# Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya

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## Abstract

**:**

## 1. Introduction

#### 1.1. Background of Study

_{2}).

#### 1.2. Statement of the Problem

#### 1.3. Justification of the Study

#### 1.4. Objectives

#### 1.4.1. Overall Objective

#### 1.4.2. Specific Objective

## 2. Literature Review

#### 2.1. The Relevance of Hybrid Systems in Off-Grid Electrification Projects

#### 2.2. Alternative Methodologies for Off Grid Electrification Projects

#### 2.3. Systems Optimization

#### 2.4. Resource Potential

^{2}. Buoyed by this positive outcome, the Ministry of Energy and Petroleum commenced wind data logging in specific high potential areas in December 2009. In 2013, these data were analyzed, leading to higher resolution wind maps that confirmed the huge potential for wind energy development. Incidentally, the areas with adequate wind resources are in the remote areas on northern Kenya, which are not served by grid connected electricity.

## 3. Materials and Methods

#### 3.1. Study Location

#### 3.2. Data Collection

#### 3.3. Mini-Grid Technical Specification

#### 3.4. Data Analysis

#### 3.4.1. Market Growth and Energy Production

#### 3.4.2. 2014—Yearly Analysis

#### 3.4.3. Operational Costs

#### 3.5. Simulation and Optimization with HOMER PRO

#### 3.6. The Hybrid Optimization Model and Problem Formulation

_{ann_tot}) represents the annual cost of the project in ($/year), which includes the initial costs (C

_{capann}), replacement costs (C

_{repann}), and O & M costs (C

_{O}

_{&Mann}), and is expressed mathematically as:

_{ann_tot}= C

_{capann}+ C

_{repann}+ C

_{O&Mann}.

_{ann_tot}= C

_{NPC}× CRF(i,N).

_{NPC}) into a flow of equal annual payments over a specified time, and calculates this value based on the annual interest rate (i) and number of years (N), and is expressed mathematically as:

_{N}(1 + i)

_{N}− 1

_{NPC}represents all the costs that occur within the project lifecycle, with future cash flows discounted to the present using the discount rate. NPC includes the initial costs (IC), replacement costs, and O & M costs. Besides, salvage value that occurs at the end of the project lifetime that reduces the total NPC. The salvage value (S) is the value remaining for each component after a project’s lifetime is completed and is computed using:

_{rep}R

_{rem}R

_{comp},

_{comp}is the lifetime of the component (years), R

_{rem}is the remaining lifetime of the component (years), and C

_{rep}is the replacement cost of the component ($).

_{NPC}= C

_{ann}

_{_tot}CRF(i,N)),

_{PV},

_{annual-demand}< E

_{PV},

_{Battery}+ E

_{PV}= E

_{BS}+ E

_{Losses}.

- (i)
- The energy output of the PV array (E
_{PV}) must always be positive, as given in Equation (6), and must be at least 10% of the total annual demand (E_{annual-demand}). The factors influencing the solar energy generation are the peak capacity of the PV array (Y_{PV}) in kW, the peak solar hour (PSH) in hours, and PV efficiency, which represents the relationship between the target yields (f_{PV}) and the actual target. The mathematical modeling in HOMER calculates the total annual energy contribution of the solar array [28] and is expressed as:E_{PV}= Y_{PV}× PSH × f_{PV}× 365 day/year. - (ii)
- To ensure a balance between demand and production power, the energy production of the sources (PV array and battery (E
_{Battery})) should cover the needs of the BS (E_{BS}) plus the losses (E_{Losses}) incurred by a DC-DC regulator, inverter, and active cooling.

_{min}, P

_{max}), where P

_{min}is the minimum state of charge and P

_{max}is the maximum state of charge of the battery, which is also the nominal capacity of the battery bank. Moreover, the DOD, efficiency, days of autonomy (A

_{B}), and lifetime of the battery (L

_{B}) are important, as they significantly affect the system’s total cost. The DOD refers to the maximum energy delivered from the battery and is defined using equation [28]:

_{min}100,

_{min}is the lower limit provided in the battery datasheet so that the battery does not discharge below the minimum state of charge.

_{aut}) is a critical factor representing the potential number of days that the battery bank can supply the required energy load without any PV array contribution. This value is expressed as the ratio of the battery bank size to the BS load [28]:

_{B}= N

_{bat}× B

_{V}× B

_{Q}× B

_{DOD}× (24 h/d)L

_{BS}.

_{V}is the nominal voltage of a single battery in V, N

_{bat}is the number of batteries in the battery bank, L

_{BS}is the average daily B

_{S}load in kWh, and B

_{Q}is the nominal capacity of a single battery in Ah.

_{B}= min(N

_{bat}× Q

_{lifetime}Q

_{thrpt},R

_{batt,f}).

