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[Music] we do have the CU energy club here tonight they're a University of Colorado at Boulder student group who hosts energy related events and programs now they recently hosted the Energy frontiers 2020 where they held a career networking fair a panel discussion on harnessing demand-side flexibility as well as a keynote talk by byron Kemetic who some of you might remember actually spoke for VRS not too long ago I'll quickly introduce each group before they go starting with Kayla Carrie and Nathan scholar their presentation is titled energy policy analysis with Guzman energy the presentation will cover a preview of the policy research analysis and proposals that were part of both speakers 2020 masters of the environment capstone project topics they will cover will include Colorado PUC and General Assembly proceedings pertinent to Guzman energy which is a full-service wholesale power provider as well as a research project that they did that will discuss the wider energy transition in Colorado and the Intermountain West now to give you a quick bio on both of the first speakers Kayla Kerry is pursuing a master's degree in Environmental Policy with a focus on energy policy and regulation previously she worked for the energy efficiency business coalition and conducted research at Australia's largest climate change facility she also graduated magna laude from cu-boulder with a BA in ecology and evolutionary biology then our second speaker is Nathan Stotler he is also pursuing a master's degree in Environmental Policy with a focus on energy policy and regulation he previously worked as a project manager for the LA I design group which is a land planning firm in Denver and as a designer for TVG Partners a landscape architecture and planning firm in Houston Texas he also graduated magna laude from North Dakota State University with a bachelor's of landscape architecture and a Bachelor of Science and environmental design so they will be your first group for tonight and let's kick them off good evening everybody I'm Nathan styler and I'm Kayla Carey and we are candidates in the masters of the environment program and see you we've partnered with energy for our capstone project which is entitled cooperatives at a crossroads identifying the opportunities and challenges of Electric Cooperative exits so first a little bit about Guzman energy and who they are they're a private sector wholesale power provider and energy consultant they supply power at wholesale to their clients which are mainly electric co-ops municipal utilities and tribal utilities now their clients then sell that power at retail to consumers within their service territories Guzman started his footprint in Colorado in New Mexico but it has been expanding nationally a goose Mont separates itself from its competitors in a few different ways they start by offering affordable prices and flexible contracts and they also offer customized support to their clients because they recognize that each one has different needs they also offer greater local control to the communities that are served by their clients and they offer buyout funding to help potential clients lead custom leave cumbersome contracts so we've worked over the course of this past semester in school to narrow down on the scope of our project and the first step we took in doing that was to decide to focus on Rural Electric Cooperative's which are one of Guzman's primary customer groups there are currently over 900 total electric cooperatives you need in the United States that covers 75% of the land area of the country they own 42% of its distribution power lines and they serve 11 percent of its population now those 900 plus coops are split into two groups on one hand we have distribution coops or what we're going to call de seas and on the other hand we have generation and transmission co-ops which we're going to call G and T's de seas were the first of these to form back in the mid 1930s during the Great Depression and they purchase power at wholesale and distribute it to their rural and small-town customers not long after their formation de seas began to band together to form G and T coops which are essentially then cooperatives whose members are all cooperatives and they did this that they could effectively own their own generation and transmission infrastructure and in an affordable way de CS are generally very small have that many customers so it's not very efficient for them to each build their own generation and transmission infrastructure so by grouping together they could pool their resources build bigger generation plants bigger transmission infrastructure and secure economies of scale to lock in lower prices for their customers alright because of this de CS and G and T's have had a mutually beneficial relationship for the better part of the last century but recent changes in the energy market including the rapid growth of renewables and distributed resources as well as the advance of climate change because that means and ones of some Dec stage gt's have largely been unable or unwilling to adapt to that change and reluctant to let Dec change the terms of their contracts or let them out of their long-term commitments which as you can see are fairly substantial at 40 to 75 years these contracts are long and they also place restrictions on how much power each Dec can generate for themselves usually something around 5% on top of that GN T's investments in coal have made them unable to switch quickly to renewables which is caused rising wholesale rates for de seas and has also locked those de seas into a dirtier energy mix because of these reasons the GMT model is no longer functioning as it was intended which has left some dissatisfied dec s looking for new solutions to find cleaner more affordable and more locally generated electricity