Objective of the Monte Carlo Simulation
The DER-CAM analysis presented earlier was used to optimize the design of the EcoBlock microgrid, assuming typical patterns of weather, residential load shapes, and EV charging. It also assumes perfect prior knowledge of these factors to allow optimal control of the energy assets. In reality, there is significant variation in these factors over time, as well as significant uncertainty in future conditions for which the microgrid controls need to optimize. To better understand how this uncertainty affects the performance of the microgrid, the study team performed a Monte Carlo simulation to quantify the performance of the PV, storage, and EVSE sizes produced by DER-CAM, under different environmental and usage conditions. The Monte Carlo simulation randomly generates sample house demand profiles, EVSE charging profiles, and solar generation profiles. From the randomly generated profiles it uses a basic decision model to optimize the use of electricity, minimizing energy cost and environmental impact. The performance metrics to be tested in this simulation were economic performance and energy performance. The economic performance is measured in electricity cost savings; the energy performance is measured in energy demand and energy generation.
Simulation Methodology
The Monte Carlo simulation is divided into two main components: (1) data generation and (2) simulation and control. The first program, data generation, is designed to address the
stochastic nature of load generation and load demand by generating random scenarios of
energy generation, house load demand profiles, and EVSE demand profiles. The second component of the Monte Carlo simulation, simulation and control, is design to make optimal decisions to control the assets (flywheel charging, EVSE fleet, and electricity from the grid) and to minimize cost.
Data Generation
The data are generated first by the Monte Carlo Simulator and then stored in one file with a protocol to access them, but to generate feasible random variables, we needed to have an idea of the statistical distributions of those variables. Thus, numerous real data was collected from the following sources:
EcoBlock load demand profiles. The project team periodically canvassed block
residents, primarily to collect authorizations from them to release their utility data. The project team used various methods, including door-to-door canvassing, office hours, and cash incentives to collect the information. Twenty-three residents provided utility data to the project team via Utility API. The data that was gathered included historic electric consumption. The historic electric demand readings varied among residents, depending on when their service with PG&E started and when their smart meter was installed. For some residences, the team was able to obtain historic consumption in one-hour intervals, dating back to the first quarter of 2016.
EcoBlock EVSE load demand profiles. The team used Pecan Street EVSE demand profile data to generate the EcoBlock EVSE fleet load demand profile. Pecan Street is a program focused on advancing university research and accelerating innovation in water and energy. The program is collecting considerable information of real consumption,
including real EVSE load demand profiles. The team imported EVSE demand profiles for Pecan Street into the model, and from these profiles the model estimated the total EVSE demand of the 24-vehicle fleet proposed by DER-CAM.
PV generation. This was computed by obtaining irradiance data from the National Renewable Energy Laboratory (NREL) and from Equation 1.
E=A*r*H*PR (Eq. 1)
Where:
E = energy produced in kilowatt-hours A = solar panel area
r = solar panel yield efficiency (12 percent)
H = global horizontal irradiance per hour (data obtained from NREL) PR = performance coefficient (60 percent)
The team generated and tested a total of 560 load-generation and load-demand profiles. To achieve heterogeneity, 28 different scenarios were analyzed with 20 simulations for each scenario. The scenarios were selected depending on different energy consumption and energy generation patterns in the data. An example of two different scenarios would be a 24-hour period during winter versus a 24-hour period during the summer. Figure 3-22 presents the results of the data generated from Simulation 1, Simulation 14, Simulation 18, and
Simulation 26. Simulation 1 corresponds to a 24-hour period in winter, Simulation 14
corresponds to a 24-hour period in spring, Simulation 18 corresponds to a 24-hour period in summer, and Simulation 26 corresponds to a 24-hour period in fall.
Figure 3-22. Load vs. Time of Day for Four Simulations
Green represents PV generation; red represents EcoBlock load demand, and blue represents EVSE fleet load demand.
Simulations 1, 14, 18, and 26 correspond to 20 different scenarios performed for a 24-hour periods during winter, spring, summer, and fall, respectively.
Source: UC Berkeley
Simulation and Control
To conduct the analysis, a decision-making algorithm was used to balance supply and
consumption at each hour. The main objective of the algorithm was to balance load demand by using PV generation, flywheel energy and, if needed, energy from the grid. Figure 3-23
illustrates the algorithm’s control logic. The algorithm prioritizes the use of electricity
generated by the PV array. This means that at every hour, the system will try to use the energy generated in the EcoBlock, regardless of the price of energy from the grid, thus minimizing environmental impact. If excess energy is produced, it will be stored in the flywheel for later use. Energy will be purchased from the grid when the PV generation and the available flywheel energy output are not able to satisfy the total energy demand.
Figure 3-23. Control Scheme Overview
Source: UC Berkeley
Each simulation was plotted and the results recorded in a database to obtain an aggregate set of results for the complete number of simulations performed. Figure 3-24 and Figure 3-25 represent the control scheme results for two of the 560 simulations performed. Figure 3-24 is a simulation performed during the winter and Figure 3-25 represents a simulation performed during the summer. At each hour the load demand from the houses and the load demand for the EVSEs was satisfied by using an aggregation of energy from PV generation, flywheel, and load from the grid. In the initial hours of the day, before energy from the sun is available for PV generation to start, all the electricity must be purchased from the grid. This follows the
assumption that flywheel starts the simulation with no initial load. As PV generation starts, the energy purchased from the grid (load from grid) decreases. During days with enough solar radiation, the total energy demand is satisfied by PV generation and there is no need to purchase energy from the grid. As excess energy is available, the flywheel starts charging at a rate that increases as the available excess energy increases. In the simulation presented in Figure 3-25, the excess energy is sold as the flywheel reaches charge capacity. In both simulations presented below, the energy stored in the flywheel was used to satisfy the total load demand when PV production decreased. In both simulations, energy needed to be
purchased at the end of the day, meaning that the energy stored in the flywheels was not able to satisfy the complete load demand or that price to buy energy from the grid was attractive enough to use energy from the grid.
Figure 3-24: 24-hour Stimulation During Winter
Source: UC Berkeley
Figure 3-25: 24-hour Stimulation During Summer
Source: UC Berkeley