The addition of storage technologies such as lead-acid batteries, flow batteries, or heat storage can potentially improve the economic and environmental attractiveness of on-site generation such as PV, fuel cells, reciprocating engines or microturbines (with or without CHP), and can contribute to enhanced demand response. Preliminary analyses for a Californian nursing home indicate that storage technologies respond effectively to time-varying electricity prices, i.e., by charging batteries during periods of low electricity prices and discharging them during peak hours. While economic results do not make a compelling case for storage, they indicate that storage technologies significantly alter the residual load profile, which may lower carbon emissions as well as energy costs depending on the test site, its load profile, and DER technology adoption

Aki, Hirohisa

Coffey, Brian

Division, Environmental Energy Technologies

Firestone, Ryan

Lacommare, Kristina S H

Lai, Judy

Marnay, Chris

Siddiqui, Afzal

Stadler, Michael

UNT Digital Library

 LBNL-___  Microgrid Selection and Operation for Commercial Buildings in California and New York States  Chris Marnay,a Michael Stadler,a,b Hirohisa Aki,a,c Brian Coffey,a,d Ryan Firestone,e Judy Lai,a, and Afzal Siddiqui f  a Lawrence Berkeley National Laboratory, Berkeley CA  b Center for Energy and Innovative Technologies, Austria,  c National Institute of Advanced Industrial Science and Technology, Japan, d Department of Architecture, University of California, Berkeley,  e Summit Blue Consulting, Walnut Creek, CA  f Department of Statistical Science at University College London, U.K.   Environmental Energy Technologies Division    May 2008    http://eetd.lbl.gov/EA/EMP/emp-pubs.html    The work described in this paper was funded by the Office of Electricity Delivery and Energy Reliability, Renewable and Distributed Systems Integration Program in the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY   Disclaimer  This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or The Regents of the University of California.  Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer.  paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    Microgrid Selection and Operation for Commercial Buildings in California and New York States 1 Chris Marnay, a) Michael Stadler, a,b) Hirohisa Aki, a,c) Brian Coffey, a,d) Ryan Firestone, e) Judy Lai, a), and Afzal Siddiqui f) a)Lawrence Berkeley National Laboratory, Berkeley CA b)Center for Energy and Innovative Technologies, Austria, c)National Institute of Advanced Industrial Science and Technology, Japan, d)Department of Architecture, University of California, Berkeley, e)Summit Blue Consulting, Walnut Creek, CA  f)Department of Statistical Science at University College London, U.K. Contact: C_Marnay@lbl.gov, +1.510.486.7028 1 Cyclotron Rd MS 90R4000, BERKELEY, CA 94720-8136, http://der.lbl.gov ABSTRACT The addition of storage technologies such as lead-acid batteries, flow batteries, or heat storage can potentially improve the economic and environmental attractiveness of on-site generation such as PV, fuel cells, reciprocating engines or microturbines (with or without CHP), and can contribute to enhanced demand response. Preliminary analyses for a Californian nursing home indicate that storage technologies respond effectively to time-varying electricity prices, i.e. by charging batteries during periods of low electricity prices and discharging them during peak hours. While economic results do not make a compelling case for storage, they indicate that storage technologies significantly alter the residual load profile, which may lower carbon emissions as well as energy costs depending on the test site, its load profile, and DER technology adoption. Introduction In this paper, a microgrid is defined as a cluster of electricity sources and (possibly controllable) loads at one or more locations that are connected to the traditional wider power system, or macrogrid, but which may, as circumstances or economics dictate, disconnect from it and operate as an island, at least for short periods (Hatziargyriou, N. et al. 2007). The Berkeley Lab has developed the Distributed Energy Resources                                                  1 The work described in this paper was funded by the Office of Electricity Delivery and Energy Reliability, Renewable and Distributed Systems Integration program of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    Customer Adoption Model (DER-CAM), (Siddiqui et al. 2003, Stadler et al. 2006). Its optimization techniques find both the combination of equipment and its operation over a typical year to minimize the site’s total energy bill, typically for electricity and natural gas purchases, as well as amortized equipment costs. The latest version also includes storage technologies such as regular batteries (e.g. lead-acid batteries), flow batteries as well as heat storage. The Distributed Energy Resources - Costumer Adoption Model (DER-CAM) DER-CAM (Siddiqui et al. 2003) is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS®). Its objective is to minimize the annual costs for providing energy services to the modeled