19th European Conference on Mathematics for Industry: book of Abstracts, June, 13-17, 2016, Santiago de Compostela (Spain) (original) (raw)
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Water, 2017
The hydrodynamics of many hydropower reservoirs are controlled by the operation of their power plant, but the associated water quality impact is often poorly understood. In particular, significant hydropeaking operations by hydropower plants affect not only the downstream ecosystem but also the reservoir water temperature. This paper contributes to understanding that link. For this, we coupled a hydrodynamic model (Estuary, Lake and Coastal Ocean Model, ELCOM) to a grid-wide power system scheduling model. In a case study (Rapel, Chile), we observe the behavior of variables related to the flow regime and water quality (including sub-daily hydrologic alteration, seasonal and sub-daily thermal pollution of the downstream river, and vertical mixing in the reservoir). Additionally, we evaluate how environmental constraints (ECs) can improve the conditions for a wet, normal and dry water-type year. We found that the unconstrained operation produces a strong sub-daily hydrologic alteration as well as an intense thermal pollution of the outflow. We show that these effects can clearly be avoided when implementing ECs. The current (unconstrained) vertical mixing makes the reservoir susceptible to algae blooms. Implementing ECs may intensify the stratification in the reservoir near the dam in some scenarios. The grid-wide economic cost of Rapel's ECs is a modest 0.3%.
Seasonal Arima Inflow Models for Reservoir SIZING1
JAWRA Journal of the American Water Resources Association, 2001
The reliable sizing of reservoirs is a very important task of hydraulic engineering. Although many reservoirs throughout the world have been designed using Rippi's mass curves with historical inflow volumes at the dam site, this technique is now considered outdated. In this paper, synthetic series of monthly inflows are used as an alternative to historical inflow records. These synthetic series are generated from stochastic SARIMA (Seasonal Autoregressive Integrated Moving Average) models. The analyzed data refer to the planned Almopeos Reservoir on the Almopeos River in Northern Greece with 19-year monthly inflow series. The analysis of this study demonstrates the ability of SARIMA models, in conjunction with the adequate transformation, to forecast monthly inflows of one or more months ahead and generate synthetic series of monthly inflows that preserve the key statistics of the historical monthly inflows and their persistence Hurst coefficient K. The forecasted monthly inflows would be of help in evaluating the optimal real time reservoir operation policies and the generated synthetic series of monthly inflows can be used to provide a probabilistic framework for reservoir design and to cope with the situation where the design horizon of interest exceeds the length of the historical inflow record.
Optimization of hydro storage systems and indifference pricing of power contracts
2015
We present a medium-term planning model for hydropower production based on multistage stochastic programming (MSP). The model determines a production schedule for a planning horizon of one year where decisions are made on generation, pumping or spillover. While in reality a production schedule must be determined with (at least) hourly time resolution, the MSP model cannot decide in hourly steps due to the curse of dimensionality. To overcome this issue, we exploit a special aggregation technique based on occupation levels for prices. The system under consideration consists of several interconnected seasonal reservoirs and produced electricity is sold on the spot market. Thus, we consider stochastic inflows and stochastic electricity prices which are modeled with a regime-switching approach to take also extreme price movements (spikes) into account. Scenario paths are generated with Monte Carlo simulation and then aggregated to scenario trees which serve as input for the stochastic o...
