An Overview of Energy Access Solutions for Rural Healthcare Facilities
Abstract
:1. Introduction
1.1. Background
1.2. Theoretical Analysis
1.2.1. Effects of No Access to Reliable Energy Sources in Rural Healthcare Centres
1.2.2. Significance of Accessing Reliable Electricity in Rural Healthcare Centres
1.2.3. Rural Healthcare Centre Power Supply Alternative the Off-Grid Renewable Energy Systems
- Strategic conservation: this is simply an energy conservation method where high-power-consuming electrical appliances are replaced with low-rated appliances with similar functionality. This approach ensures less power consumption and a reduction in load demand. Appliances such as DC lighting, low-wattage lamps, and replacing Cathode Ray Tube (CRT) TV sets with Plasma or Liquide Crystal Display (LCD) are typical strategic conservation which increase the HRES performance.
- Peak shifting: This approach allows the operation of non-priority loads during off-peak periods. The operation of a shiftable peak load is suspended till an off-peak period when the demand is adequately low [37].
2. Methodology/Procedure
2.1. Energy Access Solutions and Demand-Side Management Strategies
2.2. Social and Economic Sustainability of HRES
2.3. Modelling Tools for Hybrid Energy Access Solutions
3. Conclusions and Policy Directions
- Initially, electrification is identified as a primary factor in improving the standard of healthcare administration in Sub-Saharan Africa and attracting healthcare personnel into rural areas for quality services.
- Available natural and renewable resources were identified as optimal approaches to powering rural healthcare centres. The deployment of mini-grids powered primarily by renewable energy sources and associated technologies is a solution to energy accessibility for healthcare facilities in rural or urban areas with low power supply.
- Small-scale hydropower via mini-grid or off-grid is an energy provision solution in rural areas, which possess the potential to serve healthcare facilities and provide nearby communities with sustainable energy. However, the small-scale hydropower may require modification to suit the local situation and grid size. Thus, higher and more reliable energy access in rural healthcare centres can be achieved through Hybrid Renewable Energy Sources (HRES) microsystems, such as Microgrids incorporated with solar panels and battery storage.
- Providing backup with solar panels and battery storage systems to healthcare facilities and households in urban or rural areas is considered a better, cost-effective, and operationally less-burden alternative than small-scale hydropower. Furthermore, the HRES can be aided by implementing intelligent devices developed from artificial intelligence and internet of things technologies; this allows for combining solar and wind in energy production.
- Technical maintenance of renewable energy microsystems is a necessary procedure to avoid breakdown. Therefore, as a policy measure, the benefitting communities should carry out preventive and corrective maintenance of the system. This approach ensures ownership and responsibility; the preliminary instruction given to the user will help them perform elementary maintenance, such as periodically adding distilled water to the battery.
- Since the local council, which owned the rural healthcare centre, has an insufficient budgetary allocation for the maintenance of HRES, providing funding through public-private-partnership to maintain HRES is a sure approach to prevent the early breakdown of the system. Additionally, the course of installation, training, and enlightenment of the healthcare staff on the usage and maintenance of the HRES can reduce the maintenance cost of the system. One critical component of the HRES is using an energy storage device. The storage system enhances the utilisation of the renewable energy system, reduces the need for a generator set and reduces fuel consumption.
- The adoption of DSM strategies in the HRES deployment in rural healthcare facilities is seen to lessen the start-up cost of installation and improve efficiency. In addition, DSM strategy through an energy management system can be adapted to meet the energy needs of a rural health care centre by minimising carbon emissions and guaranteeing low-cost energy.
- The financial burden of the HRES–DSM system procurement can be limited by leasing the system to the beneficiary. Alternatively, the project can be contracted out to the system operator for continuous energy system operation. As for the rural community, where the finance might be huge to handle or meet the vendor’s requirement, the government can intervene to execute the project as a national development program.
