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Big Data Algorithm for Resource Potential Awareness Response Optimization on the Power User Side Based on IoT Edge Computing

Jiang Du

Jiang Du

China Southern Power Grid Company LimitedGuangzhou, China
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Du, Jiang
, 
Xinlei Cai

Xinlei Cai

Electric Power Dispatching Control Center of Guangdong Power Grid Co., LtdGuangzhou, China
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Cai, Xinlei
, 
Tingzhe Pan

Tingzhe Pan

Electric Power Research Institute, CSGGuangzhou, China
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Pan, Tingzhe
, 
Jiale Liu

Jiale Liu

Electric Power Dispatching Control Center of Guangdong Power Grid Co., LtdGuangzhou, China
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Liu, Jiale
, 
Zhangying Cheng

Zhangying Cheng

Electric Power Dispatching Control Center of Guangdong Power Grid Co., LtdGuangzhou, China
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Cheng, Zhangying
 e 
Xin Jin

Xin Jin

Electric Power Research Institute, CSGGuangzhou, China
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Jin, Xin
  
27 feb 2025
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Pubblicato online: 27 feb 2025
Ricevuto: 19 set 2024
Accettato: 10 gen 2025
DOI: https://doi.org/10.2478/amns-2025-0113
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© 2025 Jiang Du et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Schematic diagram of dynamic decision model
Schematic diagram of dynamic decision model

Figure 2.

Architecture diagram of edge IoT data processing platform
Architecture diagram of edge IoT data processing platform

Figure 3.

Random Forest performance curve
Random Forest performance curve

Figure 4.

Relationship between computing power and task data size
Relationship between computing power and task data size

Figure 5.

Analysis of system energy consumption results
Analysis of system energy consumption results

Figure 6.

Task completion performance comparison
Task completion performance comparison

Figure 7.

Impact of server computing power on system latency
Impact of server computing power on system latency

Figure 8.

Market electricity price in each period before and after demand response
Market electricity price in each period before and after demand response

Figure 9.

Power clearing situation
Power clearing situation

Figure 10.

Feature selection results
Feature selection results

Marginal benefits under scenarios

Scene Social marginal benefit Marginal cost of power generation plus Weighted average User marginal benefit plus Weighted average
1 651.61 776.44 1564.07
2 664.13 765.57 1565.86
3 676.02 760.24 1573.05
4 699.39 737.59 1573.84

Overall prediction results

Consider unit start-stop Regardless of unit start-stop
Power generation side income 75348 0
Electricity side income -73554 1484
Total revenue 1794 1484
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Inglese
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