Abstract
In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering
① 24-hour continuous power supply; ② load shedding percentage; ③ diesel power generator (genset) curtailment; ④ the worst meteorological conditions; ⑤ the use of renewable energy sources including battery energy storage systems (BESSs); and ⑥ the use of genset. A mathematical programming language (AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand (without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.
ACCESS to essential resources for daily activities is a recurring issue in some isolated communities in developing countries. Electricity is crucial as it provides access to schooling and technology, and it is closely linked to other primary necessities such as water. The national government unilaterally specified the selling price of fuels in Ecuador through a price-fixing system, which lacked the flexibility to adapt to active changes in the global market. The new reality underwent a significant modification from 2020, when the selling price of oil-derived fuels such as diesel began to reflect the fluctuations resulting from international supply and demand imbalances. This transition has directly impacted isolated communities in Ecuador that rely on fossil fuels for local energy generation [
In this context, hybrid microgrids appear as an alternative to stimulate the incorporation of renewable resources and reduce fossil fuel consumption in isolated communities, with better environmental performances [
Reference | Year | Location | Objective function | Optimization algorithm |
---|---|---|---|---|
[ | 2017-2019 | Somalia, USA | Energy produced with fossil fuels and investment cost | Linear programming and mixed-integer programming |
[ | 2018 | Small tropical island | Annual cost including initial investment, operation and maintenance (), and power shortage penalty | PSO |
[ | 2019 | Iran | Total life cost | Chaotic search, harmony search, and simulated annealing |
[ | 2020 | Kenya | Net present cost as a weighted sum of capital expenditure (CAPEX) and operational expenditure (OPEX) | PSO |
[ | 2020 | Saudi Arabia | Annual system cost and probability of power outage | Supply-demand based optimization |
[ | 2020 | Japan | Total cost over the microgrid lifetime | PSO |
[ | 2021 | Russia | Net present cost | Iterative Gauss-Seidel |
[ | 2022 | Philippines | Levelized cost of electricity, probability of power outage, and greenhouse gas emissions | Multi-objective PSO |
[ | 2020-2022 | USA, Malaysia, Bangladesh, India, Namibia, Spain, and Pakistan | Net present cost | HOMER Pro software |
[ | 2022 | Algeria | Cost of energy and probability of power outage | Multi-objective slap swarm algorithm |
[ | 2022 | USA | Cost per kWh | Nonlinear reduced gradient method |
One of the main constraints of the problem must ensure the balance between the power produced by different sources and the system demand. Then, the optimization constraints manage the state of charge (SOC) of the battery energy storage systems (BESSs) and the fossil generators dispatch.
On the other hand, the generation capacity of renewable energy sources strongly relies on resource availability according to geographical and meteorological conditions. Solar radiation or wind speed data vary greatly depending on the season or time period.
The forecast problem of solar radiation and wind speed has become increasingly important over the last decade [
Furthermore, [
This paper proposes a method for the optimal design of hybrid microgrids to fulfill energy needs in isolated communities such as Cerrito de Los Morreños Island in Ecuador. This method integrates renewable and conventional energy sources alongside machine-learning algorithms for selecting representative values of meteorological parameters, particularly air temperature and solar irradiance. The clustering analysis introduces a factor related to renewable generation into the optimization model. This process represents a tangible solution that is applicable and replicable in isolated communities and shares characteristics similar to the case study conducted in this paper. On-site data on consumption and installed equipment are collected through an information survey to improve the accuracy of design outcomes. This process involves manual collection. The collected information can be used in future research and enhances the strength and utility of the proposed method. The obtained hybrid microgrid is rigorously examined under six operating scenarios to provide a comprehensive assessment. These scenarios encompass: ① 24-hour continuous power supply; ② load shedding percentage; ③ diesel generator (genset) curtailment; ④ the worst meteorological conditions; ⑤ the use of renewable energy sources including BESSs; and ⑥ the use of genset. The optimization model is proposed and addressed using a mathematical programming language (AMPL) tool, and the results highlight significant findings.
