Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach (2107.07772v1)

Abstract: A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users' energy consumption and electric vehicles' behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

• The paper introduces a bi-level model that optimizes community energy systems and EV charging by addressing renewable fluctuations and demand response flexibility.
• It employs a dynamic pricing mechanism combining time-of-use and real-time signals to stimulate adaptive energy consumption.
• Simulation results in a North China case validate improvements in renewable utilization and significant cost reductions in energy operations.

Coordinating Flexible Demand Response and Renewable Uncertainties in Community Integrated Energy Systems: A Bi-level Approach

This paper presents a bi-level optimization framework for managing a Community Integrated Energy System (CIES) inclusive of an Electric Vehicle Charging Station (EVCS), amidst variable renewable generation and flexible demand response scenarios. The model introduced acknowledges the intricacies of modern energy systems, wherein the unpredictability of renewable outputs and surging electric vehicle (EV) adoption complicates effective scheduling and load management.

Overview of Objectives and Methodology

The primary aim of this research is to harmonize demand response strategies with the uncertainties inherent in renewable energy generation, to minimize operational costs while ensuring stakeholder interests are aligned. The proposed solution involves a bi-level dispatching model. At the upper level, the model optimizes the CIES operations by accounting for grid power, renewable generation, shifting and interruptible electric and thermal loads. At the lower level, it focuses on the EVCS, taking into consideration the charging and discharging schedules of EVs. The synchronization between the two levels is achieved through a dynamic pricing mechanism that incorporates both time-of-use (TOU) and real-time (RT) pricing strategies.

Key Contributions and Analytical Insights

1. Bi-level Model Framework: The delineation of the model into two levels allows for targeted optimization of complex variables—CIES and EVCS scheduling are approached from a multi-stakeholder perspective, integrating EV charging, renewable output fluctuations, and demand response flexibilities.
2. Dynamic Pricing Integration: A dynamic pricing mechanism bridges the upper and lower model levels, encouraging users to adjust their consumption patterns and EV charging activities in response to real-time pricing signals, thus enhancing demand responsiveness and reducing operational costs.
3. Renewable Generation Accommodation: The model emphasizes maximizing the utilization of renewable energy by effectively scheduling EV charging and demand response events, enhancing the overall accommodation of wind and solar outputs while mitigating curtailments.
4. Simulation and Performance Validation: The model's effectiveness is validated through simulations on a CIES in North China, indicating substantial improvements in renewable consumption rates and cost reductions. These results underscore the model's practical applicability in real-world energy systems with significant renewable penetration.

Implications and Future Directions

The proposed coordination framework has significant practical implications for energy system operators grappling with the uncertainties of renewable energy and the complexities of increased EV penetration. By quantifying and addressing renewable generation variability and integrating flexible demand response, this model contributes to the sustainable and economically efficient operation of modern energy systems.

From a theoretical standpoint, the model paves the way for further research into more detailed V2G (vehicle-to-grid) interactions and the integration of additional forms of energy storage and conversion technologies, such as power-to-gas (P2G) systems. Future studies could also explore more granular modeling of EV user behavior and preferences in the context of dynamic pricing, as well as expanding bi-level models to accommodate other emerging technologies in smart grids.

In conclusion, this paper presents a robust framework that successfully marries theoretical optimization techniques with practical energy challenges, demonstrating the potential for significant advancements in both the efficiency and sustainability of integrated energy systems.