# Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes

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## Abstract

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## 1. Introduction

#### 1.1. Background and Motivation

#### 1.2. Critical Literature Review

- The existing studies did not provide a thorough review of optimal planning of RAES systems. This includes the optimization process, input data, methods, objective functions, study based on the country, and design constraints.
- The technical challenges of the existing studies were not found by the review papers.
- The advantages and disadvantages of applied methodologies and data uncertainties for RAES optimal planning were not described by the review studies.
- The potential future directions were not introduced for researchers. Since the optimal planning problem of RAES systems is extremely critical, future perspectives should be identified to develop more significant studies.

#### 1.3. Contribution

- Overviewing the optimization problem of RAES systems’ planning.
- Conducting a review on the state of the art in optimal planning of RAES systems.
- Classifying the existing studies on optimal planning of RAES systems.
- Identifying the current technical challenges on optimal planning of RAES systems.
- Outlooking the future research trends in optimal planning of RAES systems.

#### 1.4. Article Organization

## 2. Overview on Optimal Planning of RAES Systems

#### 2.1. System Components

#### 2.2. Input Data

#### 2.3. Objective Functions

#### 2.3.1. Financial Objective Functions

#### 2.3.2. Reliability Objective Functions

#### 2.3.3. Emission and Technical Objective Functions

#### 2.4. Feasibility Constraints

#### 2.5. Operation Strategies

#### 2.6. Solving the RAES Optimal Planning

#### 2.6.1. Metaheuristic Methods

#### 2.6.2. Other Optimization Methods

#### 2.6.3. HOMER Software

## 3. Review on Existing Studies and Technical Challenges

#### 3.1. Hybrid RAES Systems with/without ESS

#### 3.1.1. HOMER Software for Hybrid RAES Systems

#### 3.1.2. Metaheuristic Methods for Hybrid RAES Systems

#### 3.1.3. Non-Metaheuristic Optimization Algorithms for Hybrid RAES Systems

#### 3.2. Clean (Renewable-Storage) RAES Systems

#### 3.2.1. HOMER Software for Renewable-Storage RAES Systems

#### 3.2.2. Metaheuristic Methods for Renewable-Storage RAES Systems

#### 3.2.3. Non-Metaheuristic Optimization Algorithms for Renewable-Storage RAES Systems

#### 3.3. Discussion

#### 3.3.1. Electricity Supply Cost for RAES Systems

#### 3.3.2. Discussions on Methods

#### 3.3.3. Technical Challenges

- High capacity of BES in clean remote area energy supply systems.
- Demand response strategies for optimal planning in RAES systems.
- Robust optimal planning of components for clean RAES systems.
- Neglecting guidelines for customers in RAES systems.
- Neglecting distribution network constraints in the optimal planning model.

## 4. Recent Developments

#### 4.1. EV Charging Stations and Diesel Generator

#### 4.2. Integrated Energy System with Solar PV and Biogas

#### 4.3. Hybrid Energy Storage and PV

#### 4.4. Optimal Configuration

#### 4.5. Accurate Battery Lifetime Estimation and Technology Selection

#### 4.6. Concentrating Solar Power Plant

#### 4.7. Cooperation of a Diesel Generator and Flywheel with Incentive DR

## 5. Future Scopes

#### 5.1. Incentive Demand Response

#### 5.2. Distribution Network Constraints

#### 5.3. Considering Voltage and Frequency Control

#### 5.4. New Software Tools for Optimal Planning of RAES Systems

#### 5.5. Guidelines for RAES Customers

#### 5.6. Feed-in-Tariff in RAES

#### 5.7. Robust Optimal Planning

#### 5.8. Resilient Optimal Planning

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**A general view of the technical roadmap of this review study on optimal planning of RAES systems.

