# Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan

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## Abstract

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## 1. Introduction

#### 1.1. Background

#### 1.2. Literature Review

#### 1.3. Contributions

- Providing one of the first extensive investigations into the load demand and PV power generations of rural areas in Jordan;
- Designing an optimal 100% PV and ESS system for a microgrid and rural areas in Jordan;
- Employing new metaheuristic optimization algorithms (GROM and PSO) to improve the economic and energy performance of 100% PV and ESS systems for the microgrid.

#### 1.4. Outline of Paper

## 2. The Description of the 100% PV and ESS Systems and Methodology

#### 2.1. Area and Load Demand Analysis

#### 2.1.1. Demand Trend and Analysis

- Overview of the town demand patterns based on the hourly, daily, and monthly load consumption;
- The seasonal demand (winter and summer).

#### Seasonal Analysis

#### 2.2. Sizing of the PV System

^{2}per day which introduced this location as a significant location for installing PV systems with a high level of solar radiation, as shown in Table 1 and Figure 12. In addition, from April to September, the irradiance level is more than 6 kWh/m

^{2}per day. The solar radiation in summer is significantly higher and reaches above 8 kWh/m

^{2}per day in June and July, whereas in winter it still records a sufficient level of radiation with an amount of around 3 kWh/m

^{2}per day in December and January.

- Scenario 1: The monthly average load demand;
- Scenario 2: 1.5 of the monthly average load demand to cover maximum days of demand as presented in the previous section;
- Scenario 3: The average of the highest daily power consumption in the month over a year, as shown in Table 3.

#### 2.3. Sizing of ESS

#### 2.4. Optimization Operation Method for the BESS

## 3. Results

- Firstly, the results of the optimal BESS controllers for the power network with the three PV system scenarios are presented;
- Then, the impact of the optimal BESS controllers on the sizing of BESS is investigated under a specific case study;
- Finally, the feasibility and economic results of using the BESS with different operation scenarios are presented.

#### 3.1. Three Scenarios of PV Systems for the Isolated Power Grid

#### 3.2. Sizing of BESS

- Case 1: The calculated size of BESS (9535.725 kWh) is presented in Section 2.3 and Table 6;
- Case 2: 80% of the calculated size of BESS (7628.580 kWh);
- Case 3: 60% of the calculated size of BESS (5721.435 kWh).

#### 3.3. Economic Results

#### 3.4. ESS Location Results

## 4. Conclusions and Discussion

- The size of the PV system depends on different factors such as the scale of the project (small, medium, or large scale) and location. The size of PV is usually calculated based on the monthly average of the demand. However, the results showed that is recommended to take safety factors and increase the size of PV to cover the seasonal and night peaks for a 100% renewable network model;
- The type and size of ESS selected based on the main role of the ESS in the network. In this work, the ESS was used to cover 100% of the demand with the PV. Therefore, the size was calculated based on the maximum peak and demand during the night and based on the PV generation. However, the results showed that it can be relied upon to minimize the size of ESS and cover 100% of load demand by using load scheduling and shaving algorithms;
- It is important to develop and implement an effective controller for the ESS to improve the network performance. The results showed that the optimal controller can help to cover the load demand and reduce peaks and blackouts.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

PV | Photovoltaic | ${\mathrm{Z}}_{PV}$ | Size of PV |

ESS | Energy Storage System | ${E}_{daily}$ | Average daily demand |

GROM | Golden Ratio Optimization Method | ${S}_{daily}$ | Direct sunlight (hours) |

PSO | Particle Swarm Optimization | ${\mathsf{\eta}}_{pv}$ | PV system efficiency |

G(t) | Main energy source | $\mathrm{BESS}$ | Size of the battery |

E(t) | Energy of ESS | ${L}_{daily}$ | Average daily-night demand |

L(t) | Load demand | ${D}_{atu}$ | the days of Autonomy |

t | Time t | $DoD$ | Depth of discharge |

EDCO | Electrical Distribution Co | ${\mathsf{\eta}}_{\mathrm{inv}}$ | Efficiency of inverter |

GHI | Global horizontal irradiation | ${\mathsf{\eta}}_{\mathrm{dis}}$ | Charge/discharge efficiency |

DNI | Direct normal irradiation | $f$ | Safety factor |

DIF | Diffuse horizontal irradiation | ${\mathrm{ITHD}}_{lim}$ | ITHD limitation |

DE | Differential Evolution | $L$ | load demand |

GTI | Global tilted irradiation at optimal angle | $\mathrm{SoC}$ | State of Charge |

${\mathrm{SoC}}^{\mathrm{min}}$ | Minimum SoC | ${\mathrm{SoC}}^{\mathrm{max}}$ | Maximum SoC |

NPV | Net Present Value | IRR | Internal Rate of Return |

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**Figure 1.**The energy flow directions of the PV system equipped with ESS to feed 100% of the electricity demand.

**Figure 6.**The average hourly minimum and maximum power consumption recorded for all days of the year.

**Figure 14.**The percentage of daily demand cover for different PV system scenarios and operations for the BESS (set-point control model).

