# Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies

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## Abstract

**:**

_{2}) emissions. The maximum NPC and CO

_{2}reduction in comparison with the base case (with incentive policies) are 22.87% and 56.13%, respectively. The simulations are conducted using the hybrid optimization model for electric renewables (HOMER) software.

## 1. Introduction

_{2}) emissions. Furthermore, the conversion of only one-third of fuel energy into electrical power in bulk power generation units, compounded with the high voltage transmission network’s poor efficiency, makes microgrids (MGs) in distribution networks more viable [1,2]. Realization of MGs, as one of the main components of smart grids, has become an emergent research area in the electrical industry [3]. Due to the high investment cost of the local energy resources, however, realizing the MG concept to satisfy the load is not a very practical plan in developing countries. Therefore, it seems that incentive policies should be defined by the government to encourage investors to realize the renewable energy sources (RESs)-based MG. Hence, this paper aims to formulate an optimal plan for a RESs-based MG considering the incentive policies which in turn decrease the net present cost (NPC) and CO

_{2}emission.

_{2}emissions reduction, and incentive policies. Therefore, this study investigates the planning problem of a renewable-energy-based MG for an educational complex in Iran considering incentive policies. Three new incentive policies, appropriate for developing as well as developed countries, are developed in which the NPC and the quantity of CO

_{2}emissions are taken into account. It should be noted that the considered incentive policies are technically feasible, scalable, and implementable in real power systems. Therefore, the following are the key contributions of this paper:

- Tackling the incentive policies of the governments in the optimal planning problem formulation of grid-connected RES-based MGs;
- Determining the optimum size of the RES-based MG’s components considering three new incentive policies;
- Investigating the social, economic, and technical impacts of grid-connected RES-based MGs in developing countries;
- Proposing a strategy for implementing incentive policies in commercial software.

## 2. System Modeling

#### 2.1. System Components Modeling

#### 2.1.1. Solar Photovoltaic Panel Modeling

#### 2.1.2. Diesel Generator Modeling

#### 2.1.3. Battery System

_{ESS,rated}, Δt, ζ

_{c}, and ζ

_{d}are the nominal energy capacity, charge/discharge time, and charging and discharging efficiencies of the battery, respectively.

_{1}); in contrast, the energy that is chemically bonded and hence not immediately available for transmission is known as bound energy (Q

_{2}). Hence, one could write:

_{1}+ Q

_{2}

^{−1}and 0.305, respectively. Similarly, the maximum amount of power that the storage may discharge over a given period (∆t) can be computed by [22]:

#### 2.1.4. Converter

#### 2.2. Economical Modeling

#### 2.2.1. Net Present Cost

#### 2.2.2. Total Annualized Cost

#### 2.2.3. Renewable Fraction

#### 2.3. CO_{2} Emissions Calculation

_{2}emissions from two points of view as follows:

## 3. Input Data

#### 3.1. The Solar Radiation

#### 3.2. Load Consumption

#### 3.3. System Description and Requirements

- To generate more electricity, 10 batteries are connected in series, forming a battery string.
- The batteries’ initial and minimum SOCs are set to 100% and 40%, respectively.

