Hydro Generation Scheduling Research Articles

Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

The short-term hydropower generation scheduling (STHGS) is a complicated problem in the utilization of hydropower and water resources. An improved binary-coded whale optimization algorithm (IBWOA) is proposed in this paper to solve the complex nonlinear problem. The STHGS problem is divided into unit combination (UC) subproblem and economic load distribution (ELD) subproblem. For the UC subproblem, we use the sigmoid function (SF) to generate a binary array representing the start/stop state of the unit. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. Each generation solution is optimized by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint. For ELD subproblem, the optimal stable load distribution table (OSLDT) is used to distribute the load quickly. The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. Finally, the proposal is applied to solve the STHGS of the Three Gorges Hydropower Station. When the water head is 75 m,88 m, and 107 m, the minimum water consumption calculated by the IBWOA algorithm is 1,058,323,464 m3, 892,524,696 m3, and 745,272,216 m3, respectively. Compared with the traditional whale optimization algorithm, the water consumption of the IBOWA algorithm corresponding to 75 m, 88 m, and 107 m water heads is reduced by 0.76%, 0.26%, and 0.05%, respectively. The comparison between the IBWOA algorithm and other heuristic algorithms shows that the IBWOA has good feasibility and high optimization accuracy.

A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems

The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

Efficient Tabu Search Procedure for Short-Term Planning of Large-Scale Hydropower Systems

AbstractShort-term hydrogeneration scheduling aims at minimizing water consumption for the next 7–15 days on an hourly basis, while satisfying the electrical load as well as many operational, regul...

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

Series division method based on PSO and FA to optimize Long-Term Hydro Generation Scheduling

The fundamental requirement of power system hydro scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Long-Term Hydro Generation Scheduling (LHGS) is a complicated nonlinear, non-convex and nonsmooth optimization problem with discontinuous solution space. The model considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro units caused by power variations, start-up, and shut-down of hydro units. To deal with this complicated problem, Series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed in this paper. The SDM is to make a division on the Swarm Intelligence (SI) algorithm which is to be a number of particles searching collections that properly can be regarded as divisions. Whereas, each division is a developmental algorithm which used to get the global point. The extent of the SDM is often offered a quicker convergence so as to accomplish the best initial operation to swarm's algorithm research. The proposed SDM are tested on two test systems actual observed system operator (AOSO) and Standard System Operation (SSO) and compared with some recent research works in the area. The results point out the Series Division Firefly Algorithm (SDFA) is robust and has good efficiency and superiority.

Duality Based Risk Mitigation Method for Construction of Joint Hydro-Wind Coordination Short-Run Marginal Cost Curves

This study analyzes the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation, which quantifies the hourly cost of mitigating risk, and the water shadow price, which quantifies the marginal cost of electricity production. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves. The proposed method is expected to be of great interest to investors as it enables risk mitigation for investors with diverse risk preferences, from risk-averse to risk-seeking.

An efficient stochastic algorithm for mid-term scheduling of cascaded hydro systems

Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging issue. This paper proposes a novel stochastic optimization algorithm using Latin hypercube sampling and Cholesky decomposition combined with scenario bundling and sensitivity analysis (LC-SB-SA) to address this problem. To deal with the uncertainty of natural inflows, Latin hypercube sampling is implemented to provide an adequate number of sampling scenarios efficiently, and Cholesky decomposition is introduced to describe the correlated natural inflows among cascaded stations. In addition, to overcome the difficulties in solving the objectives of all the scenarios, scenario bundling and sensitivity analysis algorithms are developed to improve the computational efficiency. Simulation results from both two-station and ten-station systems indicate that the proposed method has the merits in accuracy as well as calculation speed for the mid-term cascaded hydro generation scheduling. The consideration of natural inflow correlation makes the formulated problem more realistic.

Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed.