**Table 1.**The mathematical formulation of financial objective functions for the RAES optimal planning problem.

Objective Function | Equation | Equation Number |
---|---|---|

NPC | ${f}_{c1}=\mathrm{min}\left(\mathcal{N}\mathcal{P}\mathcal{C}\right)=\mathcal{N}\mathcal{P}{\mathcal{C}}_{k}+\mathcal{N}\mathcal{P}{\mathcal{C}}_{f}$ | (1) |

$\mathcal{N}\mathcal{P}{\mathcal{C}}_{k}=\mathcal{P}{\mathcal{C}}_{c}+\mathcal{P}{\mathcal{C}}_{m}+\mathcal{P}{\mathcal{C}}_{r}-\mathcal{P}{\mathcal{C}}_{s}$ | (2) | |

$\mathcal{N}\mathcal{P}{\mathcal{C}}_{f}=\left(\frac{{\left(1+r\right)}^{\mathit{n}}-1}{r{\left(1+r\right)}^{\mathit{n}}}\right)\times \left({\displaystyle \sum _{t=1}^{T}}\left(f\left(t\right).{C}_{f}\right)\right)$ | (3) | |

LCOE | ${f}_{c2}=\mathrm{min}\left(\mathcal{L}\mathcal{C}\mathcal{O}\mathcal{E}\right)=\frac{\mathcal{N}\mathcal{P}{\mathcal{C}}_{k}+\mathcal{N}\mathcal{P}{\mathcal{C}}_{f}}{{E}_{p}}\times \frac{d{\left(1+d\right)}^{\mathit{n}}}{{\left(1+d\right)}^{\mathit{n}}-1}$ | (4) |

TAC | ${f}_{c3}=\mathrm{min}\left(\mathcal{T}\mathcal{A}\mathcal{C}\right)={\displaystyle \sum _{t=1}^{T}}\left(f\left(t\right).{C}_{f}\right)+\mathcal{A}{\mathcal{C}}_{k}$ | (5) |

SPP | ${f}_{c4}=\mathrm{min}\left(\mathcal{S}\mathcal{P}\mathcal{P}\right)=\frac{\mathcal{P}{\mathcal{C}}_{c}}{\mathcal{A}\mathcal{P}}$ | (6) |

IRR | ${f}_{c5}=\mathrm{max}\left(\mathcal{I}\mathcal{R}\mathcal{R}\right)$ | (7) |

$-\mathcal{P}{\mathcal{C}}_{c}+{\displaystyle \sum _{y=1}^{Y}}{\mathcal{M}}_{y}\times {\left(\mathcal{I}\mathcal{R}\mathcal{R}\right)}^{y}=0$ | (8) | |

Parameters and variables | $\mathcal{N}\mathcal{P}\mathcal{C}$: Total NPC of the RAES system, $\mathcal{N}\mathcal{P}{\mathcal{C}}_{k}$: NPC of the RAES components, $\mathcal{N}\mathcal{P}{\mathcal{C}}_{f}$: NPC of the fuel consumption, $\mathcal{P}{\mathcal{C}}_{c},\mathcal{P}{\mathcal{C}}_{m},\mathcal{P}{\mathcal{C}}_{r},\mathcal{P}{\mathcal{C}}_{s}$: Present values of capital, maintenance, replacement, and salvation costs, $f$: Amount of fuel consumption, ${C}_{f}$: Fuel price, $T$: Total time period of the planning project, $\mathit{n}$: Project lifetime, $r$: Interest rate, ${E}_{p}$: Total energy demand of the RAES system, $d$: Discount rate, $\mathcal{A}{\mathcal{C}}_{k}$: Annual cost of components, $\mathcal{A}\mathcal{P}$: Annual payment of the RAES system for the external system, ${\mathcal{M}}_{y}$: is the net cash flow in year y. |

**Table 2.**The mathematical formulations of the reliability objective functions for the RAES optimal planning problem.

Objective Function | Equation | Equation Number |
---|---|---|

LPSP | ${f}_{r1}=\mathrm{min}\left(\mathcal{L}\mathcal{P}\mathcal{S}\mathcal{P}\right)=\frac{{E}_{p}+{E}_{d}+{E}_{b,ch}-{E}_{re}-{E}_{f}-{E}_{b,dis}}{{E}_{p}}$ | (9) |

EENS | ${f}_{r2}=\mathrm{min}\left(\mathcal{E}\mathcal{E}\mathcal{N}\mathcal{S}\right)={\displaystyle \sum _{t=1}^{T}}{L}_{p}.{\mathcal{D}}_{p}$ | (10) |

