Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response DOI Open Access
Boyu Zhu, Dazhi Wang

Sustainability, Journal Year: 2024, Volume and Issue: 16(8), P. 3182 - 3182

Published: April 10, 2024

With the transformation of energy market from traditional vertical integrated structure to interactive competitive structure, centralized optimization method makes it difficult reveal behavior multi-agent systems (MAIES). In this paper, a master–slave game optimal scheduling strategy MAIES is proposed based on demand response. Firstly, framework established with an management agent as leader, operation agent, storage and user aggregation followers. Secondly, in view wind solar uncertainty, Monte Carlo used generate random scenarios, k-means clustering pre-generation elimination technology are for scenario reduction. Then, according different flexible characteristics loads, multi-load multi-type response model including electric, thermal, cold built fully utilize regulation role resources. On basis, transaction decision-making models each constructed, existence uniqueness Stackelberg equilibrium solution proved. Finally, case simulations demonstrate effectiveness MAIES. Compared without considering uncertainty response, rate renewable curtailment was reduced by 6.03% carbon emissions system were 1335.22 kg paper.

Language: Английский

Blockchain Technology in Carbon Trading Markets: Impacts, Benefits, and Challenges—A Case Study of the Shanghai Environment and Energy Exchange DOI Creative Commons

Guocong Zhang,

Sonia Chien-I Chen,

Xiucheng Yue

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(13), P. 3296 - 3296

Published: July 5, 2024

This study employs the Shanghai Environment and Energy Exchange as a case to investigate effects of blockchain technology applications on transaction prices within carbon trading market. Utilizing an event methodology, research demonstrates that significantly enhances transparency, security, efficiency market, thereby exerting positive influence prices. Nonetheless, also identifies several challenges associated with applications, including increased costs, heightened energy consumption, delays, substantial learning costs. To mitigate these issues, proposes optimizing architecture, incorporating Layer 2 technologies expedite processes, developing innovative regulatory frameworks.

Language: Английский

Citations

6
Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System DOI Open Access

G. Z. Liu,

Xinfu Song,

Chaoshan Xin

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(5), P. 1908 - 1908

Published: Feb. 26, 2024

Due to the large proportion of China’s energy consumption used by industry, in response national strategic goal “carbon peak and carbon neutrality” put forward Chinese government, it is urgent improve efficiency industrial field. This paper focuses on optimization an integrated system with supply–demand coordination park. formulated as a “node-flow” model. Within model, each node designed according objective function its own operation coupling relationship. The flow model based interaction relationship between node. Based edge–cloud information mechanism transfer balance nodes proposed describe way interacts information, distributed iterative algorithm collaboration realize decision performance method this demonstrated using practical case study park Xinjiang. results show that can effectively utilization multi-energy synergy complementation park, shorten solution time more than 50% without significantly affecting accuracy solution.

Language: Английский

Citations

3
Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response DOI Open Access
Boyu Zhu, Dazhi Wang

Sustainability, Journal Year: 2024, Volume and Issue: 16(8), P. 3182 - 3182

Published: April 10, 2024

With the transformation of energy market from traditional vertical integrated structure to interactive competitive structure, centralized optimization method makes it difficult reveal behavior multi-agent systems (MAIES). In this paper, a master–slave game optimal scheduling strategy MAIES is proposed based on demand response. Firstly, framework established with an management agent as leader, operation agent, storage and user aggregation followers. Secondly, in view wind solar uncertainty, Monte Carlo used generate random scenarios, k-means clustering pre-generation elimination technology are for scenario reduction. Then, according different flexible characteristics loads, multi-load multi-type response model including electric, thermal, cold built fully utilize regulation role resources. On basis, transaction decision-making models each constructed, existence uniqueness Stackelberg equilibrium solution proved. Finally, case simulations demonstrate effectiveness MAIES. Compared without considering uncertainty response, rate renewable curtailment was reduced by 6.03% carbon emissions system were 1335.22 kg paper.

Language: Английский

Citations

2