Cascaded Hydro Plants Research Articles

Optimal Control of Cascade Hydro Plants as a Prosumer-Oriented Distributed Energy Depot

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners’ revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted.

A mathematical programming–based heuristic for coordinated hydrothermal generator maintenance scheduling and long-term unit commitment

This paper studies the coordinated optimization between generator maintenance scheduling and long-term unit commitment in hydrothermal power systems, which can deal with the increased conflicts between these two tasks brought on by the extensive integration of cascade hydropower, but encounters difficulty in finding solutions when the system scale is large. A coordinated optimization model between hydrothermal generator maintenance scheduling and long-term unit commitment is proposed, in which the network security constraints and coupling characteristics between cascade hydro plants are considered. To accelerate the solution of this complex mixed-integer linear programming problem, an improved two-stage heuristic algorithm based on the objective scaling ensemble approach is proposed. The first stage is a detection process in which a maintenance interval detection strategy is used to probe the maintenance variables to be fixed. The second stage is a fix-and-solve process in which the reduced model is solved after fixing the maintenance variables detected at the first stage. Finally, simulations on a modified IEEE 30-bus and 6-unit hydrothermal power system and a real 1348-bus and 155-unit hydrothermal power system are conducted, focusing on the solution quality and the acceleration effect of the proposed algorithms, as well as the results of the proposed model. The numerical results reveal that the proposed algorithm can speed up the process of finding a near-optimal solution for large-scale systems through a detection strategy with high accuracy and efficiency. The proposed model can plan out feasible and coordinated maintenance scheduling and long-term unit commitment scheme simultaneously.

Data-driven two-stage stochastic optimization model for short-term hydro-thermal-wind coordination scheduling based on the dynamic extreme scenario set

A data-driven two-stage stochastic optimization model for solving short-term hydro-thermal-wind coordination scheduling problem with complicated time-coupled and space-related characteristics is proposed in this paper. The generation, emission and power fluctuation costs in the day-ahead scheduling stage and power regulation costs by hydro-thermal units, wind curtailment and load shedding costs in the real-time scheduling stage is formulated as the objective function of proposed model. Moreover, a dynamic extreme scenario generation and reduction method is introduced to deal with wind power uncertainty. The relationship between hydropower output, volume, inflow, discharge and upstream water for the multi-reservoir cascaded hydro plants is converted into a series of linear constraints, and the practical constraints of prohibited operation zones for thermal units are taken into account. As a result, the proposed model can be transformed into a mixed-integer linear programming problem to solve. Finally, case studies are carried out on the modified IEEE 24-bus system comprising of six thermal units, a multi-chain cascade of four hydro plants and one wind farm, and simulation results verify the effectiveness of the proposed coordination scheduling model and method. • Two-stage stochastic optimization for hydro-thermal-wind system is proposed. • Dynamic extreme scenario set is constructed to describe wind power uncertainty. • Spatio-temporal coupling characteristics of cascaded hydro plants is considered. • A mixed-integer linear programming is built for solving the scheduling problem.

Electric Power and Heat Generation Expansion Planning

A short-term electric power and heat generation augmentation planning (EPHGAP) is portrayed and evaluated here. The objective of EPHGAP is to find out the most cost-effective and dependable expansion plan for meeting the forecasted electrical power demand and heat demand over a long-range horizon while fulfilling a large number of technical, cost-effective, reliability, and social constraints. The EPHGAP problem is a big-dimensional highly constrained mixed integer nonlinear programing (MINLP) problem. The general algebraic modeling system (GAMS) environment has been used to implement the proposed formulation, and the EPHGAP problem is solved by using Basic Open-source Nonlinear Mixed Integer (BONMIN) solver. An archetypal test system with existing thermal power plants, cascaded hydropower plants, cogeneration units, heat-only units and candidate cogeneration units, and cascaded hydro plants is considered to evaluate the proposed methodology. Simulation outcomes of the test system have been matched up to those acquired by differential evolution and particle swarm optimization. It has been observed from the comparison that the recommended BONMIN solver has the capability to bestow with superior quality solution.

Multiobjective Scheduling Method for Short-Term Peak Shaving Operation of Cascade Hydro Plants

With structural changes in power consumption, peak load demands and peak-valley differences are growing, which motivates peak shaving operations of hydropower systems. A multiobjective mixed-integer nonlinear programming model is presented for short-term hydropower generation scheduling that aims to regulate peak loads and enhance power efficiency. The combined feasible operation zones and the aggregate generation function of each plant are embedded in this model to deal with the operation constraints of units. A set of Pareto solutions of this model is generated by an improved normalized constraint method that narrows the optimization range to ensure the practicality and compact distribution of solutions, each of which represents an aggregated scheduling of cascaded hydropower plants. Then, a mixed integer nonlinear programming formulation is developed to solve the unit commitment problem for each solution, and a fuzzy-based membership value assignment method is employed to extract a best compromise solution for final implementation. A case study of the hydropower plants of the Beipanjiang cascade in China is used to test the proposed approach, verifying its success in generating a practical operating schedule while providing abundant information to decision makers for decision support.

