The value of stochastic programming in day-ahead and intra-day generation unit commitment

Schulze, Tim P.; McKinnon, Karen

doi:10.1016/j.energy.2016.01.090

Cited by 42 publications

(35 citation statements)

References 21 publications

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“…The authors of [507] demonstrate the interest of SO over deterministic optimization using such a direct reformulation. In [458], two and multi-stage stochastic programming are compared against a deterministic rolling horizon approach, showing that stochastic models lead to cost savings, but only if the scenario tree well represents the underlying uncertainty. In view of this, [346] considers the impact of uncertainty jointly with other approximations of reality (such as not considering stopping/starting curves), and proves that each feature has a non-negligeable effect on the expected costs.…”

Section: Dealing With Uncertainty In the Modelmentioning

confidence: 99%

Large-scale unit commitment under uncertainty: an updated literature survey

Ackooij¹,

et al. 2018

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The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chanceconstrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115-171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject.

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Section: Dealing With Uncertainty In the Modelmentioning

confidence: 99%

Large-scale unit commitment under uncertainty: an updated literature survey

Ackooij¹,

et al. 2018

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“…Finally, we note that since wind power is stochastic, stochastic unit commitment is generally used in 70 systems with important wind power uncertainty [23,24, 25,26, 27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fuzzy decision-making is a methodology used to find the final optimal solution considering the decision maker's preferences about the objectives after a Pareto optimal solution set is obtained [13,22]. Hozouri et al [22] uses a fuzzy decision-making method to balance minimum wind energy curtailment, minimum social cost, and maximum energy storage revenue.Finally, we note that since wind power is stochastic, stochastic unit commitment is generally used in 70 systems with important wind power uncertainty [23,24, 25,26, 27].…”

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confidence: 99%

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

et al. 2017

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Some areas in China are facing pressing air pollution problems. Measures from the power sector can be taken to cope with air pollution issues, including reducing emission levels of thermal units and integrating wind and solar power. Social welfare, emission, and renewable integration are three major concerns in modern power system operations. This paper describes three stochastic scheduling models aiming at maximizing social welfare (SW), minimizing emission (EM) and maximizing renewable production (RE).A multi-objective scheduling model (MT) is also proposed that properly balances the above objectives.Wind power uncertainty and dispatchable loads are considered in the model. The outcomes of the three models are compared through an illustrative example and a 57-node case study. Results show that model EM results in 36% of the social welfare of model SW, 27% of its emissions, and 43% of its wind spillage, while model RE results in 55% of the social welfare of model SW, 56% of its emissions and 28% of its wind spillage. Additionally, we analyze how the optimal generation scheduling is affected by the weights in model MT. This work provides insight to policy makers on how to balance social welfare, emissions and renewable production. teristics of wind power increase the possibility of energy curtailment. Wind spillage reached 15% of wind 10 production in China in 2015 [2].Besides deployment of up/down reserves by thermal units, dispatchable loads provide an appropriate mechanism to deal with uncertainty. Dispatchable loads can be scheduled and re-dispatched like generators to achieve the scheduling objective. Dispatchable loads increase the flexibility of the system, which is helpful in system with high renewable production. 15To minimize the operating cost is the main objective for most Independent System Operators (ISO), but nowadays other objectives appear. For some highly-polluted areas, the ISO may seek to minimize emissions to ensure air quality. For areas with high renewable integration, the ISO may seek to maximize renewable production to make electricity production as "green" as possible. These distinct objectives result in different generation scheduling outcomes. 20Considering the pressing problems of air pollution and wind curtailment in China, this paper focuses on the comparison of three stochastic scheduling models taking into account wind power uncertainty and dispatchable loads. The objectives are to maximize social welfare, to minimize emissions, and to maximize renewable integration, respectively. A multi-objective scheduling model that properly combines the above objectives is proposed as well. A similar methodology has not been reported in the technical literature. 25An illustrative example and a 57-node case study are considered to illustrate the proposed models. Literature ReviewRelevant works pertaining to electricity generation scheduling models with different objectives are reviewed below. Minimizing cost (or maximizing social welfare) is the most common goal [3,4] in unit commitment proble...

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“…The aim is to minimize the total expected operational cost of the EPS, DHS and NGS while assuming constant water temperature and gas pressure. That scheduling of the multi-energy system relying on multi-stage stochastic programming rather than using a deterministic approach leads to lower operational costs was also shown in [21]. Stochastic optimization has gained a lot of attention in the field of optimization under uncertainty and stochastic DA scheduling has been mostly used in the field of EPS [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing

Turk

Zhang

et al. 2020

Energy

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Secure operation of the power system is challenged by the high level of uncertainty and fluctuation introduced by renewable energy sources. More flexibility is needed to cope with the uncertainty and improve the utilization of renewable energy. A prominent solution to provide flexibility, and simultaneously increase the efficiency of the system, is the integration of different energy sectors. This paper proposes a two-stage stochastic scheduling scheme of an integrated multi-energy system, which considers the wind power uncertainty and the synergy of different energy sectors to achieve the optimal economic operation of the whole system with minimum curtailment of wind power. In the first stage, energy and reserve scheduling of generating units is performed, while accommodation of wind power production is realized through reserves in the second stage. In the proposed scheme, the electric power system, natural gas system, and district heating system are coordinated to achieve more flexibility, both in the day-ahead and real-time stage. The stochasticity of the wind power uncertainty is represented by realistic scenarios with corresponding probabilities, which are obtained from a scenario generation algorithm based on historical observations taking into account the temporal correlation of wind power. The simulation results on a smallscale test system show that both, the economic efficiency and wind power utilization, have been improved with more flexibility and more reliable scenario set. It is shown, that the total system cost is reduced and reserves are optimized.

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The value of stochastic programming in day-ahead and intra-day generation unit commitment

Cited by 42 publications

References 21 publications

Large-scale unit commitment under uncertainty: an updated literature survey

Large-scale unit commitment under uncertainty: an updated literature survey

Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production

Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing

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