LOLE | ${f}_{r3}=\mathrm{min}\left(\mathcal{L}\mathcal{O}\mathcal{L}\mathcal{E}\right)={\displaystyle \sum _{t=1}^{T}}{\displaystyle \sum _{s\u03f5S}}{F}_{s}.{T}_{s}$ | (11) |

LOEE | ${f}_{r4}=\mathrm{min}\left(\mathcal{L}\mathcal{O}\mathcal{E}\mathcal{E}\right)={E}_{p}+{E}_{d}+{E}_{b,ch}-{E}_{re}-{E}_{f}-{E}_{b,dis}$ | (12) |

SAIFI | ${f}_{r5}=\mathrm{min}\left(\mathcal{S}\mathcal{A}\mathcal{I}\mathcal{F}\mathcal{I}\right)=\frac{\sum {\lambda}_{i}{N}_{i}}{\sum {N}_{i}}$ | (13) |

SAIDI | ${f}_{r6}=\mathrm{min}\left(\mathcal{S}\mathcal{A}\mathcal{I}\mathcal{D}\mathcal{I}\right)=\frac{\sum {U}_{i}{N}_{i}}{\sum {N}_{i}}$ | (14) |

Parameters and variables | ${E}_{re}$: Total energy generation by renewable energy, ${E}_{f}$: Total energy generation by diesel generators, ${E}_{b,dis}$: Total discharged energy generation by battery, ${E}_{b,ch}$: Total charged energy generation by battery, ${E}_{d}$: Total dumped energy, ${L}_{p}$: Average annual load, ${D}_{p}$: Duration of unmet load, ${F}_{s}$: Probability of meeting state s, ${T}_{s}$: Loss of load duration, $S$: all loss of energy states, ${\lambda}_{i}$: Rate of power interruption, ${U}_{i}$: Duration of power outage, ${N}_{i}$: Number of customers for location i. |

**Table 3.**The mathematical formulations of the emission and technical objective functions for the RAES optimal planning problem.

Objective Function | Equation | Equation Number |
---|---|---|

RF | ${f}_{t1}=\mathrm{min}\left(\mathcal{R}\mathcal{F}\right)=\left(1-\frac{{E}_{f}}{{E}_{p}}\right)\times 100$ | (15) |

CE | ${f}_{t2}=\mathrm{min}\left(\mathcal{C}\mathcal{E}\right)=\alpha +\beta {\displaystyle \sum _{t=1}^{T}}{P}_{f}\left(t\right)+\gamma {\left({\displaystyle \sum _{t=1}^{T}}{P}_{f}\left(t\right)\right)}^{2}$ | (16) |

BL | ${f}_{t3}=\mathrm{max}\left(\mathcal{B}\mathcal{L}\right)=1-{D}_{b}$ | (17) |

CCL | ${f}_{t4}=\mathrm{max}\left(\mathcal{C}\mathcal{C}\mathcal{L}\right)$ | (18) |

DE | ${f}_{t5}=\mathrm{min}\left(\mathcal{D}\mathcal{E}\right)={E}_{re}+{E}_{f}+{E}_{b,dis}-{E}_{p}-{E}_{b,ch}$ | (19) |

Parameters and variables | $\alpha ,\beta ,\gamma $: Approximate emission coefficients, ${P}_{f}$: Generated power by diesel generator, ${D}_{b}$: Battery capacity degradation due to charging/discharging cycles and environmental issues |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[63] | Particle swarm optimization | Diesel generator-PV-WT-BES | Island village | Life cycle cost | Power balance Diesel generator output power, Battery constraint | Thailand | 2011 |

[64] | Grasshopper optimization algorithm | Diesel generator-PV-WT-BES | Off-grid community | LCOE | Renewable energy fraction, number of components | Nigeria | 2019 |

[65] | Harmony search algorithm | Diesel generator-PV | Remote community | NPC | LPSP, number of components | Iran | 2017 |

[66] | Particle swarm optimization | Diesel generator-PV-BES | Rural mini-grids | NPC | Power balance, fuel consumption and tank level, curtailment of PV, energy of BES | Kenya | 2016 |

[67] | Particle swarm optimization | Diesel generator-Biomass-PV-WT-BES | Small remote area community | LCOE | LPSP | India | 2017 |

