A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning

Diniz, André Luiz; Maceira, M.E.P.; Vasconcellos, Cesar Luis V.; Penna, D.D.J.

doi:10.1007/s10479-019-03419-4

Cited by 12 publications

(5 citation statements)

References 56 publications

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“…The lower layer optimization problem of the risk aversion coefficient is characterized by high dimensional order, multistage, non-linearity, and multiple constraints. To solve this type of optimization problem, researchers have proposed improved stochastic fractal search algorithm (ISFSA) (Phan et al, 2021), Coyote Optimization Algorithm (COA) (Heidari et al, 1971) and Discrete Differential Dynamic Planning (DDDP) (Bookstaber and McDonald, 1987;Diniz et al, 2020), etc. Among them, DDDP has the advantage of finding the optimal solution only in the locally feasible domain of state variables, which can effectively reduce the computational storage and computing time compared with other algorithms.…”

Section: Solving Methodsmentioning

confidence: 99%

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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Section: Solving Methodsmentioning

confidence: 99%

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

Yang

et al. 2023

Front. Energy Res.

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“…In contrast to the hybrid approach in [15], hydropower details are considered through the entire planning horizon seen by SDDP. The use of global feasibility cuts to deal with functionality that is not explicit in the SDDP formulation has previously been applied for embedding risk measures [34], [35] in LTS model formulations. Differently from those works, we use feasibility cuts to facilitate spatial decomposition, coordinating aggregated and detailed hydropower representations within the SDDP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hydropower Aggregation by Spatial Decomposition—An SDDP Approach

Helseth

2023

IEEE Trans. Sustain. Energy

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The balance between detailed technical description, representation of uncertainty and computational complexity is central in long-term scheduling models applied to hydro-dominated power system. The aggregation of complex hydropower systems into equivalent energy representations (EER) is a commonly used technique to reduce dimensionality and computation time in scheduling models. This work presents a method for coordinating the EERs with their detailed hydropower system representation within a model based on stochastic dual dynamic programming (SDDP). SDDP is applied to an EER representation of the hydropower system, where feasibility cuts derived from optimization of the detailed hydropower are used to constrain the flexibility of the EERs. These cuts can be computed either before or during the execution of the SDDP algorithm and allow system details to be captured within the SDDP strategies without compromising the convergence rate and statespace dimensionality. Results in terms of computational performance and system operation are reported from a test system comprising realistic hydropower watercourses.

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“…Although regulation does not allow residential clients to take part in the free market, the load that is represented by large industrial and commercial customers is quite relevant, and today about 30% of all energy consumed in the country is freely traded. However, the market deregulation kept some structures from the preexisting electrical system coordination, such as the price formation through computational calculations [7][8][9], which are centrally performed by the ISO and the CCEE. Thus, there is no field for the game theory application towards price formation on a short-term period-although, this theory can be applied to the pricing of bilateral contracts.…”

Section: Introductionmentioning

confidence: 99%

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Santos

Lobato

Vieira³

et al. 2022

Energies

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This paper presents a Nash equilibrium approach to model a non-cooperative game that takes place among Brazilian hydro-generating companies in the annual process called “seasonalization”. We have “remade” the seasonalizations that occurred in the period of 2013–2020 by using our model and comparing the financial outcomes with the current ones that resulted from the seasonalizations the generators made each year, with financial improvements in almost every year. By using the Nash equilibrium approach, it is possible to achieve optimal decisions concerning the seasonalization process that were not evident by using traditional methods. This approach is useful for companies willing to enhance their income and to improve their risk management by making better choices in seasonalization.

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A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning

Cited by 12 publications

References 56 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

Hydropower Aggregation by Spatial Decomposition—An SDDP Approach

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

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