Economic and Emission Dispatch Problems Using a New Hybrid Algorithm

Environmental legislation, with its increasing pressure on the energy sector to control greenhouse gases, is a driving force to reduce CO2 emissions, forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the economic power dispatch problem has become a multi-objective optimization problem. This paper sets up an new hybrid algorithm combined in two algorithm, the harmony search algorithm and ant colony optimization (HSA-ACO), to solve the optimization with combined economic emission dispatch. This problem has been formulated as a multi-objective problem by considering both economy and emission simultaneously. The feasibility of the proposed approach was tested on 3-unit and 6-unit systems. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.

Environmental/Economic Power Dispatch Problem /Renewable Energy Using Firefly Algorithm

Exploitation and development of renewable energy such as solar and wind energy is a very important alternative to reduce gas emissions, reduce the bill for power generation. This paper examines the implications of renewable energy deployment in power generation with the classical energy system, managed by an intelligent method, to minimize the cost of production of electric energy and also reduce the emission of gases. Simulation results on the 10 units power system prove the efficiency of this method thus confirming its capacity to solve the environmental/economic power dispatch problem with the renewable energy.

A novel differential evolution algorithm for economic power dispatch problem

<p style='text-indent:20px;'>In power systems, Economic Power dispatch Problem (EPP) is an influential optimization problem which is a highly non-convex and non-linear optimization problem. In the current study, a novel version of Differential Evolution (NDE) is used to solve this particular problem. NDE algorithm enhances local and global search capability along with efficient utilization of time and space by making use of two elite features: selfadaptive control parameter and single population structure. The combined effect of these concepts improves the performance of Differential Evolution (DE) without compromising on quality of the solution and balances the exploitation and exploration capabilities of DE. The efficiency of NDE is validated by evaluating on three benchmark cases of the power system problem having constraints such as power balance and power generation along with nonsmooth cost function and is compared with other optimization algorithms. The Numerical outcomes uncovered that NDE performed well for all the benchmark cases and maintained a trade-off between convergence rate and efficiency.</p>

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

Economic power dispatch for an interconnected power system based on reliability indices

Reliability indices are always one of the most important factors in the power systems. In this paper, the problem of the economic load dispatch (ELD) and the problem of economic emission load dispatch (CEELD) have been improved taking into account reliability indices. That is, the problem and reliability of ELD are proposed as combined economic load dispatch reliability (CELDR) and the problem CEELD is suggested as (CEELDR). In solving CELDR and CEELDR problems, tried to use power generators in a very reliable way to save system load, as well as minimum fuel and emission costs. In this effort, the ELD of power plants is successfully implemented in a single system containing 6 generating units, taking into account the reliability and emissions of the system with and without system power loss, inequality and inequality constraints, and valve point effects using the exchange market algorithm(EMA). The results suggest that reliability indicators in ELD can be used to create greater reliability in providing consumers with uninterrupted power.

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.