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

arXiv: https://arxiv.org/abs/2107.07772 · HTML: https://mlsanity.com/paper/2107.07772/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2021.

## Built on by (most cited first, 11 of 11)

- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning](https://mlsanity.com/paper/2308.03953/) (2023)
- [Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.17941/) (2023)
- [Out-of-Distribution-Aware Electric Vehicle Charging](https://mlsanity.com/paper/2311.05941/) (2023)
- [Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks](https://mlsanity.com/paper/2205.11743/) (2022)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling](https://mlsanity.com/paper/2308.12554/) (2023)
- [Research on short-term load forecasting model based on VMD and IPSO-ELM](https://mlsanity.com/paper/2410.05300/) (2024)
- [Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling](https://mlsanity.com/paper/2410.11932/) (2024)
- [Chargax: A JAX Accelerated EV Charging Simulator](https://mlsanity.com/paper/2507.01522/) (2025)

## Abstract (arXiv)

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.