---
# System prepended metadata

title: '[Notes on PIMS Workshop on Mathematical Sciences and Clean Energy Applications](http://www.pims.math.ca/scientific-event/190521-pwmscea)'
tags: [energy]

---

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tags: energy
disqus: colliand
---

# [Notes on PIMS Workshop on Mathematical Sciences and Clean Energy Applications](http://www.pims.math.ca/scientific-event/190521-pwmscea)

[![hackmd-github-sync-badge](https://hackmd.io/IyEoJqklTqu5GGIdRKiq9A/badge)](https://hackmd.io/IyEoJqklTqu5GGIdRKiq9A)


May 21-24, 2019

Notes on the workshop...

> Workshop Description: To avoid the worst consequences of climate change, the energy chain of the global economy must be drastically decarbonized. This exploratory workshop will build a greater dialogue between those in the mathematical sciences and the clean energy sector. The workshop will include: first-hand accounts of mathematical scientists working in clean energy projects, kind and gentle introductions to clean energy systems and mathematical tools, graduate student presentations, and panel discussions on topics such as challenges in clean energy. The goal of the workshop is to inspire interest in further exploration and to nucleate collaborations between mathematical scientists and practitioners in clean energy. Mathematical scientists with no previous experience in clean energy research are welcome.



## [Taraneh Sowlati](https://profiles.forestry.ubc.ca/person/taraneh-sowlati/) (UBC Forestry)

**Title: Use of forest-based biomass in clean energy applications: complexities and supply chain modeling approaches**

>Abstract: The importance of biomass as a renewable source of energy has been increasing. Forest-based biomass is an attractive type of biomass for  producing  bioenergy,  biofuels  and  bioproducts,  especially  in  regions  with  abundance  of  supply,  and  active  forestry  and  wood manufacturing  operations,  such  as  Canada.  Utilizing  forest-based  biomass  in  clean  energy  applications  has  the  potential  to  reduce dependency  on  fossil  fuels,  decrease  emissions  and  create  jobs  in  forestry-dependent  communities.  However,  forest-based  biomass availability,  quality,  and  cost  affects  its  competitiveness  as  an  energy  source.  Many  previous  studies  used  simulation  and  optimization modeling  approaches  to  manage,  improve  and  optimize  the  supply  chain  of  forest-based  biomass.  Recent  studies  used  hybrid approaches to incorporate variations in supply amount and quality as well as sustainability aspects of the supply chain into the models. This  presentation  provides  a  background  on  forest-based  biomass,  and  the  opportunities  and  challenges  in  utilizing  this  renewable source. Furthermore, a classification of supply chain modeling approaches is presented. Additionally, our recent projects that integrated sustainability aspects and addressed variations in forest-based biomass supply chains are discussed.

Biomass supply and demand. Biomass accounts for 10% of the global energy supply.

[World Energy Balances](https://webstore.iea.org/world-energy-balances-2018)

Share of bioenergy in different countries.

Forest based biomass is an attractive renewal source. No competition with food/fodder. Could be used to produce bioproducts from wastes.

The term "forest biomass" was introduce by Young et al. in 1964. In many scenarios, the forest-based biomass energy is extracgted from portions of the tree that are not used for lumber and traditional wood products. 

### Four main categories of biomass energy technologies

* Combustion: heat, steam, electricity; commercially available
* Fermentation: Ethanol, syngas, glucose, electricity; technically feasible
* Gasification: Heat, electricity, syngas, diesel, technically feasible
* Pyrolysis: Biooil, gas, chemicals; technically feasible

### Challenges for using forest-based biomass energy

* Bulky material, low heating value, high moisture content
* High transportation, pre-processing and handling costs
* Variability in supply in quality and quantity
* ....slide changed!

### Supply chain entities and complexities

* Interdependency among forest sectors
* Multiple decision makers

### Previous research

* Classification of forest-based biomass supply chain models
* Fewer studies looked at multi-objective optimization scenarios
  * GHG emissions from operations or LCA
  * Environmental and social aspects
* Stochastic models, simulation, hybrid models

### Tactical optimization model for a biomass energy plant

* Objective function: maximize Profit = Revenue - Rosts
  * Revenue: selling electricity to the customers
  * Costs: operating costs, biomass costs, storage and handling costs, ash disposal costs
* Constraints

We implemented optimization, monte carlo, hybrid optimization based on various parameters in the supply chain. We looked at a one year plan in three month stages. We developed a stochastic-robust optimization to incorporate variations in the biomass input.

Our analysis suggested that the profit for the company could increase by 15% compared to current profits. 

### Bioenergy and biofuels supply chain

Williams lake area was affected by mountain pine beetle infestation. The challenge was to investigate whether the infested forest areas could be used for biomass energy instead of for traditional lumber production.

The goverment of British Columbia evaluates how vulnerable a region is to changes in forest product markets. This index helps identify social benefit factors in the social optimization model.

### Conclusions

* Bioeconomy market is estimated to be more than USD 2 Trillion
* 

## [Rose Murphy](https://www.sfu.ca/emrg/People/rose-murphy.html), (PICS Research at SFU)

**Title: The math of energy-economy models CIMS and CIMS-Urban**

> Abstract: Energy-economy  models  are  appliedat  the  global,  national,  and  sub-national  (state,  province,  region)  levels  to  estimate  the  GHG emissions  reduction  from  one  or  a  portfolio  of  policies.  While  these  models  usually  represent  all  GHG  emission  sources,  they tend  to focus on energy given its importance for GHG emissions –hence the term energy-economy models. Energy-economy models simulate the  GHG-related  decisions  of  firms  and  households.  They  are  especially  focused  on  the  turnover  of  the  capital  stock,  namely: infrastructure,  buildings,  industrial  plants  and  equipment,  electricity  generation  and  other  energy  supply  technologies,  vehicles  and other  transport  equipment,  appliances  and  other  household  durable  goods.  Energy-economy  models  can  have  considerable  regional detail;  however,  they  generally  lack  city-level  detail  on  land-use.  And  as  a  growing  number  of  cities  have  set  ambitious  mid-century targets for GHG emissions reduction and increased use of renewable energy, new modeling tools are required in order to independently assess these targets. Urban planners and policy-makers focus on land-use zoning and the coordination of urban development (density, mixed  uses)  with  mobility  infrastructure  (roads,  transit,  cycle  paths)  and  perhaps  energy  infrastructure  (district  heating). Analysis  of policies operating at this level requires an energy-economy model combined with urban land-use detail. To address this need, researchers at the Energy and Materials Research Group at SFU have developed a spatially explicit version of CIMS, an existing energy-economy model, by linking it to a GIS model. The resulting model is called CIMS-Urban. In my talk I will discuss the following.
>  * The mathematical functions used in CIMS to estimate market shares of energy consuming and producing technologies. 
>  * The techniques used to estimate the values of parameters within these functions, including discrete choice analysis.
>  * How urban travel demand is allocated between private vehicles, transit, cycling, and walking based on equations that represent the quality of transportation networks within the GIS component of CIMS-Urban.
>  * Mathematical challenges we are currently facing in our modeling with CIMS and CIMS-Urban

[CIMS energy-economy model](https://www.sfu.ca/emrg/Our_Research/policy-modelling.html)

Characterizing CIMS in various terms:

* Technological explicitness
* Preference incorporation
* Equilibrium feedbacks

The heart of the model is the *CIMS technology choice algorithm*.

A new model: CIMS-Urban. Mode switching. 

## [Blake Shaffer](https://www.blakeshaffer.ca/) (Stanford, University of Calgary)

**Title: The effect of climate change on the level and timing of future electricity demand**

> Abstract: This paper examines how rising temperature due to climate change will affect the demand for electricity through mid-and end-century. We  extend  recent  literature  by  directly  incorporating  adaptation  in  the  form  of  increased  air  conditioner  penetration  into  temperature responsiveness  and  focussing  on  changes  to  both  the  level  and  timing  of  future  electricity  demand.  The  latter  is  found  to  be of  greater importance  in  colder  countries,  where  the level  effect  is  dampened  by  offsetting  reductions  in  heating  demand  from  warmer  winters. Seasonal  peaks  are  projected  to  shift  from  winter  to  summer  and  the  diurnal  range  of  hourly  demand  expands,  exacerbating  an increasing need for flexibility coming fromthe supply side due to a growing share of variable renewable energy

###  How will climage change affect variable X?

+ Economic growth
+ Eocnomoic production
+ Agriculture (Deschenes and Greenstone, 2012)
+ Mortality
+ Human Capital
+ Electricity demand

The paper does not predict actual future electricity demand. The paper does not include population effects. This paper does not include non-temperature related electrification.

Three key takeaways:

+ Annual Canadian electricity deman increases 3% by end-century
+ Peak deman shifts to summer in most provinces
+ Large increase in intraday ramping requirements across all provinces

Themes on recent related literature:

1. regional heterogeneity
2. incorporate adaptation
3. intraday shape of energy usage

Data

* original dataset of hourly demand for every province in Canada for 2001-2015. Collected from each provincial utility and/or balancing authority
* Other data
  * hourly temperature
  * observables on air conditioning behaviour....slide changed.

Interesting. This data may be shareable (with the exception of the Quebec data). That would be an interesting resource.

**TODO:** Can we connect data for hourly demand for every province in Canada with the federated [syzygy](https://syzygy.ca) JupyterHub network?

