2.1. Research Design
The research targets the following research questions: “What type of energy sharing strategy (isolated, IES, or CES) has the lowest total cost? How does the energy sharing strategy, starting month, initial charge, load variation, unit cost, number of systems, geographic location, and required reliability affect total cost and energy storage required?”
The research hypotheses are that the IES and CES operating strategy will reduce the total cost compared to isolated. Increasing the number of systems in IES and CES will reduce the total cost because this will increase the benefit of trading. Additionally, “Hot-Dry/Mixed Dry” climate will have the lowest cost and cost will decrease predictably with lower required reliability. All variables should have some effect on total cost, as given by Equation (1).
where Strategy denotes the operating strategy, Month
initial is the starting month, ESS
initial is the initial charge, Load
var designates the type of load variation, Cost
unit designates the unit cost parameters for energy storage and solar, N
systems denotes the number of systems, Location designates the geographic location, and LPSP
req designates the required reliability. One hundred trials were chosen for all cases to ensure convergence of results without significant computation time. A time horizon of one year was chosen for this initial research because this time frame worked best with the existing methodology.
2.1.1. Energy Sharing Strategy
The effect of energy sharing strategy is the focus of this research. For each case below, isolated, IES, and CES operating strategies are considered. The number of systems can only be considered for the IES and CES operating strategies.
2.1.2. Starting Month
Starting month was chosen as a variable to determine a baseline. The literature was not clear on a typical starting month to choose and if the variable mattered in short-term or long-term simulations. To determine the effect of the starting month (first day of simulation), given as Monthinitial, 100 trials were performed for each month (12 cases), with initial charge (10%), number of systems (five), geographic location (Indianapolis), and required reliability (nine hours a year or 0.1% Loss of Power Supply Probability (LPSP)) kept constant. In long term simulations, starting month should not matter after the battery is charged in the initial year. However, initial results show that starting month does have an effect in the short term, especially with low initial charge in a winter month.
2.1.3. Initial Charge
Initial charge, given as ESSinitial, was also chosen to determine a baseline. The literature was not clear on a typical initial charge of a shipped battery. To determine the effect of the initial charge, 100 trials were conducted for each of six cases (1%, 5%, 10%, 15%, 20%, and 100% initial charges) in March, in July, and in November, with number of systems (five), geographic location (Indianapolis), and required reliability (0.1% LPSP) kept constant. Initial charge values were chosen for the typical range (5% to 20%) with an outlier on each end (1% and 100%).
2.1.4. Load Variation
Load variation, given by Loadvar, was investigated as the motivational basis for energy trading. If every system has an identical load profile, the simulation would not show any instances where it is beneficial to trade. That situation would not be indicative of real-life load profiles, where different homes have different time schedules. To determine the effect of load variation, 100 trials were conducted for a case with all systems having the same load and a case with all systems having loads simulated by the load simulator, with initial charge (10%), number of systems (five), geographic location (Indianapolis), required reliability (nine hours a year or 0.1% LPSP), and starting month (June) kept constant. The load simulator introduces variability into systems by adding or subtracting a random number of hours, creating the impression that systems are on different schedules.
2.1.5. Cost
The unit cost of the solar panels and energy storage, given by Costunit, will change over time for example due to the ending or starting of subsidies, technology improvements, and changes in the market. To show how these changes in cost might affect the analysis, case studies of +/− 15% were included for the energy storage and solar panel unit costs with initial charge (10%), number of systems (five), geographic location (Indianapolis), required reliability (nine hours a year or 0.1% LPSP), and starting month (June) kept constant.
2.1.6. Climate
Climate is known to influence average solar irradiation and hourly load. For example, cold climates typically have lower solar irradiation and higher heating requirements while warmer climates have higher average solar irradiation and higher cooling requirements. To determine the effect of the climate, 100 trials were performed for 4 cases (“Cold”; “Hot-Dry/Mixed”; “Mixed Humid”; and “Cold but with lower solar irradiation”). These cases correspond with Erie, Michigan; Phoenix, Arizona; Little Rock, Arkansas; and Indianapolis, Indiana, respectively.
2.1.7. Number of Systems
The main simulation studies the size of the community as measured by the number of systems, local climate, and required reliability with starting month (June), and initial charge (10%) kept constant and load variation activated. The number of systems, given by Nsystems, was selected because it was believed that more trades would occur and the benefit of establishing a transactive microgrid would improve. To determine the effect of the number of systems, 100 trials were conducted for five cases (Nsystems = 2, 5, 10, 20, 50).
2.1.8. Required Reliability
Required reliability, given as LPSP
req, is known to influence amount of energy storage required to ensure that reliability. In 2016 the average US customer experienced 1.3 interruptions for approximately 4 h of outage per year, with significant variability among states and utility providers [
14]. A range from nine hours (0.1% LPSP) to ninety hours (1% LPSP) was chosen for this initial investigation.
