Optimization Based Microgrid and DER Modelling: Introduction to XENDEE MAE/CER Energy Seminars University of California at San Diego, 30 Oct 2019 Xendee.com Michael Stadler, PhD; Zack Pecenak, PhD MStadler@xendee.com; zpecenak@xendee.com

San Diego based software and research focused company

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Team of 14 with 1/3 in research and engineering Adib Nasle, Scott Mitchell, Michael Stadler, Alan Zhang, Zachary K. Pecenak, Patrick Mathiesen, Jisun Lee, Kelsey Taylor Fahy, Rich Goldman, Jaime Rios, Margit Temper, Andrea Ruotolo, Tristan Jackson, Nathan Johnson

Some of our partners and clients

A bit about myself

Chief Technology Officer of XENDEE Corporation since August 2017 Area head of the Microgrid and Smartgrid department at Bioenergy2020+ in Austria,

currently Senior Scientific Advisor - March 2017 to December 2018 Lawrence Berkeley National Laboratory at the University of California Berkeley 2005 –

2016 • Staff Scientist and lead of Grid Integration Group with 40 scientists and students • Presidential Award from the White House for the economic optimization engine

within XENDEE (DER-CAM) 2013/2016 240 publications, 9 copyrights, H-index 30 leading the design, implementation, and operation of the first Austrian Microgrid

testbed https://www.linkedin.com/in/StadlerMichael

https://scholar.google.com/citations?user=MHJVYuIAAAAJ&hl=en

Michael Stadler, PhD, MS

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1) Microgrid and DER commercial state

2) Introduction to DER-CAM

3) Research and improvements on DER-CAM 1) Impact of down sampling input data 2) Multiyear optimization approaches 3) Ancillary service modeling (backup slides)

4) Conclusion

Talk overview

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Microgrid and DER Background A.K.A. Why are we here today?

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What is a Microgrid? Smartgrid ≠ Microgrid

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Growing market

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• Utility bills in excess of $45M/year just for electricity

• On average 8 power outages every year • Each outage costs $2.7M

• 170 MT of CO2 emissions

• Internal distribution services over 20 nodes

• Want to improve : • cost • reliability • carbon footprint • system operations

Nutrien Potash plant

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To model such cases: Need to consider 1. customer operation 2. physical and financial constraints 3. physical circuit 4. DER costs and characteristics 5. local climate

Most use a combination of complex spreadsheets with bulk approximations of the real system and multiple power design tools

Typically takes 12-18 months and several engineers

Nutrien Potash plant

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How to compare alternatives quickly (in weeks and not months)?

Net Present Value? Internal Rate

Return? Emissions?

Grid health? Etc.

Need for a standardized approach

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XENDEE to the rescue

XENDEE aims to be an end-to-end, standardized solution to microgrid design and implementation

Research to develop a state of the art Microgrid investment and decision making platform

Four core technologies - All in one platform Security constrained economic optimization based on the Distributed Energy Resources Customer Adoption Model (DER-CAM) from Berkeley Lab Power systems analysis engine based on EPRI‘s OpenDSS Transient stability framework based on XENDEE‘s own transient solver Implementation management based on XENDEE‘s own Project Management Tool

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https://xendee.s3-us-west-2.amazonaws.com/videos/WikiMovies/Website+Intro+Vid+_+Final+_+BW+Text.mp4

Microgrid and DER deployment in one platform

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Introduction to DER-CAM How XENDEE optimizes the DER selection in a Microgrid

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• Continuous development since 2000

• 300+ publications about or using model

• Millions in cumulative funding • XENDEE only professional

entity developing DER-CAM further

• More than 80 revisions and features of XENDEE version

The Distributed Energy Resources Customer Adoption Model

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Complexity of energy flows

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Simplified objective function min 𝑐 = 𝑐tariff + 𝑐fuel + 𝑐carbon + 𝑐invest + 𝑐O&M − 𝑟sales

Subject to

Energy balance 𝐷𝑚,𝑑,ℎ + 𝐿𝑚,𝑑,ℎ = Σ𝑡𝐺𝑡,𝑚,𝑑,ℎ + 𝑃𝑚,𝑑,ℎ

Payback constraint 𝐴𝑛𝑛𝑢𝑎𝑙𝑆𝑎𝑣𝑖𝑛𝑔𝑠

𝑇𝑜𝑡𝑎𝑙𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡≤ 𝑀𝑎𝑥𝑃𝑎𝑦𝑏𝑎𝑐𝑘𝑌𝑒𝑎𝑟𝑠 *Does not show all constraints

In total there are over 230,000 equations solved in the model

DER-CAM minimizes the objective considering a typical operational year, where amortized investment costs are balanced with operational

costs

Where: 𝑐 = Total Annual Cost 𝑟 = Revenue 𝑚 = Month (typically 12) 𝑑 = Day (typically 30) ℎ = Hour (typically 24) 𝑡 = Technology

𝐷 = Demand 𝐿 = Losses 𝐺 = Generation 𝑃 = Purchases

Mixed Integer Linearized formulation ≠ simulations

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Indices can be manipulated for different scenarios: Example: 𝐷𝑒𝑚𝑎𝑛𝑑𝑚,𝑑,ℎ

• m,d,h represent months, days, and hours • Can add indices for minutes, seconds, or

years

More indices increase search space of solutions

Increases computational time non-linearly

Can down-sample indices to reduce complexity

• DER-CAM down-samples days into 3 representative day types

• Impact discussed in later slides

We solve d=3 “representative” days each month 1. Peak demand day 2. Week day 3. Weekend day

Impact of optimization indices on runtime

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Dispatch is solved using operational planning constraints, mimics microgrid controller

DER are selected to minimize total cost considering each timestep and optimized dispatch

Coupled unit commitment and investment decision

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Such modeling provides detailed financials

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The impact of down-sampling data How does using three daytypes impact accuracy of optimization?

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COMPUTATIONALLY INTRACTABLE

Prohibitive computational time

REPRESENTATIVE DEMAND PROFILES

One minute One hour One day

Need for “daytype” down-sampling

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Contributions

Quantify impact of down-sampling

Introduce demand data reduction method which captures demand peaks

Validation and comparison against existing data reduction approach

Demonstration of the importance of capturing demand peaks

Two classes of down-sampling tested

XENDEE Peak Preservation (M0, M1, M2, M3, M4, M5)

K-Means Clustering (K1, K2, K3)

Approach

Use full 8760 timestep model as benchmark and compare down sampling approaches

Testing impact of down-sampling

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• Peak demand values subtracted from total weekday demand data

• Remaining data averaged

Example: Peak, Weekday, Weekend representative profiles constructed for March

• Peak demand values subtracted from total weekend demand data

• Remaining data averaged

• Total weekend demand data summed • Total weekday demand data summed

XENDEE peak perseveration approach

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3 k-means approaches were considered (1-3 centroids)

Allows for comparison of common literature approach

K-means clustering

XENDEE

XENDEE

Methods do not preserve peak

Impact on optimization results

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More research ongoing for also islanded Microgrids

Fahy Kelsey, Michael Stadler, Zachary K. Pecenak, and Jan Kleissl, “The Impact of Representative Days on the Precision of Microgrid and DER Modeling,“ Journal of Renewable and Sustainable Energy, ISSN: 1941-7012.

Novel multiyear modeling A new approach to make multiyear modeling feasible and open up business cases

for continued investment

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Common use cases

This work is patent pending and published The energy landscape is constantly changing, projects

should be adaptive

When project financials aren’t attractive today Can model when a project should start

Prevention of unnecessary oversizing Most companies significantly oversize for

degradation and factor of safety

Investors need more accuracy over project lifetime to secure capital

Projecting current financials is not sufficient

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Multiyear optimization

Pecenak Zachary K., Michael Stadler, Kelsey Fahy, “Efficient Multi-Year Economic Energy Planning in Microgrids,“ Applied Energy Journal by Elsevier, Volume 255, 1 December 2019, ISSN: 0306-2619, https://doi.org/10.1016/j.apenergy.2019.113771

Forward looking approach XENDEE adaptive approach

• Does not change problem formulation

• Solves consecutive single year optimization • Representing different years

• Investments in previous optimizations are considered fixed assets

• Applies a new index ‘𝑦’ to the optimization problem, which represents the year in considerations

