2.1. Review of Existing Stand-Alone Microgrid Design Cases
Through the content of the business plan used to carry out government-supported projects that supersedes some parts of power standards of domestic islands by using renewable sources and ESS, we analyzed the existing modeling methods. The target island is A, which is located in the South Sea. The basic data obtained for the design include geographical data (such as longitude and latitude), weather data (such as average daily solar radiation and average monthly wind speed), power consumption data (such as maximum and average power), number of power-bearing households, and population. Based on these limited data, when building an existing stand-alone microgrid without considering the load pattern, it is assumed that only about 50% of the total power generation is covered by renewable energy, the power deficit is covered using a previously installed diesel generator, and the ESS capacity provides power for 0.7 days in the event of bad weather.
However, the time and cost incurred by preliminary investigation and analysis, including the load pattern of the island, solar radiation, and wind speed, are relatively large compared to the project size. The existing microgrid design in domestic islands, where detailed data are not available, appears to have been designed in the above manner, which negatively affects the stability and economic feasibility of their power systems.
2.2. New Method for Estimating the Load Pattern of the Microgrid
As stated above, to design a microgrid based on the limited data of power load, this paper proposes a new method to estimate the load pattern using existing load pattern data to improve reliability. The basic assumption of the estimation method of the load pattern is that if the generation pattern is similar, such as monthly or seasonal, then the hourly load pattern will also be similar. The background for this assumption is that most islands in Korea, except in special cases such as Jeju Island (tourism) and Geoje Island (shipbuilding), are highly likely to have similar patterns of lifestyle, such as fishing or a combination of agriculture and fishing, so there will be similarities in monthly or seasonal power usage patterns depending on the crops or fishing species, and there should be similarity in hourly load patterns. In this study, the most realistic estimation method is found by comparing the homogeneity of the pattern of annual (every month), seasonal, bi-monthly, and average monthly generation.
Based on the steps for estimating power load, the estimated load pattern is created assuming that only the amount of power used per island (eight existing islands) is known. By comparing the estimated load pattern with the actual load pattern on an island, the similarity in load patterns is verified statistically according to the estimation method. In addition, the optimal design method is confirmed by comparing the design results of a microgrid system reflecting the estimated load patterns produced by the new estimation method, with the similarities between the design results reflecting the actual load patterns. Finally, from an economic standpoint, by comparing the results obtained from estimating the new load pattern for the microgrids on islands, which have already been designed and deployed with the degree of optimization, the improvement performance of the optimal design through the new load pattern estimation is identified.
The annual (8760 h) power generation of the eight islands is summarized in
Table 1, representing a small island with power generation of 20 kW per hour to a large island with power generation of 2 MW per hour, as shown in
Figure 1. As each island has an independent power source involving only diesel generators, it is assumed that the power generated per hour is the power consumed each hour. In the eight islands, except for Islands 4 and 6, the annual power usage is relatively stable, and the power peak is not remarkable. On the other hand, power consumption for Islands 4 and 6 is concentrated at a specific time or a power peak is much larger than the average power consumption. Unevenness of power consumption and power peak in the period is the main cause limiting the expansion of renewable energy resources.
2.4. Microgrid Design Using Estimated Load Pattern
To verify the reliability of the power load modeling methods proposed above, the results of an optimal new and renewable energy combination, which is calculated by applying the load patterns estimated from the four proposed methods for the existing eight islands, and those of the microgrid design obtained by applying the actual load pattern, are compared. Comparing the optimal capacities of the solar generator, wind generator, and ESS based on the load pattern estimated by the four load estimations for the eight islands and the optimum capacity based on the actual load pattern, the method of load estimation derived from the most similar results is determined. Weather data from Amazon Web Services (AWS) and Modern-Era Retrospective analysis for Research and Applications (MERRA) are used.