Optimal Long-Term Hydro Generation Scheduling of Small Hydropower Plant (SHP) using Metaheuristic Algorithm in Himreen Lake Dam

This study focuses on the improvement of optimization model by applying particle swarm optimization and firefly algorithm methods to get a stable power production utility at its maximum level. Furthermore, it investigates on the minimization of utility loss in power production from the hydropower system, which is done by optimizing the variables of operation control in the hydropower plant at Lake Himreen - Diyala Dam. The variables mentioned are net turbine head, the rate of water flow and power production which had been gathered in the data during a research throughout a 10-year period. The results obtained from these two methods, namely Firefly Algorithm (FA) and Particle Swarm Optimization, (PSO) are compared. The inferences for general comparisons are created through several behavior indicators. The behavior indicators illustrate that FA's performance is better than PSO's performance, in some fields. At the end, the results show the strength of FA, as well as its efficiency and superiority.

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.

Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution

This paper present short-term combined economic and emission hydrothermal optimization, addressing total fuel costs and emissions minimization. This paper uses the fuel cost function with valve-point effect, which increases the degree of optimization problem difficulty. The optimal balance between the addressed objectives, that conflict with each other, can be obtained with appropriate hydro and thermal generation schedules. A surrogate differential evolution is applied in order to satisfy 24-h system demand and final states of hydro power plant reservoirs by minimized total fuel costs and emissions. This paper proposes a novel master–slave model optimization algorithm, where the optimal thermal schedules are obtained within the slave model. The data obtained from the slave model are saved into a matrix, which serves as a surrogate model for a master model, where the hydrothermal optimization with all objectives and constraints is conducted by using a parallel self-adaptive differential evolution algorithm. In order to show the effectiveness of the proposed method, different case studies are used: economic load scheduling, economic emission scheduling, and combined economic emission scheduling. The proposed method is verified on a model consisting of four hydro power plants and three thermal power plants.

Short-term hydro generation scheduling of Xiluodu and Xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm

Short-term hydro generation scheduling (STHGS) of cascade hydropower stations is a typical nonlinear mixed integer optimization problem to minimize the total water consumption while simultaneously meeting the grid requirements and other hydraulic and electrical constraints. In this paper, STHGS problem is decomposed into two parallel sub-problems of unit commitment (UC) and economic load dispatch (ELD), and the methodology of improved binary-real coded bee colony optimization (IB-RBCO) algorithm is proposed to solve them. Firstly, the improved binary coded BCO is used to solve the UC sub-problem with 0–1 discrete variables, and the heuristic repairing strategy for unit state constrains is applied to generate the feasible unit commitment schedule. Then, the improved real coded BCO is used to solve the ELD sub-problem with continuous variables, and an effective method is introduced to handle various unit operation constraints. Especially, the new updating strategy of DE/best/2/bin method with dynamic parameter control mechanism is applied to real coded BCO to improve the search ability of IB-RBCO. Finally, to verify the feasibility and effectiveness of the proposed IB-RBCO method, it is applied to solve the STHGS problem of Xiluodu and Xiangjiaba cascaded hydropower stations, and the simulating results are compared with other intelligence algorithms. The simulation results demonstrate that the proposed IB-RBCO method can get higher-quality solutions with less water consumption and shorter calculating time when facing the complex STHGS problem.

An MILP Based Formulation for Short-Term Hydro Generation Scheduling With Analysis of the Linearization Effects on Solution Feasibility

Mixed integer linear programming (MILP) based formulations and solution methods for short-term hydro generation scheduling (HGS) have been widely adopted by researchers, hydropower producers, and system operators in recent years. This approach calls for the nonlinear forebay level, tailrace level, penstock loss, and hydropower production functions to be replaced with their piecewise linear approximations. However, the effects of the linearization of the nonlinear functions and related constraints on solution feasibility have not been fully discussed in the literature. In this paper, the issues concerning solution feasibility are discussed in detail and a method is presented to ensure that the solution obtained based on the approximated MILP formulation remains feasible for the original nonlinear formulation. Furthermore, it is found that the real number water delay can be handled in the formulation without destroying the linear structure of the water balance constraints. Numerical testing results show that the method presented in this paper is effective.

