Lab Directorate

Currently over $300B is spent in US city economies to pay for energy. Many US cities are taking leading roles in exploring and promoting activities to improve energy efficiency and reduce green house gas (GHG) emissions. This paper summarizes a series of interviews with several leading cities regarding their needs, methods and tools they are using to model energy use and evaluate policies to reduce GHG. We also present a review of several analysis tools evaluated and used to explore urban scale design scenarios for two new major developments in the San Francisco area. We found that cities face great challenges managing data on their building stock, obtaining energy use data, and evaluating the different tools that are available to them. There is a need for better data management systems that allow tools to be more interoperable. The wide variety and features of today’s tools, and the fact that many of them are not open data models, create sub-optimal environments to conduct the energy analysis many cities are seeking to conduct.

BayREN Integrated Commercial Retrofits (BRICR) is a DOE-funded project which aims to enhance the capacity of energy efficiency programs to recruit participants, develop retrofits, and measure outcomes in small and medium-sized commercial buildings, a sector notoriously hard to reach and expensive to serve, that accounts for ⅔ of US commercial floor space. To address these barriers, BRICR leverages existing incentives, financing, data, and open source software to facilitate two paths for comprehensive improvements: a deep energy retrofit, or serial upgrades integrated into capital improvement and maintenance cycles. BRICR is developing an integrated workflow for iterative energy modeling of commercial buildings for city energy program managers and auditors - starting with mass building-scale simulation based on public records and proceeding through audit, retrofit, and measurement and verification stages. BRICR builds on existing tools including LBNL’s CBES, NREL’s OpenStudio, PNNL’s Audit Template, and DOE’s BuildingSync and SEED PlatformTM. At each stage, BRICR uses available information to inform simulations (starting with public records but iteratively augmented with observations from energy program staff) to improve the quality of the models that inform decision making. This paper presents initial results from energy models of 1699 office and retail buildings in San Francisco. Building stock data from public records were translated to create a BuildingSync file for each building which was stored in SEED. Each BuildingSync file was then translated to multiple OpenStudio Workflow files for EnergyPlus simulation to estimate energy savings of energy conservation measures (ECM). Energy savings predictions for each ECM were written back to an updated BuildingSync file for each building and re-uploaded to SEED. The distribution of baseline energy simulations was calibrated against the publicly disclosed distribution of energy benchmarking data to increase confidence in results.

Buildings in San Francisco consumed 52% of total primary energy. Improving building energy efficiency is one of the key strategies cities are adopting towards their energy and climate goals. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures for investment and to design effective incentive and rebate programs. This paper introduces methods to develop a standardized dataset of city building stock, and it demonstrates the use of a UBEM tool, City Building Energy Saver (CityBES), for an urban-scale energy retrofit analysis of building stock in the city of San Francisco. CityBES is an open web-based data and computing platform providing city-scale building energy modeling and performance visualization and benchmarking. CityBES utilizes an international standard CityGML to represent the three-dimensional building stock in cities. As an application example, 940 office and retail buildings in six districts of northeast San Francisco were modeled and analyzed with CityBES to evaluate energy savings for five selected measures. The analysis found that replacing existing lighting with LED and adding an air economizer to HVAC systems are cost-effective measures with combined savings per building between 17% to 31%. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or building energy models, which helps overcome barriers for city managers and their consultants to adopt UBEM.

Improving the sustainability of cities is crucial for meeting climate goals in the next several decades. One way this is being tackled is through innovation in district energy systems, which can take advantage of local resources and economies of scale to improve the performance of whole neighborhoods in ways infeasible for individual buildings. These systems vary in physical size, end use services, primary energy resources, and sophistication of control. They also vary enormously in their choice of optimization metrics while all under the umbrella-goal of improved sustainability. This paper explores the implications of choice of metric on district energy systems using three case studies: Stanford University, the University of California at Merced, and the Richmond Bay campus of the University of California at Berkeley. They each have a centralized authority to implement large-scale projects quickly, while maintaining data records, which makes them relatively effective at achieving their respective goals. Comparing the systems using several common energy metrics reveals significant differences in relative system merit. Additionally, a novel bidirectional heating and cooling system is presented. This system is highly energy-efficient, and while more analysis is required, may be the basis of the next generation of district energy systems.