[68] | Particle swarm optimization | Diesel generator-FT-PV-WT-BES-FW | Remote community | LCOE | Power balance, SOC, number of components, power reserve | Australia | 2020 |

[69] | Biogeography based optimization | Diesel generator-PV-WT-Hydro-BES | Remote home | Total cost | Number of components, power balance, SOC | India | 2013 |

[70] | Several algorithms | Diesel generator-PV-WT-BES | Remote village | LCOE | LPSP, power balance, SOC | Egypt | 2019 |

[71] | Hybrid simulated annealing–tabu search | Diesel generator-Biodiesel-PV-WT-BES-FC | Educational Institute | LCOE | Initial cost, unmet load, capacity shortage, fuel consumption, renewable factor, components’ size | Greece | 2012 |

[72] | Particle swarm optimization | Diesel generator-PV-BES-EV | Residential | Lifetime cost | Size of components, unit commitment constraints | India | 2019 |

[73] | Crow search algorithm | Diesel generator-PV-FC | Remote area community | NPC | LPSP, renewable energy portion | Iran | 2020 |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[74] | Multi-objective genetic algorithm | Diesel generator-PV-WT-BES | Not specified | LCOE, CE | Not specified | Spain | 2011 |

[75] | Multi-objective genetic algorithm | Diesel generator-PV-WT-BES | Residential island | Cycle cost, CE, RF | SOC | China | 2014 |

[76] | Non-dominated sorting genetic algorithm II | Diesel generator-PV-WT-BES | Island | TAC, LPSP and emission | Number of components, height of WTs, tilt angle of PV, SOC | China | 2017 |

[77] | Multi-objective line-up competition algorithm | Diesel generator-PV-WT-BES | Residential | Total TAC, total greenhouse gas | Energy of BES, power of Diesel generator, number of components, energy supply constraint | Not specified | 2017 |

[78] | Multi-objective crow search algorithm | Diesel generator-PV-FC | Not specified | NPC and LPSP | Number of components, tank energy | Iran | 2019 |

[79] | Multi-objective grey wolf algorithm | Diesel generator-PV-WT-Tidal-BES | Flinders island | LCOE, emission | Number of components, operating reserve | Australia | 2018 |

[80] | Fuzzy artificial bee colony optimization mechanism | Diesel generator-PV-WT-BES | An edge region | Annualized cost, emission | Number of components, battery’s energy | USA | 2020 |

[81] | Non-dominated sorting genetic algorithm II | Diesel generator-PV-BES | Island | LCOE, CE, grid voltage deviation | Number of components, battery’s energy | Indonesia | 2018 |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[82] | Deterministic algorithm | Diesel generator-PV-WT-BES | Not specified | NPC | Power balance, SOC, number of components | Senegal | 2011 |

[83] | Iterative approach | Diesel generator-PV-WT-BES | Residential | Energy cost | Energy of battery | Algeria | 2014 |

[84] | Developed method | Diesel generator-PV | Campus | LCOE | Not specified | Burkina Faso | 2015 |

[85] | Decision support technique | Diesel generator-PV-WT-BES | Remote village | NPC | LPSP | India | 2007 |

[86] | MILP with GAMS/CPLEX | Diesel generator-PV-WT-BES | Not specified | LCOE | Minimum Diesel generator power, battery’s energy, power balance | Portugal | 2015 |

[87] | Triangular Aggregation Model and the Levy-Harmony Algorithm | Diesel generator-PV-WT-BES | Island village | COE, TAC, loss of renewable energy, LOLP, emission, LPSP | SOC, Diesel generator output power, LPSP | Australia | 2018 |

[88] | CPLEX optimizer in JAVA | Diesel generator-PV-BES | Ten households in rural area | Capacity of battery | SOC, Diesel generator’s output power | Australia | 2018 |

[89] | Reformed electric system cascade analysis | Diesel generator-PV-WT-BES | Residential community with 100 homes | Defined based on constraints | Final Excess Energy, Renewable Energy Fraction, LPSP, Annual System Cost | USA | 2019 |

[90] | MINLP in GAMS using BARON solver | Diesel generator-PV-BES | A remote 38-bus distribution network | Annualized costs | Power flow, active and reactive power mismatch constraints, system frequency | Not specified | 2019 |

