Green Microgrid Digital Foundation Solution

Pain Points

Currently, industrial enterprises face multiple challenges in energy management, severely hindering their green and low-carbon transformation and operational efficiency improvements:

• High energy costs with a lack of refined management: Industrial electricity costs typically account for 15%-30% of total production costs. However, most enterprises still rely on manual meter reading and monthly summaries, unable to monitor energy consumption data of various production lines and equipment in real time. This makes it difficult to detect "leaks and spills," leading to significant energy waste.
• Difficulty integrating renewable energy and low microgrid operational efficiency: With the proliferation of distributed energy sources like photovoltaics (PV) and energy storage, the generation, grid, load, and storage links of enterprise microgrids lack unified dispatch, resulting in PV curtailment rates as high as 10%-20%. Energy storage charging and discharging strategies are crude, failing to maximize peak-valley arbitrage and extending the return on investment cycle.
• Passive carbon management and increasing compliance pressure: Faced with increasingly stringent carbon emission accounting and reporting requirements, enterprises still rely on manual Excel statistics, leading to inconsistent data standards and difficult traceability. This fails to meet compliance needs for carbon trading and carbon tariffs, exposing them to fines and brand reputation risks.
• Lagging equipment maintenance response and high failure losses: Key energy equipment (e.g., transformers, air conditioning units) lacks predictive maintenance capabilities. Sudden failures cause unplanned downtime, with a single outage potentially costing hundreds of thousands of dollars, along with high repair costs.
• Data silos across multiple systems, lacking a holistic decision-making perspective: Deployed systems like MES, ERP, and EMS operate independently, isolating energy data from production and financial data. Management cannot make optimal decisions from an "energy-production-cost" linkage perspective.

Solution Overview

This solution, centered on a "Green Microgrid Digital Foundation," builds a comprehensive industrial energy intelligence solution covering the entire "Generation-Grid-Load-Storage-Carbon-Maintenance" chain. Its core philosophy is to transform energy management from a "cost center" to a "value center", leveraging digital twins, AI optimization, and IoT technology to achieve deep integration and intelligent dispatch of energy, information, and carbon flows.

The solution adopts a "1 Digital Foundation + 4 Application Platforms + N Smart Terminals" architecture:

• Digital Foundation: A unified data middle platform that breaks down data silos from the device layer to the business layer, providing real-time data governance and model services.
• Application Platforms: Covering four core scenarios: microgrid dispatch, energy efficiency optimization, carbon management, and intelligent maintenance.
• Smart Terminals: Edge gateways, smart meters, sensors, etc., enabling millisecond-level data collection and control.

Unlike single-function EMS or PV monitoring systems on the market, this solution emphasizes systematic resolution: integrating PV forecasting, energy storage strategies, load response, carbon accounting, and equipment health assessment on a single platform to achieve "PV-storage coordination, load following generation, and carbon-energy integration." Its unique value lies in helping enterprises reduce comprehensive energy costs by 15%-25%, increase renewable energy consumption rates to over 95%, and meet carbon compliance requirements.

Solution Components

This solution consists of the following core components, working together to form a complete solution:

• Digital Foundation (Data Middle Platform): Unifies the collection, cleaning, and storage of data from PV, energy storage, load, environmental, and other equipment. Provides standard API interfaces for seamless integration with enterprise systems like MES and ERP. Built-in data quality monitoring and anomaly alert mechanisms ensure data availability of 99.9%.
• Microgrid Intelligent Dispatch Platform: Based on AI algorithms, dynamically optimizes PV output, energy storage charging/discharging, and load response strategies by integrating weather forecasts, electricity price curves, and production plans. Supports automatic switching between "grid-connected/off-grid" modes to achieve the optimal balance between microgrid economy and reliability.
• Energy Efficiency Optimization and Carbon Management Platform: Monitors energy efficiency indicators (e.g., energy consumption per unit product) for various production lines and equipment in real time, automatically identifies efficiency anomalies, and pushes improvement suggestions. Features a built-in carbon emission factor database, automatically generates carbon inventory reports compliant with ISO 14064 standards, and supports carbon quota compliance and carbon trading decision-making.
• Intelligent Maintenance and Predictive Maintenance Platform: Builds equipment health models using multi-dimensional data such as vibration, temperature, and current, providing 7-30 days advance warning of potential failures. Offers functions like automatic dispatch of inspection work orders, maintenance knowledge base, and spare parts management, reducing unplanned downtime by 60%.
• Edge Computing Gateways and Smart Terminals: Deployed on-site, supporting protocols like Modbus, IEC 104, and OPC UA for millisecond-level data collection and local control. Can independently run local optimization strategies even during network outages to ensure microgrid stability.
• Implementation and Training Services: Includes on-site surveys, system deployment, customized algorithm tuning, user training (targeting three levels: operations staff, management, and decision-makers), and 12 months of operational support services.