A key issue for saving energy in buildings is to assure that delivery of energy services matches building occupancy as closely as possible, to assure that energy is not wasted providing the services to empty rooms or buildings, and to assure that needed services are provided during all times when desired. Accomplishing this to date has been significantly hampered by a lack of inexpensive mechanisms to obtain and use building-wide and more granular data about occupancy. In recent years, the concept of inferential (or implicit) sensing has been proposed and explored to use data from IT systems that already exists in most buildings, to obtain data that are not perfect, but are nearly free to obtain. Past work by LBNL and others has primarily demonstrated the principle and potential for this, with a primary focus on data from Wi-Fi networks as the best near-term opportunity for inferential sensing from IT networks. This is due to its near-ubiquity, ease of explanation to many audiences, relative uniformness in deployment, low latency of detection, clear ways to mitigate privacy and security, and other benefits (Price et al., 2015). While the potential and value are clear, researchers or building managers who want to obtain the data lack a source of information to understand what data might exist, what devices may have it, and what mechanisms are available to obtain the data. The purpose of this report is to fill that gap. The report begins with a review of institutional issues in collecting such data, including very real concerns about privacy and IT security. It then describes the various system architectures used in Wi-Fi systems in commercial buildings today, and a variety of mechanisms that can be used to obtain information from them, including examples of how to use them, and sample output. Next is a summary of how to use such data for several different purposes, from retrospective analysis to dynamic building operation, and a conclusion. An appendix provides additional detail on specific mechanisms available from major equipment manufacturers. The mechanisms specified in this report can be used by building owners and operators to confirm proper operation and uncover any issues or unexpected conditions as a resource for building owners and researchers interesting in utilizing inferential sensing data.

The rapid growth of renewable generation is creating challenges for the California grid in the form of the “duck curve,” with increasingly steep ramping required for conventional generation resources in the morning and evening, and growing curtailment of solar resources in midday periods. Time-varying electricity tariffs have received considerable attention as a tool to address these challenges, with a renewed recent focus on the potential for dynamic tariffs that vary to reflect conditions on the grid in near-real time. Consideration of dynamic tariffs may raise concerns about the financial impact on utility customers, especially for those who have limited flexibility to modify their electricity consumption in response. Specific areas of concern include electricity bills, bill volatility, and equity implications related to cost shifting among customer groups. In this paper we leverage smart meter data for more than 400,000 California utility customers, spanning residential, commercial, industrial, and agricultural customers, to assess potential customer bill impacts arising from a multi-component dynamic tariff . Specifically, we compute impacts on customer bills and bill volatility under the assumption of fully inelastic demand, i.e., where customers do not change their consumption patterns in response to the tariff. We also assess various approaches designing subscription load shapes that customers can pre-purchase as a hedge that may provide a measure of protection against large negative impacts, while still incentivizing the modification of loads on the margin. We compare and contrast the relative impacts on different customer classes and discuss benefits and pitfalls of different dynamic tariff structures and subscription load shapes.

The electricity grid is transitioning from a centralized and uncoordinated set of large generators and loads to a framework that also includes decentralized and coordinated "distributed energy resources" (DER). Advances in renewable generation, energy storage, efficiency, and controls technology present a significant opportunity for demand-side investment that is matched to the needs of the future grid infrastructure and operations, but the complex interplay of controls technology and grid operations makes estimating and realizing the potential of DER a significant challenge. Supply curves for conserved energy have long been used to synthesize energy efficiency opportunities for electricity system planners and show how demand side resources compete with building new power plants. We have developed a similar approach for supporting policymakers who now face a range of technology options for DER, with a focus on describing the potential for demand response (DR) to provide flexibility to the grid. We describe our modeling approach using supply curves for demand response across four key dimensions: reshaping with rates, shedding at critical time, shifting to capture renewables, and fast-response "shimmy" to balance the grid. In a California-focused study, we find a significant potential for DR to support the grid, and a need for integration between DR and energy efficiency. The combined efficiency benefits from a better-controlled and commissioned facility can lead to significant reductions in the cost of DR, increasing the quantity that is cost competitive by 5-200%. The benefit stream from DR can alternatively be framed to "buy down" the cost of EE investment.