exiting of GMT contracts has emerged as one route the de seas can take in pursuit of their needs so we've decided to explore these coop exits as the focus of our capstone project taking a step back for a second in developing the purpose for our project we employed the system's approach of identifying guiding and near stars so our guiding star or big picture goal for the project is to accelerate a national clean energy transition with the target of limiting global climate change this is obviously not something that we're going to solve right here with our capstone project so we also set a near star our short-term goal that's going to be more achievable within the scope of our project so our near star is to or the potential for coop exits to accelerate the clean energy transition we plan on doing this by objectively identifying the challenges and opportunities of coop exits for meeting Dec needs now Guzman Energy's role in this study will be lending us their unique insight into the coop exit process as a tool for identifying these challenges and opportunities you Thank You Nathan so through our capstone project we aim to produce work that will contribute to the existing literature and also shape the future of rural energy markets we plan to do this by conducting an objective analysis on the opportunities and challenges related to cloth exits so we have identified three main goals to achieve this outcome first is to engage a representative set of Dec boards and members to identify their view of the challenges and opportunities related to contract exits and by representative we mean diverse as in we want to talk with people from various places and income to avoid bias in our findings our second goal is to analyze current and proposed policies to assess their potential for effecting de seas efforts to meet their needs so for national we will mainly focus on the federal energy regulatory commission also known as FERC and for state regulations we will focus on the Public Utilities Commission along with legislation from the Colorado General Assembly and other state legislators throughout the country and finally our last goal is to develop a white paper containing a detailed study of past or ongoing Dec exit attempts that impartially lays out the challenges and opportunities that arose during the process and it will also include a description of how called autonomy relates to the clean energy transition so there will be three major methods of developing our whitepaper research outreach and analysis throughout our research we will expand on the literature review that we already completed during the spring semester and perform case studies of coops that either completely or partially engage in exit process in addition that we will use tools that we learned in our system's thinking course to map out the components and connections of the utility system and at the end of the research phase we will be able to answer the question what back on information is already available regarding cooperative exits and next we'll move into the outreach phase where we will engage Dec leadership and complete interviews or surveys to determine their perceived and experience challenges and benefits surrounding coop exits the final method is analysis where we will analyze existing pending policies to answer the question what type of policies make cooperative exits more challenging or more accessible for DC's and after we complete these three stages we'll move on to some asus and writing so our final deliverable will be a neutral white paper that synthesizes the findings from our research outreach and analysis phase it will include an extensive literature review of case studies and policy analysis and along the way we will also be creating other smaller deliverables to inform our research and advance our knowledge on the topic so these may include messaging efforts for guzman and other miscellaneous work like systems maps or jurisdictional boundary diagrams so here's our timeline you can see that our project is organized into four major phases and we are currently at the conclusion of phase 1 which is project conception and initiation throughout the summer we will be focused on the research outreach analysis phase along with this sense synthesis of that information to write our white paper and in the fall we'll move on to the final phase of presentation polishing and publication so during that time we will work with Guzman to identify a medium to share our findings and meet our degree requirements so ultimately we would like to try to publish our report potentially in an academic journal and so similar to any project we have encountered some speed bumps along the way but we have also made some progress so one action that has helped us so far has been participating in a weekly policy call so this is a meeting with our capstone partner where they discuss relevant actions that have occurred in the policy space that week along with upcoming legislation and regulation this call has been a really valuable learning experience for us because it gives us insight into Guzman stance on current and pending policies and it also keeps us updated on what is going on in the political and regulatory realm and on an internal level we have also established weekly team meetings where we send agenda and define clear action items so these internal standing meetings have helped us to keep on track and stay efficient nevertheless over the course of planning our project we have had concerns about overcoming learning curves of a complicated industry along with narrowing the scope of our capstone so there's so much going on in the complex energy space this project can go in a lot of different directions and it can quickly snowball into something unmanageable within our limited time frame so we have to constantly think about and talk through what is realistically both