A Framework for Sustainable Power Demand Response: Optimization Scheduling with Dynamic Carbon Emission Factors and Dual DPMM-LSTM

Qian Zhang, Xunting Wang, Jinjin Ding, Haiwei Wang, Fulin Zhao, Xingxing Ju () and Meijie Zhang
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Qian Zhang: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Xunting Wang: Electric Power Science Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei 230601, China
Jinjin Ding: Electric Power Science Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei 230601, China
Haiwei Wang: State Grid Hefei Electric Power Supply Company, Hefei 230022, China
Fulin Zhao: State Grid Anhui Electric Power Co., Ltd., Hefei 230041, China
Xingxing Ju: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Meijie Zhang: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China

Sustainability, 2025, vol. 17, issue 20, 1-24

Abstract: In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response methods often overlook the dynamic characteristics of power system carbon emissions and fail to accurately characterize the complex relationship between power consumption and carbon emissions, which results in suboptimal emission reduction results. To address this challenge, this paper proposes and validates an innovative low-carbon demand response optimization scheduling method as a sustainable tool. The core of this method is the development of a dynamic carbon emission factor (DCEF) assessment model. By innovatively integrating marginal and average carbon emission factors, it becomes a dynamic sustainability indicator that can measure the environmental performance of the power grid in real time. To characterize the relationship between power consumption behavior and carbon emissions, we employ an adaptive Dirichlet process mixture model (DPMM). This model does not require a preset number of clusters and can automatically discover patterns in the data, such as grouping holidays and working days with similar power consumption characteristics. Based on the clustering results and historical data, a dual long short-term memory (LSTM) deep learning network architecture is designed to achieve a coordinated prediction of power consumption and DCEFs for the next 24 h. On this basis, a method is established with the goal of maximizing carbon emission reduction while considering constraints such as fixed daily power consumption, user comfort, and equipment safety. Simulation results demonstrate that this approach can effectively reduce regional carbon emissions through accurate prediction and optimized scheduling. This provides not only a quantifiable technical path for improving the environmental sustainability of the power system but also decision-making support for the formulation of energy policies and incentive mechanisms that align with sustainable development goals.

Keywords: dynamic carbon emission factors; dirichlet process mixture model; LSTM deep learning network; demand response optimization model; sustainable power development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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