Sizing Assessment of Islanded Microgrids Considering Total Investment Cost and Tax Benefits in Colombia
Abstract
:1. Introduction
2. Proposed Methodology
2.1. PV Array Model
2.2. BESS Model
2.3. Diesel Generator
2.4. Economic Indicators
2.5. Reliability Indicator
2.6. Fiscal Incentives
3. Proposed Optimization Model
3.1. Energy Balance Constraint
3.2. Reliability Constraint
3.3. PV Constraints
3.4. Diesel Generator Constraint
3.5. BESS Charge and Discharge Constraints
4. Tests and Results
4.1. Load Profile
4.2. Solar Radiation and Temperature
4.3. Input Parameters
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
G | Sets of systems components |
T | Time horizon to evaluate the study case |
Price per kW of PV energy installed in (USD/kW) | |
Price per kW of Diesel energy installed in (USD/kW) | |
Price per kW of BESS energy installed in (USD/kW) | |
Cost of energy not supplied in (USD/kWh) | |
Maximum loss of power supply probability allowed in (%) | |
Load to meet at time t in kWh | |
Maximum energy available from PV array at time t in kWh | |
Minimum energy available from PV array at time t in kWh | |
Minimum energy available from diesel generator in kWh | |
Is the rated power of diesel generator in kWh | |
Positive BESS constant | |
Maximum state of BESS charge in kW | |
Minimum state of BESS charge in kW | |
Maximum number of allowed charging/discharging cycles for the BESS | |
Maximum flow of energy to avoid overheating the BESS | |
Energy supplied to load from PV array in kWh | |
Energy supplied to load from diesel generator in kWh | |
Energy supplied to load from BESS in kWh | |
Energy supplied to BESS from PV array and diesel generator in kWh | |
Energy supplied to BESS from PV array in kWh | |
Energy supplied to BESS from diesel generator in kWh | |
Energy not supplied to load in kWh | |
State of BESS charge at time in t in kWh | |
Binary variable that determines if the diesel generator is Used | |
Binary variable that determines if the BESS is charging | |
Binary variable that determines if the BESS is discharging | |
Number of charging/discharging cycles in the BESS |
References
- Saldarriaga-Zuluaga, S.D.; Lopez-Lezama, J.M.; Muñoz-Galeano, N. Protection Coordination in Microgrids: Current Weaknesses, Available Solutions and Future Challenges. IEEE Lat. Am. Trans. 2020, 18, 1715–1723. [Google Scholar] [CrossRef]
- Bani-Ahmed, A.; Rashidi, M.; Nasiri, A.; Hosseini, H. Reliability Analysis of a Decentralized Microgrid Control Architecture. IEEE Trans. Smart Grid 2019, 10, 3910–3918. [Google Scholar] [CrossRef]
- Saldarriaga-Zuluaga, S.D.; López-Lezama, J.M.; Muñoz-Galeano, N. Optimal coordination of over-current relays in microgrids considering multiple characteristic curves. Alex. Eng. J. 2021, 60, 2093–2113. [Google Scholar] [CrossRef]
- Saldarriaga-Zuluaga, S.D.; López-Lezama, J.M.; Muñoz-Galeano, N. An Approach for Optimal Coordination of Over-Current Relays in Microgrids with Distributed Generation. Electronics 2020, 9, 1740. [Google Scholar] [CrossRef]
- Feroldi, D.; Zumoffen, D. Sizing methodology for hybrid systems based on multiple renewable power sources integrated to the energy management strategy. Int. J. Hydrogen Energy 2014, 39, 8609–8620. [Google Scholar] [CrossRef]
- Sharma, S.; Bhattacharjee, S.; Bhattacharya, A. Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. IET Gener. Transm. Distrib. 2016, 10, 625–637. [Google Scholar] [CrossRef]
- Emad, D.; El-Hameed, M.A.; Yousef, M.T.; El-Fergany, A.A. Computational Methods for Optimal Planning of Hybrid Renewable Microgrids: A Comprehensive Review and Challenges. Arch. Comput. Methods Eng. 2019, 27, 1297–1319. [Google Scholar] [CrossRef]
- López-Santiago, D.; Caicedo, E. Optimal management of electric power in microgrids under a strategic multi-objective decision-making approach and operational proportional adjustment. IET Gener. Transm. Distrib. 2019, 13, 4473–4481. [Google Scholar] [CrossRef]
- Baghaee, H.; Mirsalim, M.; Gharehpetian, G.; Talebi, H. Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system. Energy 2016, 115, 1022–1041. [Google Scholar] [CrossRef]
- Zhao, J.; Yuan, X. Multi-objective optimization of stand-alone hybrid PV-wind-diesel-battery system using improved fruit fly optimization algorithm. Soft Comput. 2015, 20, 2841–2853. [Google Scholar] [CrossRef]
