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Optimization Integrated Rule-Based Operational Algorithms for Agricultural Microgrids

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Ozan Keysan

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http://keysan.me

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Middle East Technical University

Ankara, Turkey

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March 18, 2023

iCoMET, 2023 Sukkur, Pakistan

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Solar Energy Potential

Optimization Integrated Rule-Based Operational Algorithms for an Agricultural Microgrid

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Solar Energy Potential - Pakistan

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Levelized Cost of Energy

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What is the cost of energy for off-grid applications?

A farmer with access to water can generate around 6 times more income compared to farming without irrigation.

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AgriPV : Agriculture + Solar Energy

Optimization Integrated Rule-Based Operational Algorithms for an Agricultural Microgrid

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Agrivoltaics, agrophotovoltaics, agrisolar, or dual-use solar is the simultaneous use of areas of land for both solar photovoltaic power generation and agriculture.

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Agrivoltaics Creates Synergies

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Agrivoltaics Creates Synergies

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Agrivoltaics Creates Synergies

Optimization Integrated Rule-Based Operational Algorithms for an Agricultural Microgrid

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Introduction to Agricultural Microgrids

Agricultural microgrid:

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A microgrid designed specifically for agricultural purposes to supply both electrical power and water demands.

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Introduction to Agricultural Microgrids

Importance:

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  • Sustainable energy for rural areas sometimes it means providing electricity to remote areas.
  • Local generation, consumption, and management of energy.
  • Improve harvest yield and economic benefit.
  • Efficient management of water resources.
  • Adaptation to climate change impacts

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Structure of the Microgrid under Study

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Features of the Agricultural Microgrid Structure under Study

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  • The elevated reservoir stores water with potential energy.
  • Battery lifetime is extended.
  • Variable DC link voltage.
  • Better reliability.
  • Separate control over power and water.

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Güneşköy

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Güneşköy

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Natural elevated reservoir

Agricultural Fields

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Microgrid Testbench

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Microgrid Testbench

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Chroma DC Power Supply with Solar Array Simulator

18 kW, 1800 V, 30 A

Growatt Hybrid Inverter

SPF 5000 ES 5 kW

AC, and DC inputs. BESS input.

TESCOM Solar pump drive 2.2 kW – Single Phase

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Microgrid Testbench

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Motor-Generator set to emulate electric water pump 2.2 kW

Lithium-iron-phosphate (LiFePo4) Battery packs

10 kWh

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Microgrid Testbench

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Resistive load 5 kW

Meteorological Station

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Rooftop Solar Array

Optimization Integrated Rule-Based Operational Algorithms for an Agricultural Microgrid

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PV System 25 kW

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Rule-Based Operational Algorithms

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  • Pump First

Operational Algorithm

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Rule-Based Operational Algorithms

  • BESS First

Operational Algorithm

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Rule-Based Operational Algorithms

Performance indices:

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Loss of Power Supply Probability

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  • LPSP = Σ PDef / Σ Pload

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Loss of Water Supply Probability

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  • LWSP = Σ WDef / Σ Wdem

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Loss of Supply Probability

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LSP = (LPSP + LWSP) / 2

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Optimized Operational Algorithms

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  • The proposed optimization integrated rule-based algorithms combine advantages from rule-based and optimized approaches by inserting the optimization process into the embedded equations and constraints of rule-based algorithms.

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  • The proposed optimization integrated rule-based algorithms are fast, accurate, simple and provide optimal solutions.

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Case Study

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  1. Environmental Data has been collected using a virtual meteorological station in the FieldClimate platform for the year 2021 of a private farm located near the city of Ankara, Turkey.

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  • Hourly load profile of an average household in the same area has been obtained from the Energy Exchange Istanbul (EXIST) transparency platform.

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  • Water demand is defined in the form of a pre-planned irrigation pattern.

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Simulation Results

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Simulation Results

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  1. Pump First
  2. BESS First

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Simulation Results

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Index

 

Algorithm & Window

LPSP

LWSP

LSP

Pump First Rule Based

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9.08 %

0.66 %

4.87 %

Optimization Integrated

Pump First Rule Based

1 Day

7.58 %

1.55 %

4.56 %

3 Days

7.28 %

1.44 %

4.36 %

7 Days

7.24 %

0.66 %

3.95 %

BESS First Rule Based

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5.37 %

11.66 %

8.51 %

Optimization Integrated

BESS First Rule Based

1 Day

12.15 %

3.01 %

7.58 %

3 Days

8.07 %

2.21 %

5.14 %

7 Days

7.71 %

0.97 %

4.34 %

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Conclusion

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  • A topology for an agricultural microgrid has been described.

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  • Two operational algorithms with fast response and true simplicity have been developed. Two modified versions provide optimality as well.

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  • Any further improvement in the proposed algorithms is a straightforward task.

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  • Validated by simulation results.

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Future Works

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  • Integration of wind energy.

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Thank you for your attention.

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Appendix A: Analytical Modeling

Solar PV Array:

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The output power of PV array

PPV = Voc,c × Isc,c × FF × Nc × Nm

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Slide A2

stc : Standard test condition

Kv : Voltage temperature coefficient

Ki : Current temperature coefficient

GT : Solar irradiance (W/m2)

Tc : Temperature (oC)

FF : Fill factor

Nc : Number of cells in a module

Nm : Number of modules in the array

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Appendix A: Analytical Modeling

Electric Water Pump:

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Pump power:

PPump = (ρQgH / 3600) / (η / 100)

Affinity laws:

Q1 / Q2 = n1 / n2

H1 / H2 = (n1 / n2)²

PPump,1 / PPump,2 = (n1 / n2)³

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ρ : Water density, 1000 kg/m3

Q : Flow rate (m3/h)

g : Earth gravity, 9.81 m/s2

H : Head pressure (m)

η : Pump efficiency

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Appendix A: Analytical Modeling

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Slide A4

Alarko 4SDM12 / 12 Submersible Water Pump

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Appendix A: Analytical Modeling

Battery Energy Storage System (BESS):

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Batteries’ state of charge:

SOC (t) = SOC (t – 1) + (PBESS (t) × Δt × 100 / CBESS)

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PBESS : Battery power (W)

CBESS : Battery capacity (Wh)

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Appendix B: Case Study

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Slide B1

CSUN 255-60 M Solar PV Array

Voc,m,stc

37.4 V

Isc,m,stc

8.85 A

Pmax,m,stc

254.9 W

Nc

60

Kv

-0.34 % / °C

Ki

0.05 % / °C

Alarko 4SDM12 / 12 Submersible Water Pump

PPump-rated

2200 W

PPump-min

1250 W

Rated Speed

2850 rpm

Water Reservoir

Capacity

50 m³

WU

50 m³

WL

10 m³

Battery Energy Storage System

CBESS

20 kWh

SOCU

80%

SOCL

20%

Characteristics of the simulated microgrid