Short-term hydro generation scheduling of Three Gorges–Gezhouba cascaded hydropower plants using hybrid MACS-ADE approach

Short-term hydro generation scheduling (STHGS) aims at determining optimal hydro generation scheduling to obtain minimum water consumption for one day or week while meeting various system constraints. In this paper, the STHGS problem is decomposed into two sub-problems: (i) unit commitment (UC) sub-problem; (ii) economic load dispatch (ELD) sub-problem. Then, we present a hybrid algorithm based on multi ant colony system (MACS) and differential evolution (DE) for solving the STHGS problem. First, MACS is used for dealing with UC sub-problem. A set of cooperating ant colonies cooperate to choose the unit state over the scheduled time horizon. Then, the adaptive differential evolution (ADE) is used to solve ELD sub-problem. MACS and ADE are run in parallel with adjusting their solutions in search of a better solution. Meanwhile, local and global pheromone updating rules in MACS and adaptive dynamic parameter adjusting strategy in DE are applied for enhancing the search ability of MACS-ADE. Finally, the proposed method is implemented to solve STHGS problem of Three Gorges–Gezhouba cascaded hydropower plants to verify the feasibility and effectiveness. Compared with other established methods, the simulation results reveal that the proposed MACS-ADE approach has the best convergence property, computational efficiency with less water consumption.

Improved Self-Adaptive Chaotic Genetic Algorithm for Hydrogeneration Scheduling

The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions.

An enhanced differential evolution algorithm for daily optimal hydro generation scheduling

The daily optimal hydro generation scheduling problem (DOHGSB) is a complicated nonlinear dynamic constrained optimization problem, which plays an important role in the economic operation of electric power systems. This paper proposes a new enhanced differential evolution algorithm to solve DOHGSB. In the proposed method, chaos theory was applied to obtain self-adaptive parameter settings in differential evolution (DE). In order to handle constraints effectively, three simple feasibility-based selection comparison techniques embedded into DE are devised to guide the process toward the feasible region of the search space. The feasibility of the proposed method is demonstrated for the daily generation scheduling of a hydro system with four interconnected cascade hydro plants, and the test results are compared with those obtained by the conjugate gradient and two-phase neural network method in terms of solution quality. The simulation results show that the proposed method is able to obtain higher quality solutions.

An augmented Lagrange programming optimization neural network for short-term hydroelectric generation scheduling

An approach based on augmented Lagrange programming neural networks is proposed for determining the optimal hourly amounts of generated power for the hydro-units in an electric power system. This methodology is based on the Lagrange multiplier theory in optimization and searches for solutions satisfying the necessary conditions of optimality in the state space. The equilibrium point of the network satisfies the Kuhn–Tucker condition for the problem. The equilibrium point of the network corresponds to the Lagrange solution of the problem. The proposed technique has been applied to a multi-reservoir cascaded hydro-electric system with a non-linear power generation function of water discharge rate and storage volume. The water transportation delay between connected reservoirs is also taken into account. Results obtained from this approach are compared with those obtained from the two phase optimization neural network and the conventional augmented Lagrange multiplier method. It is concluded from the results that the proposed method provides better results with respect to constraint satisfaction and is very effective in yielding optimal hydro-generation schedules.

Reservoir operation using a fuzzy and neural system for hydrogeneration scheduling

This paper presents a method using a fuzzy neural system for finding the optimum release from reservoir so that the annual hydropower generation is maximized. Inflow uncertainties are taken into account implicitly. The training set is determined by solving the deterministic operation problem for a large number of flow sequences. Inflows are used as input features and releases as outputs in the fuzzy neural system. Fuzzy rules are created from the input-output data by recursively dividing the input space into a set of subspaces using fuzzy partitions. The resultant fuzzy system is used to determine the structure of the fuzzy neural network and to set its initial weights. The training of the neural network with back propagation is very fast.