Short-Term Scheduling of Expected Output-Sensitive Cascaded Hydro Systems Considering the Provision of Reserve Services

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

Multi-region dynamic economic dispatch of solar–wind–hydro–thermal power system incorporating pumped hydro energy storage

This paper suggests chaotic fast convergence evolutionary programming (CFCEP) for solving multi-region dynamic economic dispatch (MRDED) problem, multi-region economic dispatch (MRED) problem and economic dispatch (ED) problem. MRDED problem is based on multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost function, wind and solar power units with uncertainty and pumped hydro energy storage. MRED problem deals with tie line constraints, transmission losses, valve point effect and proscribed workable area of thermal generators. ED problem deals with valve point effect, proscribed workable area and ramp rate limits of thermal generators. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficacy of the proposed technique has been verified on convoluted three-area system for MRDED problem, four-area system for MRED problem and 140-unit Korean system for ED problem. Test results acquired from the suggested CFCEP technique have been fit to that acquired from FCEP, differential evolution (DE) and particle swarm optimization (PSO). It has been observed from the comparison that the recommended CFCEP technique has the capability to bestow with better-quality solution.

Economic environmental dispatch of solar-wind-hydro-thermal power system

• EED is a significant chore in solar-wind-hydro-thermal power system. • It is an extremely restricted multi-objective optimization problem. • NSGA-II has been suggested for solving EED of solar-wind-hydro-thermal power system. Economic environmental dispatch (EED) is a significant chore in solar-wind-hydro-thermal power system comprising multi-reservoir cascaded hydro plant with time delay, thermal plants with non-smooth fuel cost, NO x emission level and SO 2 emission level functions, wind power generating units, solar PV plant and battery energy storage system. It is an extremely restricted multi-objective optimization problem linking conflicting objectives with both equality and inequality constraints. Here, non-dominated sorting genetic algorithm-II (NSGA-II) has been suggested for solving EED of solar-wind-hydro-thermal power system with battery energy storage system where generations are allocated among the on line units such that cost, NO x emission level and SO 2 emission level are optimized simultaneously while fulfilling all operational constraints. Test results of two test systems acquired from the suggested technique have been fit to that acquired from strength pareto evolutionary algorithm 2 (SPEA 2).

Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system

Multi-area dynamic economic emission dispatch (MADEED) is a significant chore of hydro-wind-thermal power system. This system comprises multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost and emission level functions, wind power generating units with the effect of reserve coefficient, penalty coefficient and wind power uncertainty. This paper suggests nondominated genetic algorithm–II (NSGA-II) for solving multi-area dynamic economic emission dispatch (MADEED) of hydro-wind-thermal power system where power generations are allocated among the on line units in such a manner that total cost and emission level are optimized simultaneously while fulfilling all operational constraints. Test results of a four-area system acquired from the suggested technique have been fit to that acquired from strength pareto evolutionary algorithm 2 (SPEA 2).

Network‐constrained hydrothermal unit commitment using benders and optimality condition decompositions

This paper presents a new network‐constrained hydrothermal unit commitment (NCHTUC) model that considers nonlinear hydro generation functions and AC power flow constraints. In addition, the proposed NCHTUC includes dynamic water balance equations of cascaded hydro plants. To cope with the large‐scale and mixed‐integer nonlinear nature of the model, a new hybrid computational strategy consisting of generalized Benders decomposition and optimality condition decomposition is proposed. By using this method, the NCHTUC problem is decomposed into a master problem, a hydro plant subproblem, and a thermal plant subproblem with independent OPF (optimal power flow) solutions for each time period. In order to verify the effectiveness of the proposed model and decomposition strategy, numerical tests are performed on three test systems of up to 703 buses over a 24‐h horizon. Test results indicate that the developed methodology has excellent convergence and is capable of solving large‐scale NCHTUC problems without loss of accuracy. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Research on joint generation scheduling of cascade hydro plants in the dry season

Abstract As more and more reservoirs are built, concentrated water releasing in the dry season will bring about additional flow compensation and the joint operation of cascade hydro plants is quite important. This paper discusses the water level decline strategy of four cascade hydro plants in the Jinsha River and analyses the relationship between guaranteed output and total power generation. Considering stochastic inflows, an implicit stochastic optimization method is employed and a multi-objective parallel differential evolution algorithm is proposed to extract dispatching rules. Finally, a method which combines discriminant method and dispatching rules is proposed for practical operations and achieves good performance. Compared with routine scheduling, the power generations of the proposed method are improved observably in different typical years. The average power generation increases about 3% with the same cascade minimum output.