site, including utility electricity and natural gas purchases, amortized capital, and maintenance costs for distributed generation (DG) investments. It outputs the optimal DG and storage adoption combination and an hourly operating schedule, as well as the resulting costs, fuel consumption, and carbon emissions. Figure 1 shows a high-level schematic of the energy flow as modeled in DER-CAM. Optimal combinations of equipment involving PV, thermal generation with heat recovery, thermal heat collection, and heat-activated cooling can be identified in a way that would be intractable by trial-and-error enumeration of possible combinations. The economics of storage are particularly complex, both because they require optimization across multiple time steps and because they are heavily influenced by complex tariff structures (on-peak, off-peak, demand charges, etc.). Note that facilities with on-site generation will incur electricity bills more biased toward demand (peak power) charges and less toward energy charges, thereby making the timing and control of chargeable peaks of particular operational importance. DER Equipment Including Storage Technologies  The menu of available equipment options to DER-CAM for this analysis together with their cost and performance characteristics are shown in Tables 1, 2, and 3. While the paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    current set of available technologies is limited in this analysis, any candidate technology may potentially be included. Technology options in DER-CAM are categorized as either discretely or continuously sized. This distinction is important to the economics of DER because some equipment is subject to strong diseconomies of small scale. Continuously sized technologies are available in such a large variety of sizes that it can be assumed that close to optimal capacity could be implemented, e.g. battery storage. The installation cost functions for these technologies are assumed to consist of an unavoidable cost (intercept) independent of installed capacity representing the fixed cost of the infrastructure required to adopt such a device, plus a variable cost proportional to capacity. Results The northern Californian nursing home is the first of several California and New York being studied. The home has a peak total electrical load of 958 kW. Table 4 shows its local Pacific Gas and Electric (PG&E) rates. Carbon emission intensities of purchased electricity and natural gas from PG&E are assumed to be 140 g/kWh (marginal value) and 49 g/kWh, respectively. Six DER-CAM runs were performed: 1. a do nothing case in which all DER investment is disallowed, i.e., the nursing home meets its local energy demands solely by purchases; 2. an invest case, which finds the optimal DER investment; 3. a low storage and PV price; 4. to assess the value of storage systems, a run was performed forcing the same investments as run 3, but with storage disallowed; 5. a low storage, PV, and solar thermal price run; and 6. a low storage price and 60% PV price reduction/subsidy run. The number of installed Tecogen® reciprocating engine stays constant in all performed runs because CHP is attractive to this site because of the coincidence of heat and electric loads. DER-CAM also provides an optimal schedule for each installed technology, which is illustrated using the low storage cost runs 3 and 6 (Figure 2 to 4). Note that since electric cooling loads can be offset by the absorption chiller, there are paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    four possible ways to meet cooling loads: utility purchases of electricity, on-site generation of electricity, absorption chiller offsets, and stored electricity in batteries.  At the assumed price levels, neither electric nor thermal storage is economically attractive (see run 2). Including low-cost storage of US$50/kWh for solar thermal and US$60/kWh for electric storage lowers annual operating costs by almost 5% (see run 3); however, the elemental carbon reduction is only ca. 12% meaning that elemental carbon emission reduction is lower with the adoption of electric and thermal storage than without it (run 2). This finding is proven by run 4, which forces the same results as in the low storage cost run 3, but prohibits storage adoption. The major driver for electric storage adoption is the objective to reduce energy costs, and this can be effectively reached by avoiding electricity consumption during on-peak hours. Batteries are charged by cheap off-peak electricity and displaces utility consumption during on-peak hours (see also Figure 3). Assuming the same marginal carbon emission rate during on- and off-peak hours results in additional carbon emissions (efficiency losses); however, as shown in run 6 (see Table 5), the combination of PV and electrical storage brings together the positive economic effects of batteries with the positive environmental effects of PV.  Conclusions The results show a wide range in the complexity of optimal systems but fairly similar costs and diverse carbon emissions. Heat, electric load profile, tariff structure, available solar insolation, and installed DG equipment all have strong effects on the site’s achievable energy cost and carbon abatement. The demand charge is a significant driver for the adoption of electric storage technologies and so storage is discharged during productive PV hours, raising carbon emissions overall.  