Optimal load distribution between units in a power plant

Bortoni, Edson C.; Souza, Luiz Edival de

doi:10.1016/j.isatra.2007.03.003

Cited by 22 publications

(5 citation statements)

References 2 publications

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“…As a drawback, spillage cannot be duly used as a decision variable in this approach, as it becomes impossible to calculate how much the variation in the reservoir water will change its level. Furthermore, the fact that all GUs are identical allows the authors to assume that power demands are equally distributed among online GUs [30], which greatly simplifies the problem.…”

Section: Problem Formulationmentioning

confidence: 99%

Hydroelectric Operation Optimization and Unexpected Spillage Indications

et al. 2020

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It is widely known that hydroelectric power plants benefit from optimized operation schedules, since the latter prevent water and, therefore, monetary wastes, contributing to significant environmental and economic gains. The level of detail on the representation of such systems is related to how far ahead the planning horizon is extended. Aiming at the very short-term optimization of hydroelectric power plants, which usually requires the most detailed models, this paper addresses an undesired effect that, despite being already mentioned in the literature, has not been properly explored and explained yet. This effect is given by the indication of spillage by the optimizer, even when the reservoir does not reach its maximum capacity. Simulations implemented in Julia language using real power plant data expose this phenomenon. Possible ways to circumvent it are presented. Results showed that, in specific cases, spillage allows the achieving of more efficient operating points by reducing the gross head and increasing the amount of water that flows through turbines. Furthermore, it was verified that applying water outflow-based objective functions prevents undesired spillage indications, despite causing machines to operate at lower efficiency levels, compared with the utilization of power losses-based objective functions.

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Section: Problem Formulationmentioning

confidence: 99%

Hydroelectric Operation Optimization and Unexpected Spillage Indications

et al. 2020

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“…The problem of optimal load distribution among hydropower plant units is to minimize the total water discharge while ensuring the power system reliability [2,4,6]. This problem is modeled as follows:…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In this paper, the OLD problem is defined as a deterministic optimization problem with a fixed hydraulic head and a single time step [6], namely economic dispatch. The objective is to minimize the total water discharge by optimizing the load distribution among multiple generator units.…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of optimization methods have been applied to solving the OLD problem, including mixed integer linear programming (MILP) [7][8][9][10][11][12], mixed integer nonlinear programming [13], dynamic programming (DP) [14][15][16], Lagrangian relaxation [12,17,18], hierarchical optimization [6,[19][20][21][22][23], genetic algorithm (GA) [18], particle swarm optimization (PSO) [2,[24][25][26][27] and cultural algorithm [28]. These methods generally produce a single optimal or near-optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant

et al. 2012

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Optimal load distribution (OLD) among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. However, many practical constraints on hydropower plant operation are very difficult, if not impossible, to be modeled, and the optimal solution found by those models might be of limited practical uses. This motivates us to find multiple optimal solutions to the OLD problem, which can provide more flexible choices for decision-making. Based on a special dynamic programming model, we use a modified shortest path algorithm to produce multiple solutions to the problem. It is shown that multiple optimal solutions exist for the case study of China's Geheyan hydropower plant, and they are valuable for assessing the stability of generator units, showing the potential of reducing occurrence times of units across vibration areas.

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“…Therefore, managing water resources properly is advised so that water shortages or excessive increase in the reservoir level are avoided as often as possible during dry or wet periods, respectively. An effort to prevent the former scenario, i.e., low availability of resources, is done by optimizing the operation of the HPP, as in Arce et al (2002), Bortoni et al (2007), and Finardi et al (2016), which minimizes costs, maximize efficiency, and minimizes resources usage, respectively. This paper, however, focuses on preventing the latter scenario, i.e., avoiding that unexpected and expressive incoming water flows reach the reservoir and increase its level up to unsafe values.…”

Section: Introductionmentioning

confidence: 99%

Spillage Forecast in Hydroelectric Power Plants via Machine Learning

Nascimento¹,

Abritta²,

Panoeiro³

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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Brazilian hydroelectric power plants often use telemetry stations to extract information about the environment. These equipment are usually installed in several strategic spots of rivers that "feed" the reservoir, and are capable of providing important information such as precipitation, river level, and water flow. This paper presents an analysis of Machine Learning applied to the forecasting of spillage occurrences over a set amount of time in a Brazilian power plant. To achieve this goal, telemetry stations' data were utilized together with the plant's operations historical, which provides information about previous spillages, turbines' flows, among others. The Machine Learning approach has shown to be promising in this problem, and the developed model presented the potential to effectively support decisions by helping the operators prepare for significant incoming water flows.

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Optimal load distribution between units in a power plant

Cited by 22 publications

References 2 publications

Hydroelectric Operation Optimization and Unexpected Spillage Indications

Hydroelectric Operation Optimization and Unexpected Spillage Indications

Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant

Spillage Forecast in Hydroelectric Power Plants via Machine Learning

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