**Table 3.**The MG components description and specification [50].

Item | Specification | Item | Specification |
---|---|---|---|

- PV panel
| Minimum load ratio (%) | 30 | |

Model | MF100-EC4 | Lifetime | 15,000 h |

Rated power | 250 kW | - Battery
| |

Capital cost (USD) | 7300/kW | Type | Surrette-6CS25P |

Replacement cost (USD) | 2974/kW | Capital cost (USD) | 1229/single cell |

O&M cost (USD) | 10/year | Replacement cost (USD) | 1229/single cell |

Temperature coefficient | −0.5%/°C | O&M cost (USD) | 10/year |

De-rating factor (%) | 80 | - Inverter
| |

Lifetime | 25 years | Type | Leonics GTP519S |

- Diesel generator
| Rated power | 900 kW | |

Type | Perkins | Capital cost (USD) | 300/kW |

Rated power | 250 kVA | Replacement cost (USD) | 300/kW |

Capital cost (USD) | 500/kW | O&M cost (USD) | 10/year |

Replacement cost (USD) | 500/kW | Efficiency (%) | 90 |

O&M cost (USD) | 0.03/hours | Lifetime | 10 years |

**Table 4.**Grid purchase and sell tariffs [50].

Selling Energy Cost (USD/kWh) | Buying Energy Cost (USD/kWh) | |
---|---|---|

Off-peak | 0.16 | 0.0011 |

Normal | 0.16 | 0.0047 |

Peak | 0.16 | 0.0155 |

#### 3.4. System Control and Constraints

- The project lifetime is considered as 10 years with an annual discount rate of 18%, and an inflation rate of 19% [51].
- The system’s fixed capital cost and fixed O&M cost are considered as USD 10,000 for the entire project, and 10 USD/year, respectively.
- A maximum annual capacity shortage restriction is established throughout the simulation process. This value is set to 0 to assess the system’s ability to deliver peak demand even in the event of a short fault or interruption.
- The penalty for CO
_{2}pollution is considered 50 (USD/t). - The discharge efficiency is assumed to be unity.
- The operational reserves are defined as 10% of hourly loads and 25% of PV output.

## 4. Simulation Results

_{2}emissions. To this end, the first sub-section investigates the impact of RES-based MG generation on NPC and CO

_{2}emissions. Then, the impact of incentive policies on the MG design from technical and economic points of view is investigated. The detailed methodology used for analysis and modeling is shown in Figure 6. The general description of the figure can be explained as follows:

- The technical features of PV panels, DGs, batteries, and converters, as well as their O&M and capital costs, are fed into HOMER software as input data.
- One of the incentive policies is chosen.
- Various sizes of the components are defined as a search space for the problem.
- An optimum solution is determined by utilizing the following data: temperature data, daily average solar radiation with the clearness index, system constraints and project economics, project lifetime, the main grid parameters, total load, and sensitivity variables.
- Optimization is completed for a different combination of devices.

#### 4.1. Results of the MG Design without Considering Incentive Policy

#### 4.1.1. Base Case

_{2}emissions are calculated to be 734,498 kg/year, while a DG’s total fuel consumption is 106,417 L. Figure 7 shows the energy contributions from the main grid, energy from the DG, total load, and excess energy for July. From Figure 7, it can be seen that excess energy is increased when a DG provides the load by generating more electrical power than the actual demand. As a result, it may be regarded as a financial benefit in countries such as Iran, where diesel is a national product and a low-cost fuel. However, to reduce air pollution, DG usage should be kept to a minimum.

#### 4.1.2. The Base Case with at Least 20% Penetration Rate of RESs

_{2}emission and consumes 81,453 L of fuel. This scenario reduces CO

_{2}emissions compared to the base case without RES penetration because of the increased contribution of RESs and lower consumption of diesel fuel. The NPC for this system, however, is significantly higher due to the fact that the initial capital cost, replacement cost, and operating cost are all higher than the base scenario. The optimization results of the MG planning for this case are presented in Table 6. Figure 8 depicts the contribution of MG components to meet demand, main grid energy, total load, and excess energy for May.

#### 4.1.3. The Base Case with at Least 40% Penetration Rate of RESs

_{2}emissions, in this case, are 546,175 kg/year, while the excess energy is 91,232 kWh/year (7.35 percent). The DG’s total fuel consumption is 63018 L, and 26,020 kWh of MG energy is sold to the main grid. The MG component generations and status for November are shown in Figure 9. As shown in Figure 9, the battery is charged by the PV panels, but when the PV generation is insufficient to meet the load, the battery is discharged. In this case, the DG will meet the load and maintain a stable battery charge level.

#### 4.2. Incentive Policies’ Results

- A.
- Reducing the investment cost of MG equipment;
- B.
- Increasing the price of selling energy by the MG to the main grid;
- C.
- Reducing the price of purchasing energy by the MG from the main grid.

_{2}emissions is 586,959 kg/year, and the total fuel consumption by a DG is 81,453 L. While the components’ contributions in this scenario are the same as the base case with a 20% RES penetration level, the NPC is decreased in response to the definition of an incentive scheme.

_{2}emissions is 322,182 kg/year is significantly lower than the other cases due to RESs’ higher penetration. The system is also financially efficient, with a 138,834 kWh energy sell-back rate to the main grid.

_{2}emissions is 586,959 kg/year. The amount of increase in CO

_{2}emissions, in this case, is increased due to the lower RES penetration rate than in case A.1.

_{2}emissions are 459,834 kg/year, and the total fuel consumption by the DG is 66,163 L.

_{2}emissions are 586,959 kg/year, while the total fuel use by the DG is 81453 L. Furthermore, the excess energy is 2.03% (22,787 kWh/year) of the total load, with 108 kWh sold to the main grid.

_{2}emissions are 455,400 kg/year. The DG consumes 65,756 L of fuel in total.

