Hydroelectric Generation Scheduling Research Articles

Challenges in the Management of Hydroelectric Generation in Power System Operations

The management of hydroelectric generation in the context of power system operations has been a difficult and important problem since the inception of power systems more than a century ago; however, various current developments are leading to important new associated challenges and opportunities: massive integration of variable renewable energy and other disruptive technologies, climate change effects on the availability of hydro inflows, and also new efficient techniques for optimization under uncertainty. Multistage stochastic optimization and stochastic dual dynamic programming are currently the dominant techniques for hydroelectric generation scheduling problems; however, there are many recent extensions and improvements on such techniques, and alternative approaches are being developed with significant potential for future concrete applications from power system operators and policy makers. In this context, this paper presents a literature review on hydroelectric generation scheduling models, and a discussion on the critical challenges, open research questions, and future lines of research associated to this problem.

Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.

Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach

This paper uses multi-pass iteration particle swarm optimization (MIPSO) to solve short term hydroelectric generation scheduling of a power system with wind turbine generators. MIPSO is a new algorithm for solving nonlinear optimal scheduling problems. A new index called iteration best (IB) is incorporated into particle swarm optimization (PSO) to improve solution quality. The concept of multi-pass dynamic programming is applied to modify PSO further and improve computation efficiency. The feasible operational regions of the hydro units and pumped storage plants over the whole scheduling time range must be determined before applying MIPSO to the problem. Wind turbine power generation then shaves the power system load curves. Next, MIPSO calculates hydroelectric generation scheduling. It begins with a coarse time stage and searching space and refines the time interval between two time stages and the search spacing pass by pass (iteration). With the cooperation of agents called particles, the near optimal solution of the scheduling problem can be effectively reached. The effects of wind speed uncertainty were also considered in this paper. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms.

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.

Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System-Based Optimization Approaches

In this paper, an ant colony system (ACS)-based optimization approach is proposed for the enhancement of hydroelectric generation scheduling. To apply the method to solve this problem, the search space of multistage scheduling is first determined. Through a collection of cooperative agents called ants, the near-optimal solution to the scheduling problem can be effectively achieved. In the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Because this method can operate the population of agents simultaneously, the process stagnation can be better prevented. The optimization capability can be thus significantly enhanced. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results demonstrate the feasibility and effectiveness of the method for the application considered.

Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches

In this paper, an ant colony system (ACS) based optimization approach is proposed for the enhancement of hydroelectric generation scheduling. To apply the method to solve this problem, the search space of multi-stage scheduling is first determined. Through a collection of cooperative agents called ants, the near-optimal solution to the scheduling problem can be effectively achieved. In the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Because this method can operate the population of agents simultaneously, the process stagnation can be better prevented. The optimization capability can be thus significantly enhanced. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results demonstrated the feasibility and effectiveness of the method for the application considered.

A noise annealing neural network for hydroelectric generation scheduling with pumped-storage units

A new approach based on neural network is proposed for the hydroelectric generation scheduling with pumped-storage units at Taiwan power system. The purpose of hydroelectric generation scheduling is to determine the optimal amounts of generated powers for the hydro units in the system. To achieve an economical dispatching schedule for the hydro units including two large pumped-storage plants, a neural network is employed to reach a schedule in which total fuel cost of the thermal units over the study period is minimized. The neural network model presented can solve nonlinear constrained optimization problems with continuous decision variables. Incorporating the noise annealing concepts, the model is able to produce such a solution which is the global optimum of the original problem with probability close to 1. The proposed approach is applied to hydroelectric generation scheduling of Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper hydro generation schedules.

A hybrid method for hydroelectric generation scheduling using an artificial neural network

This paper presents a decomposition method for finding an optimal operating policy of interconnected hydroelectric power plants using an artificial neural network. The coupling constraints on reservoir storage at the end of the planning horizon are relaxed using coordinating multipliers that result in interval wise decomposition of the overall problem. Resulting subproblems are solved sequentially, which reduces the complexity of the problem. Each subproblem is solved using a two-phase neural network approach. An efficient heuristic algorithm is developed to find the feasible solution. A case study considering scheduling of the Bhakra-Beas reservoir system is also presented in this paper. The new method demonstrates the potential of achieving an improved performance.

00/00279 Application of grey relation analysis to hydroelectric generation scheduling

00/00279 Application of grey relation analysis to hydroelectric generation scheduling

Application of grey relation analysis to hydroelectric generation scheduling

An approach based on grey relation analysis is proposed for the scheduling of hydroelectric generations. The purpose of hydroelectric generation scheduling is to figure out the optimal amount of generated power for the hydro units in the system for the next N h in the future. In the proposed approach, grey relation analysis is developed in order to reach preliminary generation schedules. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible suboptimal schedule which satisfies all practical constraints. The effectiveness of the proposed approach is demonstrated by short-term hydro scheduling of Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper hydro generation schedules.

