Ryan Firestone

Optimal Technology Selection and Operation of Commercial-Building Microgrids

The deployment of small ( < 1-2 MW ) clusters of generators, heat and electrical storage, efficiency investments, and combined heat and power (CHP) applications (particularly involving heat-activated cooling) in commercial buildings promises significant benefits but poses many technical and financial challenges, both in system choice and its operation; if successful, such systems may be precursors to widespread microgrid deployment. The presented optimization approach to choosing such systems and their operating schedules uses Berkeley Labs Distributed Energy Resources Customer Adoption Model (DER-CAM), extended to incorporate electrical and thermal storage options. DER-CAM chooses annual energy bill minimizing systems in a fully technology-neutral manner. An illustrative example for a hypothetical San Francisco hotel is reported. The chosen system includes one large reciprocating engine and an absorption chiller providing an estimated 11% cost savings and 8% carbon emission reductions under idealized circumstances.

Energy manager design for microgrids

On-site energy production, known as distributed energy resources (DER), offers consumers many benefits, such as bill savings and predictability, improved system efficiency, improved reliability, control over power quality, and in many cases, greener electricity. Additionally, DER systems can benefit electric utilities by reducing congestion on the grid, reducing the need for new generation and transmission capacity, and offering ancillary services such as voltage support and emergency demand response. Local aggregations of distributed energy resources (DER) that may include active control of on-site end-use energy devices can be called microgrids. Microgrids require control to ensure safe operation and to make dispatch decisions that achieve system objectives such as cost minimization, reliability, efficiency and emissions requirements, while abiding by system constraints and regulatory rules. This control is performed by an energy manager (EM). Preferably, an EM will achieve operation reasonably close to the attainable optimum, it will do this by means robust to deviations from expected conditions, and it will not itself incur insupportable capital or operation and maintenance costs. Also, microgrids can include supervision over end-uses, such as curtailing or rescheduling certain loads. By viewing a unified microgrid as a system of supply and demand, rather than simply a system of on-site generation devices, the benefits of integrated supply and demand control can be exploited, such as economic savings and improved system energy efficiency.

Distributed energy resources customer adoption modeling with combined heat and power applications

In this report, an economic model of customer adoption of distributed energy resources (DER) is developed. It covers progress on the DER project for the California Energy Commission (CEC) at Berkeley Lab during the period July 2001 through Dec 2002 in the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. CERTS has developed a specific paradigm of distributed energy deployment, the CERTS Microgrid (as described in Lasseter et al. 2002). The primary goal of CERTS distributed generation research is to solve the technical problems required to make the CERTS Microgrid a viable technology, and Berkeley Labs contribution is to direct the technical research proceeding at CERTS partner sites towards the most productive engineering problems. The work reported herein is somewhat more widely applicable, so it will be described within the context of a generic microgrid (mGrid). Current work focuses on the implementation of combined heat and power (CHP) capability. A mGrid as generically defined for this work is a semiautonomous grouping of generating sources and end-use electrical loads and heat sinks that share heat and power. Equipment is clustered and operated for the benefit of its owners. Although it can function independently of the traditional power system, or macrogrid, the mGrid is usually interconnected and exchanges energy and possibly ancillary services with the macrogrid. In contrast to the traditional centralized paradigm, the design, implementation, operation, and expansion of the mGrid is meant to optimize the overall energy system requirements of participating customers rather than the objectives and requirements of the macrogrid.

Distributed energy resources in practice: A case study analysis and validation of LBNL's customer adoption model

LBNL -52753 E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Distributed Energy Resources in Practice: A Case Study Analysis and Validation of LBNL’s Customer Adoption Model Owen Bailey, Charles Creighton, Ryan Firestone, Chris Marnay, and Michael Stadler Environmental Energy Technologies Division February 2003 Download from http://eetd.lbl.gov/EA/EMP The work described in this report was funded by the Assistant Secretary of Energy Efficiency and Renewable Energy, Distributed Energy and Electric Reliability Program of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.

Integrated Energy System Dispatch Optimization

On-site cogeneration of heat and electricity, thermal and electrical storage, and curtailing/rescheduling demand options are often cost-effective to commercial and industrial sites. This collection of equipment and responsive consumption can be viewed as an integrated energy system (IES). The IES can best meet the sites cost or environmental objectives when controlled in a coordinated manner. However, continuously determining this optimal IES dispatch is beyond the expectations for operators of smaller systems. A new algorithm is proposed in this paper to approximately solve the real-time dispatch optimization problem for a generic IES containing an on-site cogeneration system subject to random outages, limited curtailment opportunities, an intermittent renewable electricity source, and thermal storage. An example demonstrates how this algorithm can be used in simulation to estimate the value of IES components.