- Fetanat, A.; Khorasaninejad, E. Size optimization for hybrid photovoltaic–wind energy system using ant colony optimization for continuous domains based integer programming. Appl. Soft Comput. 2015, 31, 196–209. [Google Scholar] [CrossRef]
- Ogunjuyigbe, A.; Ayodele, T.; Akinola, O. Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building. Appl. Energy 2016, 171, 153–171. [Google Scholar] [CrossRef]
- Cavanini, L.; Ciabattoni, L.; Ferracuti, F.; Ippoliti, G.; Longhi, S. Microgrid sizing via profit maximization: A population based optimization approach. In Proceedings of the 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), Poitiers, France, 19–21 July 2016; pp. 663–668. [Google Scholar] [CrossRef]
- Dali, A.; Abdelmalek, S.; Nekkache, A.; Bouharchouche, A. Development of a Sizing Interface for Photovoltaic-Wind Microgrid Based on PSO-LPSP Optimization Strategy. In Proceedings of the 2018 International Conference on Wind Energy and Applications in Algeria (ICWEAA), Algiers, Algeria, 6–7 November 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Ahamad, N.B.; Othman, M.; Vasquez, J.C.; Guerrero, J.M.; Su, C.L. Optimal sizing and performance evaluation of a renewable energy based microgrid in future seaports. In Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 20–22 February 2018; pp. 1043–1048. [Google Scholar] [CrossRef]
- Chen, J.; Zhang, W.; Li, J.; Zhang, W.; Liu, Y.; Zhao, B.; Zhang, Y. Optimal Sizing for Grid-Tied Microgrids With Consideration of Joint Optimization of Planning and Operation. IEEE Trans. Sustain. Energy 2018, 9, 237–248. [Google Scholar] [CrossRef]
- Oviedo, J.; Duarte, C.; Solano, J. Sizing of Hybrid Islanded Microgrids using a Heuristic approximation of the Gradient Descent Method for discrete functions. Int. J. Renew. Energy Res. 2020, 10, 13–22. [Google Scholar]
- Diab, A.A.Z.; Sultan, H.M.; Mohamed, I.S.; Kuznetsov, O.N.; Do, T.D. Application of Different Optimization Algorithms for Optimal Sizing of PV/Wind/Diesel/Battery Storage Stand-Alone Hybrid Microgrid. IEEE Access 2019, 7, 119223–119245. [Google Scholar] [CrossRef]
- Ghiani, E.; Vertuccio, C.; Pilo, F. Optimal sizing and management of a smart Microgrid for prevailing self-consumption. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June–2 July 2015; pp. 1–6. [Google Scholar] [CrossRef]
- Suhane, P.; Rangnekar, S.; Mittal, A.; Khare, A. Sizing and performance analysis of standalone wind-photovoltaic based hybrid energy system using ant colony optimisation. IET Renew. Power Gener. 2016, 10, 964–972. [Google Scholar] [CrossRef]
- Aldaouab, I.; Daniels, M.; Hallinan, K. Microgrid cost optimization for a mixed-use building. In Proceedings of the 2017 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA, 9–10 February 2017; pp. 1–5. [Google Scholar] [CrossRef]
- Kharrich, M.; Sayouti, Y.; Akherraz, M. Optimal microgrid sizing and daily capacity stored analysis in summer and winter season. In Proceedings of the 2018 4th International Conference on Optimization and Applications (ICOA), Mohammedia, Morocco, 26–27 April 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Li, P.; Li, R.X.; Cao, Y.; Li, D.Y.; Xie, G. Multiobjective Sizing Optimization for Island Microgrids Using a Triangular Aggregation Model and the Levy-Harmony Algorithm. IEEE Trans. Ind. Informa. 2018, 14, 3495–3505. [Google Scholar] [CrossRef]
- Pham, M.; Tran, T.; Bacha, S.; Hably, A.; An, L.N. Optimal Sizing of Battery Energy Storage System for an Islaned Microgrid. In Proceedings of the IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 1899–1903. [Google Scholar] [CrossRef] [Green Version]
- Martínez, R.E.; Bravo, E.C.; Morales, W.A.; Garcia-Racines, J.D. A bi-level multi-objective optimization model for the planning, design and operation of smart grid projects. Case study: An islanded microgrid. Int. J. Energy Econ. Policy 2020, 10, 325–341. [Google Scholar] [CrossRef]
- Hijjo, M.; Frey, G. Multi-objective optimization for scheduling isolated microgrids. In Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 20–22 February 2018; pp. 1037–1042. [Google Scholar] [CrossRef]