A complete decomposition and coordination algorithm for large‐scale hydrothermal optimal power flow problems

This paper presents a complete decomposition and coordination algorithm to solve large‐scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first‐order Karush–Kuhn–Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large‐scale test systems with up to 3120 buses and 7 531 915 primal–dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65–75%. With parallel computing, it is capable of achieving 10–20 times or even 1000 times speed without loss of optimality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

A solution to the combined hydro-thermal-wind scheduling problem of multi reservoir cascaded hydro plants is presented employing a novel ant lion optimization (ALO) algorithm. Five objectives, cost, various emissions and power loss, are simultaneously optimized. The optimal schedules of thermal, hydro and wind power (WP) units are determined for continuously varying load subject to a large number of practical operational constraints. The effect of reserve and penalty coefficients and WP uncertainty is also investigated for the multi-objective (MO) problem. The newly proposed ALO algorithm has unique features like random walk, roulette wheel, and boundary shrinking. These operations provide a judicious balance between exploration and exploitation, and create a powerful optimization technique for complex real-world problems.Finding the best compromise solution (BCS) is a tedious task when multiple objectives are involved. A composite ranking index (CRI) is proposed as a performance metrics for MO problems. The CRI helps the decision maker in ranking the large number of Pareto-optimal solutions. The developed model is tested on three standard systems, having a mix of hydro, thermal and wind generators. The performance is found to be superior to published results and comparable with established algorithms like artificial bee colony (ABC) and differential evolution (DE).

Daily Generation Scheduling of Cascade Hydro Plants Considering Peak Shaving Constraints

AbstractThis paper considers the operation of hydro plants, tackles the problem of daily hydro-generation scheduling (DHGS), and obtains the optimal hourly operation of cascade hydropower reservoirs. To meet the practical operational demands of power grid, an improved benefit-maximization model in which the peak shaving demands are taken as constraints is proposed for DHGS, and the joint peak load regulation (JPLR) method is applied to the proposed model. The model can enhance the power-generation efficiency and reduce the water spillage significantly under the condition of peak load regulation. Meanwhile, a hybrid method that combines discrete differential dynamic programming with progressive optimality algorithm is proposed to solve the JPLR problem. The complicated constraints can be handled by the proposed algorithm effectively. Moreover, the hydro-unit-commitment problem is solved by a rapid searching algorithm based on equal incremental principle and empirical methods. With this unit-commitment stra...

Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error

Reliable streamflow forecasts are very significant for reservoir operation and hydropower generation. But for monthly streamflow forecasting, the forecasting result is unreliable and it is hard to be utilized, although it has a certain reference value for long-term hydro generation scheduling. Current researches mainly focus on deterministic scheduling, and few of them consider the uncertainties. So this paper considers the forecasting error which exists in monthly streamflow forecasting and proposes a new long-term hydro generation scheduling method called forecasting dispatching chart for Xiluodu and Xiangjiaba cascade hydro plants. First, in order to consider the uncertainties of inflow, Monte Carlo simulation is employed to generate streamflow data according to the forecasting value and error distribution curves. Then the large amount of data obtained by Monte Carlo simulation is used as inputs for long-term hydro generation scheduling model. Because of the large amount of streamflow data, the computation speed of conventional algorithm cannot meet the demand. So an improved parallel progressive optimality algorithm is proposed to solve the long-term hydro generation scheduling problem and a series of solutions are obtained. These solutions constitute an interval set, unlike the unique solution in the traditional deterministic long-term hydro generation scheduling. At last, the confidence intervals of the solutions are calculated and forecasting dispatching chart is proposed as a new method for long-term hydro generation scheduling. A set of rules are proposed corresponding to forecasting dispatching chart. The chart is tested for practical operations and achieves good performance.

