Metaheuristic nature-inspired algorithms for reservoir optimization operation: A systematic literature review

The purpose of this systematic literature review (SLR) article is to discuss the findings of the state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale of this approach is to elucidate the optimal way as decision making that implemented MHNIA for several complex problems in reservoir optimization operation. Commonly, the metaheuristic optimization algorithm has always been used in hydrology field, especially in reservoir optimization. Hence, this presented study reviewed a considerable amount from the previous studies of commonly nature-based optimization algorithms applied in reservoir operations. Hence, preferred reporting items for systematic review and meta-analyses (PRISMA) has been used as guidance. The source was utilized from two primary journal databases: Scopus and web of science. According to the proposed search string, the findings managed to express into nine main themes which are optimize in water release, optimize reservoir operation problems, optimize hydropower operation, optimize condensate fluids in reservoir storage, optimize water pumped storage, optimize water quality control, optimize system performance operation, optimize water demand and optimize reservoir control as flood preventing. Overall, 24 articles that passed the minimum quality were retrieved using systematic searching strategies

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

METHODOLOGIES FOR RESERVOIR SYSTEMS ANALYSIS: APPLICATION OF OPTIMIZATION AND DEEP LEARNING

Reservoir systems operations are challenging given that they must function to meet conflicting goals. Using mathematical programming and deep learning techniques, this dissertation presents innovative methodologies to address some of the challenges. The first chapter focuses on development of a mathematical programming framework for assessing sub-daily hydropower hydropeaking operation and flow regime outcomes of a system of five large sequential hydropower facilities on the mainstem Connecticut River under various operation scenarios. A formulation for the pumped-storage Northfield reservoir is presented that uses binary decision variables to properly model the reservoir operations. The results closely match annual historical power values that indicates the model can replicate the operations. The second chapter presents a novel multiple objective optimization methodology for trade-off analysis of river basins. The novelties include a weighting scheme that normalize different objectives having different range of variabilities and formulations for quantification of ecological and flood control objectives as frequencies of meeting desirable conditions. The methodology is applied to the Connecticut River basin. In this chapter, formulations are developed that use binary decision variables to quantify ecological and flood control objectives along with other operational goals. The key trade-offs of the system objectives are identified. The results indicate hydropower revenue objective highly conflict with any other objective than flood control. Moreover, it is concluded that a balanced operation that equally weight different objectives has the potential to improve all the objectives. The third chapter presents a methodology for designing reservoir operation policy using optimization and deep learning. This chapter addresses the challenge of designing of an operation policy for a reservoir with conflicting objectives under uncertainty of hydrological and energy prices data. A deep neural network is developed to infer near-optimal operation policies under different foresight scenarios using the optimization modeling results. The methodology is applied to the Wilder reservoir on the mainstem Connecticut River. A base method is also developed that uses linear regression and is applied to the problem and the associated results are used as a comparison basis. Results indicate that the designed policies using neural networks perform better than the base method used while having foresight for a longer time improves the performance

Optimal operation of dams/reservoirs emphasizing potential environmental and climate change impacts

Mahdi studied the potential ecological and climate change impacts on management of dams. He developed several new optimization frameworks in which benefits of dams are maximized, while above impacts are mitigated. Governments and consulting engineers can use the proposed frameworks for managing dams considering environmental challenges in river basins