The Effects of Electricity Tariff Structure on DistributedGeneration Adoption in New York State

LBNL-57942 E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY The Effects of Electricity Tariff Structure on Distributed Generation Adoption in New York State Prepared for the Distributed Energy Program Principal Authors Ryan Firestone and Chris Marnay Lawrence Berkeley National Laboratory 1 Cyclotron Rd., MS 90-4000 Berkeley, California 94720 Environmental Energy Technologies Division September 2005 Download from: http://eetd.lbl.gov/EA/EMP/ The work described in this paper was funded by the Assistant Secretary of Energy Efficiency and Renewable Energy, Distributed Energy Program of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.

A business case for on-site generation: The BD biosciences pharmingen project

LBNL- 52759 E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY A Business Case for On-Site Generation: The BD Biosciences Pharmingen Project Prepared for the Transmission Reliability Program of the Distributed Energy and Electric Reliability Program Principal Authors: Ryan Firestone, Charles Creighton, Owen Bailey, Chris Marnay, and Michael Stadler Other Team Members Emily Bartholomew, Norman Bourassa, Jennifer Edwards, Kristina Hamachi LaCommare, Tim Lipman, and Afzal Siddiqui Environmental Energy Technologies Division September 2003 The work described in this report was funded by the Assistant Secretary of Energy Efficiency and Electric Reliability Program of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.

A Model of U.S. Commercial Distributed Generation Adoption

Small-scale (100 kW-5 MW) on-site distributed generation (DG) economically driven by combined heat and power (CHP) applications and, in some cases, reliability concerns will likely emerge as a common feature of commercial building energy systems over the next two decades. Forecasts of DG adoption published by the Energy Information Administration (EIA) in the Annual Energy Outlook (AEO) are made using the National Energy Modeling System (NEMS), which has a forecasting module that predicts the penetration of several possible commercial building DG technologies over the period 2005-2025. NEMS is also used for estimating the future benefits of Department of Energy research and development used in support of budget requests and management decisionmaking. The NEMS approach to modeling DG has some limitations, including constraints on the amount of DG allowed for retrofits to existing buildings and a small number of possible sizes for each DG technology. An alternative approach called Commercial Sector Model (ComSeM) is developed to improve the way in which DG adoption is modeled. The approach incorporates load shapes for specific end uses in specific building types in specific regions, e.g., cooling in hospitals in Atlanta or space heating in Chicago offices. The Distributed Energy Resources Customer Adoption Model (DER-CAM) uses these load profiles together with input cost and performance DG technology assumptions to model the potential DG adoption for four selected cities and two sizes of five building types in selected forecast years to 2022. The Distributed Energy Resources Market Diffusion Model (DER-MaDiM) is then used to then tailor the DER-CAM results to adoption projections for the entire U.S. commercial sector for all forecast years from 2007-2025. This process is conducted such that the structure of results are consistent with the structure of NEMS, and can be re-injected into NEMS that can then be used to integrate adoption results into a full forecast.

Preliminary Estimates of Combined Heat and Power Greenhouse Gas Abatement Potential for California in 2020

The objective of this scoping project is to help the California Energy Commissions (CEC) Public Interest Energy Research (PIER) Program determine where it should make investments in research to support combined heat and power (CHP) deployment. Specifically, this project will: {sm_bullet} Determine what impact CHP might have in reducing greenhouse gas (GHG) emissions, {sm_bullet} Determine which CHP strategies might encourage the most attractive early adoption, {sm_bullet} Identify the regulatory and technological barriers to the most attractive CHP strategies, and {sm_bullet} Make recommendations to the PIER program as to research that is needed to support the most attractive CHP strategies.

Microturbine Economic Competitiveness: A Study of Two PotentialAdopters

LBNL-57985 Microturbine Economic Competitiveness: A Study of Two Potential Adopters Prepared for the Distributed Energy Program Assistant Secretary for Energy Efficiency and Renewable Energy U.S. Department of Energy Principal Authors Ryan Firestone and Chris Marnay Ernest Orlando Lawrence Berkeley National Laboratory 1 Cyclotron Road, MS 90R4000 Berkeley CA 94720-8136 December 2005 This work described in this paper was funded by the Assistant Secretary of Energy Efficiency and Renewable Energy, Distributed Energy Program of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.