[91] | Dynamic programming algorithm | Diesel generator-PV-BES | Not specified | Total cost per day | Power and energy of BES | USA | 2015 |

[92] | Stochastic MINLP optimization with GAMS | Diesel generator-PV-WT-BES | Not specified | NPC | Power balance, Diesel generator constraints, operating reserve, BES constraints, budget constraint | Not specified | 2018 |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[100] | Firefly-inspired algorithm | PV-WT-BES | Group of twenty households | COE | Energy of battery, number of components, load dissatisfaction rate | Algeria | 2017 |

[101] | Water cycle algorithm | Biogas-PHES-PV-BES | Radio transmitter station | NPC | LPSP, number of components, SOC, upper reservoir volume | India | 2019 |

[102] | Four algorithms | PV-WT-BES PV-WT-FC | Not specified | TAC | Number of components, energy of tank and battery | Iran | 2014 |

[103] | Flower pollination optimization algorithm | PV-WT-FC | Rustic | NPC | Number of components | Egypt | 2020 |

[104] | Genetic algorithm | PV-WT-BES | Remote community (2240 home with 4440 population) | NPC | SOC, EENS | India | 2016 |

[105] | Discrete harmony search | MHP-Biogas-Biomass-PV-WT-BES | Remote rural households (723 homes with 3031 population) | TAC | Unmet load, number of components, energy of BES | India | 2017 |

[106] | Particle swarm optimization | PV-thermal, WT, microturbine, thermal storage, backup natural gas boiler | Not specified | TAC | LPSP, SOC of energy storage systems, thermal power, number of components | Iran | 2019 |

[107] | Hybrid harmony search and simulated annealing algorithm | Bio Diesel-PV-WT-BES | Five typical residential building | Life cycle cost | Number of components, power balance, SOC | Iran | 2018 |

[108] | Particle swarm optimization | PV-WT-Tidal-BES | Remote house | NPC | Number of components, reliability, SOC | France | 2019 |

[109] | Hybrid grey wolf optimizer-sine cosine algorithm | PV-WT-FC | Residential-commercial center | lifespan cost of hybrid system | Load interruption probability, number of components, energy at tank | Iran | 2020 |

[110] | Improved bee algorithm | PV-WT-BES-FC-Reverse Osmosis Desalination | Desalination systems and community load | Total life cycle cost | LPSP, energy at hydrogen tank, SOC, number of components | Iran | 2018 |

[111] | Particle swarm optimization | PV-WT-BES | Single house | NPC | Power balance, number of components | Australia | 2019 |

[112] | Particle swarm optimization | Biogas-PV-BES | Residential | LCOE | Constraint on deficit power of PV | Kenya | 2017 |

[113] | Whale optimization algorithm | PV-WT-FC-Tidal | Remote region | NPC | Load deficit probability Size of components | Iran | 2020 |

[114] | Four algorithms | PV-WT-BES-PHS | Remote island | NPC | Number of components, battery’s energy and SOC | China | 2020 |

[115] | Genetic algorithm | PV-WT-PHS | Coastline communities | Life cycle cost | Not specified | Nigeria | 2020 |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[116] | Multi-objective particle swarm optimization | PV-WT-BES | Residential | LPSP, LOEP, volatility, life cycle cost | Number of components | China | 2017 |

[117] | Multi-objective grey wolf algorithm | PV-WT-BES | Rural telecom tower | COE, LPSP, DE | SOC | India | 2020 |

[118] | Multi-objective grey wolf algorithm | PV-WT-BES-PHS | Isolated farmstead | COE, LPSP | Energy of battery and pump-storage hydro | Algeria | 2019 |

[119] | Multi-objective genetic algorithm | PV-WT-BES-FC | Not specified | NPC, excess energy, life cycle emission | Number of components, energy of tank | Australia | 2015 |

[120] | Imperial competitive algorithm | PV-WT-FC | Not specified | Total cost, emission | Equivalent loss factor, angle of PV array, number of components, energy stored at tank | Iran | 2015 |

[121] | Multi-objective particle swarm optimization | PV-WT-Hydro-PHS | Not specified | LPSP, LCOE, curtailment rate of wind and PV power | Not specified | China | 2020 |