_{batt,f}is the battery float life in years, Q

_{thrpt}is the annual battery throughput in kWh, and Q

_{lifetime}is the lifetime throughput of a single battery in kWh.

_{b−b}) divided by the voltage rating (B

_{V}) of one of the batteries selected:

_{seriesbatt}= V

_{b−b}B

_{V}.

_{PV}) to compute the available power, compares it with the electric load (P

_{Load}) and losses (P

_{Losses}), and finally decides how the additional power should be generated during deficits (battery discharging) or how the surplus power should be managed in times of excess (battery charging).

#### 3.7. Renewable Resources Assessment

^{2}. The average clearness index is 0.59. Based on these data, we show the assumed values for the different months in Figure 4.

#### 3.8. Components and Cost

## 4. Results

_{2}emissions by 484,649 kg/year; and (iii) the emissions of other pollutants by 3612 kg/year.

_{2}emissions by 738,656 kg/year; and (iii) the emissions of other pollutants by 5681 kg/year. Compared to the first case (no BESS), the diesel consumption is reduced by almost a half.

_{2}emissions by 951,658 kg/year; and (iii) the emissions of other pollutants by 7310 kg/year.

#### 4.1. Environmental Evaluation

#### 4.2. Economic Evaluation

#### 4.3. Sensitivity Analysis

#### 4.4. Comparison with a Probabilistic Approach

- An external Particle Swarm Optimization (PSO) procedure properly selects possible size scenarios of different components of the mini-grid.
- A year of operation is simulated several times according to a Sequential Monte Carlo procedure.
- Simple load following procedures are simulated to be used in real time to balance the system.
- The yearly OPEX of the current size scenario are evaluated, as the average operational costs obtained in the different Monte Carlo simulations.
- The Net Present Cost (NPC) of the current size scenario is assessed, based on its CAPEX and OPEX.
- When the PSO converges, the size scenario with the lowest NPC is chosen as the best design of the mini-grid.

## 5. Conclusions

- There is a growing energy demand recorded: The number of connections has almost tripled from the start-up of the mini-grid and there is a constant growth of energy production.
- The energy production supplied by the diesel generator is dominant with large emissions of GHG and other pollutants.
- The energy production cost is high and is subjected to many variations due to operation condition of the plant.

- Capital cost: The solution without storage has a lower initial cost.
- Operational cost: The solution with storage needs less fuel so the yearly cost of the plant will be lower and will be less subjected to the fuel price variations.
- Dependency on the fuel price: The fuel price is the expense that drives the cost of the plant during his life, it is variable and it is difficult to make prevision on its variation during the years.
- Environmental cost: The solution with storage needs less fuel which is the origin of the pollutants and GHG emissions.

- (1)
- Considering 25 years plant lifetime, hybrid configurations are more convenient in comparison with non-renewable configurations, such as the base case. In fact, the hybrid solutions have a lower NPC than the base case and that influences the COE of every configuration: the solutions with BESS vary their COE from 0.253 to 0.305 $/kWh, about 43% less than base case COE.
- (2)
- Hybrid solutions are more competitive at the economic level, compared to non-renewable solutions, as well as in developing countries, with weak economies and where factors such as inflation and real interest rate are unpredictable. This kind of solutions help to save money, as reported in the economic evaluation, that could be used differently, for instance investments in local enterprises and social goods;
- (3)
- Hybrid solutions enable saving fuel and hence reduction of local pollution, responsible for health problems, especially at a domestic level. Greenhouse gas emissions savings of tens to hundreds of tons of CO
_{2}every year, compared with alternative solutions based on fossil fuels, could be achieved. Thus, overall, the use and diffusion of renewable energy in developing countries, instead of traditional energy systems, represents a strong contribution to reach the objectives of greenhouse emission reduction, set by the international community in the COP21 of Paris.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 10.**Effect on varying electric load and diesel fuel price on the COE and the NPC superimposed COE–surface NPC.

**Figure 11.**Effect on varying BESS capital and replacement costs multiplier on the COE and the NPC superimposed COE–surface NPC.

Operational Data | January | February | March | April | May | June | July | August | September | October | November | December |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Consumption (L) | 35,742 | 31,359 | 35,044 | 29,888 | 28,796 | 32,879 | 33,678 | 35,758 | 33,507 | 36,047 | 30,437 | 28,150 |

Cost ($) | 46,360 | 41,318 | 46,292 | 39,481 | 38,069 | 44,155 | 45,216 | 47,809 | 37,834 | 47,705 | 37,672 | 31,292 |

Energy Production (kWh) | 92,868 | 89,808 | 83,127 | 81,364 | 95,920 | 85,016 | 103,992 | 94,028 | 96,433 | 103,216 | 89,040 | 82,604 |

Energy cost ($/kWh) | 0.50 | 0.46 | 0.56 | 0.49 | 0.40 | 0.52 | 0.43 | 0.51 | 0.39 | 0.46 | 0.42 | 0.38 |

