Big Data Analysis and Multi-Objective Optimization for Smart Grid

Lei Xu; Sergey Gorbachev; Dong Yue; Chunxia Dou

doi:10.57118/creosar/978-1-915740-01-4_9

Authors

Lei Xu
Sergey Gorbachev
Dong Yue
Chunxia Dou

DOI:

https://doi.org/10.57118/creosar/978-1-915740-01-4_9

Keywords:

Photovoltaic power forecasting, Electricity load forecasting, Multi-objective optimization, Distributed optimization

Abstract

With the growth of photovoltaic power generation, the safe operation problems are becoming increasingly important. At the same time, the high uncertainty of load also brings great pressure to the power grid. It becomes necessary to improve the accuracy of distributed photovoltaic power generation and load prediction, and various emerging prediction methods are used based on smart grid, which provides support for accurate prediction of modern power system combined with big data analysis because of its high integration of information, and it has become an important research direction. This chapter will review the distributed photovoltaic power generation and load forecasting of smart grid from the perspective of technical methods. In addition, this chapter also sorts out the multi-objective optimization methods of smart grid, and introduces the optimization methods in the smart grid scenario with high proportion of distributed generation. At the same time, the distributed optimization methods with high efficiency and privacy reflected in power grid optimization is also analyzed in this article.

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