Multiobjective Short-term Hydrothermal Scheduling Research Articles

Non-Dominated Sorting-Based Hybrid Optimization Technique for Multi-Objective Hydrothermal Scheduling

Short-term hydrothermal scheduling problem plays an important role in maintaining a high degree of economy and reliability in power system operational planning. Since electric power generation from fossil fired plants forms a major part of hydrothermal generation mix, therefore their emission contributions cannot be neglected. Hence, multi-objective short term hydrothermal scheduling is formulated as a bi-objective optimization problem by considering (a) minimizing economical power generation cost, (b) minimizing environmental emission pollution, and (c) simultaneously minimizing both the conflicting objective functions. This paper presents a non-dominated sorting disruption-based oppositional gravitational search algorithm (NSDOGSA) to solve multi-objective short-term hydrothermal scheduling (MSHTS) problems and reveals that (i) the short-term hydrothermal scheduling problem is extended to a multi-objective short-term hydrothermal scheduling problem by considering economical production cost (EPC) and environmental pollution (EEP) simultaneously while satisfying various diverse constraints; (ii) by introducing the concept of non-dominated sorting (NS) in gravitational search algorithm (GSA), it can optimize two considered objectives such as EPC and EEP simultaneously and can also obtain a group of conflicting solutions in one trial simulation; (iii) in NSDOGSA, the objective function in terms fitness for mass calculation has been represented by its rank instead of its EPC & EEP values by using the NS approach; (iv) an elite external archive set is defined to keep the NS solutions with the idea of spread indicator; (v) the optimal schedule value is extracted by using fuzzy decision approach; (vi) a consistent handling strategy has been adopted to handle effectively the system constraints; (vii) finally, the NSDOGSA approach is verified on two test systems with valve point loading effects and transmission loss, and (viii) computational discussion show that the NSDOGSA gives improved optimal results in comparison to other existing methods, which qualifies that the NSDOGSA is an effective and competitive optimization approach for solving complex MSHTS problems.

Nondominated sorting-based disruption in oppositional gravitational search algorithm for stochastic multiobjective short-term hydrothermal scheduling

This article presents a novel framework of nondominated sorting-based disruption in oppositional gravitational search algorithm (NSDOGSA) for solving an inventive stochastic multiobjective short-term hydrothermal scheduling (SMSHTS) problems. An innovative SMSHTS mathematical formulation is derived in terms of stochastic objective functions subject to stochastic hydro- and thermal constraints. Also, a versatile constraint handling procedure is proposed to satisfy all the constraints. Besides, an opposition-based learning perception is assimilated in a gravitational search algorithm (GSA) to explore the excellence of the present population and disruption operator is integrated to hasten the convergence of solutions. Moreover, a nondominated sorting procedure is hybridized to attain a set of nondominated solutions for SMSHTS problems. In addition, an elite external archive is created to keep the nondominated solutions with the help of spread indicator and also to guide the search process toward global optima. Further, a fuzzy decision making is employed for selecting the best trade-off solution among the nondominated solution set. Finally, the proposed NSDOGSA approach is validated on four test systems that consist of two fixed-head and two variable-head stochastic multiobjective hydrothermal scheduling problems. Thus, the obtained simulation results are found to be better in terms of objective function values as well to satisfy constraints compared to other methods within a reasonable execution time.

Optimal scheduling of hydrothermal system considering different Environmental Emissions using NSTLBO approach

In this paper, a simple and reliable approach of non-dominated sorting teaching learning based optimization (NSTLBO) algorithm has been prescribed to determine the optimal solution for multi-objective short-term hydrothermal scheduling (STHTS) problem. The problem has been modelled in the form of multi-objective functions which includes fuel cost, transmission loss and environmental emissions such as NOx, SOx and COx With various constraints of hydrothermal systems. Added to that, the effect of valve-point loading process has also been considered. The interaction of the present NSTLBO algorithm is to decrease the cost of the fuel, transmission losses and different kinds of emissions. By applying this algorithm a set of non-dominated solutions are created. A fuzzy decision making approach has been involved on these solution in order to identify the best comprise solution among the group of solutions. The practicability of the proposed approach has been demonstrated on a sample test system which consists of four hydro and six thermal units. The experimental finding of this method has been compared with that of well established techniques in order to validate the performance of the test results. The results confirm that the NSTLBO approach delivers a reliable solution and competitive performance for solving Multi-objective short-term hydrothermal scheduling (MOSTHTS) problem combined with emission constraints.

Modified cuckoo search algorithm for multiobjective short-term hydrothermal scheduling

Abstract This paper proposes a modified cuckoo search algorithm (MCSA) for solving multi-objective short-term fixed head hydrothermal scheduling (HTS) problem. The main objective of the multiobjective HTS problem is to minimize both total power generation cost and emission of thermal generators over a scheduling period while satisfying power balance, hydraulic, and generator operating limit constraints. The proposed MCSA method is developed for the problem based on improvements from the conventional CSA method which is a new metaheuristic algorithm inspired from the behavior of some cuckoo species laying their egg into the nest of other species to improve the optimal solution and speed up the computational process. In the MCSA method, the nests are evaluated and classified into two groups including the top group with better quality eggs and the abandoned group with worse quality eggs. Two effective strategies via Levy flights for producing new solutions are applied to the abandoned and top groups. To validate the efficiency of the MCSA method, several test systems have been tested and the result comparisons from the test systems have indicated that the proposed method can obtain higher quality solution and shorter computational time than many other methods. Therefore, the proposed MCSA method can be new efficient method for solving multiobjective short-term fixed-head HTS problems.