- Shadmand, M.B.; Balog, R.S. Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid. IEEE Trans. Smart Grid 2014, 5, 2635–2643. [Google Scholar] [CrossRef]
- Alabert, A.; Somoza, A.; de la Hoz, J.; Graells, M. A general MILP model for the sizing of islanded/grid-connected microgrids. In Proceedings of the 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, 4–8 April 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Sansa, I.; Villafafilla, R.; Belaaj, N.M. Optimal sizing design of an isolated microgrid based on the compromise between the reliability system and the minimal cost. In Proceedings of the 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, Tunisia, 21–23 December 2015; pp. 715–721. [Google Scholar] [CrossRef]
- Rigo-Mariani, R.; Sareni, B.; Roboam, X. Integrated Optimal Design of a Smart Microgrid With Storage. IEEE Trans. Smart Grid 2017, 8, 1762–1770. [Google Scholar] [CrossRef] [Green Version]
- Sawle, Y.; Gupta, S.; Bohre, A.K. Optimal sizing of standalone PV/Wind/Biomass hybrid energy system using GA and PSO optimization technique. Energy Procedia 2017, 117, 690–698. [Google Scholar] [CrossRef]
- Zhao, J.; Nian, H.; Kong, L. Multiobjective Sizing Optimization for Combined Heat and Power Microgrids Using a Triangular Evaluation Model and the Self-Adaptive Genetic Algorithm. In Proceedings of the 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China, 21–24 May 2019; pp. 1840–1845. [Google Scholar] [CrossRef]
- Scalfati, A.; Iannuzzi, D.; Fantauzzi, M.; Roscia, M. Optimal sizing of distributed energy resources in smart microgrids: A mixed integer linear programming formulation. In Proceedings of the 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), San Diego, CA, USA, 5–8 November 2017; pp. 568–573. [Google Scholar] [CrossRef]
- Rajan, G.; Kavakuntala, M.; Rajkumar, V.S.; Gnanavel, S.; Vijayaraghavan, V. Rural Indian microgrid design optimization—Intelligent battery sizing. In Proceedings of the 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, USA, 19–22 October 2017; pp. 1–5. [Google Scholar] [CrossRef]
- Ciabattoni, L.; Ferracuti, F.; Ippoliti, G.; Longhi, S. Artificial bee colonies based optimal sizing of microgrid components: A profit maximization approach. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 24–29 July 2016; pp. 2036–2042. [Google Scholar] [CrossRef]
- Almaktar, M.; Elbreki, A.M.; Shaaban, M. Revitalizing operational reliability of the electrical energy system in Libya: Feasibility analysis of solar generation in local communities. J. Clean. Prod. 2021, 279, 123647. [Google Scholar] [CrossRef] [PubMed]
- Hau, V.B.; Husein, M.; Chung, I.Y.; Won, D.J.; Torre, W.; Nguyen, T. Analyzing the impact of renewable energy incentives and parameter uncertainties on financial feasibility of a campus microgrid. Energies 2021, 2446, 2446. [Google Scholar] [CrossRef] [Green Version]
- Haghi, E.; Raahemifar, K.; Fowler, M. Investigaing the effect of renewable energy incentives and hydrogen storage on advantages of stakeholders in a microgrid. Energy Policy 2018, 113, 206–222. [Google Scholar] [CrossRef]
- Arraez-Cancelliere, O.; Muñoz-Galeano, N.; López-Lezama, J.M. Methodology for Sizing Hybrid Battery-Backed Power Generation Systems in Off-Grid Areas. In Wind Solar Hybrid Renewable Energy System; IntechOpen: London, UK, 2019; pp. 1–22. [Google Scholar] [CrossRef] [Green Version]
- Kashefi Kaviani, A.; Riahy, G.; Kouhsari, S. Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages. Renew. Energy 2009, 34, 2380–2390. [Google Scholar] [CrossRef]
- Osim Islanded Microgrids Sizing. Available online: https://github.com/osim-microgrid-tool (accessed on 10 June 2022).
- Castillo-Ramírez, A.; Mejía-Giraldo, D.; Molina-Castro, J.D. Fiscal incentives impact for RETs investments in Colombia. Energy Sources Part B Econ. Plan. Policy 2017, 12, 759–764. [Google Scholar] [CrossRef]
- Costo Incremental Operativo de Racionamiento de Energía, CREG. Available online: https://www.creg.gov.co/taxonomy/term/166 (accessed on 10 June 2022).
- Costo Incremental Operativo de Racionamiento de Energía, UPME. Available online: http://www.upme.gov.co/CostosEnergia.asp (accessed on 10 June 2022).
- Caracterización Energética de las ZNI. Available online: https://ipse.gov.co/cnm/caracterizacion-de-las-zni/ (accessed on 10 June 2022).