An oppositional learning based gravitational search algorithm for short term hydrothermal scheduling

This paper presents an oppositional based gravitational search algorithm (OGSA) for solving the short term hydrothermal scheduling (STHTS) problem. The STHTS problem involves the optimization of nonlinear constrained objective function by taking into consideration of multireservoir cascaded nature hydro plants, water transportation delay between cascaded reservoirs and scheduled time linkages, variable system active load balance, water discharge and reservoir storage limits, initial and final reservoir storage limits, reservoir flow balance and operating limits of hydro and thermal plants. A stochastic search algorithm known as gravitational search algorithm (GSA) inspired from the law of gravity and mass interactions is used to solve this complex constrained STHTS problem. In order to improve the convergence rate of GSA, the opposite numbers are utilized in the evolution process of GSA. Finally, the proposed oppositional gravitational search algorithm (OGSA) approach is evaluated on two test systems, one consisting of four hydro plants and an equivalent thermal plant and the other one with nine cascaded hydro and three thermal plants. The results obtained with the proposed approach give better solution in terms of less production cost, execution time and better convergence characteristics while comparing with the results of other methods reported in the literature.

Economic Environmental Scheduling of Hydrothermal Power System

This paper presents an interactive fuzzy satisfying method based on simulated annealing technique to determine the economic environmental scheduling of hydrothermal power system consisting of multi-reservoir cascaded hydro plants with time delay and thermal plants with nonsmooth fuel cost and emission level functions. This paper treats cost and emission as competing objectives. Assuming that the decision maker (DM) has fuzzy goals for each of the objective functions, fuzzy satisfying method based on simulated annealing technique is applied for generating a corresponding optimal noninferior solution for the DM’s goals. Test results obtained from the proposed method are compared with those obtained by using evolutionary programming.

Comparison of emerging metaheuristic algorithms for optimal hydrothermal system operation

Optimal hydrothermal system operation (OHSO) is one of the complex and hard-to-solve problems in power system field due to its nonlinear, dynamic, stochastic, non-separable and non-convex nature. Traditionally, this problem has been solved through classical optimization algorithms, which require some approximations to tackle a more tractable variant of the original problem formulation. Metaheuristic optimization has undergone a significant development in recent years, thus, there is a variety of tools with different conceptual differences, which offer a great potential for solving OHSO without extensive simplifications. This paper provides a comparative study on the application of six emerging metaheuristic algorithms to OHSO, namely, the Comprehensive Learning Particle Swarm Optimizer (CLPSO), Genetic algorithm with Multi-Parent Crossover (GA-MPC), Differential Evolution with Adaptive Crossover Operator (DE-ACO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Linearized Biogeography-based Optimization (LBBO), and the Hybrid Median-Variance Mapping Optimization (MVMO-SH). Since these tools have been successfully applied to other hard-to-solve optimization problems, the goal is to ascertain their effectiveness when adapted to tackle the OHSO problem by evaluating their performance in terms of convergence speed, achieved optimum solutions, and computing effort. Numerical experiments, performed on a test system composed by four cascaded hydro plants and an equivalent thermal plant, highlight the relevance of the adopted global search mechanisms, especially for LBBO and MVMO-SH. A nonlinear programming (NLP) algorithm is used as reference to validate the results.

Symbiosis Equilibrium Stable Range Analysis of Multi-Owner Cascade Hydro Plants

It is a huge dynamic dimensional optimization question for the optimal operation of multi-owner cascade hydro plants constrained by water use competition and market strategies cooperation. The symmetry mutualism behavior patterns between multi-owner hydro plants are described accurately based on symbiosis theory. Then the mathematical model of the cascade symbiosis alliance in symmetry mutualisms pattern is built and the equilibrium solutions are presented. We discuss the symbiosis equilibrium stability range of multi-owner cascade hydro plants. Finally, simulation results obtained demonstrate that the proposed stable range is proficient and the symbiosis evolution law is revealed.

Optimal operation of multi-reservoir system by multi-elite guide particle swarm optimization

With increasingly scarce of fossil fuel resources required for energy demand, hydropower has become one of the most important energy resources. As a renewable and sustainable energy, hydropower system has developed rapidly during recent decades. The unprecedented rate of expansion and development scale of hydropower has posed a challenge to the operation of multi-reservoir system (OMRS). The optimal operation of multi-reservoir system is a large-scaled, non-linear and multi-stage problem which subject to a series of hydraulic constraints. The main objective of OMRS is to find out the optimal hourly water discharge rate of each hydro station in the multi-reservoir system to minimize the power deficit and distribute the uniformly deficit if any. In order to solve OMRS problem effectively, in this paper, a multi-elite guide particle swarm optimization (MGPSO) is proposed by introducing archive set into standard particle swarm optimization. External archive set which can preserve elite solutions along the evolution process is employed to provide multi-elite flying directions for particles. Meanwhile, an effective constrain handling method is proposed to handle the operational constraints of OMRS problem. This proposed method is applied to a multi-reservoir system consisting of 10 cascaded hydro plants for case study. Compared with several previous methods, the simulation results of MGPSO can get better solutions with smaller energy deficit, which proves it is an alternative method to deal with OMRS problem.