Decarbonization of Campus Energy Systems · The Stanford Campus as an Ideal Case Study. 5 • Stanford’s campus is master-metered. • The SESI project is big (~36 MWe peak load)

Decarbonization of Campus Energy Systems

JA de Chalendar, PW Glynn, J Stagner and SM BensonStanford University

G R I D- F R I E N D LY E L E C TR I F I E D D I S TR I C T H E AT I N G A N DC O O L I N G W I TH T H E R M A L S TO R A G E

Shifts in the Power Grid

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Other (10%)

Electricity& Heat (42%)

Transport(23%)

Industry (23%)

Residential (6%)

World CO2 emissions from fuel emissions bysector in 2014. Adapted from IEA (2016).

• Need to reduce carbon emissions drives:• Electrification.• Large-scale renewables.

Shifts in the management of the power grid are necessary.

The CAISO “duck curve” and non-dispatchable generation in 2020

Shifts in the Power Grid

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• Turn to the demand side for new control options.• Electric loads that offer energy services and flexibility have value.Heat pumps or large manufacturing machines, but also electric

vehicles, household appliances, etc.District energy networks have historically played a small but important

role at a local level.

• Rapid urbanization prompts further questions around the “City of the Future”What is the economic case and carbon impact of electrified district

heating and cooling?

Stanford Campus as an Ideal Case Study

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CONTROLLABLE LOADS

CONTROLLABLE LOADS

The Stanford Campus as an Ideal Case Study

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• Stanford’s campus is master-metered.• The SESI project is big (~36 MWe peak load) and centrally

managed by an Energy Management System.• Source of real data and insights as well as an ideal testbed.• The entire distribution feeder can be viewed as a Virtual Power

Plant.

Stanford Campus – Electric Billing Structure

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Electricity price ($/kW

h)

Day

of y

ear

• Direct Access to California’s electricity markets.• Two-part energy tariff: hourly demand charge + energy usage price.

CONTROLLABLE LOADS


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Meeting Stanford’s energy requirements

Cooling (GJ) Heating (GJ) Power (GJe)D

ay o

f yea

rThree main streams for energy consumption.

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Each row corresponds to one day, each column to one hour.

CONTROLLABLE LOADS


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CONTROLLABLE LOADS

Objectives

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1. What does it mean to be flexible and to provide energy services?

2. What is the value of this flexibility?

Problem formulation

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• Decision variables: power to machines at every time step.• State variable: amounts of energy in thermal storage (water in tanks).• Linearize demand charge objective by introducing an auxiliary variable

to represent the monthly peak demand.• Formulate the problem of generating optimal schedules for the Central

Energy plant as a linear program with ~130k variables and 170k constraints (hourly operations for a year).

min < 𝑐𝑐, 𝑥𝑥 >Subject to 𝐴𝐴𝑥𝑥 ≤ 𝑏𝑏

𝑥𝑥 ≥ 0

Energy prices &

Power, heating, cooling loads

Hourly operations

schedule for CEP machines

Optimal operations schedule – base case

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Grid imports (GJe) Chiller output (GJ) HRC output (GJ)

Day

of y

ear

Chillers and Heat Recovery Chillers are scheduled to to avoid the expensive early evening hours and minimize the cost of the aggregate imports from the power grid.

Heatmaps for key operating variables. Each row corresponds to one day and eachcolumn to one hour.

CONTROLLABLE LOADS


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CONTROLLABLE LOADS

Decarbonizing Campus Energy Systems

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Aggregate campus emissions were reduced by 68% through the SESI project.Can we do better by leveraging the current infrastructure?

California Grid Carbon Intensity Scenarios in mid-2020s

Day

of y

ear

kgCO

2 /MW

e

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Heatmaps for the hourly AEFs (kgCO2/MWhe) of the California power grid energy mixin the mid-2020s. Each row corresponds to one day, each column to one hour, and allimages use the same colorbar.

• Emissions impact the atmosphere on a timescale of years, but scheduling decisions must be made on a timescale of hours.

Average Emissions factors can be used to determine the carbon intensity depending on the generation mix.

‘16 Actuals High solar High solar & wind

Demonstrating Short-Term Flexibility of SESI

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• Three different scenarios for CAISO AEFs in mid-2020s.• Four different operating conditions are tested for the

optimization program1. Current operations (minimize electric bill).2. Current operations without thermal storage.3. $100 per tonne carbon tax.4. Carbon optimal strategy.

• We determine the changes in cost and carbon emissions.


