Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework

Papavasiliou, Anthony; Oren, Shmuel S.; O’Neill, Richard P.

doi:10.1109/tpwrs.2011.2121095

Cited by 513 publications

(308 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

108

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

108

122

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“…This method was firstly introduced in [18]- [19] for the optimal scheduling of hydrothermal generation systems, driven by the need to model the reservoir interconnections for the future inflow sequences. SDDP has been used to model a variety of operational problems [20]. The ability of SDDP to refine solution quality around areas of the state space most likely to occur ('areas of interest') instead of searching the entire state space, facilitates the solution of high dimensional problems.…”

Section: Contributionsmentioning

confidence: 99%

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

Fatouros

Konstantelos

Papadaskalopoulos

et al. 2019

IEEE Trans. Sustain. Energy

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Abstract--The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a porder vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.Index Terms--Aggregator, distributed energy resources, multidimensional uncertainty, stochastic dual dynamic programming, vector autoregressive modeling. I. NOMENCLATURE A. IndexesIndex of time periods, running from 1 to . Index of energy storage (ES) units, running from 1 to . Index of flexible loads (FL), running from 1 to . Index of wind turbines (WT), running from 1 to . Index of micro-generators, running from 1 to . B. ParametersEnergy market price at period . Operating cost of micro-generator . Cost of demand shedding. C. VariablesPower sold to (positive)/bought from (negative) the market at period . ,Power output of micro-generator at period . Demand shed at period ., Energy level of ES unit at period . ,Power input (positive) / output (negative) of ES unit at period . , ℎChange of demand of FL at period due to load shifting. ,Demand of FL at period after load shifting., Dispatched wind power output of WT at period . II. INTRODUCTIONA. Background FUNDAMENTAL feature of the emerging Smart Grid paradigm involves the integration of a large number of distributed energy resources (DER) storage units, in order to support the economic operation of the future low-carbon power system [1]- [2]. However, the large number, small individual size and inherent stochasticity characterizing these DER have complicated system scheduling and market coordination. Furthermore, driven by the wide integration of renewable generation in power systems, there is a general consensus to move energy trading as close as possible to real-time [3]- [4], which intensifies the complexity of DER coordination. These challenges have...

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“…An alternative formulation obtained by distinguishing the commitment of slow-start and fast-start generators as first-and second-stage decisions, respectively, is given in [7], and [6] uses a similar formulation but extends it to multiple areas separated by transmission constraints. The economic effects of forecast accuracy on wind and uncertainty bounds have been analyzed by integrating a numerical weather prediction model into stochastic UC/ED in [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A general approach based on stability analysis and probability metrics is represented by [3,4], and has been widely used in power system studies. However, in recent studies of unit commitment with high levels of variable energy penetration, some doubts have been expressed about the practical utility of this approach [5][6][7]. This paper develops a heuristic scenario reduction method for use with a two-stage stochastic program for unit commitment by following the decision maker's two major concerns: reliability and cost.…”

Section: Introductionmentioning

confidence: 99%

Solution sensitivity-based scenario reduction for stochastic unit commitment

Feng

Ryan

2014

Comput Manag Sci

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A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the US, FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial. Abstract A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment (SRUC) are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if a lot of scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the U.S., FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial.

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Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework

Cited by 513 publications

References 28 publications

Electricity market clearing with improved scheduling of stochastic production

Electricity market clearing with improved scheduling of stochastic production

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

Solution sensitivity-based scenario reduction for stochastic unit commitment

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