Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

Hjelmeland, Martin N.; Helseth, Arild; Korpås, Magnus

doi:10.3390/en12010189

Cited by 6 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, alternative models must be formulated without nonlinear transformations, such as Box-Cox transformation, in the parameter estimation and scenario generation processes. This approach is used in [7][8][9][10][11][12][13], wherein a three-parameter lognormal PAR model is estimated for energy and reservoir inflows, respectively. In any case, normalized datasets and multi-lag forecast models that follow the TSM structure of [6] can generate negative inflow values, even if the parameter estimation is computed on a positive dataset.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Negative Inflows in the Long-Term Hydrothermal Scheduling Problem

Larroyd¹,

Pedrini

Beltran³

et al. 2022

Energies

View full text Add to dashboard Cite

The long-term hydrothermal scheduling (LTHS) problem seeks to obtain an operational policy that optimizes water resource management. The most employed strategy to obtain such a policy is stochastic dual dynamic programming (SDDP). The primary source of uncertainty in predominant hydropower systems is the reservoirs inflow, usually a linear time series model (TSM) based on the order-p periodic autoregressive [PAR(p)] model. Although the linear PAR(p) can represent the seasonality and autocorrelation of the inflow datasets, negative inflows may appear during SDDP iterations, leading to water balance infeasibilities in the LTHS problem. Different from other works, the focus of this paper is not avoiding negative inflows but instead dealing with the negative values that cause infeasibilities. Hence, three strategies are discussed: (i) inclusion of a slack variable penalized in the objective function, (ii) negative inflow truncation to zero, and (iii) optimal inflow truncation, among which the latter is a novel approach. The strategies are compared individually and combined. Methodological conditions and evidence of the algorithm convergence are presented. Out-of-sample simulations show that the choice of negative inflow strategy significantly impacts the performance of the resultant operational policy. The combination of strategy (i) and (iii) reduces the expected operation cost by 15%.

show abstract

Section: Introductionmentioning

confidence: 99%

Dealing with Negative Inflows in the Long-Term Hydrothermal Scheduling Problem

Larroyd¹,

Pedrini

Beltran³

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…The mathematical characteristics of these models are intrinsically linked to the market design employed in a given electrical system. In systems whose design adopts a loose-pool dispatch, the GS models are tools primarily used to maximize the expected revenue associated with energy trading in spot and future markets [2,3]. On the other hand, in the case of markets with centralized dispatch, i.e., the tight-pool, an independent system operator employs a chain of GS models to minimize the expected operating cost, considering some risk-measure [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

Abreu

Finardi

2022

Energies

View full text Add to dashboard Cite

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm3, possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in terms of mean absolute error and 0.306% to 6.356% regarding the maximum absolute error even with few approximations.

show abstract

“…Hjelmeland et al [37] use it to handle a medium-term scheduling issue for a single producer under uncertain inflows and prices within one to three-year horizon. Most related works consider the medium-term setting as Gjelsvik et al [4]; Helseth et al [38]; Hjelmeland et al [39]; and Poorsepahy-Samian et al [40]. However, as indicated by Hjelmeland et al [39], although decomposition methods can help to alleviate the solving complexity, the computation would significantly become slow with the increase of system size and decision stages.…”

Section: Introductionmentioning

confidence: 99%

“…Most related works consider the medium-term setting as Gjelsvik et al [4]; Helseth et al [38]; Hjelmeland et al [39]; and Poorsepahy-Samian et al [40]. However, as indicated by Hjelmeland et al [39], although decomposition methods can help to alleviate the solving complexity, the computation would significantly become slow with the increase of system size and decision stages. It shows the difficulty of multistage stochastic optimization with high dimensions of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Wang

Chen

2019

Complexity

View full text Add to dashboard Cite

In the location-related planning of a hydropower system, the consideration of future operations under uncertainties can make the decisions sustainable and robust. Then, it is of great importance to develop an effective approach that deals with the long-term stochasticity due to the long-lasting effects of the location selections. Thus, we propose a multistage stochastic programming model to optimize the planning decisions of cascade hydropower stations and the long-term stochastic operations in an integrated way. The first stage (i.e., the planning stage) in the model deals with the location and capacity decisions of the hydropower stations, while the subsequent stages implement the scheduling decisions under each stagewise stochastic scenario. To address the curse of dimensionality caused by the long-term stochastic operations, we further propose a novel dimensionality reduction approach based on dual equilibrium to transform the multistage model into a tractable two-stage stochastic program. The applicability of our approach is validated by a case study based on a basin of Yangtze River, China, and corresponding sensitivity analysis.

show abstract

Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty

Cited by 6 publications

References 21 publications

Dealing with Negative Inflows in the Long-Term Hydrothermal Scheduling Problem

Dealing with Negative Inflows in the Long-Term Hydrothermal Scheduling Problem

Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

Planning of Cascade Hydropower Stations with the Consideration of Long‐Term Operations under Uncertainties

Contact Info

Product

Resources

About