The remainder of this paper is structured as follows. Section II describes the method for the optimal design of hybrid microgrid, highlighting the importance of the BESS for isolated communities in the optimization model. In Section III, the results of the optimal design for hybrid microgrid is assessed in different operating scenarios. Finally, the discussion and conclusion are drawn in Sections IV and V, respectively.
The method for the optimal design of hybrid microgrid is summarized in
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F001.jpg)
Fig. 1 Summary of method for optimal design of hybrid microgrid.
The hybrid microgrid is located in the Cerrito de los Morreños community, which is in the Gulf of Guayaquil, Guayas Province in Ecuador, with coordinates at latitude 2°28’25.0” S and longitude 79°54’28.2” W, as shown in Appendix A Fig. A1. This community has a permanent population of about 800 inhabitants, which rises to 1000 on holidays. The buildings in this community consist of 96 residential houses, one church, and one school. One of the main problems for the inhabitants is the lack of power supply, as there is no main grid nearby. Currently, a 185 kVA genset supplies electricity to the entire community from 18:00 to midnight, which consumes about 20 gallons of diesel per day.
Although this community does not have a continuous power supply, each home has all types of electrical appliances that work as soon as the generator is started. Currently, the frequency of use of electrical appliances in each household is directly dependent on the operating time of the genset, which is only six hours per day. The daily and annual load profiles are constructed considering a 24-hour power supply from the microgrid. The operating time of each appliance in a new scenario of electricity availability is surveyed on each household in this community.
The design of the hybrid microgrid depends mainly on the electrical load. The baseline data for adequately constructing the load profile are based on classifying the appliances (loads) into two groups considering the duration of power consumption during their operation. Then, from a survey performed on each dwelling, the appliances are classified as follows.
1) Group 1: appliances with intermittent power consumption, which operate with on/off cycles, such as refrigerators, freezers, and washing machines.
2) Group 2: appliances with continuous power consumption during their operation, such as television (TV) sets, lamps, and computers.
After classification, we summarize the electrical load information in Cerrito de los Morreños community, which is identified from surveys conducted on inhabitants in November 2022, as shown in
Load sector | Appliance | Quantity | Power (kW) |
---|---|---|---|
Residential | Refrigerator | 24 | 2.96 |
Freezer | 37 | 4.33 | |
Washing machine | 42 | 16.86 | |
TV | 77 | 8.78 | |
Blender | 33 | 14.67 | |
Iron | 26 | 27.80 | |
Toaster | 9 | 6.22 | |
Computer | 15 | 12.26 | |
Lamps | 388 | 7.73 | |
Cell-phone | 164 | 2.69 | |
Others | 4.95 | ||
Church | Lamps | 9 | 0.18 |
Speakers | 3 | 3.30 | |
Fans | 2 | 0.16 | |
School | Fluorescent | 17 | 0.68 |
LED lamps | 14 | 0.14 | |
TV | 2 | 0.06 | |
Computers | 20 | 3.60 | |
Speakers | 2 | 1.20 | |
Street lighting | Lighting | 3 | 3.30 |
Note: the item “Others” in the residential sector corresponds to other types of appliances that are not widely used and/or are very rare in the dwellings such as printers and sewing machines.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F002.jpg)
Fig. 2 Estimated hourly load profile on a typical day in Cerrito de los Morreños community.
The lack of weather stations in Cerrito de los Morreños community is a drawback for designing a hybrid microgrid. Given this, the proper selection of representative values of each meteorological parameter is fundamental for solving optimization models related to cost minimization in microgrids. The inadequate selection of these values can make the best solution of the optimization model with over-sizing components and therefore higher life cycle cost of the hybrid microgrid. The solar irradiance and air temperature for this community are obtained from NASA meteorological datasets [
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F003.jpg)
Fig. 3 Meteorological data of solar irradiance and air temperature for year 2020. (a) Solar irradiance. (b) Air temperature.
In this study, the k-means clustering algorithm is applied to obtain hourly representative values of meteorological parameters for each month.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F004.jpg)
Fig. 4 Steps of k-means clustering algorithm and its integration with optimization model.