achievable and impactful so the meetings with our partner and our team along with the research we have conducted in the spring have helped us to overcome both of these concerns however going forward it will continue to be a challenge to Stan's cope and keep up with the changing policy landscape but clear and constant communication will help us to overcome those anticipated hurdles and also just like everybody else we will have to navigate work restrictions due to kovin so that means we will not be able to fail to have face-to-face meetings with our partner or interviewees and we will not be able to attend regulatory sessions in person however thankfully we are equipped with the technology to make up for those shortcomings and so what's next we had just finished our spring semester which concluded phase 1 of our project and then in May and June we will dive into Phase two where we will develop interview scripts determine who we will reach out to for interviews explain our research and start to identify and conduct case studies and so we hope to have the interview is completed by midsummer and that will help us to lay out the framework for our paper and start writing so thank you all for listening and now we will open it up for questions we'll do just as a quick reminder the Q&A will happen all at once at the very end moving on to our next presentation so the second presentation is titled smart grid responsive homes and is presented by Katelyn goofy Katelyn goofy will discuss her work on designing algorithms to optimally manage electricity usage in smart grid responsive homes she is a PhD candidate in electrical engineering at cu-boulder studying power system optimization renewable energy integration and smart grid technology so that'll be alright next one hi everyone my name is Caitlin Murphy I am a PhD candidate in electrical engineering at the University of Colorado Boulder and today I'm going to be talking to you about my one of my research projects which is about smart grid response poems so let's dive in so first I want to talk about why we need homes to be responsive and what I mean by that so Colorado like a lot of like many cities and states around the United States have ambitious from renewable energy targets that they would like to meet by some years such as 2030 and currently the grid is operated to meet our electricity demand every second of the day every day of the year regardless of what renewable energy is available so in this picture there's over 43,000 homes demanding electricity from the grid during a time when there is no solar and maybe some wind this highlights the point of what my work games answer so how can 43,000 homes be helping to the grid and perhaps you aligning their electricity usage with what renewable energy is available so perhaps if homes are operated in a smart way these free responsive homes could be a very powerful Avenue for helping us meet our renewable energy targets since the risk the residential sector consumes more electricity than any other sector so next what do we mean by a smart great responsive home I don't mean consume art consumer appliances like smart thermostats or like a smart oven instead what I mean is a smart algorithm which is going to control all of the electricity usage within your home so if you have an electric vehicle water heater your thermostat secondly maybe you've got PV so operating all of these together and their electricity usage to align with the renewable energy at the pole along with other things and this is going to ensure that electricity is used as efficiently as possible within a home so for to create a smart grid responsive home I developed an algorithm for a home energy management system this energy management system is going to take into account things such as whether we renew the price of electricity from Xcel and what appliances that the that is inside the home as well as the preferences of the residents so but before we dive in I want to for all of the optimization nerds out there I want to talk about what's actually going on behind I'm just going to coordinate all of these things that are being accounted for in the home energy management system so this is the quick sidebar on this whole home energy management system framework is based on model predictive control based algorithm and what's beautiful about this is that it introduces feedback and future information into the system when we're determining our optimal or optimal decisions for how we want to use electricity so for example if we're at the beginning of this time
horizon at time T we're going to take into account all of these things in the house so the the weather forecasts electricity demand the future electricity prices for example if they're on a time of use rate the appliance characteristics etc so pack this all into models and we're going to talk about these in more detail but I wanted to get the background so you see them I'm building all of this so at time T we mathematically model all this information and we we formulate an optimization problem so that we get and then we solve that to get an optimal to get a to get an optimal solution which is going to decide how we're going to use our electricity like whether it's coming from the grid or PV panels for some future prediction horizon so in this slide it's eight hours and then so we do the first time step of that so we so we do that decision but then every time step we get to reevaluate and incorporate new information or updated forecasts etc and then reoptimize so every time stuff we get to reoptimize come up with new solutions as we're moving forward in time so you can ensure that we're always optimally managing the electricity in the home so why don't you give that quick background on model predictive control and now we're going to talk about now or now we're going to go back to the to the grid responsive home