Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models
The supporting position and role of smart grids in the construction of smart cities have not been fully explored. Based on systematizing the system architecture of smart cities, we first analyze the facilitating and constraining roles of smart grids and smart cities with each other and make a quantitative analysis of the coordination and supporting roles between them; in the smart grid environment, we propose a framework of energy management system based on particle swarm optimization (PSO) dispatching model. The algorithm optimizes the operation of dispatchable loads, electric vehicles, and energy storage systems based on outdoor temperature forecasts, renewable energy power output forecasts, day-ahead tariff signals, and customer preferences to minimize customer electricity costs. The performance of the algorithm is verified through simulation experiments, and the results show that the proposed algorithm significantly reduces electricity consumption costs by 32.54% compared to algorithms that only optimize the scheduling of loads or some components of the home energy management system.
Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models
The supporting position and role of smart grids in the construction of smart cities have not been fully explored. Based on systematizing the system architecture of smart cities, we first analyze the facilitating and constraining roles of smart grids and smart cities with each other and make a quantitative analysis of the coordination and supporting roles between them; in the smart grid environment, we propose a framework of energy management system based on particle swarm optimization (PSO) dispatching model. The algorithm optimizes the operation of dispatchable loads, electric vehicles, and energy storage systems based on outdoor temperature forecasts, renewable energy power output forecasts, day-ahead tariff signals, and customer preferences to minimize customer electricity costs. The performance of the algorithm is verified through simulation experiments, and the results show that the proposed algorithm significantly reduces electricity consumption costs by 32.54% compared to algorithms that only optimize the scheduling of loads or some components of the home energy management system.
Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models
Xiaolong Yang (Autor:in) / Yanxia Xu (Autor:in) / Chao Ma (Autor:in) / Tao Yao (Autor:in) / Lei Xu (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Smart (Electricity) Grids for Smart Cities: Assessing Roles and Societal Impacts
| BASE | 2018