#### 4.3. Discussion

_{2}emissions is shown in Table 11. It shows that at a lower RES penetration rate (i.e., 20%), the scenarios reduce CO

_{2}emissions by the same amount. However, in terms of NPC and COE reductions, scenarios A, B, and C, respectively, have the highest NPC and COE reduction compared to the base case. Furthermore, according to Table 10, for an RES penetration level of at least 40%, scenario A gives leads to the highest reduction in NPC and COE. For scenarios B and C with an increasing RES penetration rate, not only does NPC not decrease, but it also increases due to the high initial cost of the equipment. Hence, these two incentive policies are not effective in decreasing NPC in systems with at least a 40% RES penetration. As a result, the proposed incentive policy in scenario A is suggested as the effective incentive policy from NPC and COE points of view. At this level of RES penetration, again, scenario A leads to a maximum reduction in CO

_{2}emissions; and between scenarios B and C, scenario C has the better performance. This is due to the fact that compared to the other cases, case A has the highest PV system penetration rate. Consequently, as shown in Table 11, reducing the investment cost of MG’s equipment may be suggested as an effective incentive policy to encourage customers to utilize MG-produced energy rather than purchasing electricity from the main grid.

## 5. Uncertainty in Key Variables

## 6. Conclusions

_{2}emission of the best plan are USD 4.94 million and 734,498 kg/year. Then, three incentive policies are defined, where for each of them, two scenarios consisting of at least 20% and 40% penetration rate of RESs are proposed. The main conclusions from applying these incentive policies to the planning problem of the MG are as follows:

- The case with an incentive policy for the investment cost of the MG’s resources has the maximum impact on the NPC reduction.
- The maximum CO
_{2}and NPC reduction occurred in the case of reducing the investment cost of the MG’s equipment. - The sensitivity analysis results, carried out based on a variation of some parameters, including the expected inflation rate, the PV lifetime, DG fuel price, and optimal reserve show a significant influence of the nominal discount rate, expected inflation rate, and PV lifetime on the NPC.
- The considering incentive policy for investors has resulted in increasing RES penetration and minimizing the dependence on harmful emissions and fossil fuels. Finally, it should be noted that the present work fails to consider uncertainties in load and weather data, which may affect simulation results. Furthermore, the results for the high penetration of inverter-based sources should consider technical aspects regarding stability rather than the economical perspective.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

Parameters | ||

$A$ | Fuel curve intercept coefficient | [unit/hour/kW] |

c | Battery capacity ratio | - |

${\mathrm{D}}^{\mathrm{PV}}$ | PV de-rating factor | [%] |

E_{ESS,rated} | Nominal energy capacity | [Ah] |

E^{Supplied} | Total supplied electrical | [kW] |

${F}_{t}^{\mathrm{DG}}$ | Fuel consumption of diesel generator | [kWh/L] |

i | Annual interest rate | [%] |

I_{Bat} | Battery current | [A] |

k | Battery rate constant | [h-1] |

N | Project lifetime | [year] |

p(t) | Battery power | [kW] |

${\overline{\mathrm{P}}}^{\mathrm{DG}}$ | The generator’s rated capacity | [kW] |

${\mathrm{P}}^{\mathrm{Demand}}$ | Total demand of the MG | [kW] |

${\overline{\mathrm{P}}}^{\mathrm{PV}}$ | The PV array’s rated capacity | [kW] |

$S{R}^{\mathrm{PV\_Stan}.}$ | Incident radiation at standard test conditions | [kW/m^{2}] |

${T}^{\mathrm{PV\_Stan}.}$ | Cell temperature under standard test conditions | [°C] |

V_{Bat} | Battery voltage | [V] |

α_{p} | Temperature coefficient of power | [%/°C] |

ζ_{c} | Charging efficiencies | [%] |

ζ_{d} | Discharging efficiencies | [%] |

Variables | ||

B | Fuel curve slope | [units/hour/kW] |

E^{Production} | Non-RES production | [kWh/year] |

Q | Total quantity of energy stored at the start of the time step | [kWh] |

Q_{1} | Available energy | [kWh] |

Q_{1,end} | Available energy at the end of Δt | [kWh] |

Q_{2} | Bound energy | [kWh] |

Q_{2,end} | Bound energy the end of Δt | [kWh] |

Q_{Bat} | Battery charge | [kWh] |

Q_{Bat,0} | Initial battery charge | [kWh] |

Q_{max} | Total capacity of the storage bank | [kWh] |

$S{R}_{t}^{\mathrm{PV}}$ | Solar radiation incident on the PV array in the current time step | [kW/m^{2}] |