details like what's actually going on inside the home and into this this framework this model predictive control framework so for this home energy management system there are pretty much three categories of our information that I'm including so there's the resident preferences the forecasts and forecasts are important because they inform renewable energy generation going on inside the house and then the models and the data that we need to to solve these and so we're going to now talk about these in a lot of detail or in more detail so first we have for our home energy management system or hence as I'm calling it the resident preferences are focused on comfort so we need to make sure that this algorithm is maintaining the thermal comfort balance for the residents or the or another example of water heat or water heater tank temperature additionally price and sensitivity of how much you're spending them all on electricity like is very important especially the residential rates that they might have access to you such as time-of-use pricing and then also an occupancy schedule so this will dictate how much electricity that they actually demand at any given time next thing in the hems or home energy management system it is incorporated are the forecasts weather is the weather forecasts are super important and we get to the butyl whole thing about the model predictive control is that we get to incorporate not just lets going on now but also what's going on in multiple time steps into the future and the weather forecasts in the form space heating and cooling based on like doctor temperature in humidity it's also going to dictate solar entered the rooftop solar generation if if residents has rooftop solar we also want to incorporate forecasts of how the electrical electric vehicle if applicable is going to be used so when does at what time would do a residency to blue leave for work so the eating is be fully charged and when we think the e me is going to come back at the end of the day like what is C in charge then that's also important and the electricity demand so over time we want to be able to account for our expected future electricity demand inside the home so forecasts are very important a the beautiful part of the model predictive control because we get to incorporate those next are kind of the the third most important or the third most important thing that kids included in the in the hems framework the home energy management system framework is the models in the data so we need rooftop solar efficiency for incorporating into the PV model again the lectures you demand that's kind of data based we also have models for the house temperature dynamics so for better controlling our HVAC our heating and cooling system we also have a model for the battery which is very important for how it's going to charge and discharge and its efficiency and similar to the battery um or the the stationary like home batteries such as the tesla powerwall we also have an electric vehicle which is just a non-stationary battery really we also have that model and then the associated kind of occupancy or not occupancy but the the similar thing for EB where we would have you want to know when the evening is be fully charged and when it's going to come home so that we can get it charged up for the next workday so being able to describe these mathematically have access so the data is really important and then all of these go into the home energy management system like framework that's based on model predictive control so so those are three components that go into it however many of you in the audience are probably thinking like oh wow a lot of these are very uncertain and you can't always describe things like models you're absolutely correct so with in addition to all of these models I also incorporate uncertainty on the weather forecast because that's going to impact the optimal decision that comes out of this model predictive control based algorithm additionally incorporating incorporating uncertainties is because we are taking these very complex things and coming up with a bottle to describe them and that that gets into whether these models are actually good enough because we want to a model that can solve really fast and so those are normally more simplified so that they're easier to incorporate into these optimization algorithms so so you have the last step of these of this of creating this type algorithm is making sure that you account for uncertainty and you incorporate that mathematically into you to your optimization problem to make to ensure that the optional solutions are getting for this for managing the electricity within home are a bit more realistic so this leads me to one of the my favorite parts of where this research project led me was looking at what type of load shifting can a what what amounts of load shifting can all types of homes achieve because not all homes are the same some are older have better insulation therefore their their HVAC model is going to be a little bit different not all homes have solar a rooftop solar or an electric vehicle or a battery for that matter so what happens as I take away or add different parts of these different parts of this model such as like taking off the battery model in this overall optimization problem looking at the effect of this from many different perspectives and so yeah this is this is kind of a really interesting part of this work so as I mentioned homes are on a spectrum some have less controllable devices available so like for example the PV or the battery or the II me and then you've got then you've got the super smart home and and what you can do there is very different with an algorithm so compared to a home that would just have you like heating sleep singing and cooling that you would get to optimize so for these so for all of for this whole spectrum of poems I apply