Energy Optimization Management Scheme for Manufacturing Systems Based on BMAPPO: A Deep Reinforcement Learning Approach
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| I publikationen: | International Journal of Advanced Computer Science and Applications vol. 15, no. 10 (2024) |
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| Huvudupphov: | |
| Utgiven: |
Science and Information (SAI) Organization Limited
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| Ämnen: | |
| Länkar: | Citation/Abstract Full Text - PDF |
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| 001 | 3131836853 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2024.0151077 |2 doi | |
| 035 | |a 3131836853 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Energy Optimization Management Scheme for Manufacturing Systems Based on BMAPPO: A Deep Reinforcement Learning Approach | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Environmental management | ||
| 653 | |a Distributed generation | ||
| 653 | |a Power flow | ||
| 653 | |a Renewable resources | ||
| 653 | |a Optimization | ||
| 653 | |a Pollution sources | ||
| 653 | |a Algorithms | ||
| 653 | |a Controllability | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Renewable energy | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Computer science | ||
| 653 | |a Emissions | ||
| 653 | |a Electricity distribution | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Prices | ||
| 653 | |a Energy storage | ||
| 653 | |a Mathematical programming | ||
| 653 | |a Control algorithms | ||
| 653 | |a Electricity | ||
| 653 | |a Alternative energy | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Cost reduction | ||
| 653 | |a Energy management | ||
| 653 | |a Linear programming | ||
| 653 | |a Demand side management | ||
| 653 | |a Optimization algorithms | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 15, no. 10 (2024) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3131836853/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3131836853/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |