An Improved Scenario Reduction Technique and Its Application in Dynamic Economic Dispatch Incorporating Wind Power

Zhou, Yating; Shi, Libao; Ni, Yixin

doi:10.1109/isgt-asia.2019.8881144

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…When developing optimization models that include power loss, approximating formulas are often sufficient. One such approximation technique is the B-matrix power loss formula shown in (6). The use of the B-matrix approximation avoids the need to calculate the loss for each transmission line, as long as the structure of the power system remains relatively uniform.…”

Section: Equality Constraintsmentioning

confidence: 99%

“…It is a highly constrained problem that obtains the optimized economic planning of the power generation units to satisfy the scheduled load in a specific period, taking into account various constraints, like power balance, unit capacity limits, and ramp rate limits. The DED problem is becoming complex and challenging to solve with the extensive incorporation of wind generators because of wind speed's uncertain nature [5,6]. A DED that incorporates wind and photovoltaic power that deals with the stochastic and unpredictable nature of wind and solar energy generation while considering the differences between actual and predicted output power to achieve an optimized cost and optimal power dispatch is solved using a hybrid heuristic flower pollination algorithm with sequential quadratic programming [7].…”

Section: Introductionmentioning

confidence: 99%

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Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

Bouddou¹,

Benhamida²,

Haba³

et al. 2020

ISI

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In this paper, a bid-based dynamic economic dispatch (BBDED) problem is solved in the electricity market system under bidding strategies, including wind energy penetration using simulated annealing (SA) algorithm. The multi-objective dispatch model allows generating companies (GENCOs) and their customers to submit supply and demand bids to a market controller known as the independent system operator (ISO) and follow a bidding strategy. ISO is responsible for the market clearing and settlement to maximize the social profit and benefit for GENCOs and customers during trading periods. To study the effect and advantages of wind energy integration in the BBDED problem, the wind energy generation is computed using the forecasted wind speeds and included in the dispatch model. In this regard, the ISO's dispatch model is formulated as a bilevel nonlinear optimization problem. The higher-level is solving the market-clearing with and without wind energy, and the lower level is maximizing GENCO's social profit. The proposed SA algorithm is evaluated for optimality, convergence, robustness, and computational efficiency tested on an IEEE 30-bus test system. The simulation results are compared with those found using different algorithm-based approaches, considering various constraints like power balancing, generator limits, ramp rate limits, and transmission losses.

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Section: Equality Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

Bouddou¹,

Benhamida²,

Haba³

et al. 2020

ISI

View full text Add to dashboard Cite

show abstract

“…However, there are reduction techniques based on other distances, such as the discrepancy distances [14] and methods which are not based on probability distances, e.g. reduction techniques based on Euclidean distances, sampling or clustering [15–17].…”

Section: Introductionmentioning

confidence: 99%

The energy distance for ensemble and scenario reduction

Ziel

2021

Phil. Trans. R. Soc. A.

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Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems, but are also used in probabilistic forecasting, clustering and estimating generative adversarial networks. We propose a new method for ensemble and scenario reduction based on the energy distance which is a special case of the maximum mean discrepancy. We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature. The energy distance is a metric between probability measures that allows for powerful tests for equality of arbitrary multivariate distributions or independence. Thanks to the latter, it is a suitable candidate for ensemble and scenario reduction problems. The theoretical properties and considered examples indicate clearly that the reduced scenario sets tend to exhibit better statistical properties for the energy distance than a corresponding reduction with respect to the Wasserstein distance. We show applications to a Bernoulli random walk and two real data-based examples for electricity demand profiles and day-ahead electricity prices. This article is part of the theme issue ‘The mathematics of energy systems’.

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“…However, there are reduction techniques which are not based on distances for probability measures, e.g. reduction techniques based on Euclidean distances, sampling or clustering Beraldi and Bruni (2014); Keko and Miranda (2015); Davendra et al (2018); Park et al (2019); Zhou et al (2019). Still, from the theoretical point of view it makes sense to consider any ensemble or scenario reduction technique based on a probability metric as the scenario can be regarded as a (weighted) sample from a multivariate probability distribution.…”

Section: Introductionmentioning

confidence: 99%

The energy distance for ensemble and scenario reduction

Ziel

2020

Preprint

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Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems. We propose a new method for ensemble and discrete scenario reduction based on the energy distance which is a special case of the maximum mean discrepancy (MMD). We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature. The energy distance is a metric between probability measures which allows for powerful tests for equality of arbitrary multivariate distributions or independence. Thanks to the latter, it to a suitable candidate for ensemble and scenario reduction problems. The theoretical properties and considered examples indicate clearly that the reduced scenario set tend exhibit better statistical properties for energy distance than a corresponding reduction with respect to the Wasserstein distance. We show applications to binary trees and a real data based examples for electricity demand profiles.

show abstract

An Improved Scenario Reduction Technique and Its Application in Dynamic Economic Dispatch Incorporating Wind Power

Cited by 6 publications

References 14 publications

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

The energy distance for ensemble and scenario reduction

The energy distance for ensemble and scenario reduction

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