In the rapidly evolving landscape of smart cities, the integration of intelligent connected transportation systems (ICTS) with energy grids is becoming a critical focus for researchers and industry professionals alike. A recent study published in the *International Journal of Electrical Power & Energy Systems* offers a novel approach to optimizing energy use in these advanced transportation networks, with significant implications for the energy sector.
Led by Junjie Hu from the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources at North China Electric Power University, the research introduces a two-stage coordinated scheduling model for ICTS. This model aims to harness the potential of distributed energy resources more effectively, addressing the complex interplay between power demand and traffic flow.
“The traditional transportation system is undergoing a significant transformation into an ICTS, where a multitude of connected devices are distributed across the road system,” explains Hu. “Our model seeks to better utilize the energy resources within these systems, ensuring economically efficient and stable operations.”
The study’s innovative approach involves two key stages. In the day-ahead stage, the model minimizes interconnection costs, electricity purchase costs, and wind/solar curtailment costs under representative scenarios identified using the Minimum Volume Enclosing Ellipsoid (MVEE) algorithm. This algorithm helps in capturing the uncertainty in wind and solar power output, which is crucial for effective energy management.
In the intraday stage, rolling optimization is employed to fine-tune the scheduling strategy based on the day-ahead interconnection solution. This dynamic adjustment ensures that the operational costs of the ICTS multi-microgrids are minimized, adapting to real-time changes in energy demand and supply.
One of the standout features of this research is the introduction of nonanticipativity constraints. These constraints ensure that energy storage decision-making does not rely on future information, making the model more practical and implementable in real-world scenarios.
“The nonanticipativity constraints are essential for making our model robust and applicable in real-time decision-making processes,” says Hu. “This ensures that our scheduling strategies are not only theoretically sound but also practically feasible.”
The adaptive PJ-ADMM algorithm used to solve the model further enhances its effectiveness. Simulation case studies demonstrate that the proposed model can significantly improve the economic efficiency and stability of ICTS operations.
The implications of this research for the energy sector are profound. As cities around the world invest in smart transportation systems, the ability to optimize energy use within these networks will become increasingly important. The two-stage coordinated scheduling model offers a promising solution for achieving this goal, potentially reducing operational costs and enhancing the reliability of energy supply.
Moreover, the study’s focus on fine-grained load modeling and coordinated optimization provides valuable insights for energy providers and grid operators. By better understanding the intricate coupling relationship between power demand and traffic flow, they can develop more effective strategies for managing energy resources.
As the world moves towards a future dominated by smart cities and connected transportation systems, research like this will play a crucial role in shaping the energy landscape. The findings from Hu’s study not only advance our understanding of ICTS energy management but also pave the way for more innovative and efficient energy solutions.
In the quest for sustainable and economically viable energy systems, this research offers a significant step forward, highlighting the importance of coordinated optimization and nonanticipativity in achieving these goals.