Microgrid Energy Dispatching: The Revolution of Multi-Energy Complementary Control Driven by Industrial Personal Computers
Under the drive of carbon neutrality goals, the global energy system is undergoing a profound transformation from centralized to distributed and from single-energy to multi-energy complementarity. By 2025, China's installed capacity of distributed photovoltaics will exceed 800 GW, and the penetration rate of wind-storage hybrid systems will surpass 35%. However, the superposition of the intermittency of renewable energy and the randomness of loads poses three major challenges to traditional microgrid energy dispatching: insufficient efficiency in multi-source heterogeneous data fusion, dynamic response delays exceeding safety thresholds, and a lack of real-time optimization strategies for energy complementarity. In this context, the deep integration of industrial personal computers and multi-energy complementary control technologies is reshaping the technological paradigm of microgrid energy dispatching.
Traditional microgrids adopt a three-tier architecture of "bottom-layer device control - central energy management - upper-layer market interaction," but suffer from data silos and decision-making delays. The industrial-grade industrial personal computer, represented by the USR-EG628, constructs a new-generation architecture of "edge perception - real-time decision-making - cloud optimization" through its quad-core ARM Cortex-A53 processor and an NPU with 1 TOPS of computing power. This controller supports dual-mode communication of LTE-V2X and 5G. In real-world tests conducted in the Qianhai Free Trade Zone in Shenzhen, it achieved millisecond-level responses from over 200 devices within a 500-meter range, with a data throughput of 100,000 items per second, representing a tenfold improvement over traditional architectures.
The introduction of digital twin technology further breaks through physical limitations. The microgrid digital twin platform developed by the Tsinghua University team constructs a virtual model incorporating the coupling of electricity, heat, and cooling networks through 12 types of sensor data collected by the USR-EG628. In a pilot project in Xiong'an New Area, this platform reduced the iteration cycle of dispatching strategies from hourly to minute-level intervals, and decreased the photovoltaic prediction error rate from 12% to 3.8%.
Multi-energy complementary control is essentially a multi-objective optimization problem that must simultaneously satisfy three major constraints: economic efficiency, environmental friendliness, and reliability. The improved genetic algorithm proposed by the research team demonstrates significant advantages in isolated microgrid dispatching:
Economic Efficiency Dimension: Through a dynamic electricity price response mechanism, the electricity purchase cost of the Lingang microgrid in Shanghai was reduced by 27%, and the lifespan of the energy storage system was extended by 40%.
Environmental Friendliness Dimension: In the joint dispatching of the Jiuquan Wind Power Base in Gansu, the game theory model of hydrogen energy storage and cascaded hydropower reduced the wind curtailment rate from 18% to 5.2%, resulting in annual CO₂ emissions reductions of 120,000 tons.
Reliability Dimension: The two-tier control strategy based on particle swarm optimization reduced the fault recovery time from 15 minutes to 90 seconds in the Jiangbei New Area of Nanjing, achieving a power supply reliability of 99.999%.
The edge computing capability of the USR-EG628 supports the implementation of complex algorithms. Its built-in TensorFlow Lite accelerator can run LSTM neural network models in real-time. In the Hangzhou Qiutao Road BRT project, it accurately predicted the arrival times of buses, controlling the signal priority response error within ±0.3 seconds.
The Chengdu Bus Group has constructed an integrated "photovoltaic-storage-charging-discharging" microgrid, integrating 5 MW of photovoltaics, 2 MWh of energy storage, and 300 V2G charging stations. The USR-EG628 achieves real-time collaboration among vehicles, the grid, and charging stations through the V2X protocol, reducing the daily charging cost of new energy buses by 32% and narrowing the grid peak-to-valley difference by 28%.
The tri-generation microgrid in the Suzhou Industrial Park integrates a 20 MW gas turbine, a 5 MW ground-source heat pump, and a 10 MWh energy storage system. Based on a three-tier control strategy of data envelopment analysis, the comprehensive energy utilization rate was increased to 85%, saving 12,000 tons of standard coal annually.
In the electricity-free region of Yushu, Qinghai, a wind-solar-storage-diesel microgrid solution was adopted. The intelligent protection module of the USR-EG628 achieves fault isolation within 0.1 seconds, and, in conjunction with energy dispatching using an improved bat algorithm, the power supply reliability was increased from 68% to 99.2%, reducing operation and maintenance costs by 65%.
To address the volatility of renewable energy, Tsinghua University developed a supercapacitor-battery hybrid energy storage system. Through millisecond-level PWM control by the USR-EG628, the cooperation efficiency between power-type and energy-type energy storage reached 92%, representing a 23% improvement over traditional solutions.
The State Grid's blockchain microgrid pilot project in Xiong'an New Area utilizes the encryption module of the USR-EG628 to enable P2P electricity trading. Through the automatic execution of smart contracts, the transaction settlement time was shortened from days to seconds, reducing transaction costs by 80%.
The deep reinforcement learning framework developed by Shanghai Jiao Tong University enables microgrids to possess self-optimization capabilities. During 30 consecutive days of testing, the system autonomously adjusted control parameters 217 times, ultimately reducing operational costs by 19% compared to manual dispatching.
Despite significant progress, multi-energy complementary control still faces three major challenges:
