# Feasibility Assessment of Rural Hybrid Microgrid Using Canal-Based Microhydel Resources: A Case Study of Renala Khurd Pakistan

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## Abstract

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## 1. Introduction

_{2}emissions. Results show that the hybrid system has an efficiency between 15–75%, while the standalone system has just around 10% efficiency [13].

#### 1.1. Hybrid Distributed Generation and Associated Potential Challenges

#### 1.2. Contribution

- A comprehensive framework and feasibility study for tapping the potential of micro-hydro into the existing distrusted generation sources for microgrid operation near the canal areas;
- Development of the optimal sizing of trigeneration sources using a genetic algorithm with detailed problem formulation of the objective functions and a set of constraints based on the fixed and variable generation sources to reduce the operational, net present cost, and cost of energy;
- Conduction of detailed economic assessment of various configurations and combinations (solar–grid, grid–hydro, grid–Solar–hydro) for detailed comparative analysis for sustainable system design;
- Reliability assessment of various configurations in terms of unmet load, grid units purchased, percentage of renewable energy and grid utilized, and excess power generation.

## 2. Methodology

#### 2.1. Load Profile Development

_{NPC}= Net present cost (NPC), C

_{ann,t}= Total annual cost, CRF = Capital recovery factor, i = Real Interest rate, and Nproj = Project lifetime in year, COE (cost of energy) is the ratio of total useful annual energy cost to total energy produced by the system [30].

#### 2.2. Annual Solar Radiation

^{2}/day) and the minimum irradiance is in December at 3.080 (kWh/m

^{2}/day). The annual daily solar irradiance is shown in Figure 6 below.

#### 2.3. Analysis of Utilizing Multiple Energy Resources for Hybrid Microgrid

- System sizing using HOMER Pro software along with economic analysis;
- System sizing using the genetic algorithm (GA) and attained optimum sizing values will be used in HOMER Pro software for economic analysis.

#### 2.4. Scenario 1: Small-Scale Hydel (SSH) and Grid

#### 2.5. Scenario 2: Grid and (SPV)

#### 2.6. Scenario 3: Small Scale Hydro (SSH), Solar PV (SPV), BATT, and Grid with GA

#### 2.7. Scenario 4: SSH, SPV, BATT, and Grid

#### 2.8. Modeling of System Components

#### 2.8.1. Solar PV System

_{pv}= P

_{n_pv}∗ (G/G

_{ref}) ∗ [1 + KT (T

_{c}− T

_{ref})]

_{c}= T

_{amb}+ (0.0256 *G)

_{c}= Cell temperature (°C), T

_{amb}= Ambient temperature (°C), G = solar radiation (W/m

^{2}), P

_{pv}= Output power from PV cell (W), P

_{n-pv}= Nominal power from PV cell at reference conditions (W), G

_{ref}= solar radiation at reference condition (1000 W/m

^{2}), K

_{T}= Temperature coefficient at maximum power (3.7 * 10

^{−3}(1/°C)), and T

_{ref}= PV cell temperature at reference conditions (25 °C).

#### 2.8.2. Hydro Turbine

_{site}= K [A

_{site}/A

_{guage}] Q

_{guage}

_{site}= Discharge at site (m

^{3}/s), Q

_{guage}= Discharge at gauge (m

^{3}/s), A

_{site}= Catchment Area (m

^{2}), A

_{guage}= Catchment Area of gauge (m

^{2}), and K = Scaling constant. The hydro power [34] is calculated from the following Equation (5):

_{hydel}= Mechnical power from turbine, ${\eta}_{total}$ = Hydraulic efficiency of turbine, $\rho $ = Density of water (1000 kg/m

^{3}), $g$ = Acceleration gravity (9.8 m/s

^{2}), $H$ = Head height (m), and $Q$ = Discharge at site (m

^{3}/s).

#### 2.8.3. Grid

_{g}is power purchased from the grid (kWh).

_{g min}is the minimum power purchased from the grid and P

_{g max}is the maximum power purchased from the grid supply. Power purchased from the grid should be within above-mentioned limits [35].