2.3. Inputs
The model inputs are project specifications, component specifications, solar irradiation data, and residential load data. Independent variables include the number of SAPV systems in the community, the number of trials, the acceptable number of outage hours, the number of years being simulated, the insolation data, the load profiles, the initial charge of battery and the starting month of simulation. Component specifications include the solar panel and energy storage performance and cost parameters, given in
Table 1 and
Table 2, respectively. The solar panel specifications correspond with a 3 kW solar module kit that includes an inverter and racking system [
19]. The battery specifications correspond with a 13.5 kWh Tesla Powerwall with an included charge controller assumed to be 100% dischargeable that does not require an enclosure [
20]. Battery efficiency is the “round-trip efficiency” considering both charging and discharging. The interconnection cost is a rough estimate taken as
$200 assuming that the houses are 50 feet apart,
$1/foot of wire, and
$150 for installation. The interconnection cost estimate could be improved with a more specific estimate of implementing the Blockchain technology and more knowledge of what controllers are necessary.
Typical Meteorological Year (TMY) data sets were used for solar irradiation data, specifically TMY3. Although not designed to consider meteorological extremes, TMY data exhibit diurnal and seasonal variations, and they represent a full calendar year of typical climatic conditions for a given location [
21]. TMY3 residential load data was selected because it coincides with TMY3 solar data, is freely available, and is in a convenient format. This dataset includes high and low residential hourly load profiles which will be useful for future work [
22]. There are five climate zones in the U.S (Marine, Hot-Dry/Mixed-Dry, Hot/Humid, Mixed/Humid, and Cold/Very Cold) from which were selected the sites and simulates residential load data [
23]. Each climate zone will have a different typical load profile.
Specific locations are chosen because they all have Class 1 (low uncertainty) data, represent a range of different yearly average Global Horizontal Irradiance (GHI) values, and provide samples from several climate zones. The four locations chosen include Erie, Pennsylvania (Cold); Indianapolis, Indiana (Cold); Little Rock, Arkansas (Mixed-Humid); and Phoenix, Arizona (Hot/Mixed Dry). The purpose of choosing multiple geographic locations was to show that the results are repeated among different data sets and that climate has a noticeable effect. San Antonio, Texas was chosen initially but was abandoned because the load data was discovered to have non-representative values (6.5 kWh days) resulting in much larger capital cost estimates (greater than $200,000). San Antonio was, however, used for validation because it provided a valuable example of how starting month and load variability could affect total cost.
2.4. Validation
Functions were validated by selecting inputs that allowed for confirming the expected output. The methods used for validation are available at [
24]. HOMER [
25] was chosen as an external validation source because it is accepted as the world’s leading microgrid modeling software company and it performs a related analysis to the proposed model (hourly numerical analysis). No existing commercial software allows for transactive microgrid modeling or simulating the energy trading between systems within a community. Because HOMER cannot simulate an energy trading system or more than one residential system, it can only be used to verify the Baseline (isolated) case. This validation also works to compare our optimal results with an established existing method. HOMER was setup to mimic PV and ESS performance parameters given in
Table 1 and
Table 2:
Tesla Powerwall 2.0. Capital cost changed to $8100, search space integers from 1 to 30, and changed the initial state of charge to match the different cases.
“Large free Converter” component included to model the integrated inverter.
The same load and solar data used for both models. The load data is larger than HOMER’s initial estimate, but our data includes electric heating.
“Generic flat plate PV” component used for the PV generation. Changed the PV capacity to 3 kW, capital cost to $8377, derating factor to 73.1%, and edited the search space to only give answers our model would look at. The temperature effects were not considered.
HOMER does not consider the effect of starting month, so June was taken as the starting month for our baseline model based on preliminary results.
Case studies considered:
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Geographic Locations: Phoenix, Arizona; Little Rock, Arkansas; Indianapolis, Indiana; Erie, Pennsylvania
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Required Reliability: 0.1%, 1% LPSP
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Initial Charge: 10%, 20%, 100%
These case studies were selected in order to validate whether the developed model compares with HOMER. The geographic locations were selected because they all have Class 1 (lowest uncertainty) data from TMY3, they represent a range of different yearly Global Horizontal Irradiance (GHI) values, and they offer examples from several climate zones. The validation results for Phoenix and Erie are included in
Table 3. Results for Little Rock and Indianapolis can be found at [
24]. The average percent difference for each location are approximated as follows: 6% for Erie, 9% for Phoenix, 21% for Indianapolis, and 5% for Little Rock. Some of the differences between the two models are that HOMER calculates reliability through capacity shortage (capacity loss/total capacity demanded) while this study measured hours of service (hours where capacity is lost/8760). It may also be due to the simulation model, battery model, and better optimization model of HOMER. HOMER selects an optimum integer value for the number of batteries while the proposed model takes an average of all the trials, resulting in non-integer values. HOMER does not contain a method for analyzing the starting month of the simulation [
25]. These results suggest that starting month should be considered when a system wants to go off-grid particularly in the winter months, because there is less solar power available, which may require additional battery storage during the first winter of operation.