- Lastly, in improving energy access, rural electrification planning is achieved through modelling tools related to energy access modelling. A careful assessment of the features of various tools offers an easy means of identifying and choosing a tool that can give perceptions of the particular research question, adequately model the processes, and retain the indispensable technological and economic properties.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
HRES | Hybrid Renewable Energy Sources | CRT | TV Cathode Ray Tube Television |
DSM | Demand Side Management | LCD | Liquid Crystal Display |
SDG | Sustainable Development Goal | LRHC | Low Rating High Cost |
ECE | Economic Computable Equilibrium | MRMC | Medium Rating Medium Cost |
PHCs | Primary Health Centres | HRLC | High Rating Low-Cost |
WHO | World Health Organisation | kW | KiloWatt |
HRES | Renewable Energy System | NGOs | Non Governmental Organisations |
PV | Photo Voltaic | ARE | Alliance for Rural Electrification |
UN | United Nation | DRE | Decentralised Renewable Energy |
AC | Alternating Current | PPA | Power Purchasing Agreement |
DC | Direct Current | RE | Renewable Energy |
DG | Distributed Generation | RES | Renewable Energy System |
CO2 | Carbon dioxide | UPS | Uninterruptible Power Supply |
RHU | Rural Health Units | LCOE | Levelized cost of energy: LCOE |
PSAT | Power Systems Analysis | COVID-19 | Corona Virus |
HO | Heuristic Optimisation | I & ODS Investment & Operation Support | |
CGE | Computable General Equilibrium | MIP | Mix Integer Programming |
LP | Linear Programming | ABS | Agent Based Simulation |
HOMER | Hybrid Optimisation of Multiple Electric Renewable Energy | ||
CMA-ES | Covariance Matrix Adaptation Evolution Strategy | ||
TETFund | Tertiary Education Trust Fund, Nigeria | ||
NPHCDA | National Primary Health Care Development Agency | ||
NREL | National Renewable Energy Laboratory | ||
REA | Rural Electrification Agency |
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INPUT: Increased financing for energy interventions, including:
| IMPACT OF FINANCING RENEWABLE SOLUTIONS |
OUTPUT: Enhanced elements of energy supply ecosystem
| ENHANCED ENERGY PERFORMANCE |
INTERMEDIATE OUTCOME: Healthcare facility access to modern energy services for electricity and thermal requirements.
| ENERGY ACCESS |
RE Solutions | Modularity & Scalability | Application Services | Deployment Area | Operations & Maintenance Cost | CO2 & Emission? | Seasonal Output? | Backup Available? | Technical Maintenance | Safety & Environ. | Investment Cost/Capital |
---|---|---|---|---|---|---|---|---|---|---|
Natural and Renewable resources [8,14,21,25,33]. | Y | All | U, R | Medium/High | N | Y—Solar, Wind & Hydro | Y | Scheduled & Periodical | H-SL | Medium & High |
Off-grid Installations [26,37] | Y | All | R | Medium | Y/N | Y—Solar, Wind & Hydro | Y | Minimal & Periodical | M-SL | Medium & High |
Fossil Fuel-based [26] | Y | All | U, R | High | Y | N | N | Scheduled & Periodical | L-SL | Medium |
Solar PV with or without Storage, [12,27,34] | Y | All, may exclude HC. | U, R | Low | N | Y | Y | Minimal & Periodical | H-SL | High |
Solar PV Home Systems [23] | Y | Exclude HC | U, R | Low | N | Y | Y | Minimal & Periodical | H-SL | High |
Lithium-ion batteries & Other Storage. [23] | Y | Exclude HC | U, R | Low | N | N | -- | Minimal & Periodical | M-SL | Medium & High |
Hybrid storage units. [23] | Y | Exclude HC | U, R | Low | N | N | -- | Minimal & Periodical | M-SL | Medium & High |
Wind Turbine. [34] | Y | All | R | Low | N | Y | N | Scheduled & Periodical | M-SL | Medium |
