Multi-level peer-to-peer collaborative optimization of smart energy system based on big data analysis

Abstract

The emergence of energy internet has changed people’s understanding of energy production, transmission, storage, conversion and consumption. However, how to promote the development of energy Internet, how to integrate it with various existing energy entities, and really play a role, still need more in-depth research and practice. To solve the problem of optimal management and control of energy and power system, a multi-level peer-to-peer collaborative optimization method for smart energy system based on big data analysis is proposed. In the constructed optimization scheduling model, combined with the demand response characteristics of different energy loads, and based on the time-sharing price of energy such as electricity and natural gas, the user IDR response is described in detail from the aspects of load reduction, transferable load and replaceable load, so that it can effectively cooperate with MES equipment in IEGS, thus minimizing the system operation cost. Multi-source energy storage mode is helpful to improve the economic efficiency of smart energy system, reduce the influence of uncertainty on the actual operation of the system, and reduce the power fluctuation burden of power grid.