Economic valuation of reserves in power systems with high penetration of wind power

Morales, Juan M.; Conejo, Antonio J.; Pérez-Ruiz, Juan

doi:10.1109/pes.2009.5260229

Cited by 139 publications

(233 citation statements)

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“…To clear the forward market using stochastic programming (Birge & Louveaux, 2011), which allows modeling future balancing needs and costs in a probabilistic framework, thus yielding the day-ahead energy dispatch that minimizes the expected system operating costs. One of the major advantages of this approach is that it endogenously solves for the optimal amount of reserve capacity to be left to the balancing market, weighing the expected costs and benefits of such capacity Bouffard & Galiana, 2008;Morales et al, 2009;Papavasiliou et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Electricity market clearing with improved scheduling of stochastic production

Morales

Zugno

Pineda

2014

European Journal of Operational Research

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105

122

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In this paper, we consider an electricity market that consists of a day-ahead and a balancing settlement, and includes a number of stochastic producers. We first introduce two reference procedures for scheduling and pricing energy in the day-ahead market: on the one hand, a conventional network-constrained auction purely based on the least-cost merit order, where stochastic generation enters with its expected production and a low marginal cost; on the other, a counterfactual auction that also accounts for the projected balancing costs using stochastic programming. Although the stochastic clearing procedure attains higher market efficiency in expectation than the conventional day-ahead auction, it suffers from fundamental drawbacks with a view to its practical implementation. In particular, it requires flexible producers (those that make up for the lack or surplus of stochastic generation) to accept losses in some scenarios. Using a bilevel programming framework, we then show that the conventional auction, if combined with a suitable day-ahead dispatch of stochastic producers (generally different from their expected production), can substantially increase market efficiency and emulate the advantageous features of the stochastic optimization ideal, while avoiding its major pitfalls.A two-node power system serves as both an illustrative example and a proof of concept. Finally, a more realistic case study highlights the main advantages of a smart day-ahead dispatch of stochastic producers.Dear Editor of European Journal of Operational Research, Today's electricity market design is the result of adapting traditional practices, such as unit commitment, economic dispatch or contingency analysis, to a competitive structure. These practices were conceived in view of a generation mix mostly formed by dispatchable power plants, and are now to be revisited so that stochastic producers can enter the competition in a fair and efficient manner.In this line, researchers have recently advocated a market-clearing mechanism that co-optimizes the forward (day-ahead) and the anticipated real-time energy dispatches using stochastic programming. Even though, ideally, this mechanism attains maximum market efficiency, it results in an energy-only market settlement that requires flexible producers to accept economic losses for some realizations of the stochastic production, which raises concerns on its practical applicability.Starting from this point, this paper shows that, if stochastic production is conveniently scheduled in the day-ahead market, the conventional settlement of this market can notably approach the behavior of the stochastic ideal, while sidestepping its theoretical drawbacks. To this end, we construct a bilevel programming formulation that determines the optimal value of stochastic production that should be considered to clear the day-ahead market under the conventional settlement.We firmly believe that this research work could positively contribute to your Journal. We thank you in advance for considering this...

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Section: Introductionmentioning

confidence: 99%

Electricity market clearing with improved scheduling of stochastic production

Morales

Zugno

Pineda

2014

European Journal of Operational Research

Self Cite

105

122

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“…In this work, we propose the use of subproblem (9). The only nonlinearity in this problem is the presence of complementarity conditions (9b), where the ⊥ operator implies that ξ(h − H∆w) = 0.…”

Section: Choice Of the Subproblem And Of Its Solution Methodsmentioning

confidence: 99%

“…The existing literature on the subject shows that system cost can be significantly reduced in expectation by jointly optimizing the day-ahead dispatch and the reserve in a stochastic programming framework, see [9]. However, models of this type require that the market operator has an accurate probabilistic description of the joint distribution of uncertain production at different locations in the grid, which is far from trivial.…”

Section: Introductionmentioning

confidence: 99%

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A robust optimization approach to energy and reserve dispatch in electricity markets

Zugno¹,

Conejo

2015

European Journal of Operational Research

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143

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To a large extent, electricity markets worldwide still rely on deterministic procedures for clearing energy and reserve auctions. However, larger and larger shares of the production mix consist of renewable sources whose nature is stochastic and non-dispatchable, as their output is not known with certainty and cannot be controlled by the operators of the production units. Stochastic programming models for the joint determination of the day-ahead energy and reserve dispatch, necessary for coping with the real-time output deviations from these sources, have been proposed in the literature. In this work, we take an alternative approach and cast the problem as an adaptive robust optimization problem. The day-ahead and reserve schedules determined in this fashion yield the minimum system cost, accounting for the cost of the redispatching decisions at the balancing stage, in the worst-case realization of the stochastic production within a specified uncertainty set. In a case-study based on a 24-node system, we assess the degree of suboptimality of the robust solution with respect to the optimal dispatch obtained with a stochastic programming approach, and compare their worst-case cost. Furthermore, we discuss the robustness of these two alternative approaches with respect to changes in the distribution of the uncertainty, as well as their computational properties. d Demand at each node of the system (considered known with certainty);

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“…Zhang et al [9] proposes conditional forecast error that makes use of copula theory to perform probabilistic analysis in a scenario-based UC, in which 3000 scenarios were trimmed down to a computationally feasible number of 20. Morales et al [10] developed a detailed scheme to allocate spinning and nonspinning reserves in systems with high wind penetration, taking into account network constraints. In the first phase of that proposal [10], 3018 scenarios were created and then narrowed down to 20 scenarios.…”

Section: Introductionmentioning

confidence: 99%

Scenario selection of wind forecast errors for stochastic unit commitment: A UK case study

Gomez-Martinez

Ortega‐Vazquez

Ochoa

2014

IEEE PES Innovative Smart Grid Technologies, Europe

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Abstract-Integration of renewable generation is crucial in future generation mix of power systems as many are the benefits that it offers. At certain penetration levels, latent risks could materialize as a result of not adequately managing the additional variability, therefore this situation prompts the necessity of enhancing traditional tools to operate power systems. In this paper a sensitivity analysis is conducted to assess key variables that improve the performance of Stochastic Unit Commitment (SUC). Focus is given to the right number of scenarios that sufficiently capture the variability of wind power forecast errors, in which a trade-off between quality of solution and computation time is considered. The proposed methodology is tested with real data from the Balancing Mechanism Reporting System aiming to represent the UK Power System.

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Economic valuation of reserves in power systems with high penetration of wind power

Cited by 139 publications

References 0 publications

Electricity market clearing with improved scheduling of stochastic production

Electricity market clearing with improved scheduling of stochastic production

A robust optimization approach to energy and reserve dispatch in electricity markets

Scenario selection of wind forecast errors for stochastic unit commitment: A UK case study

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