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

[](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.