#### 2.8.4. Battery Storage

_{bs}= Storage capacity of the battery (kWh), E

_{load}= Hourly load energy (kWh), AD = Daily Autonomy within the range of (0.1 to 1.0), ${\eta}_{inv}$ = Inverter efficiency, ${\eta}_{b}$= Battery efficiency, and $DOD$ = Depth of discharge.

#### 2.8.5. Converter

## 3. Genetic Algorithm Based System Sizing

#### 3.1. Problem Formulation

#### 3.2. Objective Function

_{t}). The cost minimization function includes solar PV system P

_{pv}, battery storage P

_{bs}, small-scale hydel generation P

_{hydel,}and grid P

_{.}The formula for evaluating the minimum power of the battery, grid, and solar PV has been discussed in the mathematical model of the power generation through these sources from Equations (7)–(10). Equation (11) elaborates all of the costs below. The cost of the solar PV, battery storage, and grid is aimed to be reduced while reducing the capacity of grid, solar PV, and battery storage. Equations (3)–(10) are used for evaluating the size of the different generations. However, while selecting each component sizing, a set of constraints are taken into consideration. The set constraints are given in the next section.

_{t}(P

_{pv}(t), P

_{bs}(t), P

_{hydel}(t), P

_{g}(t)) = C

_{pv}(t) + C

_{bs}(t) + C

_{hydel}(t) + C

_{g}(t)

_{t}= Total cost of the hybrid system, C

_{pv}= Solar PV cost, C

_{bs}= Battery storage cost, C

_{hydel}= Small-scale hydel cost, and C

_{g}= Grid cost.

#### 3.3. Constraints

#### 3.3.1. Load Power Constraint

_{.L.}should be fulfilled by any combination of the resources:

_{L}(t) = P

_{pv}(t) + P

_{bs}(t) + P

_{hydel}(t) + P

_{g}(t).

#### 3.3.2. Battery Storage Constraint

_{bs SOC}≤ P

_{bs max}

_{bs SOC}= Battery storage state of charge SOC.

#### 3.3.3. Hydel Constraints

_{g min}is the minimum power purchased from the grid and P

_{g max}is the maximum power purchased from the grid supply. Power purchased from the grid should be within the above-mentioned limits.

#### 3.3.4. Bounds Constraint

_{bs}≤ N

_{bs}, P

_{max}

_{P.V.}≤ N

_{P.V.}, P

_{max}

_{bs}= Battery bank capacity (0, N

_{bs}), N

_{bs}= Maximum number of battery, P

_{max}= Maximum capacity of solar panel, and M

_{PV}= system solar PV panel capacity.

## 4. Results and Discussions

#### 4.1. Scenario 1: SSH and Grid

#### 4.2. Scenario 2: Grid and SPV

#### 4.3. Scenario 3: SSH, SPV, BATT and Grid with GA

#### 4.4. Scenario 4: SSH, SPV, BATT and Grid

#### 4.5. Grid Utilization

#### 4.6. Annual Electricity Production

#### 4.7. Economics Comparison

#### 4.8. Environmental Comparison

_{2}while using production through renewable energy resources. The avoided emissions can be calculated as [27]. The emission factor is taken as 389 tCO

_{2}/GWh [13].

_{2,}${E}_{g}$ is the annual electricity production through renewable energy, and ${F}_{e}$ is the emission factor.

_{2}mitigation was highest in scenario 4 as solar system installation is higher in this scenario. Whereas scenario 3 has the lowest mitigation of CO

_{2.}Scenario 2, on the other hand, has a total CO

_{2}mitigation of 1,793,382,582.

#### 4.9. Comparison with the Inclusion of Battery Bank

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Canals | Area (km) |
---|---|

Main canal | 7321 |

Secondary canals (minors) | 307,056 |

Tertiary canals (Water courses) | 1.6 million |

No. of Scenario | Resources and Optimization |
---|---|

Scenario 1: SSH and GRID | Small-scale hydel (SSH) and grid resources are incorporated to fulfill the load |

Scenario 2: GRID and SPV | Grid system and solar PV (SPV) resources used in HOMER Pro optimization |

Scenario 3: SSH, SPV, BATT, and GRID with GA | Genetic Algorithm (GA) incorporates Scenario 2 resources to provide sizing to HOMER Pro software for cost optimization |