Mini-grids. [3,11,26]. | N | All | U, R | Medium | Y—fossil fuel based | Y—Night hours | Y/N | Scheduled & Periodical | M-SL | Medium & High |
Small & Micro Hydro. [29] | Y | All | R | Medium/High | N | Y—Low water heads | N | Scheduled & Periodical | M-SL | Medium & High |
Hybrid RE & Fossil Fuel Systems. [15,26,37,38,48,49] | Y | All | U, R | Medium/High | Y—Minimal | Y | Y | Minimal & Periodical | M-SL | High |
Hybrid Solar PV/Wind/DG/Battery and Wind (HRES). [2,21,36,40,49] | Y | All | U, R | Medium/High | N | Y | Y | Minimal & Periodical | M-SL | High |
Microgrids- Solar PV and Battery storage. [11,15,37]. | Y | All, may exclude HC. | U, R | Low | N | Y | Y | Minimal & Periodical | H-SL | High |
Demand Side Management (DSM). [36,40] | -- | All | U, R | -- | -- | -- | -- | -- | H-SL | Low |
HRES & DSM [36,40] | Y | All | U, R | Medium/High | Y—Minimal | Y | Y | Minimal & Periodical | M-SL | Low & Medium |
S/N | Model | Full Name | Purpose | Developer | Available | Software Type | Method/ Solver | Temporal Resolution | Modelling Horizon |
---|---|---|---|---|---|---|---|---|---|
1 | COMPETES | Comprehensive Market Power in Electricity Transmission and Energy Simulator | I & ODDS | Energy Research Centre of the Netherlands | Only ECA and Partners | AIMMS/GUROBI | Simulation | Hourly | User Define |
2 | COMPOSE | Compare Options for Sustainable Energy | ODS & S | Morten Blarke, ENERGIANALYSE.DK | AC | Standalone, CPLEX or GUROBI | MAP | User Define | User Define |
3 | DER-CAM | Distributed Energy Resources Customer Adoption Model | I & ODDS | Lawrence Berkeley National Laboratory | F | Online:-None Licensed:-GAMS | MAP | Minutes & Hourly | |
4 | DESSTinEE | Demand for Energy Services, Supply and Transmission in Europe | S, I & ODDS | Imperial College London—Iain Staffell, Richard Green | OS | VBA/Excel | Simulation | Hourly | 2050 |
5 | DIETER | Dispatch and Investment Evaluation Tool with Endogenous Renewables | I & ODDS | DIW Berlin—Alexander Zerrahn & Wolf-Peter Schill | OS | Solver + GAMS | LP | Hourly | 1 yr |
6 | EMLab- Generation | Energy Modelling Laboratory—Generation | IDS | TU Delft—Richstein, Chappin, Bhagwat & de Vries | OS | Maven JAVA | ABS | Yearly | 2050 |
7 | EMPIRE | European Model for Power system Investment with Renewable Energy | IDS | NTNU—Christian Skar et al. | UN | Xpress-Mosel | LP | User Define | 40–50 yrs |
8 | EnergyPlan | Sustainable Energy Planning Research Group | S, DIS | Aalborg University | F | Standalone | Simulation | Hourly | 1 yr |
9 | energyPro | energyPro | I & ODDS | EMD International A/S | C | Standalone | AO | Minutes | 40 yrs Max |
10 | ITEM | Energy Transition Model | S | Quintel Intelligence | OS | Online tool | Simulation | 15 min, hr & yr | 2050 |
11 | GEM-E3 | General Equilibrium Model for Economy-Energy-Environment | S | European Commission Funded Multinational Collaboration | GAMS (Solved with PATH) | CGI | 5 yrs | 2030 & 2050 | |
12 | GENESYS | Genetic Optimisation of a European Energy Supply System | IDS | RWTH-Aachen University—Alvarez, Bussar, Cai, Chen, Moraes, etc. | OS | Stand-alone | CMA-ES & HO | Hourly | 2050 |
13 | GridLAB-D | PSAT | US Department of Energy | OS | Standalone | ABS | Seconds | 3–5 yrs | |
14 | HOMER | Hybrid Optimisation of Multiple Energy Resources | I & ODDS | NREL—Peter Lilienthal | C | Standalone | Simulation | Minutes | Multi-year |
15 | HYPERSIM | PSAT | Opal-RT | C | Stand-alone | Simulation | 10 micr sec | User Define | |
16 | iHOGA | Improved Hybrid Optimisation by Genetic Algorithms | I & ODDS | Dr. Rodolfo Dufo-López—University of Zaragoza | ED | Standalone | HO | Hourly | Yearly |
