Short-term Hydrothermal Generation Scheduling Research Articles

Environmentally sound short-term hydrothermal generation scheduling using intensified water cycle approach

This paper proposes an intensified water cycle approach (IWCA) to realize an environmentally sound optimal generation schedule for short-term hydrothermal systems over a day’s time horizon considering the conflicting economic and environmental aspects of thermal units. The equality constraints of the active power balance and full utilization of the available amount of water are handled independently using proportional sharing heuristics. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration, and exploitation strategies. For intensifying the algorithm, opposition-based learning and local search are integrated with a chaotically tuned water cycle algorithm based on the natural flow of water toward the sea to balance exploration and exploitation. The numerical results show an improvement in the unified operating cost obtained from the price penalty factor to include the impact of gaseous pollutants and in the convergence performance metrics over the existing methods. The competence of the proposed approach is confirmed through illustrations of standard benchmark problems (unimodal, multimodal, and fixed dimension multimodal) and standard hydrothermal test systems. The performance of the proposed algorithm is substantiated through statistical significance tests, which include convergence curves, whisker box plots, and Wilcoxon signed-rank test.

Fuel constrained short-term hydrothermal generation scheduling

Due to gradually diminution of fossil fuel, the cost-effective utilization of available fuel for power generation has turn out to be a vital concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work suggests social group entropy optimization (SGEO) technique to solve short-term generation scheduling of a power system consisting of fuel constrained thermal generating units, cascaded hydro power plants, solar PV plants, wind turbine generators and pumped storage hydro (PSH) plants with demand side management (DSM). Simulation results of the test system have been compared with those acquired by self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Numerical results show that fuel consumption can be adequately controlled for fulfilling constraints imposed by suppliers and total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the suggested SGEO has the ability to bestow with superior-quality solution.

Optimal Scheduling of Hydro–Thermal–Wind–Photovoltaic Generation Using Lightning Attachment Procedure Optimizer

This paper presents an effective solution for the short-term hydrothermal generation scheduling (STHS) problem using an integration of wind and photovoltaic power (PV) system. Wind and PV power are integrated into the power system to minimize the total fuel cost of thermal units. In this paper, the lightning attachment procedure optimization algorithm (LAPO) is employed to solve the STHS problem using the wind and PV power integration system. The proposed method is applied for solving five test systems with different characteristics, considering the valve-point loading impact of the thermal unit. The first and third test systems include hydro and thermal units only, and the rest of the systems consist of hydro and thermal units with integrating wind and PV power-generating units to inspect the effect of renewable energy sources in the selected test systems. The simulation results are compared with other studied methods. It is found that the proposed method is superior, and it has the ability to obtain the best solutions with respect to other optimization methods that are implemented to solve the STHS problem.

Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs

Short-term hydrothermal generation scheduling (SHGS) considering various hydraulic and electric constraints is a complex non-convex optimization problem. The coupling connection of cascaded reservoirs and the valve-point effects of thermal units greatly increase the difficulty of finding optimal solution. This paper presents an efficient solution to the SHGS problem by using a novel crisscross optimization (CSO) algorithm, which generates the optimal scheduling results by applying two distinctive search operators, i.e., horizontal crossover and vertical crossover. The horizontal crossover is used as a global optimizer that can reduce the search blind spots in complex solution space through a cross-border search strategy. The vertical crossover is used to address the premature convergence problem by applying a unique dimensional crossover mechanism. Both search operators take turns to generate moderation solutions and CSO always maintains a population of historically best solutions by using a greedy strategy so as to speed up the convergence speed. To investigate the CSO’s performance on the SHGS problem, three test systems widely adopted in the literature are used to do the short-term hydrothermal generation scheduling. The results reveal that the proposed algorithm has good robustness and outperforms other state-of-the-art methods in terms of solution quality.

Lightning attachment procedure optimization algorithm for nonlinear non-convex short-term hydrothermal generation scheduling

Short-term hydrothermal scheduling (STHS) is considered an important problem in the field of power system economics. The solution of this problem gives the hourly output of power generation schedule of the available hydro and thermal power units, which leads to minimization of the total fuel cost of thermal units for a given period of a time. The optimal generation of STHS is considered as a complicated and nonlinear optimization problem with a set of equality and inequality constraints such as the valve point loading effect of thermal units, the power transmission loss and the load balance. This paper proposes lightning attachment procedure Optimization (LAPO) algorithm for solving the nonlinear non-convex STHS optimization problem in order to minimize the operating fuel cost of thermal units with satisfying the operating constraints of the system. The performance of LAPO algorithm is validated using three different test systems considering the valve point loading effects of thermal units and the power transmission losses. The obtained results prove the effectiveness and superiority of LAPO algorithm for solving the STHS problem compared with other well-known optimization techniques.

