Assessing the impact of wind variability on the long-term operation of a hydro-dominated system

Morillo, Jose L.; Pérez, Juan F.; Zéphyr, Luckny; Anderson, C. Lindsay; Cadena, Alberto

doi:10.1109/isgteurope.2017.8260317

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…The objective function intends to minimize the sum of immediate and future cost of fuel and energy deficit, under It should be noted that there are other solutions for the inclusion of wind energy, and other socalled intermittent sources, directly in the dispatch optimization. Examples are the work of Papavasiliou et al [34], who developed the multistage stochastic programming formulation; Jurasz et al [35], who developed a mixed-integer nonlinear mathematical model; Morillo et al [36], who included the expected production of wind energy in the objective function; and Raby et al [29], who considered wind power as a new thermal plant. However, one of the main objectives of this work is to propose an approach that considers the variability of the wind series, but does not change the optimization model's formulation.…”

Section: Methodsmentioning

confidence: 99%

Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning

et al. 2019

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In the past two decades, wind power’s share of the energy mix has grown significantly in Brazil. However, nowadays planning electricity operation in Brazil basically involves evaluating the future conditions of energy supply from hydro and thermal sources over the planning horizon. In this context, wind power sources are not stochastically treated. This work applies an innovative approach that incorporates wind power generation in the Brazilian hydro-thermal dispatch using the analytical method of Frequency & Duration. The proposed approach is applied to Brazil’s Northeast region, covering the planning period from July 2017 to December 2021, using the Markov chain Monte Carlo method to simulate wind power scenarios. The obtained results are more conservative than the one currently used by the National Electric System Operator, since the proposed approach forecasts 1.8% less wind generation, especially during peak periods, and 0.67% more thermal generation. This conservatism can reduce the chance of water reservoir depletion and, also an ineffective dispatch.

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Section: Methodsmentioning

confidence: 99%

Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning

et al. 2019

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“…1. As discussed earlier, popular sampling techniques include Monte Carlo simulation [17,40,61,39,21], quasi-Monte Carlo simulation [13,2,31], Latin hypercube [25,31], orthogonal arrays [24,15].…”

Section: Simplicial Approximate Stochastic Dynamic Programmingmentioning

confidence: 99%

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Zephyr,

Lamond,

Lang

2024

Comput Manag Sci

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We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method exploits the convexity properties of the value function to sample the reservoir level space based on the local curvature of the value function, which is estimated by the difference between a lower and an upper bounds (error bound). Unlike the previous approach where the state space was exhaustively partitioned into full dimensional simplices whose vertices formed a discrete grid over which the value function was approximated, here we propose instead a new randomized approach for selecting the grid points from a small number of randomly sampled simplices from which an error bound is estimated. Results of numerical experiments on three literature test problems and simulated midterm reservoir optimization problems illustrate the advantages of the randomized approach which can solve models of higher dimensions than with the exhausitive approach.

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“…It is well known that chanced-constrained problems may be difficult to handle numerically, due to potential non-convexity of their feasible domains [35]. Except for special cases, e.g., under suitable distributional assumptions on the net load process [21,22], it is no easy task deriving analytical expressions for the probabilistic load balance (6).…”

Section: Approximation Approachesmentioning

confidence: 99%

“…In this vein, [16] and [17] proposed chance constrained optimal power flow formulations, under renewable-based uncertainty. Multiple chance-constrained UC formulations were developed in [13,12,18,19,20] to account for variable load [13], high penetration of wind [12,18,20,21,22], generator outages [19] and risk measures [18]. However, chance-constrained problems are usually non-convex [23], and as such may be challenging to solve.…”

Section: Introductionmentioning

confidence: 99%

Approximate Chance-Constrained Unit Commitment Under Wind Energy Penetration

Zéphyr¹,

Guo²,

Wang³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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We study a multi-period unit commitment problem under wind energy penetration, in which the load balance is enforced with a predefined confidence level across the whole system and over the planning horizon. Since, except for special cases, chance-constrained problems are non-convex, we analyze two relaxations of the load balance based upon robust optimization ideas and estimated quantiles of the marginal distributions of the net load processes. The approximation proposals are benchmarked against the well-known scenario approximation. Under the scenario approach, we also analyze a simple decomposition strategy to find a lower bound of the approximate problem, when the latter becomes intractable due the size of the set of scenarios. The reliability of the obtained solutions as well as their runtimes are examined on three widespread test systems.

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Assessing the impact of wind variability on the long-term operation of a hydro-dominated system

Cited by 5 publications

References 10 publications

Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning

Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Approximate Chance-Constrained Unit Commitment Under Wind Energy Penetration

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