**Figure 15.**The number of hours with power off for different BESS scenarios (set-point control model).

Description | Value |
---|---|

Global horizontal irradiation (GHI) | 5.485 kWh/m^{2} per day |

Direct normal irradiation (DNI) | 5.766 kWh/m^{2} per day |

Diffuse horizontal irradiation (DIF) | 1.752 kWh/m^{2} per day |

Global tilted irradiation at the optimal angle (GTI) | 6.089 kWh/m^{2} per day |

Scenario 1 | Scenario 2 | Scenario 3 | |
---|---|---|---|

Size of PV system | 908.7 kWp | 1363.02 kWp | 1104.1 kWp |

Day | kWh |
---|---|

30 January | 4294.0 |

19 February | 4630.8 |

19 March | 3241.6 |

19 Apr | 2242.5 |

31 May | 3652.5 |

30 June | 5838.1 |

30 July | 6788.5 |

8 August | 6533.5 |

5 September | 7284.6 |

5 October | 5572.7 |

5 November | 4024.7 |

25 December | 3306.8 |

Month | Load Demand (KWh) | PV Generation (KWh) | ||
---|---|---|---|---|

Scenario 1 | Scenario 2 | Scenario 3 | ||

January | 105,979.4 | 98,543.5 | 162,060.76 | 122,614.3 |

February | 90,028.1 | 82,154.5 | 127,728.19 | 101,974.3 |

March | 72,724.6 | 129,591.1 | 192,806.33 | 158,579.3 |

April | 61,455.3 | 126,340.6 | 179,116.61 | 153,826.9 |

May | 77,744.7 | 155,105.2 | 205,204.63 | 185,515.2 |

June | 139,174.0 | 163,629.1 | 208,850.06 | 193,707.7 |

July | 178,426.6 | 167,412.3 | 214,515.70 | 197,073.6 |

August | 182,593.0 | 163,069.1 | 218,132.98 | 191,825.3 |

September | 179,735.3 | 143,128.3 | 204,109.48 | 169,488.6 |

October | 141,340.3 | 128,551.6 | 195,558.83 | 154,242.9 |

November | 94,927.5 | 93,260.9 | 147,172.77 | 113,257.9 |

December | 93,359.9 | 89,522.3 | 147,353.35 | 110,809.2 |

Total | 1,417,488.7 | 1,540,308.5 | 2,202,609.69 | 1,852,915.2 |

Number of Days covered 100% | 190 | 349 | 295 | |

Percentage of coverage for the total daily demand by the daily PV power generation over a year. | 52.3% | 95.61% | 80.72% |

Month | Consumption (kWh) |
---|---|

January | 63,469.6 |

February | 52,734.9 |

March | 42,675.6 |

April | 37,162.1 |

May | 48,360.4 |

June | 86,575.9 |

July | 110,432.4 |

August | 115,169.3 |

September | 114,193.7 |

October | 89,793.9 |

November | 58,932.1 |

December | 55,880.1 |

Year/Total | 875,380.0 |

Average | 72,948.3 |

Parameter | Description |
---|---|

BESS | 9535.7 kWh |

DoD, SoC | 80%, 20% |

${\mathsf{\eta}}_{\mathrm{inv}}$ | 85% |

${\mathsf{\eta}}_{\mathrm{dis}}$ | 90% |

$f$ | 1.2 |

PV System Scenario | Set-Point | GROM | PSO |
---|---|---|---|

Scenario 1 | 84.9% | 89.5% | 88.6% |

Scenario 2 | 100% | 100% | 100% |

Scenario 3 | 93.3% | 97.4% | 96.9% |

Case Number | Set-Point | GROM | PSO |
---|---|---|---|

Case 1 | 0 | 0 | 0 |

Case 2 | 30 | 21 | 23 |

Case 3 | 81 | 49 | 52 |

Capital Costs | Value |
---|---|

BESS (Case 1) | USD 2,860,717 (300 USD/kWh) |

PV includes panels, inverters, and cables. (Scenario 2) | USD 559,870 |

Total capital costs | USD 3,420,587 |

BESS | NPV (K USD) | IRR (%) | Payback Period (Years) |
---|---|---|---|

Case 1 | 940 | 5.8 | 11 |

Case 2 | 1473 | 7.9 | 10 |

Case 3 | 2377 | 13.2 | 7 |

**Table 11.**The percentage of covering daily demand for the different ESS location and optimal BESS controllers.

ESS Location | Set-Point | GROM | PSO |
---|---|---|---|

Central ESS | 84.9% | 89.5% | 88.6% |

Distributed ESS | 80.1% | 85.4% | 84.8% |

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**MDPI and ACS Style**

Alasali, F.; Salameh, M.; Semrin, A.; Nusair, K.; El-Naily, N.; Holderbaum, W.
Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan. *Sustainability* **2022**, *14*, 8124.
https://doi.org/10.3390/su14138124

**AMA Style**

Alasali F, Salameh M, Semrin A, Nusair K, El-Naily N, Holderbaum W.
Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan. *Sustainability*. 2022; 14(13):8124.
https://doi.org/10.3390/su14138124

**Chicago/Turabian Style**

Alasali, Feras, Mohammad Salameh, Ali Semrin, Khaled Nusair, Naser El-Naily, and William Holderbaum.
2022. "Optimal Controllers and Configurations of 100% PV and Energy Storage Systems for a Microgrid: The Case Study of a Small Town in Jordan" *Sustainability* 14, no. 13: 8124.
https://doi.org/10.3390/su14138124