Microgrid Energy Management during High-Stress Operation
We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/18/6589 |
| _version_ | 1827661445471404032 |
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| author | Thomas Price Gordon Parker Gail Vaucher Robert Jane Morris Berman |
| author_facet | Thomas Price Gordon Parker Gail Vaucher Robert Jane Morris Berman |
| author_sort | Thomas Price |
| collection | DOAJ |
| description | We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements. |
| first_indexed | 2024-03-10T00:09:30Z |
| format | Article |
| id | doaj.art-c34dc21ac100445e8bfd4897ad46f329 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T00:09:30Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c34dc21ac100445e8bfd4897ad46f3292023-11-23T16:02:20ZengMDPI AGEnergies1996-10732022-09-011518658910.3390/en15186589Microgrid Energy Management during High-Stress OperationThomas Price0Gordon Parker1Gail Vaucher2Robert Jane3Morris Berman4Mechanical Engineering-Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USAMechanical Engineering-Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USAUS Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, White Sands Missile Range, NM 88002, USAUS Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, Adelphi, MD 20783, USAUS Army Research Laboratory, US Army Combat Capabilities Development Command, US Army Futures Command, Adelphi, MD 20783, USAWe consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements.https://www.mdpi.com/1996-1073/15/18/6589clear skyelectrical load forecastfuel consumptionenergy management systemenergy storage requirementsmicrogrid |
| spellingShingle | Thomas Price Gordon Parker Gail Vaucher Robert Jane Morris Berman Microgrid Energy Management during High-Stress Operation Energies clear sky electrical load forecast fuel consumption energy management system energy storage requirements microgrid |
| title | Microgrid Energy Management during High-Stress Operation |
| title_full | Microgrid Energy Management during High-Stress Operation |
| title_fullStr | Microgrid Energy Management during High-Stress Operation |
| title_full_unstemmed | Microgrid Energy Management during High-Stress Operation |
| title_short | Microgrid Energy Management during High-Stress Operation |
| title_sort | microgrid energy management during high stress operation |
| topic | clear sky electrical load forecast fuel consumption energy management system energy storage requirements microgrid |
| url | https://www.mdpi.com/1996-1073/15/18/6589 |
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