Scenario 4: SSH, SPV, BATT and GRID | Small-scale hydel, solar PV, battery storage (BATT), and grid resources incorporated in HOMER Pro software for optimization |

Renala Khurd Power Plant Attributes | ||
---|---|---|

Capacity of Power Channel | 3000 CUSEC | |

Discharge Unit | 623 CUSEC | |

Water Level | ||

Max | Min | |

Up stream | 612 | 600 |

Downstream | 604 | 594 |

Working Head | 10 | 6 |

Capacity of Power Station | 1.1 MW | |

No. of Generating Units | 5 | |

The capacity of Each Unit | 0.22 MW | |

Generating Voltage | 3.3 KV | |

No. of Outgoing Feeders | 2 | |

Type of Turbine Francis | Horizontal |

Economics | |
---|---|

Grid unit purchased ($/kWh) | 0.13 |

1 kW Solar PV setup ($) | 545 |

Battery storage ($/kWh) | 235 |

Real Discount Rate (%) | 5.88 |

Inflation Rate (%) | 2 |

Components | Optimal Sizing Values |
---|---|

Solar PV system | 880 kW |

Battery Storage | 14 kAH |

Grid | 1600 units |

Scenarios | Annual Production | Tons Mitigation of CO_{2} |
---|---|---|

Scenario 2 | 4,610,238 | 1,793,382,582 |

Scenario 3 | 1,498,846 | 583,051,094 |

Scenario 4 | 8,088,534 | 3,146,439,726 |

NPC ($) | NPC Comparison with Scenario 3 (%) | COE ($/kWh) | Operating Cost ($) | Grid Utilization (%) | Unmet Load (%) | Grid Size (kW) | Solar PV Size (kW) | Small Scale Hydel Size (kW) | Excess Electricity (kWh/yr) | Production (kWh/yr) | |
---|---|---|---|---|---|---|---|---|---|---|---|

Scenario 1 | 14,884,780 | 36.03602 | 0.0771 | 1,151,402 | 59.3 | 0 | 999,999 | 1108 | 0 | 14,925,157 | |

Scenario 2 | 19,409,410 | 50.947 | 0.0988 | 1,286,646 | 54.0 | 0 | 999,999 | 4749 | 2,078,705 | 17,581,963 | |

Scenario 3 | 9,520,898 | --- | 0.0672 | 913036 | 47.8 | 3.31 | 1200 | 880 | 1108 | 4149 | 14,509,901 |

Scenario 4 | 11,495,240 | 17.1753 | 0.0556 | 701,399 | 34.1 | 0 | 999,999 | 2707 | 1108 | 291,619 | 16,494,026 |

Comparison with the Battery Bank | |||||||||||

Scenario 3 | 12,038,950 | --- | 0.08477 | 987674 | 48 | - | 1200 | 880 | 1108 | 2434 | 14,539,654 |

Scenario 4 | 11,430,020 | 17.1753 | 0.09827 | 1,287,556 | 54.1 | 0 | 999,999 | 2707 | 1108 | 1,965,962 | 17,566,844 |

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**MDPI and ACS Style**

Sattar, M.; Azeem, F.; Memon, Z.; Zidan, H.; Baig, S. Feasibility Assessment of Rural Hybrid Microgrid Using Canal-Based Microhydel Resources: A Case Study of Renala Khurd Pakistan. *Sustainability* **2022**, *14*, 15417.
https://doi.org/10.3390/su142215417

**AMA Style**

Sattar M, Azeem F, Memon Z, Zidan H, Baig S. Feasibility Assessment of Rural Hybrid Microgrid Using Canal-Based Microhydel Resources: A Case Study of Renala Khurd Pakistan. *Sustainability*. 2022; 14(22):15417.
https://doi.org/10.3390/su142215417

**Chicago/Turabian Style**

Sattar, Misbah, Fawad Azeem, Zulfiqar Memon, Hasan Zidan, and Sobia Baig. 2022. "Feasibility Assessment of Rural Hybrid Microgrid Using Canal-Based Microhydel Resources: A Case Study of Renala Khurd Pakistan" *Sustainability* 14, no. 22: 15417.
https://doi.org/10.3390/su142215417