Integrated Renewable Energy Sources for the minimization of Emission and Economic Operation of Power System

Document Type : Primary Research paper

Authors

1 Professor & Head, Dept of EEE, Vidya Jyothi Institute of Technology, Hyderabad, India

2 Associate Professor, Dept of EEE, Academy of Maritime Education and Training (AMET), Chennai

3 Professor, Dept of EEE, Vidya Jyothi Institute of Technology, Hyderabad, India

4 Assistant Professor, Dept of EEE, Jeppiaar Institute of Technology, Chennai

Abstract

Carbon foot print is latest hot discussion and all the countries are started the initiation to minimize it. One of the main carbon emission industries is thermal electrical power generating company. For the social welfare the emission has to minimize to maximum extend. The electrical power generation company as well needs to minimize the generation cost for the better operation. Minimizing carbon foot print and minimal power generation cost are opposite to each other. When the optimization focused to minimize the generation cost it lead to increase the carbon foot print and vice versa. This chapter addresses the constraint the optimization problem which needs the objective of reducing both emission and generation cost. Electrical power generating cost of thermal power plant is nonlinear and non-convex in nature. Likewise the emission produced by the thermal power plant is complex mathematical problem. Intelligent algorithms are best suitable to solve these types of practical problems. In this paper differential evolution technique is adopted to find the constrained emission minimization and cost minimization. Differential evolution technique has better mutation process and hence it is good in exploration of solution. Renewable energy sources PV solar and wind energy from the wind mills are green energy and helps the power systems to reduce the pollution. Solar PV system converts the light energy of the Sun into the electrical energy without any intermediate energy conversion. Solar energy is renewable and green energy suitable for low, medium and high voltage and power generation. The running cost is least as compared to the thermal power plant and suitable for both minimization of emission as well the operating cost. Wind is formed by indirect solar action and free in cost. Wind has enormous kinetic energy; wind mills are suitable device which converts the kinetic energy into mechanical energy. Wind mill is the system which houses wind turbine, generator and other ancillary devices to convert the winds’ kinetic energy into electrical energy. The running cost for the wind is also least as like solar PV system and suitable to minimize the emission and operating cost of the power system. For the case study and implement the proposed algorithm IEEE 30 bus system is considered. The test power system has 6 thermal power generators and need to supply the demanded load. The solar PV and wind mill are installed in the sensitive buses and part of the demanded load is supplied by the renewable sources. The remaining power is supplied by the thermal power stations. As the renewable energy
sources are dependents on nature, thermal power plants are must run generators during day and night. During the availability of renewable energy sources thermal power station stress is relived and improves the performance of the power system.
This paper addresses the issues of the thermal power plants in the power system and suggests the integration of renewable energy sources. The renewable energy sources are located at the most effective location based on sensitive analysis. During the availability of the renewable energy, it is injected into the power system and reduces the generation of thermal plant. The emission is reduced due to this integration and intelligent algorithm Differential Evolution (DE) is devised to find combined emission and economic optimal solution.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 341-349

Files

Share

How to cite

Statistics