The k-means clustering algorithm works iteratively and stops when there are no changes [
The Elbow statistical method is used to determine the optimal number of clusters, i.e., k, since the number k can be selected intuitively in the k-means clustering algorithm. The Elbow statistical method is based on the sum of the squared distances between the data points and their corresponding cluster centroid [
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F005.jpg)
Fig. 5 Innertia versus number of clusters k for hourly solar irradiance and air temperature data in January.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F006.jpg)
Fig. 6 Results of k-means clustering algorithm using a meteorological dataset in January.
Figures
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F007.jpg)
Fig. 7 Hourly representative values of solar irradiance for each month.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F008.jpg)
Fig. 8 Hourly representative values of air temperature for each month.
To address the worst meteorological conditions, the proposed optimization model incorporates a key factor depending on hourly representative values for PV generation. This factor is derived from analyzing the historical data of meteorological parameters, including temperature, humidity, pressure, wind, precipitation, cloud cover, etc., over the past three years in the target community. Among these, the temperature is prioritized for the sizing of PV systems due to its significant impact on the energy production model of the PV source.
In this research, we focus on air temperature and solar irradiance as critical meteorological parameters because they play a vital role in influencing the performance and efficiency of renewable energy technologies. Moreover, both air temperature and solar irradiance are integral to the sizing calculations of hybrid microgrids. The k-means clustering algorithm applied to these meteorological parameters is used to obtain normalized daily statistical information and is employed to formulate the temperature equation on the PV panels and determine the total power output of PV panels. The total power output is calculated as a function of the system size on a representative day.
The temperature on the PV panels is given by (1) [
(1) |
where is the index of hours; is the hourly representative value of air temperature at time ; is the temperature of PV panels under standard test conditions (STC); and is the hourly representative value of solar irradiance at time .
Therefore, the total power output of PV panels considering the hourly representative values is given by:
(2) |
where is the nominal power of the PV panel under STC; and is the temperature coefficient.
From (1) and (2), the factor depends on hourly representative values for PV generation is given by (3). This factor is a non-dimensional number and one of the input variables of the proposed optimization model.
(3) |
The hybrid microgrid supplying power to the Cerritos de los Morreños community consists of a genset, BESSs, PV panels, and a power inverter. In this subsection, we show the mathematical formulation of the proposed optimization model to minimize the annual total investment cost of a microgrid, including the O&M cost of a genset in the community. Given this, the result reveals the optimal sizing of the microgrid. The linear optimization method is used to solve the proposed optimization model, which is mainly based on the fact that the decision variables are continuous and the objective function about these decision variables is linear [
To minimize the annual total investment cost of the microgrid, the objective function is defined based on the linear cost functions of each component:
(4) |
(5) |
where is a conversion unit equal to 1 hour; is the rate for the sale of electricity from genset to microgrid; is the power delivered from the genset to the microgrid at time t; is the unit cost of load shedding; is the load demand at time t; is the load shedding percentage at time t; is the investment cost of PV panels; is the total power output of PV panels; is the investment cost in a genset whose maximum power output is equal to or less than the existing genset in the community; is the maximum capacity of genset; is the cost of charging or discharging power from the BESS to the microgrid; is the maximum charging or discharging power of the BESS; is the unit cost for energy storage of the BESS; and is the maximum storage capacity of the BESS.
The first term of (4) describes the operation cost of the genset per year; the second term of (4) refers to the penalty due to the load shedding of the microgrid per year; and the last term contains the sizing variables of microgrid, which is composed of four terms: ① , which represents the cost of using power from the PV system; ② , which represents the cost of using power from the genset; ③ , which represents the cost of using power from the BESS; and ④ , which represents the cost of energy storage of the BESS.
The constraints for the objective function are given as follows.
(6) |
where is the discharging power delivered from the BESS to the microgrid at time t; and is the charging power from the microgrid to the BESS at time t.
(7) |
(8) |
(9) |
(10) |
where is the BESS efficiency; and is the self-discharging rate of BESS.
(11) |
(14) |
where is the maximum power output of PV panels.
(15) |
where is the maximum power generation of genset.
(16) |
(17) |
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F009.jpg)
Fig. 9 Variables involved in proposed optimization model.