17 | LEAP | Long-range Energy Alternatives Planning | S | Stockholm Environment Institute | AS | Simulation & LP | Yearly | 20–30 Yrs | |
18 | LIBEMOD | Liberalisation MODel for the European Energy Markets | S | Frisch Centre & the Research Department at Statistics Norway | GAMS | ECE | Yearly | 1–2-yrs | |
19 | MESSAGE | Model for Energy Supply Strategy Alternatives and their General Environmental Impact | S, IDS | IIASA | UR | GAMS & ORACLE | LP | User Define | 50–100 yrs |
20 | NEMS | National Energy Modelling System | S | US Energy Information Administration (EIA) | Free & Commercial Veriosn | Simulation | Yearly | 2050 | |
21 | Remove (SOLPH) | Open Energy Modelling Framework | S, I & ODDS | Remove developing group (Reiner Lemoine Institut/ZNES Flensburg/OVGU) | OS | Solver + Python | LP, MIP | Sec to yrs | User Define |
22 | OSeMOSYS | The Open Source Energy Modelling System | IDS | KTH—Howells et al. | OS | GNU MathProg | LP | User Define | User Define |
23 | PLEXUS | PLEXOS Integrated Energy Model | I & ODS, S, | Energy Exemplar—Glenn Drayton | C | Standalone | User Define | User Define | |
24 | POLES | Prospective Outlook on Long-term Energy Systems | S, I & ODDS | CNRS (GAEL Energy), Enerdata, JRC-IPTS | Simulations | Yearly | 2050 | ||
25 | RAPSim | Renewable Alternative Power systems Simulation | PSAT | NES, AUU—Pöchacker, Khatib, Elmenreich et al. | OS | Standalone | Simulations | Minutes | Multi days |
26 | ReEDS | Regional Energy Deployment System | S (& IDS) | NREL | GAMS (Excel & R) | LP | 2 yr period | 2050 | |
27 | REMix | Renewable Energy Mix | I & ODS | DLR | GAMS | LP | Hourly | 2 yrs | |
28 | renpass | Renewable Energy Pathways Simulation System | ODS, S | Frauke Wiese & Gesine Bökenkamp | OS | MySQL, R, RMySQL | Simulations | Hourly | 1 yr |
29 | RETScreen | The RETSCreen Clean Energy Project Analysis Software | IDS, S | Natural Resources Canada | F | Windows with .NET | Simulation | Year/Month/Day | 100 yrs |
30 | SIREN | Sustainable Energy Now Integrated Renewable Energy Network | S | Sustainable Energy Now Inc.—Angus King | OS | Standalone | Simulation | Hourly | 1 yr |
31 | SWITCH | Solar, Wind, Transmission, Conventional Generation & Hydroelectricity | I & Odds | Fripp, Johnston & Maluenda | OS | Python | Map | Hourly | User Define |
32 | Temora | Tools For Energy Model Optimisation & Analysis | S | NC State University—K. Hunter et al. | OS | Solver + Python | LP | Yearly | User Define |
33 | WEM | World Energy Model | S | International Energy Agency | others + Vensim | Simulations | Yearly | 2040 |
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Share and Cite
Olatomiwa, L.; Sadiq, A.A.; Longe, O.M.; Ambafi, J.G.; Jack, K.E.; Abd'azeez, T.A.; Adeniyi, S. An Overview of Energy Access Solutions for Rural Healthcare Facilities. Energies 2022, 15, 9554. https://doi.org/10.3390/en15249554
Olatomiwa L, Sadiq AA, Longe OM, Ambafi JG, Jack KE, Abd'azeez TA, Adeniyi S. An Overview of Energy Access Solutions for Rural Healthcare Facilities. Energies. 2022; 15(24):9554. https://doi.org/10.3390/en15249554
Chicago/Turabian StyleOlatomiwa, Lanre, Ahmad A. Sadiq, Omowunmi Mary Longe, James G. Ambafi, Kufre Esenowo Jack, Toyeeb Adekunle Abd'azeez, and Samuel Adeniyi. 2022. "An Overview of Energy Access Solutions for Rural Healthcare Facilities" Energies 15, no. 24: 9554. https://doi.org/10.3390/en15249554
APA StyleOlatomiwa, L., Sadiq, A. A., Longe, O. M., Ambafi, J. G., Jack, K. E., Abd'azeez, T. A., & Adeniyi, S. (2022). An Overview of Energy Access Solutions for Rural Healthcare Facilities. Energies, 15(24), 9554. https://doi.org/10.3390/en15249554