${T}_{t}^{\mathrm{PV}}$ | PV array temperature in the present time step | [°C] |

Δt | Charge/discharge time | [hour] |

${\eta}^{\mathrm{Inverter}}$ | Inverter efficiency | [%] |

Decision Variable | ||

${C}^{\mathrm{Annual}}$ | Total annual NPC | [USD/year] |

${P}_{t}^{\mathrm{DG}}$ | Electrical output of the generator | [kW] |

${P}_{t}^{\mathrm{in\_inv}.}$ | Input power of inverter | [kW] |

${P}_{t}^{\mathrm{out\_inv}.}$ | Output power of inverter | [kW] |

${P}_{t}^{\mathrm{PV}}$ | Output power from panels | [kW] |

Acronyms | ||

CO_{2} | Carbon Dioxide | - |

DG | Diesel generator | - |

GHG | Greenhouse gas | - |

LCOE | levelized cost of energy | [USD] |

MG | Microgrid | - |

NPC | Net present cost | [USD] |

O&M | Operation and Maintenance | [USD] |

PV | Photovoltaic | - |

RF | Renewable fraction | [%] |

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**Figure 1.**The average radiation and the amount of energy [49].

Ref. | MG Mode | Component | Optimization Tool | Objective Functions | Incentive Policy | |
---|---|---|---|---|---|---|

Total Cost Minimization | Pollution Emissions Minimization | |||||

[18] | Stand-alone | WT/PV/Battery | Genetic algorithm (GA), PSO and multi-objective PSO algorithms, and HOMER software | ✓ | - | - |

[19] | Stand-alone | PV/WT/Battery | HOMER and GAMS | ✓ | - | - |

[20] | Stand-alone | PV/DG/Battery | HOMER | ✓ | - | - |

[21] | Grid-connected/Stand-alone | DG/PV/WT/Micro/Hydro | HOMER | ✓ | - | - |

[22] | Stand-alone | Biogas generator/PV/DG/WT/Battery | HOMER | ✓ | - | - |

[23] | Stand-alone | PV/DG | Crow search algorithm | ✓ | ✓ | - |

[24] | Stand-alone | PV/WT/DG/Battery | HOMER | ✓ | - | - |

[25] | Stand-alone | PV/WT/Hydrogen storage/Battery | Hybrid chaotic search, harmony search, simulated annealing algorithms | ✓ | - | - |

[12] | Grid-connected/Stand-alone | PV/WT/Biogas generator/Fuel cell | HOMER | ✓ | ✓ | - |

[26] | Stand-alone | PV/WT/DG/Biogas generator/Battery | Artificial bee colony (ABC), PSO algorithms, and HOMER | ✓ | - | - |

[27] | Grid-connected | PV/WT/Biogas generator/Battery | HOMER | ✓ | - | - |

[28] | Stand-alone | WT/PV/Battery/Biomass generator | Multi-objective GA, epsilon multi-objective genetic algorithm (ε-MOGA) | ✓ | - | - |

[29] | Stand-alone | PV/Diesel/Battery | HOMER | ✓ | - | - |

[30] | Grid-connected | PV/Biomass gasifiers/Battery | HOMER | ✓ | - | - |

[31] | Grid-connected | PV/DG/Battery | HOMER | ✓ | - | - |

[32] | Grid-connected | PV/WT/Battery | MOPSO and MOGA | ✓ | - | - |

[33] | Grid-connected | PV/WT/Microturbine/Fuel cell/Battery | Improved differential evolutionary and PSO techniques | ✓ | ✓ | - |

[34] | Grid-connected | PV/WT/Battery/Fuel cell/Electrolyzer/Hydrogen tank | HOMER | ✓ | - | - |

[35] | Stand-alone | PV/DG/Battery | HOMER | ✓ | ✓ | - |

[36] | Stand-alone | PV/WT/Battery | HOMER | ✓ | - | - |

[37] | Stand-alone | PV/WT/Battery/DG | HOMER | ✓ | ✓ | - |

This paper | Grid-connected | PV/DG/Battery | HOMER | ✓ | ✓ | ✓ |

Particulars | Total Energy Consumption (%) |
---|---|

Uninterrupted power supply | 35 |

Air conditioning | 26 |

Air handling unit | 10 |

Lighting | 15 |

Others | 14 |

Scenario | Grid (kW) | DG (kW) | PV (kW) | Converter (kW) | Energy Storage (n) | Initial Capital (USD in Millions) | Operating Cost (USD/year) | COE (USD) | NPC (USD in Millions) | RF (%) |
---|---|---|---|---|---|---|---|---|---|---|

Base case | 600 | 350 | 0 | 0 | 0 | 0.35267 | 187,276 | 0.193 | 4.94 | 0 |

**Table 6.**The best optimization results of the MG planning for the base case with at least 20% penetration rate of RESs.