Variable | Value |
---|---|
60.00 kW | |
464.60 $/kW | |
0.65 $/kW | |
997.28 $/kW | |
0.16 $/kWh | |
185.00 kW | |
197.92 $/kW | |
197.92 $/kWh | |
0 kWh | |
0.95 | |
0.02 |
The fundamental structure of the current electrical system in Cerritos de los Morreños community is depicted in
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F010.jpg)
Fig. 10 Fundamental structure of current electrical system in Cerrito de los Morreños community.
Initial scenario: the community solely relies on energy generated by the genset without the BESS, which makes the system operate with power available only when the generator is activated, meeting immediate load demands. However, this structure poses significant challenges regarding stability and power quality. The existing genset struggles to meet the load demand, leading to persistent undervoltage issues that swiftly damage connected appliances. A comprehensive survey reveals that almost every household has at least one appliance that is damaged due to the substandard quality of power supply. Common household appliances like washing machines, refrigerators, and TVs are frequently affected. This predicament is compounded by the limited financial resources of local inhabitants. Using the cost data outlined in
The hybrid microgrid is designed in six operating scenarios, where certain conditions such as 24-hour continuous power supply and load shedding percentage are considered, as shown in
Scenario No. | 24-hour continuous power supply | Load shedding percentage | Genset curtailment | The worst meteorological conditions | Use of renewable energy sources | Use of genset |
---|---|---|---|---|---|---|
1 | N | Y | N | Y | Y | Y |
2 | Y | N | N | Y | Y | Y |
3 | N | Y | Y | Y | Y | Y |
4 | Y | N | Y | Y | Y | Y |
5 | Y | N | Y | N | Y | Y |
6 | Y | N | N | Y | Y | N |
Note: the symbol “Y” represents that the corresponding condition is considered; and the symbol “N” represents that the corresponding condition is not considered.
The description of scenarios 1-6 is as follows.
1) Scenario 1: the microgrid does not supply electricity during the day. Given this, a load shedding percentage is introduced to the proposed optimization model, which specifies both the time of the day and the optimal percentage of the load for implementing electricity shortage measures. In addition, the genset can run at any time of the day.
2) Scenario 2: the microgrid does not consider a load shedding percentage, i.e., the microgrid and genset must cover the load demand in the community.
3) Scenario 3: the microgrid has priority in the power supply to take full advantage of solar energy. Given this, the genset must be turned off from 09:00 to 16:00, considering the load shedding percentage.
4) Scenario 4: the community has a 24-hour continuous power supply considering the genset curtailment from 09:00 to 16:00. This scenario does not consider load shedding percentage.
5) Scenario 5: the sizing of microgrid does not consider the worst meteorological conditions of the site, i.e., the factor depending on hourly representive values for PV generation in June, which has the lowest solar inrradiation, is not taken into account. This condition guarantees that the outcomes remain impartial, preventing any favorable bias introduced by PV generation to the genset. Given this, we process all the monthly representative values to obtain an optimal value of the whole set, which is input to the proposed optimization model.
6) Scenario 6: the load demand in the community is wholly covered with renewable energy source during the day, considering the factor depending on houly representative values for PV generation in June.
Scenario No. | (kW) | (kW) | (kW) | (kWh) | Operation cost of genset ($/year) | Cost of load shedding ($/year) | Total cost of microgrid ($/year) |
---|---|---|---|---|---|---|---|
1 | 0 | 32.40 | 2.56 | 4.73 | 36461.2 | 7215.04 | 77426.7 |
2 | 0 | 31.70 | 22.25 | 23.42 | 38686.9 | 0 | 79338.4 |
3 | 65.16 | 29.79 | 12.34 | 60.47 | 20505.3 | 3897.96 | 98794.2 |
4 | 65.16 | 29.93 | 17.59 | 74.06 | 21731.2 | 0 | 99992.2 |
5 | 65.16 | 27.00 | 19.47 | 59.75 | 19560.2 | 0 | 92440.4 |
6 | 279.20 | 0 | 92.50 | 591.89 | 0 | 0 | 265169.0 |
The load demand in Scenario 1 is almost entirely covered by the genset, although there is also a small contribution from the BESS. This scenario prioritizes an economic generation source to supply power because the genset has no operating restrictions. Given this, the optimization solution indicates that the total power output of PV panels should be 0 kW because of its high cost of power generation. In addition, the maximum power output of the genset is estimated to be 32.4 kW, and the maximum storage capacity of the BESS is estimated to be 4.73 kWh. Between the genset and BESS, the proposed optimization model finds an optimal dispatch of power consumption through load shedding so that the grid can continue to supply power to consumers. Load shedding indicates scheduled power outages during specific hours of the day.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F011.jpg)
Fig. 11 Percentage of loads with power supply in Scenario 1.