• Solves entire horizon in one optimization

Standard DER-CAM formulation

min 𝑐 = Σ𝑦 𝑐tariff𝑦+ 𝑐fuel𝑦 + 𝑐carbon𝑦

+ 𝑐investy + 𝑐O&My

− 𝑟sales𝑦

Subject to

Energy Balance 𝐷𝑦,𝑚,𝑑,ℎ + 𝐿𝑦,𝑚,𝑑,ℎ = Σ𝑡𝐺𝑡,𝑦,𝑚,𝑑,ℎ + 𝑃𝑦,𝑚,𝑑,ℎ

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Different approaches to multiyear modeling

• Forward looking approach is unpredictable and can increase runtime by 12000%

• Adaptive approach is linear and predictable

• Both approaches improve over the single year projection

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Comparison of approaches

• When considering forecasting error, forward looking approach is dangerous since it assumes foresight for e.g. 25 years

• Making sequential investment decisions with information only is a safer play

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Forecast error is impetus for adaptive approach

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New business cases for continuous investments

Conclusions What did we discuss?

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Summary • There is a market need for

smart decision making tool

• DER-CAM is a flexible optimization framework

• XENDEE has researched a number of improvements

• Impact of data down-sampling

• Multiyear method

• Ancillary services

• Stochastic behavior • Outages • Solar/wind potential

• Sub hourly optimization time-scales

• Variable equipment lifetimes

• Fast 8760 optimization

• Much more…

Ongoing work

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Ancillary Service Modeling An example of modeling a value stream in XENDEE’s DER-CAM

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• Four AS products considered:

– Spinning reserves

– Non-Spinning Reserves

– Up-Regulation

– Down-Regulation

• Key assumptions:

– Building/microgrid is a price taker

– Historic market clearing prices are used

– Bids are won according to user defined ratio (WinRatio)

– Currently only generators and storage systems can provide AS

– Bid is called for delivery according to utilization factor (𝛼)

Ancillary Services (AS) modelling

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Generation capacity limits 𝑃𝑔 ≤ 𝐺𝑒𝑛𝑈𝑠𝑒g,m,d,h,e + 𝐺𝑒𝑛𝑆𝑒𝑙𝑙g,m,d,h,e + 𝐺𝑒𝑛𝑅𝑠𝑟𝑣g,m,d,h ≤ 𝑃𝑔

Continuous bid requirements (applies a binary to ensure bid length requirements) Σ𝑔𝐺𝑒𝑛𝑅𝑠𝑟𝑣𝑔,𝑚,𝑑,ℎ ≤ 𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ × MaxBid Σ𝑔𝐺𝑒𝑛𝑅𝑠𝑟𝑣𝑔,𝑚,𝑑,ℎ ≥ 𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ × MinBid

Σℎ 𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ ≥ 𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ − 𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ−1 ∗ MinBidTime ℎ ≡ {ℎ, ℎ + 1,… , ℎ + MinBidTime}

𝑏𝑖𝑛𝑅𝑠𝑟𝑣𝑚,𝑑,ℎ ∈ 0, 1

Revenue from market participation 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑅𝑠𝑟𝑣 = Σ𝑚,𝑑,ℎ,𝑔 𝐺𝑒𝑛𝑅𝑠𝑟𝑣𝑔,𝑚,𝑑,ℎ ∗ 𝑀𝑟𝑘𝑡𝑃𝑟𝑖𝑐𝑒𝑚,𝑑,ℎ ∗ WinRatio

0 ≤ winRatio ≤ 1

Cost to participate

𝐹𝑢𝑒𝑙𝐶𝑜𝑠𝑡𝑚,𝑑,ℎ =𝐺𝑒𝑛𝑅𝑠𝑟𝑣𝑔,𝑚,𝑑,ℎ × α

𝜂𝑔∗ FuelCost

𝑂&𝑀𝐶𝑜𝑠𝑡𝑚,𝑑,ℎ = 𝐺𝑒𝑛𝑅𝑠𝑟𝑣𝑔,𝑚,𝑑,ℎ × α ∗ O&MVarCost

Similar equations applied for other markets, as well as storage devices

Modeling spinning reserve with generators

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Benefits • Value stacking for storage and generator during times where they would traditionally sit idle • Lower O&M costs and longer life, since traditional use periods can be offset with reserve

(stationary) activity and compensated

Can impact investment decisions, making those DER more attractive

Impacts of ancillary reserve participation

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