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Research on cross-building energy storage management system based on reinforcement learning

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Publicado en:Journal of Physics: Conference Series vol. 2936, no. 1 (Jan 2025), p. 012018
Autor principal: Xin, Ming
Otros Autores: Wang, Yanli, Zhang, Ruizhi, Zhang, Jibin, Liu, Xinan
Publicado:
IOP Publishing
Materias:
Routers
Computing costs
Linear programming
Dynamic programming
Algorithms
Energy storage
Mixed integer
Integer programming
Stochastic programming
Linear functions
Acceso en línea:Citation/Abstract
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Resumen:This study considers a cross-building energy storage system in which the objective function of each step is a piecewise linear function of decision variables and state variables. Therefore, the objective function can be modeled as piecewise linear programming and then transformed into a mixed integer linear programming (MILP) problem. However, as a multi-stage stochastic programming problem in which we utilize approximate dynamic programming (ADP) to tackle the computational issues, we need to solve the objective function multiple times. To further decrease computational cost, we propose several approximate algorithms to determine variable splitting, which degrades the problem to a linear programming problem. We use approximate techniques to solve the problem and design experiments to verify our conclusion. Numerical experiments show that our algorithm greatly reduces the time needed to solve the problem under the condition of minimal loss of accuracy. The simulation experiment in a Python environment further proves that the cross-building energy storage management system based on energy routers and control centers is better than the energy system of each building working alone to maximize the benefits.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2936/1/012018
Fuente:Advanced Technologies & Aerospace Database