References Hatziargyriou, N. et al., “Microgrids, An Overview of Ongoing Research, Development, and Demonstration Projects,” IEEE Power & Energy, July/August 2007. paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    PG&E commercial electricity and natural gas tariffs. http://www.pge.com/nots/rates/tariffs/CommercialCurrent.xls http://www.pge.com/tariffs/pdf/E-19.pdf http://www.pge.com/nots/rates/tariffs/GNR2_Current.xls http://www.pge.com/tariffs/pdf/G-NR2.pdf Siddiqui, A.S., R. Firestone, S. Ghosh, M. Stadler, C. Marnay, and J.L. Edwards, “Distributed Energy Resources Customer Adoption Modeling with Combined Heat and Power Applications.” Lawrence Berkeley National Laboratory, June 2003. Lawrence Berkeley National Laboratory Report LBNL-52718. Stadler, M., R. M. Firestone, D. Curtil, and Chris Marnay, “On-Site Generation Simulation with EnergyPlus for Commercial Buildings“, ACEEE Summer Study on Energy Efficiency in Buildings, August 13-18, 2006, Pacific Grove, California, ISBN 0-918249-56-2.  Figure 1. Schematic of DER-CAM  decoupling  by electric storage decoupling by thermal storage decoupling by thermal storage paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88     Table 1. Energy Storage Parameters  description electrical flow battery thermal charging efficiency (1) portion of energy input to storage that is useful 0.9 0.84 0.9 discharging efficiency (1) portion of energy output from storage that is useful 1 0.84 1 decay (1) portion of state of charge lost per hour 0.001 0.01 0.01 maximum charge rate (1) maximum portion of rated capacity that can be added to storage in an hour 0.1 n/a 0.25 maximum discharge rate (1) maximum portion of rated capacity that can be withdrawn from storage in an hour 0.25 n/a 0.25 minimum state of charge (1) minimum state of charge as apportion of rated capacity 0.3 0.25 0   Table 2. Menu of Available Equipment Options, Discrete Investments.   reciprocating engine fuel cell capacity (kW) 100 200 sprint capacity 125  installed costs (US$/kW) 2400 5005 installed costs with heat recovery (US$/kW) 3000 5200 variable maintenance (US$/kWh) 0.02 0.029 efficiency (%), (HHV) 26 35 lifetime (a) 20 10  paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88     Table 3. Menu of Available Equipment Options, Continuous Investments  electrical storage thermal storage flow battery absorption chiller solar thermal photo-voltaics intercept costs (US$) 295 10000 0 20000 1000 1000 variable costs (US$/kW or US$/kWh) 193 US$/kWh 100 US$/kWh 220 US$/kWh / 2125 US$/kW 127 US$/kW 500 US$/kW 6675 US$/kW lifetime (a) 5 17 10 15 15 20   Table 4. Commercial Energy Prices (source: PG&E, effective Nov 2007) Summer (May – Oct.) Winter (Nov. – Apr.) Electricity electricity (US$/kWh) demand (US$/kW)electricity (US$/kWh)demand (US$/kW)on-peak 0.16 15.04   mid-peak 0.12 3.58 0.12 1.86 off-peak 0.09  0.10  fixed (US$/day) 9.04  Natural Gas 0.04 US$/kWh4.96 fixed (US$/day) summer on-peak: 12:00 – 18:00 during weekdays summer mid-peak: 08:00 – 12:00 and 18:00 – 22:00 during weekdays summer off-peak: remaining hours and days winter mid-peak: 08:00 – 22:00 during weekdays;   paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    Table 5. Annual Results for the Northern California Nursing Home  run 1 run 2 run 3 run 4 run 5 run 6   do nothing invest in all technologies  low storage costs and PV incentive of 2.5US$/W  force low storage / PV results low storage costs and PV incentive of 2.5US$/W & low solar thermal costs (minus 10% of original costs)  low storage costs and 60% PV price reduction equipment Tecogen 100 kW with heat exchanger (kW) 300 300 300 300 300abs. Chiller (kW in terms of electricity) 48 46 46 85 40solar thermal collector (kW) 134 109 109 443 43PV (kW) 0 0 0 0 517electric storage (kWh) 0 4359 n/a 4148 2082thermal storage (kWh) n/a 0 123 n/a 196 47annual total costs (kUS$) total 964 926 916 926 915 910% savings compared to do nothing n/a 3.94 4.98 3.94 5.08 5.60annual elemental carbon emissions (t/a) emissions 1088 945 960 946 944 834% savings compared to do nothing n/a 13.14 11.76 13.05 13.24 23.35 paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    Figure 2. Jan. Weekday low Storage and PV Price (run 3) Diurnal Heat Pattern   Figure 3. Jul. Weekday low Storage and PV Price (run 3) Diurnal Elec. Pattern   paperrr    prrress enn tt ed   att    tt hh e   44 ttt hh    EE uu rrropeann    PP V-- Hyy brrr iiid   ann d   Miiinn iii -- Grrr iiid   Conn ff errrenn ce,,,    Glllyy ff ada,,,    Grrreece,,,   22 99 –– 33 00    Mayy    22 00 00 88    Figure 4. Jul. Weekday low Storage Price and 60% PV Price Reduction (run 6) Diurnal Elec. Pattern  

Microgrid Selection and Operation for Commercial Buildings in California and New York States

https://digital.library.unt.edu/ark:/67531/metadc894482/m2/1/high_res_d/929424.pdf

Microgrid Selection and Operation for Commercial Buildings in California and New York States

Abstract

Similar works

Full text

Available Versions

UNT Digital Library