Microgrid Optimization Services

We provide advanced analysis and modeling for energy management in microgrids using AI technologies.

An aerial view of a building with a large array of solar panels on its roof. The structure is surrounded by greenery, pathways, and some smaller structures or landscaped areas. The environment appears to be a mix of natural and man-made elements, suggesting a focus on sustainability.

An aerial view of an urban landscape showcasing a building with a rooftop solar panel installation. The scene is surrounded by lush greenery, curved pathways, and additional buildings, suggesting a blend of nature and modern architecture.

AI Algorithm Design

Developing machine learning models for renewable energy prediction and energy storage system optimization.

Mathematical Modeling

Creating mathematical models for renewable generation, load forecasting, and energy storage system operations.

AI Innovations

Reviewing research on ESS, microgrids, and AI solutions.

A complex network of electrical infrastructure composed of metal towers, transformers, wires, and high voltage equipment, set against a partially cloudy sky. The landscape includes green vegetation in the background and gravel areas around the structures.

Modeling Systems

Mathematical models for renewable energy and ESS operations.

A large industrial generator with a green casing is situated indoors. Several black battery units connected by cables are placed on the floor beside the generator. The environment appears to be clean and organized, with a grey painted floor and white walls. Additional equipment is visible in the background, contributing to the machine's operation.

AI Algorithms

Designing algorithms for energy prediction and optimization tasks.

Several rows of solar panels are arranged on a grassy hillside, surrounded by dense green trees, suggesting a focus on renewable energy.

Research Gaps

In existing research, most models of energy storage systems and microgrids are based on idealized assumptions, which make it difficult to accurately reflect complex and changeable actual working conditions. For example, when describing the charging and discharging characteristics of lithium batteries, some models do not fully consider the impact of factors such as battery aging and temperature changes on performance, resulting in deviations between the predicted results and the actual results. In addition, there is a lack of universal and targeted models for different types of microgrids (such as island microgrids and industrial park microgrids). The existing models have poor adaptability in complex topological structures and multi-energy coupling scenarios, and cannot provide a reliable basis for the optimal scheduling of energy storage systems.

Microgrid Design

Configure power generation equipment according to the geographical location and resource conditions of the microgrid. In areas with sufficient sunlight, install high-conversion-efficiency monocrystalline silicon photovoltaic modules and use string inverters to improve power generation efficiency to adapt to power output under different lighting conditions. In areas with rich wind resources, select small wind turbines that start at low wind speeds and combine them with wind direction tracking systems to improve wind energy capture capabilities. At the same time, in scenarios with industrial waste heat such as industrial parks, configure waste heat power generation devices to achieve cascade utilization of energy.

Innovating Microgrid Energy Solutions

We conduct comprehensive research on energy storage systems in microgrids, exploring AI applications and developing mathematical models for optimization and predictive analytics.

A line of yellow wind turbines with attached solar panels is situated in front of a cityscape. The background features dry, mountainous terrain under a clear blue sky with a few scattered clouds.