## [Sara Muir Owen](https://pics.uvic.ca/about/staff) (Pacific Institute for Climate Solutions [PICS])

PICS intertwines UVic, UBC, UNBC, SFU.

In 2006, there were strong connections between California and British Columbia. 

Tim Flannery wrote "The Weather Makers"

In a 2007 throne speech, BC legislated a 33% GHG emission reduction by 2020. In 2008, BC launched the Climate Action Plan. A 94M endowment went to UVic to establish PICS. The PIMC Mandate is to produce leading climate solutions research that is actively used by decision-makers to develop effective mitigation.... slide changed.

Main project (aligned with BC sectors):

+ Forest carbon management
+ Energy efficienty in the built environment
+ BC Natural Gas Development
+ Low carbon pathways to 2060
+ Transportation futures

Evolving scope for climate soluitons

+ Mitigation and adaptation
+ solutions generated and **used**
+ Diverse portfolio -- topics and locations
+ International external review
+ Partnership engagement

PICS Supported activities

+ Internship and student engagement
+ collaboration/networking/events
+ Mitigation and adaptation solutions

Student internship program

+ Up to $10K per year for each project
+ 10 projects per year

Opportunity projects program

+ $60K per year for each project for up to three years duration
+ 34 proposals received this years; 4 projects awarded.
+ Current call: Deadline on August 2.

Theme Partnership Program

+ $250K per yar for each project, up to four year duration
+  Two phase application, with proposal strengthening workshops offered
+  Year 2019: Proposals due May 15

PICS Research Engagement

+ Co-develop climate soluions in areas of high complexity
+ Build capacity through partnerships
+ Establish international leadership in critical areas

## [Mike Thiessen](https://www.linkedin.com/in/mikethiessen/) ([Genus Capital Management](https://genuscap.com/))

**Title: Opportunities and Challenges within Clean Energy Investing**

> Abstract: The clean energy industry is going throughdrastic changes and continues to see growth despite political headwinds. Among the parties interested  in  the  industry  advancement  are  investors. We  have  seen  investments  in  fossil  fuel  extractors  and  processors  decline  as  IT and  Financials  replace  them as  the  most  valuable  companies.  Traditional  investors  are  now  looking  for  clean  technology  and  utility companies for their portfolios and a new breed of investors called “impact investors” are striving to make a difference with their investment capital. This has led to  the Divest/Invest movement which is backed by research showing  that divestment from fossil fuels has historically not hurt returns.When looking for cleaner investments, investors have a growing number of options and financial firms are consistently coming out with new products. However, there are challenges that investors face, such as political risk, regulatory risk, and  a  lack  of  pure  play  equity  investment  options. The  energy  and  financial  industries  need  to  solve  these  issues  in  order to  mobilize more capital towards the growth of clean energy.

Agenda:
1. Growth of clean energy
2. divest/invest movement
3. clean energy investments
4. challenges

Growth in renewals. Source (IRENA). Doubled over the last nine years. Wind is growing at 4X over the last 9 years. Solar energy is off a smaller base from 9 years ago. Solar grew by 30X in Canada and by 25X in the US.

Government support has been helping these advances. There have been recent headwinds...

Emerging markets and China are investing eavily in renewables. In many cases, these are the first investments in power service. In other cases, cities are growing so quickly. This is not necessarily a replacement of dirty energy sources. Much of this investment is driven by governments rather than by investors. Some financing coming from private equity and public equity. 

Genus provides clients an opportunity to divest (from e.g. tobacco, apartheid, fossil fuels) and move their funding toward other investment portfolios.

Three waves of a divestment campaign:

1. religious groups and industry-related public organizations
2. universities, cities and select public institutions
3. wider market

Examples...David Suzuki Foundation wanted to get out of oil sands investment and are an example of an organization of wave 1. We are now entering wave 2 with universities choosing to divest from fossil fuels. Cities are starting to divest. New York City, San Francisco, Paris are divesting. 

Question: How can they validate the supply chain on the so-called "fossil fuel free" funds? Is there a trusted broker of "fossil fuel content" for publicly traded firms?

Climate change and fiduciary duties.

Fossil free performance comparison?

Question: Does Genus make equity investments in clean energy startups?

