The parameterization-simulation-optimization framework for the management of hydroelectric reservoir systems

A. Efstratiadis, D. Bouziotas, and D. Koutsoyiannis, The parameterization-simulation-optimization framework for the management of hydroelectric reservoir systems, Hydrology and Society, EGU Leonardo Topical Conference Series on the hydrological cycle 2012, Torino, doi:10.13140/RG.2.2.36437.22243, European Geosciences Union, 2012.



The optimal control and management of large-scale hydroelectric reservoirs remains a challenging issue in water resources modelling and its importance increases, as the growing penetration of renewable sources in the actual energy scene creates additional requirements for energy regulation and storage. In this respect, it is essential to review both the current management policies and the related methodologies for supporting decision-making in reservoir management problems, which are rather insufficient. Older approaches, based on systems analysis (i.e. linear, nonlinear, dynamic or stochastic dynamic programming), as well as more advanced concepts and tools, such as fuzzy logic and neural networks, fail to provide the essential holistic approach, with regard to the various complexities of the problem. Such drawbacks arise due to the large number of variables, the nonlinearities of system dynamics, the inherent uncertainty of future conditions (inflows, demands), as well as the multiple and often conflicting water uses and constraints that are involved in the management of such systems. On the other hand, the parameterization-simulation-optimization (PSO) framework provides a feasible and general methodology applicable to any type of hydrosystem, including complex hydropower schemes. This uses stochastic simulation to generate synthetic system inputs and represents the operation of the entire system through a simulation model as faithful as possible, without demanding a specific mathematical form that would possibly imply oversimplifications. Such representation fully respects the physical constraints, while at the same time evaluates the system operation constraints and objectives in probabilistic terms, through Monte Carlo simulation. Finally, to optimize the system performance and evaluate its control variables, a stochastic optimization procedure is employed (in particular, the evolutionary annealing-simplex method). The latter is substantially facilitated if the entire representation is parsimonious, i.e. if the number of control variables is kept as small as possible. This is ensured through a suitable system parameterization, in terms of parametric expressions of operation rules for the major system controls (e.g. reservoirs, power plants). The PSO framework is implemented within the “Hydronomeas” decision support system (DSS), which has been successfully applied for the operational management of water resource systems of various levels of complexity, including the water supply system of Athens. Recently, both the modelling background and the functionalities of the DSS were upgraded to also handle hydropower generation components, as well as pumping-storage facilities. This new version is tested in a challenging case study, involving the simulation of the Acheloos-Thessaly hydrosystem. Acheloos is characterized by very high runoff and hosts 1/3 of the installed hydropower capacity of Greece. Apart from the existing scheme of projects, future configurations are also investigated, involving the diversion of part of the upstream water resources to the adjacent plain of Thessaly. For each configuration, the optimal management policy is located, on the basis of multiple performance criteria that account for both economy and reliability. Various formulations of the objective function are examined, combining different types of benefits from water and energy production (distinguishing for firm and secondary energy) and costs (due to pumping). Finally the sensitivity of solutions against the assumptions of the stochastic simulation model is examined. Emphasis is given on the effect of long- vs. short-term persistence of the simulated inflows.

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Our works that reference this work:

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