Promoting the access of multi-energy complementary microgrids

As microgrids develop towards multi-energy complementarity, they cover a variety of energy forms such as electricity, heat, and natural gas. Energy storage systems can not only store electrical energy, but also work in conjunction with other energy storage methods (such as heat storage and gas storage) to achieve conversion and optimal configuration between different energy sources. For example, in a combined cooling, heating, and power microgrid, the electric-thermal energy storage system can convert excess electrical energy into thermal energy and store it, and release thermal energy for heating or hot water supply when needed, thereby improving the comprehensive energy utilization efficiency of the microgrid and promoting the smooth access of multi-energy complementary microgrids to the energy network.

Microgrid Research

In-depth analysis of energy storage systems and AI applications.

Model Development

In the development of models for energy storage systems applied to microgrid energy management and access, the first task is to clarify goals and requirements. The goal is to optimize the operation strategy of energy storage systems in microgrids by building accurate models, and to improve energy efficiency and microgrid stability. Specific requirements cover many aspects: the need to accurately simulate the charging and discharging characteristics of energy storage systems to reflect their performance under different working conditions; to achieve dynamic prediction of renewable energy generation and load demand in microgrids to support the optimal scheduling of energy storage systems; and to meet the stability control requirements of microgrids interacting with large power grids when connected to the grid, as well as the formulation of operation strategies in special scenarios such as black starts.

AI Algorithms

In microgrids, renewable energy sources such as solar and wind power have significant intermittency and volatility. LSTM neural networks can effectively capture long-term dependencies in time series data. By learning historical meteorological data (such as light intensity, wind speed, temperature), equipment operation data, and power generation data, LSTM can accurately predict the power generation of renewable energy in the future. For example, when predicting photovoltaic power generation, LSTM can combine historical light intensity change trends, weather patterns and other information to plan the charging and discharging strategy of the energy storage system in advance. When it is predicted that the photovoltaic power generation is about to drop, the energy storage system discharges in advance to replenish electricity, smooth out power generation fluctuations, and ensure the stability of the microgrid power supply.