Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load
To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9465775/ |
| _version_ | 1819243125807251456 |
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| author | Dejan P. Jovanovic Gerard F. Ledwich Geoffrey R. Walker |
| author_facet | Dejan P. Jovanovic Gerard F. Ledwich Geoffrey R. Walker |
| author_sort | Dejan P. Jovanovic |
| collection | DOAJ |
| description | To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty. By modelling a probabilistic dependence among flow, load, and electricity tariffs, the expected cost function is obtained and used in the constrained optimization. The proposed control strategy explicitly incorporates the cycling nature of customer load. Furthermore, for daily cycling load, a fixed-end time and a fixed-end output problem are addressed. It is demonstrated that the proposed control strategy is a convex optimization problem. While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations. Also, the expected cost across the flow variations is considered. The density function of load probability improves load prediction over a progressive prediction horizon, and a nonlinear battery model is utilized. |
| first_indexed | 2024-12-23T14:50:44Z |
| format | Article |
| id | doaj.art-eb84275922084a59bf0c321db25e580f |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-23T14:50:44Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-eb84275922084a59bf0c321db25e580f2022-12-21T17:42:57ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202022-01-0110114014810.35833/MPCE.2020.0003059465775Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling LoadDejan P. Jovanovic0Gerard F. Ledwich1Geoffrey R. Walker2Institute of Electrical and Electronic Engineers,Queensland,AustraliaSchool of Electrical Engineering and Robotics, Queensland University of Technology,Brisbane,AustraliaSchool of Electrical Engineering and Robotics, Queensland University of Technology,Brisbane,AustraliaTo optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty. By modelling a probabilistic dependence among flow, load, and electricity tariffs, the expected cost function is obtained and used in the constrained optimization. The proposed control strategy explicitly incorporates the cycling nature of customer load. Furthermore, for daily cycling load, a fixed-end time and a fixed-end output problem are addressed. It is demonstrated that the proposed control strategy is a convex optimization problem. While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations. Also, the expected cost across the flow variations is considered. The density function of load probability improves load prediction over a progressive prediction horizon, and a nonlinear battery model is utilized.https://ieeexplore.ieee.org/document/9465775/Residential energy systemsbattery storagemodel predictive controlnonlinear optimizationcost of daily electricity consumption |
| spellingShingle | Dejan P. Jovanovic Gerard F. Ledwich Geoffrey R. Walker Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load Journal of Modern Power Systems and Clean Energy Residential energy systems battery storage model predictive control nonlinear optimization cost of daily electricity consumption |
| title | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load |
| title_full | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load |
| title_fullStr | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load |
| title_full_unstemmed | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load |
| title_short | Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load |
| title_sort | electricity tariff aware model predictive controller for customer battery storage with uncertain daily cycling load |
| topic | Residential energy systems battery storage model predictive control nonlinear optimization cost of daily electricity consumption |
| url | https://ieeexplore.ieee.org/document/9465775/ |
| work_keys_str_mv | AT dejanpjovanovic electricitytariffawaremodelpredictivecontrollerforcustomerbatterystoragewithuncertaindailycyclingload AT gerardfledwich electricitytariffawaremodelpredictivecontrollerforcustomerbatterystoragewithuncertaindailycyclingload AT geoffreyrwalker electricitytariffawaremodelpredictivecontrollerforcustomerbatterystoragewithuncertaindailycyclingload |