### Impact investing

1. Cleantech stocks
2. Green bounds
3. privaty equity
4. green bonds

[Greenchip Financial](http://greenchipfinancial.com/)

### Venture Capital in Clean Energy

+ [Arctern Ventures](https://www.arcternventures.com/#launch-page)
+ [Copower](https://copower.me/en/financing)
+ [InvestEco](https://investeco.com/)
+ [Cycle Capital Management](http://www.cyclecapital.com/)
+ [WUTIF Capital](http://wutif.ca/)
+ [Chrysalix Venture Captial](https://www.chrysalix.com/)

### Challenges

1. Need more products
  a. few pure play clean energy public equityes
  b. few mature private equiaty options
    1. government ownership
    2. subsidiaries of utility companies
2. risks
  a. political risks
    1. emerging markets
    2. also USA and Canada
    3. future subsidies
  b. startup risk
  c. illiquidity risk
3. high minimus
4. perception of low returns

## [Matt Davison](https://www.uwo.ca/stats/people/bios/matt-davison.html) (Western University)

**Title: Thinking about Year 21: the Economics of Ontario Wind Power after PPA expiry**

>Abstract: Wind  Power  has  become  a  major  component  of  the  Ontario  electrical  generation  mix  due  in  large  part  to  the  generous  feed  in  tariffs offered,  for  20  year  terms,  to  wind  power  producers. The  end  of  these  20  year  terms  is  in  sight  for  many  of  the  earlier  wind  farms constructed in the province. At around 20 years not only do feed in tariffs expire, many expensive components of a wind farm begin to 
10fail. At  the  same  time,  the  exchange -set  Hourly  Ontario  Electricity Price  is  very  low.  What  is  the  optimal  course  of  action  for  Ontario Wind  Farm operators? How  might  the decisions  of the first producers to reach year 21 influence  the power market for  producers who reach year 21 later? What kind of contracts might be constructed to incent decisions, by wind farm operators, which seem desirable by provincial  authorities?  This  talk  will  be  an  eclectic  mix  of  engineering  economic  analysis,  financial  real  options,  and  economic  optimal contracting. This  is  a  joint  work  with     Rupp  Carriveau  and Lindsay  Miller,  from  the Department  of  Civil  Engineering, University  of Windsor, and  Arezoo Tahmabesi from the School of Mathematical & Statistical Sciences, Western University Canada.

I sometimes look at investments from the lens of the previous talk. I also sometimes look at these with an options lens. (I didn't really follow this distinction.)

This talk will focus on the most powerful 10% of the tools in the options pricing toolbox.

The Year 21 Problem

+ Remarkable rise of Wind power in ontario has been enabled by generous, 20 year long, Feed in Tariff contracts
+ These allow wind energy produces to seel the first 20 years of their production at a fixed indivisual tariff wich substantially exceeds the market price.
+ Much of the physical wind turbine plant was expected to last about 20 years also.
+ What happens at year 21?

Large growth in Canadia Wind Power

100MW hours in 2004 to about 10,000MW today.

At year 21, what will happen?

+ close the farm and walk away?
+ What if a blad breaks in Year 18?
+ Continue? A lot of the cost of a wind farm is bound up in the land and the social license to use it, the foundation, the tower, the access road, and the transmission link.
+ replacement pieces may be better than initial install

Academic-Industrial Collaboration -- funded through NSERC CRD grant

Kruger Energy and Enbridge are the industry partners.

[Wind Energy Institute of Canada](http://www.weican.ca/) (based in PEI.)

Three Case studies

+ Generator failure case study -- age structure population modeling
+ Delta unit study -- the power of technical improvement
+ Scenario analysis for post year 21

Case study: failure modeling

+ [Weibull failure model](https://en.wikipedia.org/wiki/Weibull_distribution) that incorporates aging.
+ Analysis here is similar to actuarial science.

After this analysis was completed, a deeper investigation based on [SCADA Data](http://www.scadaminer.com/wind-farm-scada-data-analysis/) which provides deeper insights into the performance of a wind turbine.

## [Anthony Ware](http://people.ucalgary.ca/~aware/) (U. Calgary)

**Title: Reliability-constrained hydropower optimization**

>Abstract: Maximizing  the  long-term  value  of  hydropower  generation  requires  management  of  uncertain  reservoir  inflows,  potentially  variable constraints  on  outflows,  and  exposure  to  possibly  wildly  varying  power  prices.  We  describe  a  stochastic  dynamic  programming approach to the quantification of reservoir reliability (for example, measures of the risk of over-topping the reservoir or failing to satisfy 
19downstream  flow  requirements)  and  a  related  approach  to  determining  the  reservoir  flow  strategy  that  maximizes  expected  revenue, subject to defined target reliability levels.

Hydropower, dams and rivers.

We are striving here to balance the marketer's goal of maximizing revenue with the hydro scheduler's imperiative to operate within constraints.

## [Anatoliy Swischuk](http://people.ucalgary.ca/~aswish/) (U. Calgary)

**Title: Overview of Some Results in Energy Market Modelling and a Vision on Clean Renewable Energy**

>Abstract: The talk overviews my recent results in energy market modelling, including applications of weather derivatives, option pricing, variance and volatility swaps in the energy markets, pricing crude oil options using Levy processes and energy contracts modelling with delayed and jumped volatilities. I will also talk about my vision on clean renewable energy.
>


Anatoliy publishes often in the Journal of Energy Markets.

