Solving stochastic hydro unit commitment using benders decomposition and modified stochastic dual dynamic programming

Li, Zhuangzhuang; Yang, Ping; Yang, Yi; Lu, Guanpeng; Tang, Yufeng

doi:10.3389/fenrg.2022.955875

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…, j 1, 2, 3., J, and F β (Q d , α d ) can be converted into a linear function and linear constraint (Li et al, 2022).…”

Section: Solving Methodsmentioning

confidence: 99%

“…Where: Q d is the energy portfolio of the hydropower plant on operating day d, d 1, 2, 3, ., D, and D 30; E(Q d ) is the income function (Wang et al, 2005) of the energy portfolio, as shown in Eq. 2; F β (Q d , α d ) is the CVaR-based risk function (Li et al, 2022) of the energy portfolio, as shown in Eqs 10-11; λ d is the risk aversion coefficient, calculated as in Eq. 5.…”

Section: Upper Layer Objective Functionmentioning

confidence: 99%

“…The upper layer constraints include hydropower plant operating constraints (Yu et al, 2008) and energy market trading constraints (Li et al, 2022).…”

Section: Upper Layer Constraintsmentioning

confidence: 99%

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Optimal energy portfolio method for regulable hydropower plants under the spot market

Yang

et al. 2023

Front. Energy Res.

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The energy allocation method for regulable hydropower plants under the spot market significantly impacts their income. The available studies generally draw on the Conditional Value-at-Risk (CVaR) approach, which typically assumes a fixed risk aversion coefficient for generators. This assumption is based on the assumption that the total energy the power plant can allocate is constant during the decision period. However, the amount of energy that the regulable hydropower plant can generate will be affected by inflow and water level during the decision period, and the assumption of the fixed risk aversion coefficient is only partially consistent with the actual decision behavior of the hydropower plant. In this regard, the time-varying relative risk aversion (TVRRA) based method is proposed for the energy allocation of regulable hydropower plants. That method takes the change value of the hydropower plant’s energy generation as the basis for adjusting the time-varying relative risk aversion coefficient to make the energy allocation results more consistent with the actual decision-making needs of the hydropower plant. A two-layer optimal method is proposed to obtain the income-maximizing energy portfolio based on regulable hydropower plants’ time-varying relative risk aversion coefficient. The inner point method solves the optimal energy portfolio of income and risk in the upper layer. The time-varying relative risk aversion coefficient in the lower layer accurately describes the dynamic risk preference of hydropower plants for each period. The results and comparison show that the proposed method increases the income of the energy portfolio by 31%, and water disposal of regulated hydropower plants is reduced by 2%. The energy portfolio optimization method for regulable hydropower plants proposed in this paper not only improves the economic income of hydropower plants but also improves the utilization rate of hydro energy resources and enhances the market competitiveness of regulable hydropower plants.

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“…, j 1, 2, 3., J, and F β (Q d , α d ) can be converted into a linear function and linear constraint (Li et al, 2022).…”

Section: Solving Methodsmentioning

confidence: 99%

Section: Upper Layer Objective Functionmentioning

confidence: 99%

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Optimal energy portfolio method for regulable hydropower plants under the spot market

Yang

et al. 2023

Front. Energy Res.

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Unit Commitment Strategy Based on Multi Energy Power System

Du,

Yao,

Yang

et al. 2023

2023 6th International Conference on Renewable Energy and Power Engineering (REPE)

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Optimal energy portfolio allocation method for regulable hydropower plants considering the impact of new energy generation

Yang

et al. 2023

Front. Energy Res.

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With the growing penetration of new energy sources, the impact of new energy generation on the income of the energy portfolio is becoming more significant. This paper proposes a risk measurement method based on the Conditional Value-at-Risk (CVaR) approach to measure the income risk from new energy generation on the energy portfolio. The superiority is proved by comparing it with the method only considering spot price fluctuations. Then, we constructed an energy allocation model of the hydropower plant to maximize the combined income-risk utility. In solving the optimal allocation of the energy portfolio, the effects of inflow and water level on the risk aversion parameters of the hydropower plant are considered. The results and comparison show that the proposed risk measurement method increases the income of the energy portfolio by 12%, and the proposed risk aversion parameter’s adjustment strategy can increase the income of the energy portfolio by 5.8% and reduces the CVaR value by 14.5%. Moreover, the method that considers the income risk from new energy generation can provide favorable conditions for improved new energy accommodation and safe operation of the system.

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Solving stochastic hydro unit commitment using benders decomposition and modified stochastic dual dynamic programming

Cited by 3 publications

References 43 publications

Optimal energy portfolio method for regulable hydropower plants under the spot market

Optimal energy portfolio method for regulable hydropower plants under the spot market

Unit Commitment Strategy Based on Multi Energy Power System

Optimal energy portfolio allocation method for regulable hydropower plants considering the impact of new energy generation

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