The total cost of microgrid in Scenario 2 increases by 2.5% compared with that in Scenario 1. This cost difference is insignificant considering the scale of economic investment in developing a microgrid. This scenario guarantees the 24-hour continuous power supply. In addition, has a significant increase of 395% compared with that in Scenario 1. The cost of load shedding is 0. Therefore, although the cost of BESS is higher, the total cost of the microgrid in Scenario 2 does not increase significantly compared with that in Scenario 1. In terms of the power supply quality, this scenario is favorable due to the continuous power supply. However, from an environmental perspective, Scenario 2 is still unfavorable due to increased use of genset and CO2 emissions.
The result in Scenario 3 changes drastically when the genset operating restriction during peak solar irradiance hours (09:00-16:00) is introduced. Scenario 3 prioritizes the use of solar energy. In this scenario, is 65.16 kW, which corresponds to the sizing boundaries of the PV system suggested in the mathematical model considering the maximum peak demand during the 15 years of the project. The genset does not change significantly in compared with Scenarios 1 and 2. However, the maximum discharging power of the BESS to load has a reduction of almost 10 kW compared with that in Scenario 2. This is because the PV panels provide power supply to the load, thus reducing the discharging processes of BESS hour by hour. In addition, the considerable increasement in is due to the increased penetration of PV panels and BESS. Load shedding has a significant reduction. For example, from 13:00 to 18:00, a load shedding percentage of 10% is conducted, i.e., the microgrid can supply 90% of the total load demand in the community. During other hours, the load shedding percentage does not exceed 1%. This configuration could supply more than 99% of the total load demand in the community. However, despite the short duration and low intensity of the power outages, they are still a nuisance to users. Given this, we consider Scenarios 4-6 without load ledding, i.e., the 24-hour continuous power supply is enabled.
The total cost of microgrid in Scenario 4 is $99992.20. In terms of power usage and quality, there is a notable difference from Scenarios 1-3 without any load shedding. The value of does not change considerably compared with that in Scenario 3, but the values of and increase, which are expected because the load demand must be covered during 24 hours of a day even at low solar irradiance and night time. In Scenario 5, the proposed optimization model does not consider the representative values of the worst meteorological conditions with the lowest solar irradiance in June. However, other values considered are above the average for the community, so this does not bring an impossible result. In Scenario 5, the total cost of microgrid has a 8% reduction compared with that in Scenario 4. The size of PV panel does not change, but the value of changes slightly because the microgrid can provide a larger power supply to the load. The effect of considering a higher solar energy is reflected in the increased parameters of the BESS. Finally, Scenario 6 is 100% renewable, i.e., the power supply from the genset is not considered. In this scenario, the total cost of microgrid is $265169.00 due to the considerable increase in the cost of BESS. Compared with Scenario 4, the total cost of microgrid increases by more than 160% by substituting the genset with renewables energy sources. It is demonstrated that the BESS may considerably raise the total cost of a microgrid in 100% renewable scenarios.
Scenario | PV panel | BESS | Total sizing cost of microgrid ($) | ||
---|---|---|---|---|---|
Number | Investment cost ($) | Number | Investment cost ($) | ||
1 | 0 | 0 | 2 | 473 | 473.00 |
2 | 0 | 0 | 10 | 2400 | 2400.00 |
3 | 125 | 15500.63 | 26 | 6240 | 21740.63 |
4 | 125 | 15500.63 | 31 | 7440 | 22940.63 |
5 | 125 | 15500.63 | 25 | 6000 | 21500.63 |
6 | 536 | 66728.63 | 247 | 59280 | 126008.80 |
The approach to the proposed optimization model focuses on finding the optimal combination of the different generation sources; therefore, elements related to the network topology are not considered in the proposed optimization model.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F012.jpg)
Fig. 12 Proposed electrical system in Cerrito de los Morreños community.
