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Energy Optimization Management Scheme for Manufacturing Systems Based on BMAPPO: A Deep Reinforcement Learning Approach

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Publicado en:International Journal of Advanced Computer Science and Applications vol. 15, no. 10 (2024)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
Environmental management
Distributed generation
Power flow
Renewable resources
Optimization
Pollution sources
Algorithms
Controllability
Multiagent systems
Renewable energy
Deep learning
Machine learning
Computer science
Emissions
Electricity distribution
Optimization techniques
Prices
Energy storage
Mathematical programming
Control algorithms
Electricity
Alternative energy
Genetic algorithms
Cost reduction
Energy management
Linear programming
Demand side management
Optimization algorithms
Acceso en línea:Citation/Abstract
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Resumen:To address the depletion of traditional energy sources and the increasingly severe environmental pollution, countries around the world have accelerated the deployment of renewable energy generation equipment. Energy optimization management for microgrids can address the randomness of factors such as renewable energy generation and load, ensuring the safe and stable operation of the system while achieving objectives such as cost minimization. Therefore, this paper conducts an in-depth study of energy optimization management schemes for microgrids and designs a multi-microgrid energy optimization management model and algorithm based on deep reinforcement learning. For the joint optimization problem among multiple microgrids with power flow between them, a two-layer energy optimization management scheme based on the multi-agent proximal policy optimization (PPO) algorithm and optimal power flow (BMAPPO) is proposed. This scheme is divided into two layers: first, the lower layer uses the multi-agent proximal policy optimization algorithm to determine the output of various controllable power devices in each microgrid; then, based on the lower layer's optimization results, the upper layer uses a second-order cone relaxation optimal power flow model to solve the optimal power flow between multiple microgrids, achieving power scheduling among them; finally, the total cost of the upper and lower layers is calculated to update the network parameters. Experimental results show that compared with other schemes, the proposed scheme achieves multi-microgrid energy optimization management at the lowest cost while ensuring online execution speed.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0151077
Fuente:Advanced Technologies & Aerospace Database