[122] | Multi-objective genetic algorithm | PV-WT-BES | A residential home with four occupants | Life cycle cost, embodied energy, LPSP | SOC | USA | 2014 |

[123] | Multi-objective particle swarm optimization | PV-WT-FC | Not specified | TAC, LOEE, LOLE | Energy at tank, number of components, PV tilt angle | Not specified | 2016 |

[124] | Non-dominated sorting genetic algorithm II | PV-BES-FC | Residential (10 houses) | LPSP, system cost, potential energy waste | Number of components | China | 2019 |

[125] | Mutation adaptive differential evolution | PV-BES | Rural area | Life cycle cost, LOLP, LCOE | SOC | Malaysia | 2020 |

Ref. | Applied Method | System Components | RAES Type | Objective Function | Feasibility Constraints | Country | Year |
---|---|---|---|---|---|---|---|

[126] | ε-constraint method | PV-WT-BES-FC | Not specified | NPC, LPSP, DE | SOC, energy in hydrogen tank, number of components | Not specified | 2018 |

[127] | Hybrid multi-criteria decision-making method | PV | Water pumping | Life cycle cost, LOLP, excess water volume | Not specified | Malaysia | 2018 |

[128] | Sensitivity analysis | PV-WT-BES-PHS | Remote island | Life cycle cost | Not specified | Hong Kong | 2014 |

[129] | Simulink Design Optimization | PV-BES-FC | Not specified | Cost | Not specified | Spain | 2013 |

[130] | Iterative technique | PV-WT-BES | Remote residential household | LPSP and LCOE | SOC, number of components | Algeria | 2011 |

[131] | Power Pinch Analysis | PV-BES | Remote community | Cost | Not specified | Bhutan | 2017 |

[132] | Object-Oriented Programming | PV-WT-BES | Not specified | NPC | LPSP, SOC | Algeria | 2014 |

[133] | Probabilistic simulation | PV-BES | A refrigerator used for medical supply in remote area | Loss of load hour, energy not supplied | Not specified | USA | 1998 |

[134] | Linear programming based on a cascade calculation | PV-WT-Tidal-BES | Island | Equivalent loss Factor | SOC | France | 2016 |

[135] | Enumerative method | PV-BES | House | LCOE | Unmet load percentage, number of days of autonomy | Spain | 2018 |

[136] | Pattern search-based optimization | PV-WT-BES | Not specified | Total system cost | SOC, load constraint for DR, EENS, energy index of reliability | USA | 2014 |

[137] | Iterative method in MATLAB | PV-WT-BES-FC | Pumping system (centrifugal pump) | Deficiency Power Supply, NPC | SOC, tank energy | Tunisia | 2018 |

[138] | Iterative simulation-optimization | PV-WT-BES-FC | Not specified | LCOE | LOLE | Iran | 2016 |

[139] | An iterative method | PV-WT-BES | Ten houses in a remote island | NPC | LPSP, COE | China | 2019 |

[140] | MILP | PV-WT-BES | Remote area mountain lodge | NPC | Energy of BES, power balance | Italy | 2020 |

[141] | Logical approach | PV-WT-BES | Remote community | NPC | Number of components | South Korea | 2016 |

[142] | MILP with CPLEX solver in GAMS | PV-WT-BES | Forestry camp | NPC | BES energy and charge/ discharge, demand response constraint | Iran | 2017 |

[143] | Stochastic optimization | WT-concentrating solar power (CSP) plant-BES | Island | Overall cost | SOC, power balance, output power of components | China | 2020 |

[144] | Sensitivity based method | PV-WT-FC-PHS | University | RES fraction | Not specified | Cyprus | 2020 |

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**MDPI and ACS Style**

Khezri, R.; Mahmoudi, A.; Aki, H.; Muyeen, S.M. Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes. *Energies* **2021**, *14*, 5900.
https://doi.org/10.3390/en14185900

**AMA Style**

Khezri R, Mahmoudi A, Aki H, Muyeen SM. Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes. *Energies*. 2021; 14(18):5900.
https://doi.org/10.3390/en14185900

**Chicago/Turabian Style**

Khezri, Rahmat, Amin Mahmoudi, Hirohisa Aki, and S. M. Muyeen. 2021. "Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes" *Energies* 14, no. 18: 5900.
https://doi.org/10.3390/en14185900