Application of genetic based fuzzy systems to hydroelectric generation scheduling

An application of genetic based fuzzy systems to hydroelectric generation scheduling is presented in this paper. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm was included to further enhance the process of tuning membership functions. By this way, membership mappings for those important parameters can be optimally adjusted. The computational performance is thus improved. The proposed approach has been tested on the Taiwan Power System (Taipower) through the utility data. Test results have demonstrated the feasibility and effectiveness of the proposed approach for the applications.

Hydroelectric generation scheduling—an application of genetic-embedded fuzzy system approach

In this paper, an application of genetic-embedded fuzzy systems is proposed to solve a hydroelectric generation scheduling problem. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm (GA) was included to further enhance the process of tuning membership functions. By using this approach, membership mappings for important parameters can be optimally adjusted and as a result, the computational performance is improved. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results have demonstrated the feasibility and effectiveness of the proposed approach for the applications.

A coordinated approach for real-time short term hydro scheduling

The paper describes a coordinated approach to short-term hydroelectric power generation scheduling and dispatch that has been developed as a part of the Tasmanian Hydro Electric Commission's new energy management system, which is being delivered by Landis and Gyr Energy Management. Tasmania's hydroelectric power generation system consists of 40 reservoirs in six river catchments. The daily water release for each plant is scheduled using the HEC's mid-term operation policy.

Short-term hydro-scheduling using Hopfield neural network

An approach based on the Hopfield neural network is proposed for short-term hydro-scheduling. The purpose of short-term hydro-scheduling is to determine the optimal amounts of generated powers for the hydro-units in the system for the next N (N = 24 in this work) hours in the future. The proposed approach is basically a two-stage solution method. In the first stage, a Hopfield neural network is developed to reach a preliminary generation schedule for the hydro-units. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed in the second stage to reach a feasible suboptimal schedule which satisfies all practical constraints. The proposed approach is applied to hydroelectric generation scheduling of the Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper hydro-generation schedules.

Hydroelectric generation scheduling using self-organizing feature maps

Abstract An approach based on self-organizing feature maps is proposed for the scheduling of hydroelectric generations. The purpose of hydroelectric generation scheduling is to figure out the optimal amount of generated powers for the hydro units in the system for the next N ( N = 24 in this work) hours in the future. In the proposed approach, self-organizing feature maps are developed in order to reach preliminary generation schedules. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible suboptimal schedule which satisfies all practical constraints. The effectiveness of the proposed approach is demonstrated by short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach is very effective in reaching proper hydro generation schedules.

Fuzzy linear programming: an application to hydroelectric generation scheduling

A new approach using fuzzy linear programming is proposed for solving the hydroelectric generation scheduling problem. A characteristic feature of this approach is that the errors in the forecast hourly loads and natural inflows can be taken into account by using fuzzy set notation, making the approach superior to the conventional linear programming method in which the hourly loads and natural inflows are assumed to be exactly known and there are no errors in the forecast loads and natural inflows. To reach an optimal schedule under the uncertain environment, a fuzzy linear programming model in which the hourly loads, the hourly natural inflows and the cost are all expressed in fuzzy set notations is developed. The developed fuzzy linear programming approach is applied to schedule the generation in the Taiwan power system which contains ten hydroplants including cascaded ones. It is found that the proposed approach is very effective in obtaining proper hydrogeneration schedules in uncertain conditions.

Scheduling of hydroelectric generations using artificial neural networks

An approach based on artificial neural networks (ANNs) is proposed for the scheduling of hydroelectric generations. The purpose of hydroelectric generation scheduling is to figure out the optimal amounts of generated powers for the hydro units in the system for the next N (N=24 in the work) hours in the future. Input data include system hourly loads and the natural in flow of each reservoir. In the proposed ANN approach, a clustering ANN is first developed to identify those days with similar hourly load patterns and natural inflows. These days with similar load patterns and inflows are said to be of the same group. A total of four groups are used in the work. Then a multilayer feedforward ANN is developed for each group to reach a preliminary generation schedule for the hydro units. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible suboptimal schedule which satisfies all practical constraints. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of 10 hydro plants. It is concluded that the proposed approach is very effective in reaching proper hydro generation schedules. Moreover, the proposed approach is much faster than conventional dynamic programming approach.

Two effective approaches for hydroelectric generation scheduling

Abstract This paper presents and compares two systematic and effective approaches for the hydroelectric generation scheduling problem (HSP). The objective of the HSP is to determine the best substitution of hydro energy for thermal energy in electric generation so that the fuel cost is minimized while meeting the system load and constraints. In the first approach, HSP is formulated as an optimal control problem, for which a multiplier method‐based differential dynamic programming algorithm developed by Chen et al. [4] is adopted as the solution algorithm. The second approach exploits the network structure of the water balance relation and takes linear approximation of the first formulation to formulate HSP as a minimum cost, linear network flow problem. Bertsekas and Tsengs’ RELAX code is applied to solve such a large‐scale network optimization problem. Numerical results indicate that both approaches generate results close to TPC's empirical schedules, are efficient in computation and suggest predictive o...