This section describes the microgrid control software architecture. The purpose of the control software is to manage the microgrid assets to achieve two objectives: (1) provide reliable
electric power delivery, and (2) minimize operating cost. The control architecture is divided into two hierarchical levels, each with its own time scale. The control system also contains
forecasting algorithms, a centralized database, and a web-based management and visualization platform.
Overview
As shown in Figure 3-10, the assets include the energy storage system (ESS), EV chargers, PV inverters, a grid-tied bidirectional converter, and controllable appliances.
Figure 3-10: Schematic of Control Architecture for an EcoBlock Microgrid
Source: UC Berkeley
Two-Layer Control Scheme
Figure 3-11 shows the hierarchical, two-layer control architecture. The upper layer provides power set-points for economic dispatch at a 15-minute (or similar) time scale. The lower layer provides distributed voltage control at the device level at a 1 second (or similar) time scale.
The role of the upper layer is to provide optimal economic dispatch for the microgrid assets.
This achieves the second objective—economic power delivery—which is described above. That is, it provides power set points to each asset to minimize a given objective function, which could be economic cost for electricity imported from PG&E, marginal CO2 emissions, or any other parameter of interest. This task is performed via a system of model-predictive control (MPC). The MPC algorithm solves a numerical optimization problem that finds the trajectory of asset power set points over a finite future time horizon (e.g., six hours), which minimizes the given objective function. Since the MPC incorporates a model of the microgrid power system over a future horizon, it requires forecasts of the PV power and loads (described next). The optimization is executed at each 15-minute time step over a finite forward horizon, so the MPC periodically re-plans the optimal trajectory of power set points as new measurements and forecasts become available. An example output of this upper layer is depicted in Figure 3-12.
Figure 3-11: Schematic of Two-layer Control Architecture
Includes a centralized model predictive control (upper layer), and distributed voltage control (lower layer)
Source: UC Berkeley
Figure 3-12: Example Output of Power Setpoints From an Upper-layer Model Predictive Control Scheme to Minimize the PG&E Electricity Bill
Source: UC Berkeley
The role of the lower layer is to provide voltage control across the network. This achieves the first objective described above—reliable power delivery. The voltage control algorithms are distributed, and exist on or within each major component in the microgrid (particularly the battery charge controller, but also the other major power conversion equipment and major end-use loads). That is, each asset measures voltage locally and adjusts its power electronics duty ratios and/or loads to regulate voltage. Algorithmically, this is achieved with a simple closed-loop control using proportional integral (PI) controllers, yet the PI controllers have a distributed communication architecture to ensure network stability.
Forecasting Algorithms
The upper-layer MPC controller requires forecasts of three assets: PV generation, EV charging loads, and uncontrollable loads. Separate algorithms (and hardware in the case of PV) will be
Upper Layer:
Power Setpoint Control at 15-minute (or similar) time-scale
Lower Layer Blocks:
Voltage Control at 1-sec (or similar) time-scale Operates at device-level
Physical Microgrid
The PV forecaster is comprised of a microforecasting technology capable of predicting irradiance 5 to 300 seconds in advance. The microforecaster contains two components: far-infrared sensors for sky imaging (shown in Figure 3-13) and self-adaptive neural network algorithms for converting the images into power forecasts. Forecasting at short time scales is critical for capturing transient shading from clouds, which can cause rapid fluctuations in power generation and threaten power reliability.
The EV charging load forecasting engine is comprised of a stochastic process model—a Markov chain model—estimated using historical EV charging data. Figure 3-14 visualizes the probability of EV state-of-charge at plug-out, given the EV SOC at plug-in. Using this conditional probability distribution, one can compute the distribution of EV loads. This distribution of EV charging loads is then sent to the MPC forecasting algorithm for optimal economic dispatch.
Figure 3-13: Far-infrared Sensors for the Solar Micro Forecaster
Source: UC Berkeley
Figure 3-14: Probabilistic Model of EV Charging Load
Given as conditional probability of state-of-charge at plug-out given state-of-charge at plug-in.
Source: UC Berkeley
Finally, the uncontrollable-load forecasting algorithm is comprised of a stacked ensemble learning method with moving horizon optimization. This algorithm trains multiple models to predict loads, given features such as time of day, day of week, ambient temperature, and more.
Then, it computes a linear combination of these model outputs to generate the final load prediction. Importantly, the weights in the linear combination are reoptimized over a rolling retrospective horizon. This enables the load forecasting model to adapt to varying behavior over
time. Sample results from several buildings at the UC Berkeley campus are provided in Figure 3-15, which visualizes the mean absolute percentage error (MAPE) compared to the true load. The red hexagons represent the ensemble approach. The other colored markers represent individual models. This visualizes an important differentiating advantage of the ensemble approach—it performs well across all building types and as well or better than individual forecasting models.
Figure 3-15: Mean Absolute Percent Error (MAPE) for the Ensemble Approach, Compared to Competing Individual Models
Mean absolute percent error for ensemble approach, across various buildings, compared to competing models.
Source: UC Berkeley
Database and User Interfaces
All measured data and actuated signals will be stored in a centralized database and organized into a tree structure to efficiently capture the hierarchical structure of the EcoBlock microgrid.
The database structure was specifically designed for time-series data and control applications.
There will be two user interfaces designed for two different audiences: the Oakland EcoBlock microgrid manager and the Oakland EcoBlock residents. The Oakland EcoBlock microgrid manager will be a centralized interface to monitor and manage the entire microgrid operations.
It will provide a global view of the historical, real-time, and forecasted power of each asset.
Moreover, it will monitor voltage at measured nodes. This manager will be able to redirect power, adjust set points, tune automated control parameters, and troubleshoot any issues.
The Oakland EcoBlock residents themselves will be the second user interface. Unlike other microgrid demonstration projects, the Oakland EcoBlock will collect multiple building owners and ratepayers behind a single grid interconnection point, which is traditionally where the meter that generates utility bills resides. Because most or all of the residents’ power will not be metered and billed by the utility, it is very important to provide feedback to the residents about their power utilization and economic benefits. For this reason, we will develop a
smartphone/tablet-based app that provides a visual dashboard of their energy usage. A prototyped example is shown in Figure 3-16, which visualizes the power consumption or
use, while understanding their cost savings. They can also opt-out of automated control of their controllable appliances.
Figure 3-16: Prototype Power Use Dashboard for EcoBlock Residents
Source: UC Berkeley
Summary
The EcoBlock microgrid control architecture will achieve two primary objectives: (1) reliable power delivery, and (2) economic power delivery. The two-layer architecture provides optimal economic dispatch to each asset to minimize cost, and regulates voltage to ensure reliable operation. Various forecasting technologies predict the PV, EV charging load, and
uncontrollable loads for the model predictive control algorithm. Finally, a centralized database and user interfaces provide interfaces for monitoring and control to the microgrid electricity manager and EcoBlock residents.