Evaluating and optimizing the operation of the hydropower system in the Upper Yellow River: A general LINGO-based integrated framework

Abstract

The hydropower system in the Upper Yellow River (UYR), one of the largest hydropower bases in China, plays a vital role in the energy structure of the Qinghai Power Grid. Due to management difficulties, there is still considerable room for improvement in the joint operation of this system. This paper presents a general LINGO-based integrated framework to study the operation of the UYR hydropower system. The framework is easy to use for operators with little experience in mathematical modeling, takes full advantage of LINGO’s capabilities (such as its solving capacity and multi-threading ability), and packs its three layers (the user layer, the coordination layer, and the base layer) together into an integrated solution that is robust and efficient and represents an effective tool for data/scenario management and analysis. The framework is general and can be easily transferred to other hydropower systems with minimal effort, and it can be extended as the base layer is enriched. The multi-objective model that represents the trade-off between power quantity (i.e., maximum energy production) and power reliability (i.e., firm output) of hydropower operation has been formulated. With equivalent transformations, the optimization problem can be solved by the nonlinear programming (NLP) solvers embedded in the LINGO software, such as the General Solver, the Multi-start Solver, and the Global Solver. Both simulation and optimization are performed to verify the model’s accuracy and to evaluate the operation of the UYR hydropower system. A total of 13 hydropower plants currently in operation are involved, including two pivotal storage reservoirs on the Yellow River, which are the Longyangxia Reservoir and the Liujiaxia Reservoir. Historical hydrological data from multiple years (2000–2010) are provided as input to the model for analysis. The results are as follows. 1) Assuming that the reservoirs are all in operation (in fact, some reservoirs were not operational or did not collect all of the relevant data during the study period), the energy production is estimated as 267.7, 357.5, and 358.3×108 KWh for the Qinghai Power Grid during dry, normal, and wet years, respectively. 2) Assuming that the hydropower system is operated jointly, the firm output can reach 3110 MW (reliability of 100%) and 3510 MW (reliability of 90%). Moreover, a decrease in energy production from the Longyangxia Reservoir can bring about a very large increase in firm output from the hydropower system. 3) The maximum energy production can reach 297.7, 363.9, and 411.4×108 KWh during dry, normal, and wet years, respectively. The trade-off curve between maximum energy production and firm output is also provided for reference.

Combining Grey Relational Analysis and a Bayesian Model Averaging Method to Derive Monthly Optimal Operating Rules for a Hydropower Reservoir

“…The equivalent transformation [37] is to maximize the hydropower generation with a penalty function for a violation of firm power output (Equation (3)). The reservoir optimization model operates by determining an optimal release or water level for a reservoir over the whole operation period.…”

“…(1) and the other is to maximize the firm output. A maximin model [37] is selected to optimize the firm power, as shown in Equation 2, which has been proved to be capable of obtaining the same minimal output as the equal output method with more direct and reliable results.…”

Performance Evaluation Of The Hydropower Plants Using Various Multi-Criteria Decision-Making Techniques

“…The prioritization of the criterion based on beneficial (Max) and non-beneficial (Min) as done previously for WASPAS method is made. Evaluation matrix for the computation of ranks by PROMETHEE method is same as that for TOPSIS method and is shown previously in Table-IV, while the normalized evaluative matrix is calculated on the basis of (23) and (24).…”

“…TOPSIS is grounded on the idea that a certain attribute must have the least and farthest remoteness from the positive best result and he negative best result respectively [24], [25], [26], [27] [3], [28] and [29]. The steps involved in TOPSIS method are as follows:…”

Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

“…Since both the revenue from energy production and cost from energy purchase would affect the net revenue, determining the optimal levels of the two decision variables necessitates the analysis of their conflicting mechanisms from the background supports of reservoir operation. Previous studies [23,24] have noted that the maximization in total energy production is conflicted with the maximization of firm power output, due to difference between the mechanism of energy maximization and the mechanism of firm power output maximization. Specifically, the maximization in total energy production could result in heterogeneous power output when the peak power output is often produced at the highest water head, while the maximization in firm power output may yield homogenized power output sequences for maximizing the minimum power output over the planning horizon.…”