Modelling Simulation and Performance Assessment of Multi objective Economic and Emission Dispatch Problem Using Improved Particle Swarm Optimization

In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

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An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms

Sustainability ◽

10.3390/su12187253 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7253

Author(s):

Motaeb Eid Alshammari ◽

Makbul A. M. Ramli ◽

Ibrahim M. Mehedi

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Wind Farms ◽

Clean Energy ◽

Optimization Techniques ◽

Chance Constraint ◽

Swarm Optimization ◽

Multi Objective ◽

Emission Dispatch ◽

Economic Emission Dispatch

In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems—including wind farms—are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated.

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