Non-dominated Sorting Disruption-based Gravitational Search Algorithm with Mutation Scheme for Multi-objective Short-Term Hydrothermal Scheduling

The short-term economical/environmental hydrothermal scheduling problem is formulated as a non-linear and non-convex constrained multi-objective optimization problem considering transmission losses and valve point loading effects. This article presents a non-dominated sorting disruption-based gravitational search algorithm with mutation to solve fixed-head and variable-head short-term economical/environmental hydrothermal scheduling problems. In this solution technique, a set of non-dominated solutions are obtained by using the concept of non-dominated sorting and an external archive. Thereafter, a fuzzy decision-making approach has been applied to achieve a suitable and the best compromising solution from the non-dominated solution set. Finally, the non-dominated sorting disruption-based gravitational search algorithm with mutation approach is demonstrated on three test systems: fixed-head two hydro and two thermal plants, two hydro and four thermal plants, and variable-head cascaded four hydro and three thermal plants. Simulation results obtained from this approach are compared with the other methods reported in the literature and it has been found that the proposed approach yields better solutions and is efficient for solving short-term economical/environmental hydrothermal scheduling problems.

An Efficient Cuckoo-Inspired Meta-Heuristic Algorithm for Multiobjective Short-Term Hydrothermal Scheduling

This paper proposes an efficient Cuckoo-Inspired Meta-Heuristic Algorithm (CIMHA) for solving multi-objective short-term hydrothermal scheduling (ST-HTS) problem. The objective is to simultaneously minimize the total cost and emission of thermal units while all constraints such as power balance, water discharge, and generation limitations must be satisfied. The proposed CIMHA is a newly developed meta-heuristic algorithm inspired by the intelligent reproduction strategy of the cuckoo bird. It is efficient for solving optimization problems with complicated objective and constraints because the method has few control parameters. The proposed method has been tested on different systems with various numbers of objective functions, and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is more efficient than many other methods for the test systems in terms of total cost, total emission, and computational time. Therefore, the proposed CIMHA can be a favorable method for solving the multi-objective ST-HTS problems.

Application of non‐dominated sorting gravitational search algorithm with disruption operator for stochastic multiobjective short term hydrothermal scheduling

The hydrothermal scheduling (HTS) is formulated as a stochastic multiobjective short term HTS problem by considering stochastic production cost, NO x emission, SO2 emission and CO2 emission curves for thermal generation plants and uncertainty in load demand. This study presents, a non-dominated sorting gravitational search algorithm integrated with disruption operator (NSGSA-D) to solve this problem. In this approach, a set of Pareto optimal solutions are obtained by adopting the concept of non-dominated sorting. Further, an elite external archive is introduced to keep the Pareto optimal solutions and guide the search process. In addition, a disruption operator is utilised to intensify the search process and also speed up the convergence of the solutions. Furthermore, the most suitable and efficient solution from the non-dominated solution set is obtained with the help of fuzzy decision making policy. The effectiveness of NSGSA-D approach is demonstrated on three sample test systems and simulation results thus obtained are compared with the results reported in literature. The obtained results affirm that the NSGSA-D approach yields good quality solutions and competitive performance for solving stochastic multiobjective short term HTS, while handling the diverse constraints of the problem effectively.

Civilized swarm optimization for multiobjective short-term hydrothermal scheduling

This paper presents a solution technique for multiobjective short-term hydrothermal scheduling (MSTHTS) through civilized swarm optimization (CSO) which is the hybrid of society–civilization algorithm (SCA) and particle swarm optimization (PSO). The intra and inter society communication mechanisms of SCA have been embedded into the food-searching strategy of PSO to form CSO. The MSTHTS problem is formulated by considering economic and emission objectives. A new ideal guide method has been proposed to find out the Pareto-optimal front. Multi-reservoir cascaded hydro power plants having nonlinear generation characteristics and thermal power plants with non-smooth cost and emission curves are considered for analysis. Other aspects such as, water transport delay, water availability, storage conformity, power loss and operating limits are fully accounted in the problem formulation. The performance of the proposed CSO is demonstrated through two MSTHTS problems and the results are compared with those presented in the literature. CSO along with the new ideal guide method outperforms all the previous approaches by providing quality Pareto-optimal fronts.

05/00275 An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling: Basu, M. Electric Power Systems Research, 2004, 69, (2–3), 277–285

05/00275 An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling: Basu, M. Electric Power Systems Research, 2004, 69, (2–3), 277–285

An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling

Abstract This paper presents an interactive fuzzy satisfying method based on evolutionary programming technique for short-term multiobjective hydrothermal scheduling. The multiobjective problem is formulated considering two objectives: (i) cost and (ii) emission. Assuming that the decision maker (DM) has fuzzy goals for each of the objective functions, evolutionary programming technique based fuzzy satisfying method is applied for generating a corresponding optimal noninferior solution for the DM’s goals. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. Thermal plants with nonsmooth fuel cost and emission level function are also taken into consideration. Results of the application of the proposed method are presented.

Fuzzy decision-making in stochastic multiobjective short-term hydrothermal scheduling

In the multiobjective framework, fuzzy decision-making methodology is exploited to decide the generation schedule of a short-range fixed-head hydrothermal problem. The multiobjective problem is formulated considering five objectives: (i) cost, (ii) NOx emission, (iii) SO2 emission, (iv) CO2 emission and (v) variance of generation mismatch with the explicit recognition of statistical uncertainties in the thermal generation cost, NOx, SO2 and CO2 emission curves and power demand, which are random variables. The solution set of such formulated problems is non-inferior due to contradictions among the objectives taken. The weighting method is used to simulate the trade-off relation between the conflicting objectives in the non-inferior domain. Once the trade-off has been obtained, fuzzy set theory helps the system operator to choose the weighting pattern and thus the operating point that maximises the satisfaction of all the objectives. The results are demonstrated on three sample systems.