Adaptive Reservoir Operation Model Incorporating Nonstationary Inflow Prediction
Long-term changes in reservoir inflow due to climate change and human interferences have caused doubts on the assumption of hydrologic stationarity in reservoir design and operation. Incorporating uncertain predictions that consider nonstationarity into an adaptive reservoir operation is a promising strategy for handling the challenges that result from nonstationarity. This study proposes rules for multistage optimal hedging operations that incorporate uncertain inflow predictions for large reservoirs with multiyear flow regulation capacities. Three specific rules for determining the optimal numerical solution are derived. A solution algorithm is then developed based on the optimality conditions and the three rules. The optimal hedging rules and the solution algorithm are applied to the Miyun Reservoir in China, which exhibited a statistically significant decline in reservoir inflow trend from 1957 to 2009, to determine an annual operating schedule from 1996 to 2009. Nonstationary inflows are predicted by using an autoregressive integrated moving average (ARIMA) model on a period-byperiod basis. The actual operation (AO) of the reservoir is compared with different operational policy scenarios, including a standard operating policy (SOP; matching the current demand as much as possible), a hedging rule (i.e., leaving a certain amount of water for the future to avoid the risk of a large water deficit) with a prediction from ARIMA (HR-1), and a hedging rule with a perfect prediction (HR-0). With a predefined benefit function, the utility of the reservoir operation under HR-1 is 3.7% lower than that under HR-2, but the utility under HR-1 is 3.1% higher than that of AO and 13.7% higher than that of SOP.
Journal of Hydroinformatics, 2010
This paper presents two Stochastic Dynamic Programming models (SDP) to investigate the potential value of inflow forecasts with various lead times in hydropower generation. The proposed SDP frameworks generate hydropower operating policies for the Ertan hydropower station, China. The objective function maximizes the total hydropower generation with the firm capacity committed for the system. The two proposed SDP-derived operating policies are simulated using historical inflows, as well as inflow forecasts with various lead times. Four performance indicators are chosen to assist in selecting the best reservoir operating policy: mean annual hydropower production, Nash-Sutcliffe sufficiency score, reliability and vulnerability. Performances of the proposed SDP-derived policies are compared with those of other existing policies. The simulation results demonstrate that including inflow forecasts with various lead times is beneficial to the Ertan hydropower generation, and the chosen operating policy cannot only yield higher hydropower production, but also produces reasonable storage hydrographs effectively. Key words | hydropower operation, inflow forecasts with various lead times, quantitative precipitation forecasts, stochastic dynamic programming stochastic nature of the inflows (Tejada-Guibert et al. 1995). Although several options for the hydrologic state variables are available, the most common choice is the current or previous period's inflow (Stedinger et al. 1984).
Medium Term Hydroelectric Production Planning -A Multistage Stochastic Optimization Model
Multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. One of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices) that can be described by stochastic processes in discrete time. We apply this methodology to hydrosystem operation assuming random electricity prices and random inflows to the reservoir system. After describing the multistage stochastic model a simple case study is presented. In particular we use the model for pricing an electricity delivery contract in the framework of indifference pricing.
Journal of Water Resources Planning and Management
The drawback of this additive model is the non-neglegible probability of negative discharge values. This is to be avoided, because negative discharges have no physical sense. Existing solutions dealing with negative discharges [Stedinger and Taylor , 1982; Pereira et al., 1984; Bezerra et al., 2012] use non-linear transformations that make these models not usable in SDDP. The monthly time-step preserves the process as markov, but it risks to underestimate the system adaptivity to changing conditions. Such a large time-step, in fact, may not take into account the adaptivity at a smaller time-step, and it can be a limitation to the analysis of system response to fast processes, such as flood, resulting in an underestimation References
In a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits for the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. Specifically, the model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power and total energy. The study considered three scenarios: deterministic, probabilistic, and risky, with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. The values obtained from th...
Stochastic Dynamic Programming Models for Reservoir Operation Optimization
Most applications of stochastic dynamic programming have derived stationary policies which use the previous period's inflow as a hydrologic state variable. This paper develops a stochastic dynamic programming model which employs the best forecast of the current, period's inflow to define a reservoir release policy and to calculate the expected benefits from future operations. Use of the best inflow forecast as a hydrologic state variable, instead of the preceding period's inflow, resulted in substantial improvements in simulated reservoir operations with derived stationary reservoir operating policies. While these results are for a dam at Aswan in the Nile River Basin, operators of other reservoir systems also have available to them information other than the preceding period's inflow which can be used to develop improved inflow forecasts.