A multi-objective stochastic approach to hydroelectric power generation scheduling

The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

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“…Papers [85,87] are also regarding cascade reservoirs operation optimization. However, the focus is financial.…”

Section: Results Analysismentioning

confidence: 99%

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Bernardes

Santos

Abreu

et al. 2022

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“…In all the OMs, a unit commitment problem has to be solved. The task formulation for the three cascaded poundage HPPs in the RF mode has been presented in previous articles [8], [9]. In the present paper, the reformulation of the optimization task (Fig.…”

Section: A Definition Of the Problemmentioning

confidence: 93%

“…The operation of HPPs is subject to numerous limitations due to environmental and safety concerns. The constraints imposed by bank erosion, reservoir capacity, integrity of dam facilities and various other factors [8], [9] are summarized in Table I.…”

Section: A Definition Of the Problemmentioning

confidence: 99%

Estimating the Costs of Operating Reserve Provision by Poundage Hydroelectric Power Plants

Petrichenko

Baltputnis

Sobolevsky

et al. 2018

2018 15th International Conference on the European Energy Market (EEM)

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The transmission system operators are entrusted with the task of ensuring stable and reliable functioning of the power system. In a modern power system, there are three groups of market actors who can be called upon by TSOs to aid in power system operation management: controllable generation, energy storage and controllable load. The owners of these assets can rightfully expect remuneration for their services from the TSO to recoup investment costs and operational expenses. This study is focused on poundage hydroelectric power plants and it strives to estimate the cost of operating reserve provision. Data of the large hydroelectric power plants located on the River Daugava is used for the calculation of costs and validation of the proposed algorithms.

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“…Let us point out that the division of time into weeks is possible in power systems that do not contain long-term energy storage plants. Detailed forecasting algorithms are described in our earlier studies [25], [26].…”

Section: Forecasting Of Processesmentioning

confidence: 99%

“…• Hydropower plant optimization software OptiBidus-HES [27]; • Thermal power plant mode calculation software OptiBidus-TEC; • Generalized energy storage software [28], which is adaptable to technologies of various kinds, as well as the software specifically intended for Kruonis pumped storage hydropower plant [29]; • Forecasting software products, for example, for water inflow [25] and heat load [26], etc.…”

Section: F the Tools To Be Usedmentioning

confidence: 99%

Modelling the Future of the Baltic Energy Systems: A Green Scenario

Petrichenko

Sauhats

et al. 2021

Latvian Journal of Physics and Technical Sciences

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The electricity sector in Europe and in the world is undergoing rapid and profound changes. There is a sharp increase in the capacity of renewable energy sources, coal and nuclear power plants are being closed and new technologies are being introduced. Especially rapid changes are taking place in the energy systems of the Baltic States. Under these conditions, there is an emerging need for new planning tools particularly for the analysis of the power system properties in a long-term perspective. The main contribution of this article lies in the formulation and solution of optimization problems that arise when planning the development of power systems in the Baltic States. To solve this problem, it is necessary to use models of various power plants and make a number of assumptions, the justification of which requires the following actions: to briefly review the current situation of the production and demand of energy in the Baltic power systems; to conduct an overview of the Baltic interconnections and their development; to make forecasts of energy prices, water inflow, energy production and demand; to set and solve the problems of optimization of power plant operation modes; to demonstrate the possibility and limitations of the developed tools on the basis of real-life and forecast data. In this paper, a case study is performed using the main components of the overall modelling framework being developed. It focuses on the Baltic power systems in 2050 under the conditions of significant expansion in the installed capacity of renewable energy sources (RESs) and diminished fossil fuel power plant activity. The resulting electricity generation mix and trade balance with neighbouring countries is assessed, showing that even with significant RES expansion, the Baltic countries remain net importers and because of the intermittency of RESs, there are hours within the year when the demand cannot be met.

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A multi-objective stochastic approach to hydroelectric power generation scheduling

Cited by 15 publications

References 16 publications

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Hydropower Operation Optimization Using Machine Learning: A Systematic Review

Estimating the Costs of Operating Reserve Provision by Poundage Hydroelectric Power Plants

Modelling the Future of the Baltic Energy Systems: A Green Scenario

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