AI Algorithms Development

We design advanced AI algorithms for optimizing microgrid energy management and enhancing renewable energy predictions.

Model Development

We create mathematical models for renewable energy generation and load-demand forecasting in microgrid systems.

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Research Analysis

Our team conducts in-depth research reviews to identify gaps and opportunities in microgrid energy management.

We develop innovative AI solutions for energy storage systems, enhancing efficiency and operational performance.

Optimization Strategies

A large industrial power plant with two prominent smokestacks rises in the background, adorned with the word 'POWER' in bold red letters. The foreground is dominated by a complex maze of electrical grid structures, wires, and transformers. Chain-link fences and caution signs suggest restricted access to the facility. The sky above is partly cloudy, offering a contrast to the industrial scene below.

AI Innovations

Exploring integration of AI in microgrid energy management systems.

Energy Models

Mathematical models for renewable generation and load forecasting.

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A series of solar panels are installed on a large industrial rooftop under a clear sky at sunset. The sunlight casts a warm glow across the panels and the surrounding area, highlighting the geometric patterns formed by the panels.

Research Gaps

In the design of multi-energy complementary microgrids, it is necessary to establish energy conversion models and coordinated control strategies, but the current research on the technical integration of the energy conversion stage is relatively low. The coordinated control of heat storage, gas storage equipment and electric energy storage systems such as lithium batteries lacks unified data interaction standards and control logic, resulting in the inability of various energy storage devices to cooperate efficiently in actual operation. For example, in the winter multi-energy supplementary system, the timing of the charging and discharging capacity of the heat storage system and the power supplementary system is difficult to accurately match, making the comprehensive energy utilization efficiency far lower than expected, and unable to give full play to the advantages of the multi-energy complementary microgrid.

The design of microgrids emphasizes cost control and revenue improvement, but existing research does not comprehensively calculate the cost of the entire life cycle of energy storage systems. In addition to equipment procurement and operation and maintenance costs, the recycling and processing costs of retired energy storage equipment and the secondary investment costs of replacing batteries are often overlooked. In terms of revenue assessment, the revenue forecast of participating in ancillary service transactions in the electricity market is difficult to accurately estimate due to the changing market rules and fierce competition; the profit model of signing energy service agreements with users faces problems such as low user acceptance and complex pricing mechanisms in actual promotion, resulting in a large deviation between economic feasibility analysis and actual operating results.