Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation
Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of t...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Advances in Applied Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792423000100 |
| _version_ | 1797807208768995328 |
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| author | Dylan Wald Kathryn Johnson Jennifer King Joshua Comden Christopher J. Bay Rohit Chintala Sanjana Vijayshankar Deepthi Vaidhynathan |
| author_facet | Dylan Wald Kathryn Johnson Jennifer King Joshua Comden Christopher J. Bay Rohit Chintala Sanjana Vijayshankar Deepthi Vaidhynathan |
| author_sort | Dylan Wald |
| collection | DOAJ |
| description | Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case. |
| first_indexed | 2024-03-13T06:19:07Z |
| format | Article |
| id | doaj.art-e1c694d9d40b473ea76fc998eb313e79 |
| institution | Directory Open Access Journal |
| issn | 2666-7924 |
| language | English |
| last_indexed | 2024-03-13T06:19:07Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj.art-e1c694d9d40b473ea76fc998eb313e792023-06-10T04:28:43ZengElsevierAdvances in Applied Energy2666-79242023-06-0110100131Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generationDylan Wald0Kathryn Johnson1Jennifer King2Joshua Comden3Christopher J. Bay4Rohit Chintala5Sanjana Vijayshankar6Deepthi Vaidhynathan7Corresponding author.; National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA; Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA; Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USANational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USARenewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case.http://www.sciencedirect.com/science/article/pii/S2666792423000100Commercial building controlModel predictive controlForecastingElectric vehicle charging controlShifting demandBattery energy storage systems |
| spellingShingle | Dylan Wald Kathryn Johnson Jennifer King Joshua Comden Christopher J. Bay Rohit Chintala Sanjana Vijayshankar Deepthi Vaidhynathan Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation Advances in Applied Energy Commercial building control Model predictive control Forecasting Electric vehicle charging control Shifting demand Battery energy storage systems |
| title | Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation |
| title_full | Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation |
| title_fullStr | Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation |
| title_full_unstemmed | Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation |
| title_short | Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation |
| title_sort | shifting demand reduction in necessary storage capacity through tracking of renewable energy generation |
| topic | Commercial building control Model predictive control Forecasting Electric vehicle charging control Shifting demand Battery energy storage systems |
| url | http://www.sciencedirect.com/science/article/pii/S2666792423000100 |
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