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Table 1: CEP yearly summary – associated carbon emissions (ktonnes)‘16 Actuals Increased Solar Increased Wind & Solar

Base operations 23.5 19.7 14.9No storage 26.0 22.4 17.7100$ carbon tax 23.3 17.7 13.5Carbon optimal 22.6 16.0 12.0

Table 1: CEP yearly summary – electric bill (M $)‘16 Actuals Increased Solar Increased Wind & Solar

Base operations 4.439 4.39 4.39No storage 4.711 4.711 4.711100$ carbon tax 4.440 4.440 4.440Carbon optimal 4.584 4.584 4.584


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Table 1: CEP yearly summary – associated carbon emissions change (%)‘16 Actuals Increased Solar Increased Wind & Solar

Base operations +0.0 -16.3 +0.0 -36.6 +0.0No storage +10.5 -5.0 +13.5 -24.7 +18.9100$ carbon tax -0.8 -24.6 -9.9 -42.5 -9.18Carbon optimal -4.0 -32.0 -18.8 -48.9 -19.3

Table 1: CEP yearly summary – electric bill change (%)‘16 Actuals Increased Solar Increased Wind & Solar

Base operations +0.0 +0.0 +0.0No storage +6.13 +6.13 +6.13100$ carbon tax +0.02 +0.55 +0.38Carbon optimal +3.27 +4.22 +4.23


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Conclusions

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• The Central Energy Plant is a flexible load.

• The CEP represents only a fraction of aggregate campus loads (~20% of current carbon and financial footprints). Impact of optimization on aggregate campus emissions and financials

can only be limited.

• There is high value for thermal storage if load shaping has high value.Demand Charge Management.Carbon Management with high daily variability in carbon intensity of

grid generation mix.

Q&A

Ongoing work

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• Short-term flexibility: Capacity-Based Demand Response.Shift risk from the utility to consumer.Take uncertainty into account.

• Long-term flexibility: Capacity expansion modeling.How would Stanford adapt to a change in loads?What is the value of changing the current infrastructure?How applicable are lessons from the Stanford experiment to different

climates and energy mixes?

Additional Conclusions

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• Several value propositions for thermal energy storage: Lower capital costs by enabling the use of Heat Recovery Chillers to

couple the three energy consumption streams: power, heating, cooling.

Provide flexibility: respond to dynamic prices and flatten loads in the presence of a demand-charge-based tariff.

• Can estimate the cost of cutting emissions from the change in electric bill and observed carbon reduction.Under the high solar scenario,12$/tonne in the carbon tax case vs.

50$/tonne in the carbon optimal case.Suggests strong diminishing returns for carbon policies.

Stanford Campus – Electric Billing Structure

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Electricity price ($/kW

h)

Day

of y

ear

Pric

e ($

/kW

h)• Direct Access to California’s electricity markets.• Two-part energy tariff: hourly energy usage price + demand charge.

• Energy price subject to increasingly frequent spike events (average is 5 c/kWh).

Meeting Stanford’s energy requirementsK-means clustering applied to energy loads:• Power is occupancy-driven, thermal loads

are seasonal.• Thermal loads are more difficult to predict

than electric power consumption.Cooling (GJ)

Heating (GJ)Power (GJe)

Confusion matrix for weekday/weekend using

computed clusters for 757 days.

214 (1) 0 (0)

72 (.13) 471 (.87)

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Meeting Stanford’s energy requirementsClustering of normalized consumption profiles into k=4 representative

days.Identification of a shift in power consumption profiles in the Spring of 2017

– this is most visible on weekends.

Power (GJe)

Normalized power28

4.5MWe of rooftop solar come online

State of storage over the yearCold water (GJ) Hot water (GJ)

Thermal energy in storage. Actuals (07-’15 to 09-’17).

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Interpreting anomalies in loads~4 MW of rooftop solar online

Cooling profilesJul’15 Sep’17

Sep ‘17Dec ‘15 Dec ‘16Jul ‘15

The 09/17 heat wave presented the Central Energy Plant with a major challenge.Data for the first 5 days of September is shown in red.

Daily temperatures (°F)

Power consumed by the campus buildings (GJe).Data for the Winter breaks is shown in red.

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Changes compared to 2016 baseline

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Winter (Jan 3 to 10)Summer (Aug 3 to 10)

Dec ‘16Jan ‘16

Optimized loads billed under a demand charge system have a typical flat profile.

In the Winter, HRCs are used in combination with gas-fired heaters; in the Summer,with electric Chillers.

Optimal operations schedule – base case

Documents

Decarbonization of Campus Energy Systems · The Stanford Campus as an Ideal Case Study. 5 • Stanford’s campus is master-metered. • The SESI project is big (~36 MWe peak load)