Energy production Rate (kWh/L) | 2.60 | 2.86 | 2.37 | 2.72 | 3.33 | 2.59 | 3.09 | 2.63 | 2.88 | 2.86 | 2.93 | 2.93 |

Solutions | Diesel Gen (kW) | Numbers of Starts (starts/year) | Electrical Production (kWh/year) | Fuel Consumption (L/year) | PV (kWp) | PV Energy Production (kWh/year) | BESS (kWh) | BESS Energy Out (kWh/year) |
---|---|---|---|---|---|---|---|---|

Present | 410 | n.a. | 1,042,885 | 391,285 | 30 | 38,012 | - | - |

No BESS | 410 | 602 | 495,937 | 163,241 | 569 | 863,391 | - | - |

100 | 975 | 141,861 | 43,507 | |||||

Total | - | 1577 | 637,798 | 206,748 | 569 | 863,391 | - | - |

Limited BESS | 410 | 488 | 224,775 | 75,030 | 578 | 882,471 | 1328 | 292,867 |

100 | 840 | 114,890 | 34,897 | |||||

Total | - | 1328 | 339,665 | 109,927 | 578 | 882,471 | 1328 | 292,867 |

Optim. BESS | 410 | 42 | 17,179 | 5329 | 808 | 1,233,580 | 2598 | 510,602 |

100 | 162 | 65,316 | 18,810 | |||||

50 | 349 | 11,888 | 4580 | |||||

Total | - | 553 | 94,383 | 28,719 | 808 | 1,233,580 | 2598 | 510,602 |

Solutions | Diesel Consumption (L/year) | CO_{2} Emissions (kg/year) | Other Pollutants (kg/year) | Reduction of Diesel Consumption (L/year) | Reduction of CO_{2} Emissions (kg/year) | Reduction of Other Pollutants (kg/year) |
---|---|---|---|---|---|---|

Present | 391,285 | 1,026,828 ^{1} | 8170 | - | - | - |

No BESS | 206,748 | 542,179 | 4558 | 184,537 | 484,649 | 3612 |

Limited BESS | 109,927 | 288,172 | 2488.84 | 281,358 | 738,656 | 5681 |

Optimized BESS | 28,719 | 75,170 | 860.31 | 362,566 | 951,658 | 7310 |

^{1}The CO

_{2}emissions were calculated by simulating the present system, with the present consumption, in HOMER PRO.

Solutions | Diesel Gen. (kW) | Capex ($) | PV (kWp) | BESS (kWh) | Wind Farm (kW) | Fuel Consumption (L/year) | Diesel and O & M ($/year) | COE ($/kWh) | NPC ($) |
---|---|---|---|---|---|---|---|---|---|

Present | 410 | 0 | 30 | - | 60 | 391,285 | 578,681 | 0.46 | 10,600,000 |

No BESS | 410 | 1.35 M | 569 | - | 60 | 206,748 | 285,561 | 0.354 | 7,568,600 |

100 | |||||||||

Limited BESS | 410 | 2.15 M | 578 | 1328 | 60 | 109,927 | 169,556 | 0.305 | 6,507,321 |

100 | |||||||||

Optim. BESS | 410 | 3.46 M | 808 | 2598 | 60 | 28,719 | 74,740 | 0.253 | 6,179,443 |

100 | |||||||||

50 |

**Table 5.**Optimal size of the system with the probabilistic method described in [36].

Method | NPC (M$) | PV (kW) | Battery (kWh) | Inverter (kW) | Diesel Gen. (kW) |
---|---|---|---|---|---|

HOMER PRO 3.10.1 | 6.2 | 808 | 2598 | 202 | 410 + 100 + 50 |

Stochastic optimization | 5.7 | 865 | 2066 | 208 | 136 |

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## Share and Cite

**MDPI and ACS Style**

Micangeli, A.; Del Citto, R.; Kiva, I.N.; Santori, S.G.; Gambino, V.; Kiplagat, J.; Viganò, D.; Fioriti, D.; Poli, D.
Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya. *Energies* **2017**, *10*, 2041.
https://doi.org/10.3390/en10122041

**AMA Style**

Micangeli A, Del Citto R, Kiva IN, Santori SG, Gambino V, Kiplagat J, Viganò D, Fioriti D, Poli D.
Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya. *Energies*. 2017; 10(12):2041.
https://doi.org/10.3390/en10122041

**Chicago/Turabian Style**

Micangeli, Andrea, Riccardo Del Citto, Isaac Nzue Kiva, Simone Giovanni Santori, Valeria Gambino, Jeremiah Kiplagat, Daniele Viganò, Davide Fioriti, and Davide Poli.
2017. "Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya" *Energies* 10, no. 12: 2041.
https://doi.org/10.3390/en10122041