Cost Allocation of Grid-Substituted Energy Storage Based on Activity-Based Costing and Genetic Algorithm for Transmission Tariff Integration
Year: 2025
Type: conference-paper
Abstract: With the advancement of computer-aided modeling, intelligent optimization, and data-driven decision-making, computational techniques have become essential in solving complex economic allocation problems in modern power systems. This paper presents a cost allocation framework for integrating grid-substituted energy storage (GSES) investments into transmission and distribution tariffs, combining Activity-Based Costing (ABC) with a Genetic Algorithm (GA). The ABC method systematically identifies and attributes costs to key storage functions such as peak shaving, frequency regulation, and reserve capacity, ensuring accurate operational cost tracking. A multi-objective GA is employed to optimize the allocation of costs among stakeholders—including transmission operators, distribution utilities, and regulatory bodies—under constraints of fairness, operational contribution, and policy compliance. Simulation experiments are conducted using real operational data from a provincial grid with deployed battery energy storage systems. The proposed ABC-GA model is compared against conventional linear programming (LP) and equal-proportion allocation methods. Results show that the GA-based approach improves allocation fairness by 17.1%, reduces cost deviation across entities by 22.2%, and shortens computational time by 34.8% compared to LP, while maintaining regulatory compliance. These findings validate the model's practicality and robustness, offering a scalable solution for embedding GSES costs into tariff structures and supporting the ongoing green transformation of the power sector.
Authors: Kai Zheng, Xiangxing Meng
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414701464