![](html/mpce/202402016/alternativeImage/160E32AE-0422-4e3e-AB42-B0EFB1248E4A-F013.jpg)
Fig. 13 Growth percentage for total cost of microgrid in Scenarios 1-6 compared with initial scenario.
The result in each scenario is optimal under its own operating conditions depending on the requirements and prioritization of users. For example, the total cost of microgrid in Scenario 1 reduces by -16.24% compared with the initial scenario. However, other aspects of this scenario must be considered. Among the initial scenario and Scenarios 1-6, the optimal design of microgrid that could be implemented in the community is Scenario 3, where, although there are some restrictions during peak demand hours, consumers have a 24-hour continuous power supply in a day, with a correct dispatch and prioritization of essential sources.
This paper presents the optimal design of a microgrid in six scenarios, considering a combination of sources such as PV panels, BESS, and genset. To analyze these scenarios, we consider the k-means clustering algorithm for the treatment of meteorological datasets for the sizing and optimization of the microgrid in each scenario. The inadequate selection of these values can make the best solution of the optimization model with over-sizing components and therefore higher life cycle cost of the hybrid microgrid.
The obtained hybrid microgrid is rigorously examined under six operating scenarios considering: ① continuous power supply; ② load shedding percentage; ③ genset curtailment; ④ the worst meteorological conditions; ⑤ the use of renewable energy sources including BESSs; and ⑥ the use of genset. The results obtained in each scenario include the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a microgrid. The analysis of results is the product of estimating the electrical load of the site under study, using techniques for better estimating meteorological parameters, and sizing/optimizing a microgrid. The importance of including the components of the microgrid according to operating conditions for cost minimization is demonstrated while satisfying the load demand of the community. Scenarios 1 and 3 consider load shedding but they do not have significant cost reductions compared with those that supply electricity 24 hours per day (except for the 100% renewable scenario). Besides, Scenario 3 has a higher cost than Scenario 1 due to the increase in the storage capacity of BESS and the installed capacity of PV system. It should be noted that the genset operating restriction means that genset does not operate during solar irradiance hours. Scenario 2 does not consider load shedding, i.e., the community has a 24-hour continuous power supply. However, the operation of genset without restriction reduce the costs of microgrid without considering the environmental impact. Given this, the total cost of microgrid in Scenario 2 slightly increases compared with that in Scenario 1. Among these scenarios, Scenario 1 is the most economically feasible option, but environmentally, this scenario would affect the life quality of the inhabitants in this community.
In scenarios that prioritize PV generation (Scenarios 3-5) with a genset restriction, the variation in the total cost of the microgrid is slight among these scenarios that consider the worst meteorological conditions. However, Scenario 5 shows a lower cost than Scenarios 3 and 4 because we consider the hourly representative values of meteorological parameters. However, as mentioned above, Scenario 3 considers load shedding, so Scenario 4 is a feasible option for developing a hybrid microgrid, considering the worst meteorological condition. Finally, Scenario 6 has the highest total microgrid cost among all the scenarios. This 100% renewable scenario satisfies 24-hour continuous power supply without any environmental impact on the inhabitants.
To address the power supply problems in isolated communities in Ecuador, a comprehensive analysis of the optimal design for hybrid microgrids is conducted in various scenarios. To analyze these scenarios, the k-means clustering algorithm is used for the meteorological datasets for the sizing and optimization of the microgrid. The integration of renewable energy sources, energy storage systems, and the consideration of meteorological factors are crucial aspects. The economic feasibility of different scenarios is evaluated, emphasizing the need to balance cost minimization and environmental sustainability. In summary, this paper provides valuable insights for the optimization of microgrid to improve the power supply quality in isolated communities, with a clear awareness of both economic and environmental considerations. For future work, we will incorporate the analysis of excess electricity in the daytime into the optimization model. Likewise, CO2 emissions from the genset will also be determined.
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