Modern buildings are heavy power consumers. For instance, of the total electricity consumed in the US, 75% is consumed in the residential and commercial buildings. This consumption is not evenly distributed over time. Typical consumption proﬁle exhibits several peaks and troughs. The peakiness, in turn, dictates the electric grid’s generation, transmission and distribution costs, and also the associated carbon emissions. This thesis discusses challenges involved in achieving the sustainability goals in buildings and electric grids.
It investigates building and grid energy footprint optimization techniques to achieve the following goals: 1) making buildings energy eﬃcient, 2) cutting building’s electricity bills, 3) cutting utility’s costs in electricity generation and distribution, 4) reducing carbon footprints, and 5) making the aggregate electricity consumption proﬁle grid-friendly. In this thesis, we ﬁrst design SmartCap, a system to enable homes ﬂatten their consumption/demand by scheduling background loads (such as A/Cs, refrigerator), without causing user discomfort and without direct user involvement. Demand ﬂattening facilitates aggregate peak reduction, which in turn enables grids to 1) reduce carbon emissions, and 2) cut installation and operational costs.
Our results demonstrate that SmartCap can decrease the average deviation from mean power by over 20% across all periods with “high” deviation, thereby ﬂattening the “peaky” demand. Next, we present SmartCharge, an intelligent battery charging system that shifts a building’s electricity consumption to oﬀ-peak periods by storing low-cost energy for use during high-cost periods, without active user involvement. We show that SmartCharge can typically save 10-15% in bills and can reduce the grid-wide peak demand by up to 20%. We then extend SmartCharge to GreenCharge, which integrates on-site renewables in a building’s electricity consumption. Our experiments show that GreenCharge can cut user electricity bills up to 20%. After GreenCharge, we investigate the use of large-scale distributed energy storage at buildings throughout the grid to ﬂatten grid demand, while 1) maintaining end-user incentives for storage adoption at grid-scale, and 2) ensuring grid stability. We design PeakCharge, an online peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge.
Empirical evaluations show that total storage capacity required by PeakCharge to ﬂatten grid demand is within 18% of the capacity required by a centralized system. Finally, we examine the eﬃciency of employing diﬀerent combinations of energy storage technologies at diﬀerent levels of the grids distribution hierarchy to cut electric utility’s daily operational costs. We present an optimization framework for modeling the primary characteristics of various energy storage technologies and important tradeoﬀs in placing diﬀerent storage technologies at diﬀerent levels of the distribution hierarchy. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.
BACKGROUND AND RELATED WORK
Green Computing is concerned with designing systems with low energy consumption and low carbon footprints. There are two important aspects of green computing: ﬁrst, greening of computing, i.e., making computing devices energy eﬃcient; second, application of computing for greening, i.e., employing computer science methods to make physical systems green or energy-eﬃcient.
SMARTCAP: FLATTENING PEAK ELECTRICITY DEMAND IN SMART HOMES
The focal point of SmartCap’s architecture is an intelligent smart home gate way. The home gateway serves as the interface between a smart home and the smart grid. As shown in Figure 3.1, the gateway receives information from multiple potential sources, including real-time electricity prices and demand-response signals from the grid, generation data from on-site renewables, and consumption data from each household load. The gateway’s data sources inform its load scheduling policy.
We combine LSF with a target capacity threshold to determine how many loads to power, and how much power to supply to battery chargers. Once the sum of the background loads power usage reaches the capacity threshold, the scheduler stops powering additional background loads. Figure 3.7 depicts how LSF scheduling ﬂattens demand for a real power signal, assuming three A/Cs turn on near each other as in Figure 3.6. As in our example, LSF ﬂattens the demand proﬁle by interleaving the on periods.
SMARTCHARGE: CUTTING THE ELECTRICITY BILL IN SMART HOMES WITH ENERGY STORAGE
Figure 4.2 depicts Smart Charge’s architecture, which utilizes a power transfer switch that is able to toggle the power source for the home’s electrical panel between the grid and a DC→AC inverter connected to a battery array. A gateway server continuously monitors 1) electricity prices via the Internet, 2) household consumption via an in-panel energy monitor, and 3) the battery’s state of charge via voltage sensors. Before the start of each day, the server solves an optimization problem based on the next day’s expected electricity prices, the home’s expected consumption pattern, and the battery array’s capacity and current state of charge, to determine when to switch the home’s power source between the grid and the battery array.
