Renewable energy sources have evolved into a technologically and economically feasible choice for use by electrical utilities. Furthermore, the widespread usage of renewable energy sources is driving policymakers and utilities to increase green energy’s role to ensure long-term economic growth. The most influential non-conventional energy source for generating power is solar energy. Electric power systems must be designed, built, and run in such a way that the majority of energy demand for loads is supplied reliably, cost-effectively, and in an environmentally responsible manner. In 4-E dispatch, the term “energy” refers to generated power in the scheduled period, the term “emission” refers to the amount of emission released in the scheduled period, and the term “environment” refers to health and environmental damage costs in the scheduled period and the term “economic” refers to power generating cost in the scheduled period. In this paper a hybrid of particle swarm optimization (PSO) and teaching learning-based optimization (TLBO), named as PSOTLBO is proposed, developed, and effectively applied to attain the best or optimum solutions for the 4-E (Energy-Emission-Environment-Economic) dispatch problem for scenarios involving ten thermal power plants and thirteen solar photovoltaic (PV) plants.

Thermal-hydro-solar scheduling is the most difficult power system optimization issue in the modern day. The core mean of the arrangement of thermal-hydro-solar is to decide the most favorable power from thermal, hydro, and solar sources while meeting the various constraints of thermal, hydro, solar, and network. This paper describes the optimum hourly generation schedule plan in a thermal-hydro-solar power network utilizing particle swarm optimization (PSO) approach to attain the best or optimum solutions for scenarios involving three thermal power plants, four hydro power plants and ten solar photovoltaic (PV) plants. The conclusion of the simulation shows that the suggested PSO method seems to be able to minimize fuel costs, and emissions and has improved outcomes performance and strong integration than other approaches.

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with day by day increasing population and standard of human being increases the consumption of electrical energy and this increasing in the consumption of electrical energy, increases the number of generators, transmission lines to full fill the daily needs of the electrical energy. So the power system has become more complex and main source of gaseous emission. So arranging and the task of intensity framework of power system must be done in such a way that energy and emission arising due to power generation, the retribution paid by the power plant because of emission and cost paid in generation need to tackle all the while. This paper shows a reliable and effective hybrid of particle swarm optimization (PSO) algorithm and teaching learning based optimization (TLBO) for combined emission and economic dispatch (CEED) problems. The outcomes have been shown for combined emission and economic dispatch issues of standard 3 and 6-generators frameworks with consideration of transmission losses.