The microgrid design includes consideration of diesel generators, wind turbines, solar generators, and ESS installations. The objective function of the design is to optimize the total cost, considering both installation and maintenance costs, reducing these to a minimum. When analyzing the economic feasibility of the ESS installed at grid-connected commercial and industry buildings, the basic electric charge reductions through discharge in power peak time are a major factor. However, the stand-alone microgrids covered in this study are subject to analysis at the expense of installation and maintenance, ignoring the effect of the basic electric charges. In addition, it is necessary to account for the Renewable Energy Certificate (REC), the costs of the carbon-trading rights transactions, etc., to reflect the reality in the market being driven by government policies. However, these are not considered for modeling stand-alone microgrids as there are no obvious applications and the cost-varying factors associated with policy changes are very large. The list of cost-varying factors is shown in
Table 5.
The facility’s capacity is constructed to minimize the costs of annual generation, the design is performed using the optimization derivation program developed to calculate the operation strategy, and detailed application function forms and a limiting condition are applied, as shown below.
The mathematical model for a stand-alone microgrid design is modeled to minimize the objective function of the total cost, as shown in Equation (1):
: Installation operating costs of PV ($/kW)
: Capacity of PV (kW)
: Installation operating costs of wind power ($/kW)
: Capacity of wind (kW)
: Operating costs of battery ($/kWh)
: Capacity of battery (kWh)
: Unit price of diesel ($/kWh)
: Diesel power generation (kWh)
As a physical constraint on the objective function shown in Equation (1), the application of the demand–supply balance is expressed as in Equation (2):
: Electrical energy demand by hour
: Efficiency of the power conversion system (PCS)
: PV power generation per hour
: Wind power generation per hour
: Battery discharge per hour
: Battery charge per hour
As a diesel generator is an AC generator, it supplies electricity directly to the AC loads. However, renewable power sources and batteries are DC sources, so electricity generated from them is converted into AC power through the PCS and then supplied to loads. Therefore, the efficiency of the PCS should be considered.
The conditions for battery storage energy are expressed by Equation (3):
where
is electrical energy stored in battery per hour and
is efficiency of battery.
Equation (4) reflects the condition that it is not possible to charge and discharge the battery simultaneously:
In practice, the above nonlinear conditions cannot be directly considered in a linear plan. However, it is possible to add a very small number
(
) to the objective function, multiplied by the charge and discharge term (
), to derive a valid result considering the above conditions. The battery storage energy limit is expressed as in Equation (5):
In the above equation, and are constants representing the minimum and maximum electrical energy stored in the battery that must be maintained for the battery condition, expressed in terms of the percentage of battery capacity. In Equation (5), typical operating conditions, where depth of discharge (DOD) is 15% (minimum) and 85% (maximum), are applied.
In addition, as the constraint to deploy a system in which more than a certain percentage of total electrical energy demand is covered by renewable generation based on the given meteorological and load data, the conditions in Equation (6) are reflected, where
is the target rate of renewable power generation:
A technique is applied to perform the renewable energy optimization process using the objective function, with the code written on MATLAB (2017a, MathWorks, Natick, MA, USA) using the Gurobi optimizer under a high-speed workstation. It also helps to find the lowest cost sources of power generation and battery configurations that meet a given percentage of total annual power demand with renewable energy. The optimization simulation was performed by changing the share of renewable energy generation from 10% to 90% in increments of 10%. The calculation time for each step took approximately 15 min. Linear planning, the most popular technique for solving optimization problems, is performed under the assumption that the PCS performance curves are linear and there are no losses during battery filling operations.
The configuration of the energy system for the reliability improvement analysis of the microgrid design is shown in
Figure 9. PV power is supplied to consumers (loads) rather than to the battery. For wind generators, in response to their intermittency, the produced electricity is supplied directly to the demand source if there is demand, but stored in the battery if there is no demand [
11,
12,
13].
The installation costs for the system configuration are applied as shown in
Table 6.
With the application of the load pattern calculated using the four load pattern estimation methods for the eight islands, a microgrid power system that achieves the lowest generation unit price while satisfying the renewable energy ratio ranging from 10% to 90% (changing by policy) with 10% gaps has been designed. The estimation method of the load pattern with the most similar facility configurations using the method of least squares through the differences in capacity of each facility according to the actual load patterns is shown in
Table 7.
Unlike the formative similarity in the load pattern identified in
Section 2.3.5 above, the least squares method in
Table 7 clearly shows that most of the annual results (six out of eight islands) are at the minimum, meaning that the annual (every month) estimation would follow typical loads more closely.