To better understand SmartCharge’s potential for savings, it is useful to consider a worst-case scenario where 100% of the home’s consumption occurs during the day’s highest rate period. Consider our home’s hourly electricity use on January 3rd, 2012, as depicted in Figure 4.4. On this day, the home consumed 43.7 kWh, primarily due to the occupants running multiple laundry loads after returning from a holiday trip.
GREENCHARGE: MANAGING RENEWABLE ENERGY IN SMART BUILDINGS
Figure 5.1 depicts GreenCharge’s architecture, which utilizes a power transfer switch that is able to toggle the power source for the home’s electrical panel between the grid and a DC→AC inverter connected to a battery array. On-site solar panels or wind turbines connect to, and charge, the battery array. A smart gateway server continuously monitors 1) electricity prices via the Internet, 2) household consumption via an in-panel energy monitor, 3) renewable generation via current transducers, and 4) the battery’s state of charge via voltage sensors. Our SmartCharge system, which we compare against in this work, utilizes the same architecture, but does not use renewables.
Figure 5.4 then compares GreenCharge using renewable produc tion from Figure 5.3 with a home has only energy storage but not renewables (labeled SmartCharge), and home with no energy storage or renewables. Now consider our home’s hourly electricity use on January 3rd, 2012, as depicted in Figure 5.4 in red.
SCALING DISTRIBUTED ENERGY STORAGE FOR GRID PEAK REDUCTION
There are two primary ways to control a battery’s rate of discharge. A simple approach is to install multiple switches capable of switching separate fractions of a building’s load between grid and battery power. For example, the system may be able to individually switch each circuit. In this case, the system controls the rate of discharge by monitoring the load on each circuit and switching some subset of circuits to the battery to achieve a speciﬁc rate of discharge. An alternative, cleaner approach depicted in Figure 6.2 is to connect the battery in parallel to the grid and use a discharge controller to programmatically limit the rate of discharge.
Figures 6.1 and 6.7 has an average electricity usage of ∼1kW, billing solely based on peak demand would allow the consumer to use 7X more electricity at no extra cost. Thus, utilities must balance the size of the peak demand surcharge with the electricity rates to encourage ﬂattening without incentivizing consumers to use signiﬁcantly more electricity.
INTEGRATING ENERGY STORAGE IN ELECTRICITY DISTRIBUTION NETWORKS
In general, multiple distribution transformers may be connected in parallel. However, due to a lack of access to the distribution graph of an existing network and for simplicity, in this chapter, we assume the topology of the distribution network as shown in Figure 7.1. We base this simple model on information that is available in public domain and use it in our experimental evaluation.
For computational tractability, so far, we have presented results on a single day. However, to show that the savings hold over longer periods, we conducted experiments oven an entire month. Figure 7.10 shows the average daily cost savings for the month of March, 2014 from our traces. Due to space constraints, savings are shown only for low and high cap-ex for day-ahead pricing; three types of deployments are shown: lead-acid at homes, multi-level lead-acid, and multi-level hybrid.
SUMMARY AND FUTURE WORK
This thesis has explored energy optimization techniques in buildings and distribution networks to make them energy eﬃcient, cut end user’s electricity bills, and cut electric utility’s expenses in electricity distribution. We have proposed a set of techniques to achieve these optimizations without active user involvement and user inconvenience, while making aggregate electricity consumption proﬁle grid-friendly.
Appliance Scheduling for Demand Flattening: Demand-side energy management is challenging, since it often requires active consumer involvement. Forcing people to think about how they use power is not eﬀective in encouraging broader adoption of demand-side management. Therefore, ﬁrst, we focused on quantifying the beneﬁts of scheduling transparent background loads (such as A/Cs, refrigerators).
Here we present some of the future research directions that have emerged from the work in this dissertation. Energy Optimizations for Group of Buildings: Modern buildings are get ting increasingly smarter by having integrated building management systems (BMSs) that enables ﬁner monitoring and control of the mechanical and electrical equipment in the buildings. In the future, we would like to leverage the emerging technology trends such as ubiquitous wireless communication, mobile computing, and cloud computing to design and implement large-scale extended-BMSs.
Source: University of Massachusetts
Author: Aditya K. Mishra