control of carbon emissions in zero- net-energy buildings ... · control of carbon emissions in...

Lawrence Berkeley National Laboratory, Environmental Energy Technologies Division, USA

Control of Carbon Emissions in Zero-Net-Energy Buildings by Optimal Technology Investments in Smart Energy Systems and Demand-Side-Management

presented at the 32nd IAEE International Conference, San Francisco, CA, USA

June 24, [email protected]

der.lbl.gov

Chris Marnay, Michael Stadler, Afzal Siddiqui, and Hirohisa Aki

Environmental Energy Technologies Division

Outline

Introduction

Global concept - The Distributed Energy Resources - Customer Adoption Model (DER-CAM)

Test sites and data

CA and NY examples

Multi-criteria objective function

Conclusion

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Introduction

Zero-Net-Energy (ZNE) Commercial Building Initiative (CBI) to make ZNE buildings marketable by 2025

Use of energy efficient technologies and on-site (renewable) energy generation with / without combined heat and power (CHP)

How can such buildings be implemented within the context of a cost-or carbon-minimizing microgrid? We use a mixed-integer linear (MILP) program to answer that question.

For a CA and NY nursing home the energy balance is constrained such that energy consumed equals energy exports ZNEB

Impact on PV, solar thermal, other distributed generation (DG) technologies, as well as demand response, is shown.

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Global Concept

original service demand

reduced service demand

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DER-CAM Model

Mixed Integer Linear Program (MILP), written in the General Algebraic Modeling System (GAMS®)

Minimizes annual energy costs, carbon emissions, or multiple objectives of providing services on a microgrid level (typically buildings with 250-2000 kW peak)

Produces technology neutral pure optimal results with highly variable run times

Has been designed for more than 7 years by Berkeley Lab and is under license by researchers in the US, Germany, Spain, Belgium, Japan, and Australia

Commercialization plans

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on an hourly basis

Constraints: financial constraints as payback constraint, technical constraints as area constraint for PV panels, etc.

DER-CAM Concept

DSM input parameter

Energy sales

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DER-CAM Concept,Representative MILP

Objective function, e.g. min. annual energy bill for a test year:+energy purchase costs+amortized DER technology capital costs+annual O&M costs+ carbon costs- energy sales

Energy balance+energy purchase+energy generated onsite= onsite demand + energy sales

Operational constraints-generators, chillers, etc. must operate within performance limits-heat recovered is limited by generated waste heat-solar radiation / footprint constraint

Regulatory constraints-minimum efficiency requirement-emission limits-carbon tax-CA min. eff. requirement for subsidy and (in future) feed-in tariff-ZNEB

Financial constraints-max. allowed payback period, e.g. 12 years

Storage and DSM constraints-electricity stored is limited by battery size-heat storage is limited by reservoir size-max. DSM potential for heating and electricity

Simplified* DER-CAM

model

*does not show all constraints

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DER-CAM Concept

Multi-criteria objective function to capture different strategies of building as cost minimization, carbon minimization, or combinations

( )

objectivesareatCarbonandaCostlessdimensionfunctionobjectivemaketoers...parametatMaxCarbon,aMaxCost

(0..1)factorw...weightMaxCarbon

Carbonw1MaxCost

Costwmin

)/()/($)/()/($ −

⎭⎬⎫

⎩⎨⎧ −+

ZNEB constraint:( )

basisenergyannualanon;0fficiencyMacrogridE

=+

−−

ConsumedGasNatural

GenerationOnsiteotherfromyElectricitExportedPVfromyElectricitPurchasedyElectricit

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Test Sides and Data

A San Francisco Bay Area nursing home with approx. 960 kW electric peak load, an annual electr. and NG consumption of 5.76 GWh and 194 522 therms respectively

A NYC nursing home with >1 MW electric peak load, an annual electr. and NG consumption of 6.02 GWh and 243 563 therms respectively

CA tariffs: demand charges (up to $15/kW) and TOU-tariffs that vary with the season and hour (TOU variation: 78%), also moderate NG prices of approx. $1.06/therm

NY tariffs: almost flat electric tariffs, approx. 35% higher NG prices than in CA

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reciprocating engine

fuel cell

capacity (kW) 100 200sprint capacity 125installed costs ($/kW) 2400 5005installed costs with heat recovery ($/kW) 3000 5200variable maintenance ($/kWh) 0.02 0.029efficiency (%), (HHV) 26 35lifetime (a) 20 10

Test Sides and Data

Discrete technologies

Continuous technologieselectrical storage

(lead acid)

thermal storage

flow battery

absorption chiller

solar thermal photovoltaics

intercept costs ($) 295 10000 0 20000 1000 1000

variable costs ($/kW or $/kWh)

193 100 220 / 2125 127 500 6675

lifetime (a) 5 17 10 15 15 20

fixed unavoidable costs

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DSM is modeled by storage systems and (at this point)

abstract efficiency measures that also capture behavioral

changes


Runs

run 1: a do-nothing case in which all DER investments and DSM adoption are disallowed, i.e., the site meets its local energy demands solely by purchases from utilities; no ZNEB constraint is considered

run 2: an invest case that finds the optimal DER and DSM adoption at current price levels; no ZNEB constraint is considered

run 3: please see full paper

run 4: a ZNEB invest case that finds the optimal DER and DSM adoption at current price levels, considering the ZNEB constraint

run 5: a ZNEB low storage and low PV price run, with low storage prices of $50/kWh for thermal storage, $60/kWh for electric storage, and $2670/kW for PV; both the ZNEB constraint and DSM are considered.

