Combined Economic-Emission Dispatch of External Optimization Integrating Renewable Energy Resources Stochastic Wind and Solar

Abstract

Economic dispatch in the standard power system network frequently prioritizes instant economic gains while overlooking the detrimental environmental effects of gas discharges from thermal power stations. Economic emission dispatch (EED) has received a lot of consideration in current years as a way to alleviate this shortage. The unpredictability and intermittence of renewable energy sources like wind and solar power are raising the difficulty of electric grid planning as renewable production penetration rises. This research proposes a modified MMOCE to improve dispatch efficiency with considerable renewable energy consumption. A congestion calculation method and a new external storage process are added to the traditional kernel function method to address multi-objective optimization issues. The energy and transportation sectors are two of the most significant producers of GHG emissions. Improved vehicle productivity is a partial answer, but to limit the rate of GHG emissions, it is important to use RESs in the network. It is, however, critical to evaluate the efficacy of each approach. In this research, the environmental and economic aspects of employing RESs will be examined using a mixture of generating cost and GHG emissions from the two businesses stated. To overcome the ELD problem, an enhanced PSO is employed. However, solutions for reducing time consumption for the high-dimensional ELD issue are not considered in this study.

Research on Safety Control Method of Power Grid Energy Storage System Based on Neural Network Model

“…The main structure of the network can be roughly divided into two major structures: cyclic structure and dimension compression. In this method, an activation function is added before the output layer, and a completely connected layer is used to connect the main structure of the whole network with the output [27] . At the same time, LSTM network can also effectively overcome the problems of unobvious gradient or abrupt change in the traditional recursive process of error transfer, and show good results in many sequential prediction tasks such as traffic, voice and handwritten words.…”