Research on Low-Carbon Generation Schedule Optimization for Multiple Generation Companies Considering Heterogeneous Flexible Loads

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a mathematical framework for optimizing low-carbon generation schedules with heterogeneous flexible loads and carbon constraints, which can inform further research on demand-side integration in decarbonized power systems.

🏢実務担当者:Power generation companies can apply the proposed model to improve both economic and low-carbon performance of their generation schedules, especially when facing carbon quotas.

🏛政策担当者:The results demonstrate the value of combining price elasticity and incentive-based demand response for achieving low-carbon targets, which can inform the design of carbon pricing and demand response policies.

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules.

• openalex https://doi.org/10.3390/a19060499first seen 2026-06-26 05:02:23