Scenario | Grid (kW) | DG (kW) | PV (kW) | Converter (kW) | Energy Storage (n) | Initial Capital (USD in Millions) | Operating Cost (USD/year) | COE (USD) | NPC (MUSD) | RF (%) |
---|---|---|---|---|---|---|---|---|---|---|

Base case with at least 20% penetration rate of RESs | 600 | 380 | 164 | 248 | 0 | 1.48 | 158,079 | 0.210 | 5.30 | 20.1 |

**Table 7.**The best optimization results of the MG planning for the base case with at least 40% penetration rate of RESs.

Scenario | Grid (kW) | DG (kW) | PV (kW) | Converter (kW) | Energy Storage (n) | Initial Capital (USD in Millions) | Operating Cost (USD/year) | COE (USD) | NPC (MUSD) | RF (%) |
---|---|---|---|---|---|---|---|---|---|---|

Base case with at least 40% penetration rate of RESs | 600 | 380 | 362 | 322 | 120 | 3.07 | 129,616 | 0.246 | 5.91 | 40.1 |

Scenario | RF (%) | CO_{2} Emissions (kg/year) | COE (USD) | NPC (MUSD) |
---|---|---|---|---|

Base case | 0 | 734,498 | 0.193 | 4.94 |

Base case with at least 20% penetration rate of RESs | 20.1 | 586,959 | 0.210 | 5.30 |

Base case with at least 40% penetration rate of RESs | 40.1 | 546,175 | 0.246 | 5.91 |

Plan | Grid (kW) | DG (kW) | PV (kW) | Converter (kW) | Battery (n) | Initial Capital (USD in Millions) | Operating Cost (USD) | COE (USD/kWh) | NPC (USD in Millions) | RF (%) |
---|---|---|---|---|---|---|---|---|---|---|

A.1 | 600 | 380 | 164 | 248 | 20 | 1.2 | 129,544 | 0.172 | 4.03 | 20.1 |

B.1 | 600 | 380 | 164 | 248 | 0 | 1.48 | 128,424 | 0.183 | 4.29 | 20.1 |

C.1 | 600 | 380 | 164 | 248 | 40 | 1.5 | 128,390 | 0.183 | 4.31 | 20.1 |

Plan | Grid (kW) | DG (kW) | PV (kW) | Converter (kW) | Battery (n) | Initial Capital (USD in Millions) | Operating Cost (USD) | CoE (USD/kWh) | NPC (USD in Milliona) | RF (%) |
---|---|---|---|---|---|---|---|---|---|---|

A.2 | 600 | 400 | 661 | 451 | 140 | 3.17 | 29,182 | 0.144 | 3.81 | 60.8 |

B.2 | 600 | 410 | 377 | 367 | 80 | 3.09 | 98,270 | 0.217 | 5.24 | 40.2 |

C.2 | 600 | 410 | 390 | 346 | 0 | 3.05 | 96,068 | 0.217 | 5.26 | 41.0 |

Case A | Case B | Case C | ||||
---|---|---|---|---|---|---|

A.1 | A.2 | B.1 | B.2 | C.1 | C.2 | |

NPC compared to the base case (%) | −18.42 | −22.87 | −13.15 | +0.06 | −12.75 | +0.06 |

COE compared to the base case (%) | −10.88 | −25.38 | −0.01 | +12.43 | −0.01 | +12.43 |

CO_{2} emissions compared to the base case (%) | −20.8 | −56.13 | −20.8 | −37.39 | −20.8 | −37.99 |

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**MDPI and ACS Style**

Amini, S.; Bahramara, S.; Golpîra, H.; Francois, B.; Soares, J.
Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies. *Energies* **2022**, *15*, 8285.
https://doi.org/10.3390/en15218285

**AMA Style**

Amini S, Bahramara S, Golpîra H, Francois B, Soares J.
Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies. *Energies*. 2022; 15(21):8285.
https://doi.org/10.3390/en15218285

**Chicago/Turabian Style**

Amini, Shiva, Salah Bahramara, Hêmin Golpîra, Bruno Francois, and João Soares.
2022. "Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies" *Energies* 15, no. 21: 8285.
https://doi.org/10.3390/en15218285