Input | Symbol | Value |
---|---|---|
Lifetime of the project (years) | R | 20 |
Loss of power supply probability (%) | 5 | |
Real interest rate (%) | 8.08 | |
Cost of energy not supplied (USD/kWh) | 0.7434 | |
Tax reduction factor (%) | 91.47 | |
Years income tax (years) | 15 | |
Years accelerated depreciation (years) | 10 | |
Corporate tax income rate (%) | 33 |
Input | Symbol | Value |
---|---|---|
Maximum Power | 300 | |
Module Efficiency (%) | 18.33 | |
(C) | 45 | |
Price per kWh generated (USD/kWh) | 0.003 | |
PV derating factor (%) | 85 | |
factor initial investment (%) | 1 | |
Price per kW installed (USD/kW) | 1.5 | |
Power Temperature Coefficient (%/C) | −0.39 |
Input | Symbol | Value |
---|---|---|
Positive battery constant | 0.01 | |
Self-Discharge rate | 0.2 | |
Capacity Rate (h) | 5 | |
Maximum depth of Discharge (%) | 50 | |
Maximum number of cycles | 3000 | |
Price per kWh generated (USD/kWh) | 0.12 | |
Battery cell capacity (kW) | 0.84 | |
DC system voltage (V) | 48 | |
Battery Voltage (V) | 2 | |
Inverter efficiency (%) | 95 | |
factor initial investment (%) | 2 | |
Factor initial capital cost invested (%) | 70 | |
Lifecycle (years) | 10 | |
Price per kWh installed (USD/kW) | 144.5 | |
Discharge efficiency (%) | 100 | |
load efficiency (%) | 90 | |
Number of batteries in parallel | 2 |
Input | Symbol | Value |
---|---|---|
DG Power (kW) | 10 | |
Price per installed kWh (USD/kW) | 2041 | |
Minimum ratio allowed | 0.9 | |
Diesel efficiency (%) | 100 | |
Price per generated kWh (USD/kWh) | 0.22 | |
Factor of the initial invested capital cost (%) | 70 | |
Specific consumption of fuel (gal/kWh) | 0.0974 | |
Specific consumption of oil (gal/kWh) | 0.0005 | |
Life-cycle (years) | 10 | |
Average price of oil (USD/gal) | 21.4 | |
Average price of fuel (USD/gal) | 2.4 |
Parameter | Unit | PV-BAT-DG | PV-BAT |
---|---|---|---|
Units | 190.00 | 405.00 | |
Units | 48.00 | 120.00 | |
Units | 2.00 | 5.00 | |
kW | 40.32 | 100.80 | |
Number | 229.00 | 116.30 | |
kW | 10.00 | - | |
USD | 85,500.00 | 182,250.00 | |
USD | 5826.24 | 14,565.60 | |
USD | 20,411.00 | - | |
USD/year | 855.00 | 1822.50 | |
USD/year | 116.52 | 291.31 | |
USD/year | 4002.99 | - | |
USD | 2737.40 | 6843.50 | |
USD | 9589.90 | - | |
% | 5.00 | 4.99 | |
% | 10.00 | 10.00 | |
kWh/year | 1823.30 | 1821.20 | |
USD/year | 1355.44 | 1353.93 | |
USD/year | 17,016.74 | 22,479.72 | |
USD/kWh | 0.54 | 0.84 | |
USD/year | 16,237.73 | 20,800.89 | |
USD/kWh | 0.51 | 0.78 | |
Hours | 19 | 21 |
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Ropero-Castaño, W.; Muñoz-Galeano, N.; Caicedo-Bravo, E.F.; Maya-Duque, P.; López-Lezama, J.M. Sizing Assessment of Islanded Microgrids Considering Total Investment Cost and Tax Benefits in Colombia. Energies 2022, 15, 5161. https://doi.org/10.3390/en15145161
Ropero-Castaño W, Muñoz-Galeano N, Caicedo-Bravo EF, Maya-Duque P, López-Lezama JM. Sizing Assessment of Islanded Microgrids Considering Total Investment Cost and Tax Benefits in Colombia. Energies. 2022; 15(14):5161. https://doi.org/10.3390/en15145161
Chicago/Turabian StyleRopero-Castaño, Wilmer, Nicolás Muñoz-Galeano, Eduardo F. Caicedo-Bravo, Pablo Maya-Duque, and Jesús M. López-Lezama. 2022. "Sizing Assessment of Islanded Microgrids Considering Total Investment Cost and Tax Benefits in Colombia" Energies 15, no. 14: 5161. https://doi.org/10.3390/en15145161
APA StyleRopero-Castaño, W., Muñoz-Galeano, N., Caicedo-Bravo, E. F., Maya-Duque, P., & López-Lezama, J. M. (2022). Sizing Assessment of Islanded Microgrids Considering Total Investment Cost and Tax Benefits in Colombia. Energies, 15(14), 5161. https://doi.org/10.3390/en15145161