Using a footprint constraint of 30 000m2 for the solar / PV systems

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CA Nursing Home, Cost Minimization (w = 1)

marginal carbon emission rateutility: 140 g/kWh (constant)

can reach ZNEB at a cost increase of approx. 85%

utilizing a subsidy of $4005/kW for PV and $133/kWh for batteries carbon emission reduction cost of $950/tC compared to a $65/tC market price

CHP techn. plays a role

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CA Nursing Home , Cost Minimization (w=1)

run5, diurnal electricity pattern on a July weekday

original electricity load

battery charging: electric storage is mostly charged by cheap off‐peak electricity and not PV

battery discharging

fossil based DG/CHPload reduction

PV

utility

electric sales

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NY Nursing Home, Cost Minimization (w=1)

ZNEB cannot be reached for the NYC nursing home with the same techology/ies and DSM input parameters due to higher climate-related loadsAn increase in efficiency measure (annual energy consumption decrease by approx. 14%) allows DER-CAM to find a valid optimumThe result then show an adoption of 300kW of reciprocating engines with CHP. All other cases show now adoption due to the flat electric and higher NG tariffs than in CAWaste heat utilization plays a role in ZNEBCombustion engines are not eliminated in ZNEB

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Multi-Criteria Objective Function

w0 1

There is not much experience with such buildings

• 300 kW reciprocating engines• 251 RT absorption chillers • 6456 kWh of electric storage• 6476 kWh of heat storage • 2097 kW of PV, and • 2858 kW of solar thermal

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Conclusion

Cost minimization: PV is not used for battery charging and both are in competitionCO2 minimization results in unsustainable high energy costs for thesite consideration of efficiency measures within DER-CAM and in reality necessary. CO2 minimization can result in 80% CO2 reductionA huge amount of PV is necessary to fulfill the ZNEB constraint Future work: stochastic energy prices, CO2 prices and tariffs as well as unreliable equipment; risk hedging strategy that uses a portfolio of physical equipment as well as financial instruments and delivers an innovative solution for more sustainable provision and consumption of energy

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Thank You

Thank You!Questions and comments are very welcome!

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What is the Definition of ZNEB?

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EISA TITLE IV - Energy Savings in Buildings & Industry Sec. 401. DEFINITIONS

(20) ZERO-NET-ENERGYCOMMERCIAL BUILDING. The term “zero-net-energy commercial building” means a commercial building that is designed, constructed, and operated to

(A) require a greatly reduced quantity of energy to operate; (B) meet the balance of energy needs from sources of energy

that do not produce greenhouse gases; (C) therefore result in no net emissions of greenhouse gases;

and (D) be economically viable.


Test Sides and Data

DSM is modeled by storage systems and (at this point) abstract efficiency measures that also capture behavioral changes

DER-CAM picks optimal operating hours for measures to minimize costs, carbon emissions, or other objective, & delivers schedules

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Test Sides and Data

Used DSM input data

electricityvariable

cost($/kW)

max. contribution (% of total load in

any hour)

max. hours (hours)

low 0.00 30 4380

mid 0.06 10 8760

high 1.00 5 760

heating variable cost ($/kW)

max. contribution (% of total load in

any hour)

max. hours (h)

low 0.00 30 1095

mid 0.03 20 8760

high 0.05 10 8760

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Test Sides and Data

description electrical flow battery thermal

charging efficiency portion of energy input to storage that is useful 0.9 0.84 0.9

discharging efficiency portion of energy output from storage that is useful 1 0.84 1

decay portion of state of charge lost per hour 0.001 0.01 0.01

maximum charge rate maximum portion of rated capacity that can be added to storage in an hour 0.1 n/a 0.25

maximum discharge rate maximum portion of rated capacity that can be withdrawn from storage in an hour 0.25 n/a 0.25

minimum state of charge minimum state of charge as a portion of rated capacity 0.3 0.25 0

Energy storage parameters

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Test Sides and Data

Tariffs PG&Esummer (May – Oct.) winter (Nov. – Apr.)

electricity electricity ($/kWh)

demand ($/kW)

electricity ($/kWh)

demand ($/kW)

on-peak 0.163 15.040mid-peak 0.124 3.580 0.116 1.860off-peak 0.094 0.098fixed ($/day) 9.035

natural gas0.035 for summer and

0.037 for winter $/kWh

1.026 for summer and 1.084 for winter $/therm

4.955 fixed ($/day)

summer on-peak: 12:00-18:00 during weekdayssummer mid-peak: 08:00-12:00 and 18:00-22:00 during weekdays, all other hours and days: off-peakwinter mid-peak: 08:00-22:00 during weekdays, all other hours and days: off-peak.

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Test Sides and Data

Tariffs ConEdsummer (June – Sep.) winter (Oct. – May)

electricity electricity ($/kWh)

demand ($/kW)

electricity ($/kWh)

demand ($/kW)

all day long 0.12 14.21 0.12 11.36fixed ($/month) 71.05

natural gas0.049 $/kWh 1.436 $/therm

0.419 fixed ($/day)

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