The U.S. Department of Energy has launched the commercial building initiative (CBI) in pursuit of its research goal of achieving zero-net-energy commercial buildings (ZNEB), i.e. ones that produce as much energy as they use. Its objective is to make these buildings marketable by 2025 such that they minimize their energy use through cutting-edge, energy-efficiency technologies and meet their remaining energy needs through on-site renewable energy generation. This paper examines how such buildings may be implemented within the context of a cost- or CO2-minimizing microgrid that is able to adopt and operate various technologies: photovoltaic modules (PV) and other on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and passive/demand-response technologies. A mixed-integer linear program (MILP) that has a multi-criteria objective function is used. The objective is minimization of a weighted average of the building's annual energy costs and CO2 emissions. The MILP's constraints ensure energy balance and capacity limits. In addition, constraining the building's energy consumed to equal its energy exports enables us to explore how energy sales and demand-response measures may enable compliance with the ZNEB objective. Using a commercial test site in northern California with existing tariff rates and technology data, we find that a ZNEB requires ample PV capacity installed to ensure electricity sales during the day. This is complemented by investment in energy-efficient combined heat and power (CHP) equipment, while occasional demand response shaves energy consumption. A large amount of storage is also adopted, which may be impractical. Nevertheless, it shows the nature of the solutions and costs necessary to achieve a ZNEB. Additionally, the ZNEB approach does not necessary lead to zero-carbon (ZC) buildings as is frequently argued. We also show a multi-objective frontier for the CA example, which allows us to estimate the needed technologies and costs for achieving a ZC building or microgrid.

Connection of electric storage technologies to smartgrids or microgrids will have substantial implications for building energy systems. In addition to potentially supplying ancillary services directly to the traditional centralized grid (or macrogrid), local storage will enable demand response. As an economically attractive option, mobile storage devices such as plug-in electric vehicles (EVs) are in direct competition with conventional stationary sources and storage at the building. In general, it is assumed that they can improve the financial as well as environmental attractiveness of renewable and fossil based on-site generation (e.g. PV, fuel cells, or microturbines operating with or without combined heat and power). Also, mobile storage can directly contribute to tariff driven demand response in commercial buildings. In order to examine the impact of mobile storage on building energy costs and carbon dioxide (CO2) emissions, a microgrid/distributed-energy-resources (DER) adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs applying CO2 taxes/CO2 pricing schemes. The problem is solved for a representative office building in the San Francisco Bay Area in 2020. By using employees' EVs for energy management, the office building can arbitrage its costs. But since the car battery lifetime is reduced, a business model that also reimburses car owners for the degradation will be required. In general, the link between a microgrid and an electric vehicle can create a win-win situation, wherein the microgrid can reduce utility costs by load shifting while the electric vehicle owner receives revenue that partially offsets his/her expensive mobile storage investment. For the California office building with EVs connected under a business model that distributes benefits, it is found that the economic impact is very limited relative to the costs of mobile storage for the site analyzed, i.e. cost reductions from electric vehicle connections are modest. Nonetheless, this example shows that some economic benefit is created because of avoided demand charges and on-peak energy. The strategy adopted by the office building is to avoid these high on-peak costs by using energy from the mobile storage in the business hours. CO2 emission reduction strategy results indicate that EVs' contribution at the selected office building are minor.

The addition of storage technologies such as flow batteries, conventional batteries, and heat storage can improve the economic, as well as environmental attraction of micro-generation systems (e.g., PV or fuel cells with or without CHP) and contribute to enhanced demand response. The interactions among PV, solar thermal, and storage systems can be complex, depending on the tariff structure, load profile, etc. In order to examine the impact of storage technologies on demand response and CO2 emissions, a microgrid's distributed energy resources (DER) adoption problem is formulated as a mixed-integer linear program that can pursue two strategies as its objective function. These two strategies are minimization of its annual energy costs or of its CO2 emissions. The problem is solved for a given test year at representative customer sites, e.g., nursing homes, to obtain not only the optimal investment portfolio, but also the optimal hourly operating schedules for the selected technologies. This paper focuses on analysis of storage technologies in micro-generation optimization on a building level, with example applications in New York State and California. It shows results from a two-year research project performed for the U.S. Department of Energy and ongoing work. Contrary to established expectations, our results indicate that PV and electric storage adoption compete rather than supplement each other considering the tariff structure and costs of electricity supply. The work shows that high electricity tariffs during on-peak hours are a significant driver for the adoption of electric storage technologies. To satisfy the site's objective of minimizing energy costs, the batteries have to be charged by grid power during off-peak hours instead of PV during on-peak hours. In contrast, we also show a CO2 minimization strategy where the common assumption that batteries can be charged by PV can be fulfilled at extraordinarily high energy costs for the site.

The increasing concern about climate change as well as the expected direct environmental economic impacts of global warming will put considerable constraints on the US building sector, which consumes roughly 48percent of the total primary energy, making it the biggest single source of CO2 emissions. It is obvious that the battle against climate change can only be won by considering innovative building approaches and consumer behaviors and bringing new, effective low carbon technologies to the building / consumer market. However, the limited time given to mitigate climate change is unforgiving to misled research and / or policy. This is the reason why Lawrence Berkeley National Lab is working on an open source long range Stochastic Lite Building Module (SLBM) to estimate the impact of different policies and consumer behavior on the market penetration of low carbon building technologies. SLBM is designed to be a fast running, user-friendly model that analysts can readily run and modify in its entirety through a visual interface. The tool is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies. It also incorporates consumer preferences and passive building systems as well as interactions between technologies (such as internal heat gains). Furthermore, everything is based on service demand, e.g. a certain temperature or luminous intensity, instead of energy intensities. The core objectives of this paper are to demonstrate the practical approach used, to start a discussion process between relevant stakeholders and to build collaborations.

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment & planning and b) the operations module, which builds on the investment & planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analyzed.