Large-scale Hydropower System Research Articles

Double Effects Progressive Optimality Algorithm for Optimization of Large-Scale Hydropower System

Double Effects Progressive Optimality Algorithm for Optimization of Large-Scale Hydropower System

Improved decomposition–coordination and discrete differential dynamic programming for optimization of large-scale hydropower system

With the construction of major hydro plants, more and more large-scale hydropower systems are taking shape gradually, which brings up a challenge to optimize these systems. Optimization of large-scale hydropower system (OLHS), which is to determine water discharges or water levels of overall hydro plants for maximizing total power generation when subjecting to lots of constrains, is a high dimensional, nonlinear and coupling complex problem. In order to solve the OLHS problem effectively, an improved decomposition–coordination and discrete differential dynamic programming (IDC–DDDP) method is proposed in this paper. A strategy that initial solution is generated randomly is adopted to reduce generation time. Meanwhile, a relative coefficient based on maximum output capacity is proposed for more power generation. Moreover, an adaptive bias corridor technology is proposed to enhance convergence speed. The proposed method is applied to long-term optimal dispatches of large-scale hydropower system (LHS) in the Yangtze River basin. Compared to other methods, IDC–DDDP has competitive performances in not only total power generation but also convergence speed, which provides a new method to solve the OLHS problem.

Parallel discrete differential dynamic programming for multireservoir operation

The curse of dimensionality and computational time cost are a great challenge to operation of large-scale hydropower systems (LSHSs) in China because computer memory and computational time increase exponentially with increasing number of reservoirs. Discrete differential dynamic programming (DDDP) is one of the most classical algorithms for alleviating the dimensionality problem for operation of LSHSs. However, the computational time performed on DDDP still increases exponentially with increasing number of reservoirs. Therefore, a fine-grained parallel DDDP (PDDDP) algorithm, which is based on Fork/Join parallel framework in multi-core environment, is proposed to improve the computing efficiency for long-term operation of multireservoir hydropower systems. The proposed algorithm is tested using a huge cascaded hydropower system located on the Lancang River in China. The results demonstrate that the PDDDP algorithm enhances the computing efficiency significantly and takes full advantage of multi-core resources, showing its potential practicability and validity for operation of LSHSs in future.

Parallel stochastic dynamic programming for long-term generation operation of cascaded hydropower stations

Stochastic Dynamic Programming (SDP) for long-term generation operation of cascaded hydropower stations will bring about the curse of dimensionality, resulting in the rapid increase of computational time and the decrease of computational efficiency. Therefore, alleviate the dimensionality problem and improve the computational efficiency are always difficult issues for long-term generation operation of cascaded hydropower stations. On the basis of the parallelism analysis for SDP, a parallel stochastic dynamic programming (PSDP) based on Fork/Join parallel framework was proposed. In this method, all computational tasks for the returns from all discrete combinations in one stage were taken as parent task, which was decomposed into several subtasks by divide-and-conquer method. After this, the decomposed subtasks were solved in different cores respectively for achieving fine-grain parallel computation. The proposed approach was implemented to long-term generation operation of cascaded hydropower stations located on Lancangjiang River, and 3 different schemes with different discrete number of variables were established for testing the computational efficiency in multi-core environment. The result shows that the computational time, compared with serial computation, decreased respectively about 50% in 2-core environment and 70% in 4-core environment, making full use of multi-core resources. In addition, the larger computational scale can reduce more computational time in multi-core environment. Hence, the proposed approach is effective for operation of large-scale hydropower system, and can provide guidance for other applications.

Short-term management of hydropower assets of the Federal Columbia River Power System

We focus on the short-term optimization of large-scale hydropower systems with a mixture of storage reservoirs and run-of-the-river projects. If sufficient operational flexibility is available within the operational constraints, the system is capable of balancing the transmission network by compensating for load fluctuations and power production of other renewables such as solar and wind resources. The proposed optimization model is based on a nonlinear system representation of the hydropower system for forecasting state trajectories over a forecast horizon in combination with nonlinear programming for computing optimal release trajectories for specific hydropower projects. We present the application of a deterministic version of the approach to the short-term management of the Federal Columbia River Power System in the Pacific Northwest of the USA during the chum spawning season. An assessment of the computational performance of the approach for different optimization algorithms shows a superior performance of the Interior Point OPTimizer (IPOPT) in combination with the HSL/MA27 linear equation solver. In particular, the scaling properties are promising and will enable an extension of the deterministic approach towards a multi-stage stochastic optimization for taking into account the forecast uncertainty.

Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid

This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.

Short-Term Scheduling of Large-Scale Hydropower Systems for Energy Maximization

This paper describes a short-term hydro generation optimization program that is required by Fujian Electric Power Company Ltd. (FEPCL) to maximize hydropower production, given the beginning-of-study content of each reservoir and the end-of-study contents of the reservoirs with long-term storage. The optimal hydro scheduling problem is formulated as nonlinear programming with linear constraints and is solved by a direct search procedure. A cycling module is used to estimate the end-of-study contents of the reservoirs with short-term storage and the discharge delay releases from upper reservoirs, and then a transition module is solved to obtain the optimal generation scheduling. The system model is already operational for large-scale hydro systems, and currently is being used by FEPCL’s operation engineers to determine hourly or half-hourly generation schedules over a period of up to three days.

Optimization of Large-Scale Hydropower System Operations

A practical monthly optimization model, called SISOPT, is developed for the management and operations of the Brazilian hydropower system. The system, one of the largest in the world, consists of 75 hydropower plants with an installed capacity of 69,375 MW, producing 92% of the nation's electrical power. The system size and nonlinearity pose a real challenge to the modelers. The basic model is formulated in nonlinear programming ~NLP!. The NLP model is the most general formulation and provides a foundation for analysis by other methods. The formulated NLP model was first linearized by two different linearization techniques and solved by linear programming ~LP!. A comparative analysis was made of the results obtained from the linearized and the NLP models. The results show that the simplest linearized model ~referred to as the LP model! without iteration is suitable for planning purposes. For example, the LP model could be used in system capacity expansion studies or to explore various design parameters in connection with feasibility studies, where details in storage variation are not as important as the power production. With a good initial policy provided by the LP model, the successive linear programming ~SLP! model produced excellent results with fast convergence. The NLP model, the most complex and accurate model in the suite, is particularly suited for setting up guidelines for real-time operations using inflow forecast with frequent updating. The performance of the NLP model was checked against the historical operational records, and the comparison yields indica- tions of superior performance.