Maximizing On-Bill Savings through Battery Management Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912017229545472 |
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| author | Carmona, Rene Yang, Xinshuo Bhela, Siddharth Zeng, Claire |
| author_facet | Carmona, Rene Yang, Xinshuo Bhela, Siddharth Zeng, Claire |
| contents | In many power grids, a large portion of the energy costs for commercial and industrial consumers are set with reference to the coincident peak load, the demand during the maximum system-wide peak, and their own maximum peak load, the non-coincident peak load. Coincident-peak based charges reflect the allocation of infrastructure updates to end-users for increased capacity, the amount the grid can handle, and for improvement of the transmission, the ability to transport energy across the network. Demand charges penalize the stress on the grid caused by each consumer's peak demand. Microgrids with a local generator, controllable loads, and/or a battery technology have the flexibility to cut their peak load contributions and thereby significantly reduce these charges. This paper investigates the optimal planning of microgrid technology for electricity bill reduction. The specificity of our approach is the leveraging of a scenario generator engine to incorporate probability estimates of coincident peaks and non-coincident peaks into the optimization problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximizing On-Bill Savings through Battery Management Optimization Carmona, Rene Yang, Xinshuo Bhela, Siddharth Zeng, Claire Optimization and Control 9305 (Primary) 93E12 (Secondary) In many power grids, a large portion of the energy costs for commercial and industrial consumers are set with reference to the coincident peak load, the demand during the maximum system-wide peak, and their own maximum peak load, the non-coincident peak load. Coincident-peak based charges reflect the allocation of infrastructure updates to end-users for increased capacity, the amount the grid can handle, and for improvement of the transmission, the ability to transport energy across the network. Demand charges penalize the stress on the grid caused by each consumer's peak demand. Microgrids with a local generator, controllable loads, and/or a battery technology have the flexibility to cut their peak load contributions and thereby significantly reduce these charges. This paper investigates the optimal planning of microgrid technology for electricity bill reduction. The specificity of our approach is the leveraging of a scenario generator engine to incorporate probability estimates of coincident peaks and non-coincident peaks into the optimization problem. |
| title | Maximizing On-Bill Savings through Battery Management Optimization |
| topic | Optimization and Control 9305 (Primary) 93E12 (Secondary) |
| url | https://arxiv.org/abs/2409.03942 |