management in Smart Cities

Fokaides

Apanavičienė

Klumbytė

2018

Comprehensive Energy Systems

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The Degree Certificates are giving reference to the joint programme. The doctoral candidates are jointly supervised, and must pass a joint examination procedure set up by the three institutions issuing the degrees. This Thesis is a part of the examination for the doctoral degree. The invested degrees are official in Spain, the Netherlands and Sweden, respectively. SETS Joint Doctorate was awarded the Erasmus Mundus excellence label by the European Commission in year 2010, and the European Commission's Education, Audiovisual and Culture Executive Agency, EACEA, has supported the funding of this programme. The EACEA is not to be held responsible for contents of the Thesis. vi Energy Management of Smart Cities Christian F. Calvillo Muñoz Energy Management of Smart Cities 1 Christian F. Calvillo Muñoz Chapter 7 Complete smart city energy model: aggregated DER and transport systems with active market participation Chapter 8 Conclusions, contributions and future work Chapter 1 Energy Management of Smart Cities 9 Christian F. Calvillo Muñoz 2 2 LITERATURE REVIEW This chapter presents the identification of energy systems task, corresponding to the smart city energy model methodology described in section 1.3. This includes the evaluation of energy systems available in a smart city, assessing their importance and possible synergies, in addition to the review of the existing models and tools, to select which ones could be used. The first part of the chapter introduces an overview of energy related work on planning and operation models within the smart city by classifying their scope into five main intervention areas. These areas are generation, storage, infrastructure, facilities, and transport. Then, more complex urban energy models integrating more than one intervention area are reviewed, outlining their advantages and limitations, existing trends and challenges, as well as some relevant smart cities applications. With this revision, important elements and possible synergies in the smart city are identified, and a framework for developing a comprehensive smart city energy model is proposed, along with some additional recommendations. Therefore, the main contributions of this chapter are, first, a comprehensive literature review of smart city energy systems and related modelling tools, classifying them in five intervention areas, and second, a framework with guidelines and recommendations for developing a comprehensive smart city energy model.

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Economic assessment of a price-maker energy storage facility in the Alberta electricity market

Cited by 62 publications

References 26 publications

Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

5.12 Energy Management in Smart Cities

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