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Optimal scheduling of distributed energy resources in microgrid systems based on electricity market pricing strategies by a novel hybrid optimization technique

A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid

This article proposes an optimal energy scheduling method for power transmission networks using novel genetic algorithm (nGA) for solving the dynamic economic dispatch (DED) problem combined with machine learning based short-term load forecasting (STLF). The STLF is implemented based on a multilayer artificial neural network (MANN) to estimate the day-ahead variations in the demand. The efficacy of the proposed energy scheduling model together with the STLF is verified using a modified IEEE 30-bus system using real data of the power plants located in the Ereymentau region of Kazakhstan. The simulation results suggest that the proposed model offers a cost effective, reliable, and efficient dynamic energy scheduling in power transmission systems.

A Novel Genetic Algorithm Based Dynamic Economic Dispatch With Short-Term Load Forecasting

In this paper, the combined wind and thermal power plant systems are operated optimally to reduce the total fossil fuel cost (TFFC) of all thermal power plants and supply enough power energy to loads. The objective of reducing TFFC is implemented by using antlion algorithm (ALA), particle swarm optimization (PSO) and Cuckoo search algorithm (CSA). The best method is then determined based on the obtained TFFC from the three methods as dealing with two study cases. Two systems with eleven units including one wind power plant (WPP) and ten thermal power plants are optimally operated. The two systems have the same characteristic of MFSs but the valve loading effects (VLEs) on thermal power plants are only considered in the second system. The comparisons of TFFC from the two systems indicate that CSA is more powerful than ALA and PSO. Furthermore, CSA is also superior to the two methods in terms of faster search process. Consequently, CSA is a powerful method for the problem of optimal generation for wind-thermal power plant systems with consideration of MFSs from thermal power plants.

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Optimal generation for wind-thermal power plant systems with multiple fuel sources

In the power system, "Economic load dispatch" means the generation of electricity is low of cost and the power system constraints such as Real power and Reactive power vary within the specified limits while fulfill the load demand of the power system. Main objective of the ELD problem is to minimize the total fuel cost. The total cost of operation includes the cost of fuel, transmission cost, labor cost and cost of maintenance. The two major factors to be considered while dispatching power to generating units are the cost of generation and the quantity of power supplied. The relation between the cost of generation and quantity of power supplied is expressed in a polynomial equation and it is solved by applying of mathematical technique. The suggested optimization techniques have a merit in finding an optimal solution. In this research paper we apply basically genetic algorithm and simulated algorithm. We have tested the applied algorithm on IEEE 30 bus system and 20-unit generator system.

Anurag Sonkar

Papers

Economic Load Dispatch Using Genetic Algorithm Optimization Technique