MPC-based control for a stand-alone LVDC microgrid for rural electrification. (December 2022)
- Record Type:
- Journal Article
- Title:
- MPC-based control for a stand-alone LVDC microgrid for rural electrification. (December 2022)
- Main Title:
- MPC-based control for a stand-alone LVDC microgrid for rural electrification
- Authors:
- Negri, Simone
Giani, Federico
Massi Pavan, Alessandro
Mellit, Adel
Tironi, Enrico - Abstract:
- Abstract: Electricity access in developing countries, where the availability of public distribution grids is still poor, is considered a key factor for improvement of people life conditions. In these situations, the lack of a reliable grid can be mitigated by the introduction of stand-alone DC microgrids, including small Photovoltaic (PV) generators and storage devices. This paper focuses on optimal energy management and power supply reliability of such a microgrid. In particular, a Model-Predictive-Control (MPC) - based control system is introduced to optimally manage storage devices and coordinate load shedding actions. Additionally, an Artificial-Neural-Network (ANN) - based predictor is introduced to manage unpredictable solar irradiance and temperature variations. The availability of reliable adaptive forecasts provided by the ANN-based predictor increases the efficiency of the optimization performed by the MPC-based control over the prediction horizon, avoiding the well-known issues related to optimization performed on unreliable forecast. In this paper, the proposed control approach is detailed for a specific case study and its advantages with respect to traditional controller algorithms are highlighted by comprehensive numerical simulations. The presented results highlight that the proposed MPC controller provides a substantial increment in power supply reliability with respect to standard controls, especially for priority loads. This is obtained at the expense of anAbstract: Electricity access in developing countries, where the availability of public distribution grids is still poor, is considered a key factor for improvement of people life conditions. In these situations, the lack of a reliable grid can be mitigated by the introduction of stand-alone DC microgrids, including small Photovoltaic (PV) generators and storage devices. This paper focuses on optimal energy management and power supply reliability of such a microgrid. In particular, a Model-Predictive-Control (MPC) - based control system is introduced to optimally manage storage devices and coordinate load shedding actions. Additionally, an Artificial-Neural-Network (ANN) - based predictor is introduced to manage unpredictable solar irradiance and temperature variations. The availability of reliable adaptive forecasts provided by the ANN-based predictor increases the efficiency of the optimization performed by the MPC-based control over the prediction horizon, avoiding the well-known issues related to optimization performed on unreliable forecast. In this paper, the proposed control approach is detailed for a specific case study and its advantages with respect to traditional controller algorithms are highlighted by comprehensive numerical simulations. The presented results highlight that the proposed MPC controller provides a substantial increment in power supply reliability with respect to standard controls, especially for priority loads. This is obtained at the expense of an increased battery stress, which is acceptable for electricity access applications where power supply reliability is usually the foremost need. … (more)
- Is Part Of:
- Sustainable energy, grids and networks. Volume 32(2022)
- Journal:
- Sustainable energy, grids and networks
- Issue:
- Volume 32(2022)
- Issue Display:
- Volume 32, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 32
- Issue:
- 2022
- Issue Sort Value:
- 2022-0032-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12
- Subjects:
- Model predictive control -- Neural networks -- Photovoltaic generation -- LVDC -- Microgrids -- Rural electrification
Renewable energy sources -- Periodicals
Smart power grids -- Periodicals
Electric power systems -- Periodicals
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/23524677/ ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.segan.2022.100777 ↗
- Languages:
- English
- ISSNs:
- 2352-4677
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24638.xml