Toward cost-efficient and reliable unit commitment under uncertainty

Pandžić, Hrvoje; Dvorkin, Yury; Wang, Yishen; Qiu, Ting; Kirschen, Daniel

doi:10.1109/pesgm.2016.7741080

Cited by 40 publications

(106 citation statements)

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“…Commonly adopted approaches to address these challenges include advanced forecasting techniques [1][2][3] and improved system operational uncertainty modeling [4][5][6][7][8][9][10][11][12][13][14][15][16]. Traditionally, the system operators make the commitment and dispatch decisions based on a deterministic renewable point forecast.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many researchers have applied different operational schemes to incorporate stochastic wind power generation [4][5][6][7][8][9][10][11][12][13][14][15][16]. These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15]. The traditional DUC typically keep contingency reserves to address the possibility for a generation or line outage.…”

Section: Introductionmentioning

confidence: 99%

“…Ramping capabilities between these bounds are considered to ensure the operational feasibility in extreme situations [8]. Pandzic et al [9] modify the IUC approach by determining the ramping requirements with a data-driven method to reduce the over-conservativeness within the original formulation. Liu et al [10] further improve the IUC formulation with a fuzzy set approach to transform the crisp interval bounds into a fuzzy one, and shows that if wind curtailment cost is zero, then only the lower bound is necessary.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating stochastic methods in power system operations with wind power

Wang

Zhou

Liu

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

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Abstract-Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatiotemporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating stochastic methods in power system operations with wind power

Wang

Zhou

Liu

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

View full text Add to dashboard Cite

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“…A master problem is solved first and a sub problem considering the wind uncertainty is then solved and fed to the master problem. Pandžić et al [41] compared the three methods of stochastic, robust and interval optimization in order to solve the unit commitment problem considering the uncertainties in wind speed. It was found that the stochastic approach gives the more realistic objective value but has a higher computational cost as compared to the other two approaches.…”

mentioning

confidence: 99%

Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies

Sharifzadeh

Lubiano-Walochik

Shah

2017

Renewable and Sustainable Energy Reviews

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Renewable energies can play an important role in mitigating the emissions associated with electricity power generation and diversification of the energy supply. The challenge is that renewable resources such as wind and solar have intermittent production patterns and their incorporation into conventional electricity grids will increase the degree of uncertainty. The present research proposes a comprehensive framework in which design and operation of the electricity grid are considered simultaneously and the uncertainties in the wind and solar generation as well as demand are systematically taken into account. The case of retrofitting the current UK electricity grid to include 50% renewable power generation by 2030 was posed as the demonstrating example. The research problem was formulated as a piece-wise linear mixed integer optimization under uncertainty and solved using CPLEX v12.0 in GAMS. The results suggested that it is possible to retrofit the electricity grid using renewable generators while optimizing the overall profitability and ensuring the secure supply of electricity. It was also observed that at the price of higher computational costs, stochastic optimization generates more realistic and robust solutions for the design and operation of the smart electricity grid. KeywordsIntegrated renewable energy systems, wind power, solar energy, stochastic mixed-integer optimization, uncertainty. , , ( ) Power generated by i natural gas power plant in scenario s at node n at time t , , ( ) Power generated by k nuclear power plant in scenario s at node n at time t , ( ) Wind power generated in scenario s at node n at time t , ( ) Solar power generated in scenario s at node n at time t , ( ) Power pumped from storage to node n in scenario s at time t , ( ) Power pumped to storage from node n in scenario s at time t cite this publication at: Mahdi Sharifzadeh, Helena Lubiano-Walochik, Nilay Shah. Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and

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Scenario reduction based on correlation sensitivity and its application in microgrid optimization

Liu

2021

Int Trans Electr Energ Syst

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Summary In the stochastic programming involving multiple renewable energy sources such as solar and wind, uncertainties and correlations exist simultaneously in various random variables. To accurately describe them, a large number of scenarios are necessary to be generated, which may render the original programming problem intractable and require critical scenario reduction operations. This paper addresses two crucial issues in scenario reduction: (a) how to preserve the correlation properties of the generated scenarios after reduction and (b) how to guarantee the quality of decision results after reduction. For the first issue, the concepts of correlation sensitivity and partial correlation loss are proposed to specifically retain some key correlations that have an important impact on the solution stability. For the second issue, this paper proposes a novel scenario reduction method that aims to minimize the partial correlation loss and maximize the probabilistic similarity degree before and after reduction. Comparative studies are performed to evaluate the validity and universality of the proposed method. Numerical results of the microgrid economic operation optimization problem show that the reduced scenario set obtained by eliminating 97.8% scenarios can provide approximate target value to the problem with less than 5% accuracy errors and CPU time reduction of around 93.4%.

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Toward cost-efficient and reliable unit commitment under uncertainty

Cited by 40 publications

References 0 publications

Evaluating stochastic methods in power system operations with wind power

Evaluating stochastic methods in power system operations with wind power

Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies

Scenario reduction based on correlation sensitivity and its application in microgrid optimization

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