Short-Term Reservoir Optimization By Stochastic Optimization To Mitigate Downstream Flood Risks

An important objective of the operation of multi-purpose reservoirs is the mitigation of flood risks in downstream river reaches. Under the assumptions of reservoirs with finite storage volumes, a key factor for its effective use during flood events is the proper timing of detention measures under consideration of forecast uncertainty. Operational flow forecasting systems support this task by providing deterministic or probabilistic inflow forecasts and decision support components to assess optimum release strategies. We focus on the decision support component and propose a deterministic optimization and its extension to an adaptive multi-stage stochastic optimization. These techniques are used to compute release trajectories of the reservoirs over a finite forecast horizon of up to 15 days by integrating a nonlinear gradient-based optimization algorithm and a simulation model of the water system. The framework has been implemented for a reservoir system operated by the Brazilian Companhia Energética de Minas Gerais S.A. (CEMIG). We exemplary present results obtained for the operation of the Tres Marias reservoir in the Brazilian state of Minas Gerais with a catchment area of near 55,000 km2. The focus of our discussion is the impact of forecast uncertainty and its consideration in the optimization procedure. We compare the performance of the deterministic and multi-stage stochastic optimization techniques and show the superiority of the stochastic approach

Short-Term Reservoir Optimization By Stochastic Optimization To Mitigate Downstream Flood Risks

An important objective of the operation of multi-purpose reservoirs is the mitigation of flood risks in downstream river reaches. Under the assumptions of reservoirs with finite storage volumes, a key factor for its effective use during flood events is the proper timing of detention measures under consideration of forecast uncertainty. Operational flow forecasting systems support this task by providing deterministic or probabilistic inflow forecasts and decision support components to assess optimum release strategies. We focus on the decision support component and propose a deterministic optimization and its extension to an adaptive multi-stage stochastic optimization. These techniques are used to compute release trajectories of the reservoirs over a finite forecast horizon of up to 15 days by integrating a nonlinear gradient-based optimization algorithm and a simulation model of the water system. The framework has been implemented for a reservoir system operated by the Brazilian Companhia Energética de Minas Gerais S.A. (CEMIG). We exemplary present results obtained for the operation of the Tres Marias reservoir in the Brazilian state of Minas Gerais with a catchment area of near 55,000 km2. The focus of our discussion is the impact of forecast uncertainty and its consideration in the optimization procedure. We compare the performance of the deterministic and multi-stage stochastic optimization techniques and show the superiority of the stochastic approach

On degree sums of a triangle-free graph

For a simple triangle-free k-chromatic graph G with k >= 2 the upper bound m(n-f (k-2)) on the sum Sigma(2)(G) = Sigma(x is an element of V(G))d(2)(x) of the squares of the degrees of G is proved, where n, m, and f(1) are the order of G, the size of G, and the minimum order of a triangle-free l-chromatic graph, respectively. Consequences of this bound are discussed. Moreover, we generalize the upper bound on Ep (G) = Sigma(p)(G) = Sigma(x is an element of V(G))d(x)) for p = 2 to P >= 3

Simulation of Coordinated Market Grid Operations considering Uncertainties

Within the venture"REGEES" (REGenerative rEnewable Electricity System) a new approach for a Coordinated Market Grid Operation Management (CMGOM) was developed. This approach is investigated in a simulation considering uncertainties. Therefore, the energy time series generator as a method to generate time series for demand and feed-in is described and used to generate simulation input in the form of forecast-scenarios for load and feed-in. Uncertainties in the form of forecast errors and stochastic time series properties are taken into account. The optimization problem, which represents the mathematical description for the acquisition and balancing process of the Balance Responsible Party (BRP), is introduced. Finally, the energy system simulation is described for a test case in order to evaluate the CMGOM approach with consideration of uncertainty. For this purpose, deterministic optimization and two versions of optimization with uncertainties are compared

A smart market approach to coordinate market and grid operations

Within the venture “REGEES” (REGenerative rEnewable Electricity System) a new approach for a Coordinated Market Grid Operation Management (CMGOM) was developed. It envisages the extension of the balancing process of Balance Responsible Party (BRP) including information of current or estimated grid situation. This paper discusses two different approaches to calculate the restrictions for BRPs by the DSO and the resultant consequences for BRPs optimization. The approaches are applied and assessed in test cases that show their benefits and disadvantages