Nicholas DeForest

Applications of Optimal Building Energy System Selection and Operation

Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model for several years. Given load curves for energy services requirements in a building microgrid (µ·grid), fuel costs and other economic inputs, and a menu of available technologies, the model finds the optimum equipment fleet and operating schedule. This capability is being applied using a Software as a Service (SaaS) model. The evolution of this approach is demonstrated by description of four past and present projects: (1) a public access web site focused on solar photovoltaic generation and battery viability for large non-residential customers; (2) a building CO2 emissions reduction operations problem for a university dining hall with potential investments considered; (3) a battery and rolling operating schedule problem for a large county jail; and (4) the direct control of the solar-assisted heating ventilation and air conditioning system of a university building by providing optimised daily schedules that are automatically implemented in the building’s energy management and control system. Together these examples show that optimisation of building μ·grid design and operation can be effectively achieved using SaaS.

Web-based economic and environmental optimization of microgrids

Even as distributed generation and distributed energy resources (DER) become a more appealing option to meeting building electricity, heat, and cooling demands, the process of selecting the appropriate equipment mix remains as a complex and time-consuming obstacle. This is the motivation behind Lawrence Berkeley National Laboratorys (LBNL) development of WebOpt, a flexible web-based software as service (SaaS) approach for optimizing the selection and operation of DER equipment, and running the Distributed Energy Resources Customer Adoption Model (DER-CAM), the optimization platform that supports it. DER-CAM solves energy systems for microgrids [1], [2] (e.g. combined heat and power (CHP) or electric storage) holistically, taking into account service levels for multiple building end-uses, including heating, cooling and electricity. Given an individual microgrids hourly energy requirements, available technologies and the economic environment as defined by tariff structure, DER-CAM finds the economically or environmentally optimal combination of equipment to install and an optimal schedule to operate it [3], [4].

A green prison: The Santa Rita Jail campus microgrid

A large microgrid project is nearing completion at Alameda Countys twenty-two-year-old 45 ha 4,000-inmate Santa Rita Jail, about 70 km east of San Francisco. Often described as a green prison, it has a considerable installed base of distributed energy resources (DER) including an eight-year old 1.2 MW PV array, a five-year old 1 MW fuel cell with heat recovery, and considerable efficiency investments. Fig. 1 is an aerial depiction of the Jail with the PV rooftop modules clearly visible.

A Two-Layer Framework for Quantifying Demand Response Flexibility at Bulk Supply Points

Demand response (DR) currently plays a significant role in the operation of the electric grid. As a result, quantification of DR flexibility is an important aspect in the utilization of various DR resources. Generally, the evaluation of DR flexibility at bulk supply points (BSPs) is a challenging problem, especially without the monitoring of downstream customers’ load profiles in some areas. To solve this problem, we develop a two-layer DR flexibility estimation framework. In the top layer, a top-down optimization approach is proposed to disaggregate the BSP load into different building categories based on a suite of prototype building (PB) load profiles. In the bottom layer, simplified DR estimation models are deployed to quantify the theoretical DR flexibility of each PB type. Key advantages of this framework include: 1) quantifying DR flexibility at BSPs without relying on smart meter data or detailed customer surveys and 2) providing day-ahead, hour-ahead, and near real-time prediction of DR resources based on weather forecasts and other data. Case studies demonstrate the effectiveness of load disaggregation and DR flexibility quantification at a BSP. The prediction is compared with detailed physical models, and the mean relative errors for upper/lower DR capacity at the BSP are 1.5% and 3.1%, respectively.

Introduction to the Buildings Sector Module of SEDS

E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Introduction to the Buildings Sector Module of SEDS Nicholas DeForest, Florence Bonnet, Michael Stadler, Chris Marnay Environmental Energy Technologies Division December 31, 2010 http://eetd.lbl.gov/EA/EMP/emp-pubs.html The work described in this document was funded by the Planning, Budget, and Analysis section of the Office of Energy Efficiency and Renewable Energy, and Distributed Systems Integration Program in the U.S. Department of Energy under Contract No. DEAC02-05CH11231