Integration and management technique of renewable energy resources in microgrid

Abstract

Ever increasing demand for electricity supply along with higher power quality and reliability, available fossil fuels restrictions and environmental pollutions led to aggregation of clean energy resources (distributed generations) and developing microgrids. Integration of distributed generations such as wind power and solar energy are challenging with various problems such as non-deterministic nature of available wind power and solar energy. On the other hand, power systems are subject to other uncertainties such as load and energy prices in day-ahead (DA) and balancing markets. Hence, intermittence could be highlighted as the main obstacle of distributed generations’ aggregation which cause to imbalance charges set by uncertain market prices and accordingly economic losses. To this end, a comprehensive study should be performed to elaborate aforementioned issues. In this chapter, a stochastic model with the goal of profit maximization and imbalance cost minimization is presented. Unlike previous works, in the proposed model all existent uncertainties related to wind power, solar energy, load, day ahead and imbalance market prices altogether are considered by the means of scenario based investigations. In order to generate probable scenarios, uncertain parameters should be predicted. In this framework, a new method based on neural network theory is proposed for predicting wind speed and solar radiation. Afterwards, pumped-storage plant and demand response program are utilized as two complementary resources to compensate power imbalances. Storage devices are used as flexible resources to exchange power between low consumption—cheap hours and peak hours. Finally, to investigate efficiency of the proposed method two operating modes, namely coordinated and uncoordinated operation of clean energy resources, are assumed and testified on a test microgrid.