Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health

Abstract

Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.

Identification of Degradation Areas of Ecological Environment and Degradation Intensity Assessment in the Yellow River Basin

“…This directly or indirectly affects the ecological environment (Qiu et al, 2019). Reservoir dispatch management has important practical significance for further improving the ecological health of the YRB (Bai et al, 2020). In this paper, we chose the number of reservoirs and the total capacity of reservoirs to represent the government's response to the protection of ecological environment quality in the YRB.…”

Advances in Hydrologic Forecasts and Water Resources Management

“…Due to climate change, the importance of reservoirs is likely to increase, not only for water storage purpose but also for maximizing water use benefits and mitigating climate extremes. Four papers [9][10][11][12] employ advanced optimization methods to derive reservoir operating rules for multi-reservoir systems and/or optimize multi-objective reservoir operation. In [9], the authors conduct a multi-target single dispatching study on ecology and power generation in the lower Yellow River to solve the single-objective and the multi-objective optimal schema using the genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II).…”

“…Four papers [9][10][11][12] employ advanced optimization methods to derive reservoir operating rules for multi-reservoir systems and/or optimize multi-objective reservoir operation. In [9], the authors conduct a multi-target single dispatching study on ecology and power generation in the lower Yellow River to solve the single-objective and the multi-objective optimal schema using the genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II). The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improvement on the ecological health of the lower Yellow River.…”

“…Multidisciplinary research and advanced methodologies in hydrological forecasts, especially in extreme floods and droughts, are widely implemented for water planning and management, which ultimately lead to improved optimum water resources management and effective control under a changing environment. Among them, artificial intelligence (AI) techniques are efficient tools for extracting the key information from complex highly dimensional input-output patterns and are widely used to tackle various hydrological problems such as flood forecasts discussed in this Special Issue [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Over the last decades, many studies have demonstrated that artificial intelligence (AI) techniques, such as machine learning (ML) methods, can produce flood forecasts in a few hours [15][16][17][18][19] while extending to seasonal forecasts many months in advance for larger river basins [20][21][22][23][24].…”

A Multi-Timescale Integrated Operation Model for Balancing Power Generation, Ecology, and Water Supply of Reservoir Operation

“…As an effective search algorithm for solving multi-objective problems, the NSGA-II has been widely used in the multi-objective optimization operation of reservoirs and is welcomed by people [15,16]. Bai et al [17] and Chen et al [18] used NSGA-II to solve the multi-objective scheduling model. By comparing the benefit changes between multi-objective and single-objective scheduling, the mechanism of action between objectives was analyzed.…”