Implementation Roadmap

The solution adopts a "phased, incremental" implementation strategy to reduce the risk of a one-time investment for the customer, ensuring clear deliverables and measurable value at each stage.

Phase	Time	Objective	Key Activities	Milestone
Phase 1: Foundation Building	Months 1-2	Complete data collection and digital foundation setup	On-site survey, equipment networking, edge gateway deployment, data middle platform initialization	Data access rate reaches 90%, digital foundation goes live
Phase 2: Core Applications	Months 3-5	Launch microgrid dispatch and energy efficiency optimization platforms	Algorithm model training and tuning, trial operation of dispatch strategies, energy efficiency dashboard launch	Microgrid auto-dispatch function enabled, energy efficiency indicators displayed in real time
Phase 3: Deepening Applications	Months 6-8	Integrate carbon management and intelligent maintenance	Carbon accounting module deployment, equipment health model training, maintenance work order process integration	Carbon inventory reports auto-generated, predictive maintenance alerts go live
Phase 4: Optimization and Iteration	Months 9-12	System tuning and value validation	Continuous algorithm optimization based on operational data, ROI calculation, user training and acceptance	Comprehensive energy cost reduced by over 15%, project acceptance

Risk Management: Value assessment is conducted after each phase. If expected targets are not met, root cause analysis and adjustment plans are initiated to ensure overall project risk is controllable.

Expected Outcomes

After implementation, enterprises will achieve quantifiable economic, operational, and compliance value:

Short-Term Outcomes (1-3 months)

• Energy Data Transparency: Real-time visualization of energy consumption data across the entire plant and all equipment. Anomaly detection time reduced from days to minutes.
• Microgrid Dispatch Optimization: PV curtailment rate reduced to below 5%, energy storage charging/discharging strategies optimized, peak-valley arbitrage revenue increased by 20%.

Long-Term Value (6-12 months)

• Comprehensive Energy Cost Reduction of 15%-25%: Achieved through multi-dimensional means such as energy efficiency optimization, demand response, and peak-valley arbitrage.
• Unplanned Downtime Reduced by 60%: Predictive maintenance provides early warnings, increasing equipment availability to over 98%.
• Automated Carbon Compliance: Carbon report generation time reduced from weeks to hours, meeting carbon trading and ESG disclosure requirements.
• Return on Investment Period: Project investment expected to be recovered within 12-18 months (based on typical industrial customer data).

Metric	Before Implementation	After Implementation	Improvement
Comprehensive Energy Cost	100%	75%-85%	Reduced by 15%-25%
PV Consumption Rate	80%-90%	Over 95%	Increased by 5-15 percentage points
Unplanned Downtime Events	5 times/year	2 times/year	Reduced by 60%
Carbon Report Generation Time	2 weeks	2 hours	Shortened by 98%

Reference Cases

The following cases demonstrate the successful application of this solution in similar scenarios:

• A Large Automotive Parts Manufacturer: Annual electricity consumption of 120 million kWh. After deploying this solution, PV consumption rate increased from 82% to 97% through coordinated PV + storage dispatch, saving approximately 3 million RMB in electricity costs annually. Carbon report generation time reduced from 10 days to 3 hours.
• A Chemical Industrial Park: Multiple enterprises within the park share a microgrid. Using this solution's unified dispatch platform, load-side demand response was achieved, reducing peak-hour electricity consumption by 15% annually and obtaining over 2 million RMB in grid demand response subsidies.
• An Electronic Components Factory: After introducing the predictive maintenance module, a critical air conditioning unit failure was warned 14 days in advance, preventing an estimated production outage loss of 800,000 RMB. Equipment maintenance costs were reduced by 30%.

These cases all validate the significant effectiveness of this solution in reducing energy costs, improving operational efficiency, and meeting compliance requirements.