A novel two-swarm based PSO search strategy for optimal short-term hydro-thermal generation scheduling

The optimal power generation of hydro-thermal systems to minimize total operating costs is one of the most important targets in short-term hydro-thermal scheduling (STHS) and its evaluation is complicated by several complex and interconnected equality and inequality constraints.To solve this type of problem, it is necessary to develop on one side a model of the system characterized by a fair and proper treatment of all the equality and inequality constraints and on the other a performing optimization algorithm simultaneously considering not only the minimization of the operating costs but also the feasibility of the proposed solution.The paper presents a novel two-swarm based PSO (Particle Swarm Optimization) search strategy developed for solving these kinds of STHS problems. To exclude constraint violations as far as possible during the optimization process, first a complete inequality and equality constraints treatment of the hydrothermal systems was developed, combined with a fair spillage modelling. Then, a recent evolution of the particle swarm optimization algorithm ASD-PSO (Adaptive Search Diversification-Particle Swarm Optimization) was modified, introducing a secondary swarm able to exploit the potential contribution of the infeasible solutions. Within the context of this new strategy, two different swarms were designed, each characterized by a different treatment of the infeasible solutions, and each interacting properly with the other at every iteration.The primary swarm dealt with feasible solutions, resulting from the manipulation of constraint repairing strategies, and with a limited number of infeasible solutions whose potential contribution in the search process was not wasted out but it was properly weighted on the basis of the violation degree. The secondary swarm involved less manipulated solutions that were subjected only to one repair strategy in order to foster the exploration capability of the swarm. At each iteration, the two swarms independently update their position and velocity but partially interact with each other to obtain mutual advantages from their different peculiarities.This novel two-swarm based strategy for solving short-term hydro-thermal scheduling (STHS) problems was validated by comparing the achieved performance in terms of accuracy with those of several algorithms proposed in literature. Six different test cases, widely analysed in the literature and characterized by increasing complexity, were considered. In all the considered Test Cases, the algorithm performed very well with minimum total fuel costs that were still lower than all the feasible solutions proposed in the literature, and that satisfied all the equality and inequality constraints of the system.

Short-term hydrothermal generation scheduling using improved predator influenced civilized swarm optimization technique

An improved predator influenced civilized swarm optimization (IPCSO) technique is proposed to solve short-term conventional hydro-thermal generation scheduling (HTGS) and profit-based HTGS (PB-HTGS) problems. In the proposed IPCSO technique, prey swarm is divided into the number of societies and each society is influenced by its own predator’s effect. In every society, prey particles interact with each other and the best performing prey particle acts as a society leader. The predator particle chases the society leader and society leader tries to escape from it. In this process, predator effect improves the exploitation capability of the algorithm by searching around the respective society leader. Further, society leader of each society interacts with each other and helps to improve the performance of society leaders. The best performing society leader becomes the leader of civilization. For HTGS problem, a multi-chain cascaded hydro model is undertaken along with consideration of water transport delay between reservoirs. The problem is formulated with due consideration of thermal unit valve point effect, prohibited operating zones on reservoir discharge rate and ramp rate limits on thermal unit power generation. The technique is tested on three HTGS systems and one PB-HTGS system. The obtained results have been compared with the results reported in the literature and found satisfactory. The statistical analysis of the results is carried out to verify the robustness of the proposed technique. Further, a nonparametric test is also applied to compare the performance of the proposed technique.

Short-term Hydrothermal Generation Scheduling Using a Parallelized Stochastic Mixed-integer Linear Programming Algorithm

Short-term hydrothermal generation scheduling (STHTGS) is the optimization process through which decisions are made about the commitments of thermal generators and the allocation of hydro energy resources in the planning horizon (1 day to 1 week), while satisfying a large set of technical constraints. Uncertainty in this problem may appear in different modelling parameters, but the extended stochastic version of the STHTGS problem may lead to impractical solution times. This paper discusses the application of a parallelized stochastic mixed-integer linear program (SMILP) to solve the stochastic STHTGS problem. In order to decrease simulation time a scenario-based decomposition approach based on the progressive hedging (PH) algorithm is proposed. Computational experiments are conducted in two multi-processor nodes of a cluster for different numbers of stochastic scenarios. The algorithm is tested in the Chilean Central Interconnected System using a problem instance considering a weekly horizon with hourly resolution. Results show that the PH algorithm has good convergence properties, needing only a few iterations to converge. Furthermore, as PH generates similarly sized sub-problems, the parallel version of the algorithm scales up quite well as the number of scenarios is increased.

An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem

This paper establishes a model to deal with the short-term hydrothermal generation scheduling (SHTGS) problem. The problem is composed of three interconnected parts: short-term hydrothermal coordination, thermal unit commitment and economic load dispatch. An efficient hybrid method composed of chaotic backtracking search optimization algorithm and binary charged system search algorithm (CBSA–BCSS) is proposed to solve this problem. In order to analyze the effect of the chaotic map on the performance of the method, three different chaotic maps are adopted to integrate into the proposed method and the corresponding consequences are achieved. Furthermore, efficient heuristic search strategies are adopted to handle with the complicated constraints of the SHTGS system. Finally, a hydrothermal unit commitment system is utilized to verify the feasibility and effectiveness of the proposed method. The results demonstrate the efficiency of the hybrid optimization method and the appropriation of the constraint handling strategies. The comparison of the solutions achieved by different methods shows that the proposed method has higher efficiency in terms of solving SHTGS problem.

Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

Abstract The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.

An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms.

A clonal real-coded quantum-inspired evolutionary algorithm with Cauchy mutation for short-term hydrothermal generation scheduling

The short-term hydrothermal generation scheduling (SHGS) problem considered valve-point effects and transmission losses is a complicated problem which has the characteristics of nonlinear and non-convex. This paper presents a clonal real-coded quantum-inspired evolutionary algorithm (CRQEA) with Cauchy mutation for solving SHGS problem. In this proposed algorithm, real-coded rule is adopted for handling continuous variables. Meanwhile, clonal operator and Cauchy mutation are integrated into the proposed algorithm to avoid premature convergence. Moreover, heuristic strategies are designed for dealing with all kinds of constraints effectively. Finally, three test systems are employed to verify the feasible and effectiveness the proposed CRQEA. Compared with other reported methods, the obtained results clearly illustrates that CRQEA has better performances.

Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm

Short-term hydrothermal generation scheduling aims at determining optimal hydro and thermal generations to achieve minimum fuel cost of thermal plants for a 1 day or a 1 week while meeting various hydraulic and electric system constraints. The problem is viewed as a complex and nonlinear hard problem considering valve-points effects and transmission losses with a set of operation operational and physical constraints. This paper presents a novel effective differential real-coded quantum-inspired evolutionary algorithm (DRQEA) for solving this complicated problem. Some improvements like real-coded rule, adaptive differential mutation and crossover mechanism are proposed in DRQEA to enhance the global search ability in continuous space. Meanwhile, various constraints are handled effectively by using heuristic strategies designed by their characteristics. The effectiveness of the proposed approach is demonstrated on two hydrothermal test systems, which consist of two sub-systems: hydro sub-system and thermal sub-system. The obtained results of the proposed approach are compared with other methods, and simulation and comparison results clearly show that DRQEA is able to provide better solution than other reported methods, both in the solution quality and the convergence speed. The proposed algorithm can also apply to other dynamic optimization problem with nonlinear and non-convex characteristics in power system.

Application of a Fast Evolutionary Algorithm to Short-term Hydro-thermal Generation Scheduling

This article presents a solution to the hydrothermal scheduling (HS) problem by evolutionary programming (EP). The purpose of HS is to minimize fuel costs of thermal units by allocating hydro and thermal units optimally, satisfying constraints on system operation. EP performs better than conventional methods in converging to near optimum results in the solution of the HS problem, which consists of nonlinear objective function and constraints. In EP, offsprings are generated from randomly generated initial parent vectors by Gauss or Cauchy mutations. Parent vectors and their offspring vectors compete with each other. Better individuals are selected as new parent vectors for the next iteration. As the iteration makes progress, convergence to optimum solution increases. In this study, instead of stochastic competition in existing EP algorithms, deterministic competition is utilized. The control parameter “scaling factor” is taken as variable instead of constant. Thus, better results have been obtained in convergence rate, solution time and success rate.

An adaptive chaotic differential evolution for the short-term hydrothermal generation scheduling problem

The short-term hydrothermal generation scheduling (SHGS) is a complicated nonlinear optimization problem with a set of operational and hydraulic constraints. A new approach is presented in this paper for solving the short-term hydrothermal generation scheduling problem by using an adaptive chaotic differential evolution algorithm (ACDE). In the proposed method, an adaptive dynamic parameter adjusting strategy is adopted to obtain the parameter settings in differential evolution algorithm (DE), and a chaotic local search (CLS) operation is integrated with DE to avoid premature convergence effectively. Moreover, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper for the proposed ACDE to deal with the complicated constraints effectively. The feasibility and effectiveness of the proposed ACDE method is demonstrated for two test hydrothermal power systems. The simulation results reveal that, compared with other established methods like PSO and DE, the proposed ACDE appears to be the best in terms of convergence property, computational efficiency and solution accuracy.

Short-term hydrothermal generation scheduling model using a genetic algorithm

A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.

Modular and flexible software for medium and short-term hydrothermal scheduling

The authors describe modular and flexible software developed for medium-term and short-term hydrothermal-generation scheduling. Both the medium-term and short-term programs model a wide range of system characteristics, including physical and operational constraints such as area interchange and reserve constraints. A matrix-generator generator (MGG) program is used to develop a matrix generator (MG) program for implementing the hydrothermal-scheduling problem formulation. The MG which is integrated with scheduling software, is developed so as to provide the capability for exercising different modeling options. A novel modeling approach is used for the medium-term program that divides the planning period into two parts with different time resolutions. This software has been used for operations planning studies for the hydrothermal system of Colombia in South America; these studies are used to exemplify the software's operations. >