Energy Management Model of Intelligent Park Based on Improved Depth Deterministic Gradient Strategy Algorithm

The traditional distribution automation system, demand side management system, and distributed generation access and control system, respectively, solve the problems of regional distribution network power supply, customer power consumption, and new energy utilization to varying degrees. Intelligent...

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Veröffentlicht in:Scientific programming Jg. 2022; S. 1 - 14
Hauptverfasser: Xu, Chen, Jiang, Xianwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 14.02.2022
John Wiley & Sons, Inc
Schlagworte:
Access control
Algorithms
Artificial intelligence
Automation
Communication
Consumption
Deep learning
Distributed generation
Electric power distribution
Electric vehicles
Electrical loads
Electricity
Electricity distribution
Energy management
Energy storage
Energy utilization
Environment models
Integer programming
Machine learning
Neural networks
Optimization algorithms
Parks & recreation areas
Power
Power consumption
Residential areas
Scheduling
Temperature control
ISSN:1058-9244, 1875-919X
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Zusammenfassung:The traditional distribution automation system, demand side management system, and distributed generation access and control system, respectively, solve the problems of regional distribution network power supply, customer power consumption, and new energy utilization to varying degrees. Intelligent Park evolved from the concept of intelligent park of State Grid Corporation, Modern enterprises, or residential areas that can make full use of modern communication, computer, automation, and other technologies to implement the power supply demand of the park. For the modern intelligent park with user-side temperature control load and demand response load access, an intelligent park energy management and optimal scheduling method based on deep reinforcement learning (DRL) algorithm is proposed. Through the interaction between the agent and the energy environment of the intelligent park, the control strategy is adaptively learned. This method can realize the continuous action control of energy management in intelligent park and can realize the optimal scheduling decision of intelligent park in various scenarios. Firstly, based on the characteristics and types of intelligent park aggregation unit, the environment model of intelligent park energy management system interacting with agent is established. Secondly, the basic principle of deep deterministic policy gradient deep algorithm is introduced, and on this basis, the key links of deep reinforcement learning algorithm, such as action space, state space, reward mechanism, neural network structure, and learning process, are designed. Finally, the effectiveness of the proposed algorithm is verified by an example of a city intelligent park.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/6177978