this this optimization this home energy management system that I've been talking about and I'm going to be looking at three performance metrics for how it does across a wide spectrum of homes so the first performance metric is electricity cost so making sure that the the optimization does actually result in a cheaper price of electricity so we have a flat price for I run the algorithm with a flat price but then also a time of use price where we've got a really expensive on peak period during like a peak - BAM time from 2 to 6 p.m. and then we have shoulder periods before and after that on peak period and then at night it's a it's there's a cheaper cost of electricity from 9 p.m. to 9 a.m. the second metric is is home battery operation for the homes that would have for example a power wall because I wouldn't want the optimization to be cycle to be cycling the battery because the battery life is measured in normally in the number of cycles so if the optimization is or the optimal the electricity decisions management within the home is such that the battery's being cycled that's probably not are being cycled on a daily basis it's probably not thing that a homeowner would really want even at the expense of like a really cheap electricity bill because you won't wanted to create their battery last is looking from now the utilities perspective for the performance of this is looking at the electricity demand from their side from each home so everybody knows of this of the famous curve with the rebounding effect what if I want to make sure that the algorithm that I'm suggesting doesn't create new Peaks that are even higher than what was there before without the control and that's really important because the if we have a whole bunch of homes independently optimizing their electricity usage what if we end up with a ramp or with a with a with kind of an aggregate I feel like at a bunch of homes looking at an aggregate amount or the electricity demand what would that mean for the utilities and is that something they would desire so looking at these performance metrics and across the wide spectrum of poems so I'm going to talk about some kind of overall general general results that I found so first in terms of performance time use prices in a smart home where it can optimally coordinate electricity usage always results in cheaper electricity prices across the entire spectrum however the the homes that have more of these controllable appliances see more savings and that's mainly because of the battery and and a rooftop PV so being able to store that PV for later when you don't have solar solar energy stuck in one of the downsides of these time abuse prices in the in this algorithm is that the time reduced prices incentivize the battery to charge using cheaper electricity from the grid so at night and then storing that electricity four times during high price it is what however however the downside of this while while this save the homeowners money it also led to more battery cycling so therefore lead perhaps leading to faster degradation of the battery last leaf' from running all of running this algorithm on a wide variety of homes from just homes having Justin H bus a smart smart HVAC system or heating and cooling system that I can leverage to ones that have any me a bunch of controllable devices the time of use prices actually created new and higher Peaks and electricity demand and so perhaps how a time I abused prices are engineered right now you're just giving it to feeding me these current prices into a model predictive control based algorithm where we want you know the optimal decision actually crazy like it just creates these larger Peaks and so that might not be what utility you want to see because if every house is doing this these new Peaks are bigger than what they were then we're there before which may not be desirable from utility perspective so that's very important so so yeah these are the overall results from this work I thought it was super interesting to see the impact at the time of news prices on these performance metrics so especially these peaks from the utility perspective and how that battery is being cycled for a wide variety of homes because a lot of these algorithm these home energy management system and algorithms or smart home our core algorithms really only look at the the super smart home and not how it's actually impacting homes that look more like the ones that we see when we want to walk around the neighborhood so maybe a home that has just regular cars and and just PV so and maybe a smart thermostat so that's what I really loved about working on this product so thank you for listening and I'm happy to answer any questions and my email and LinkedIn is up there for anybody who wants to follow up outside kind of the question and answer session after this so look forward to your questions thank you Thank You Caitlin all right so our final presentation of the evening will be from four different presenters Andy Bingle Caroline Cox dan Christophe ski and Raquel so they will be presenting on sea pace financing helping developers invest in clean energy Andy Bengal is pursuing a master's of the environment degree specializing in renewable and sustainable energy he graduated from the University of Puget Sound with a BA in comparative politics for the past five years he worked as a program manager in education with the multinational conservation organization the International crane foundation Caroline Cox is a master's of the environment student specializing in renewable energy and sustainable energy she has spent the last five years working for an environmental nonprofit that works with national and international brands and retailers and other stakeholders across the value chain to improve the sustainability and recoverability of consumer packaging is the main goal of her work there Dan Christophe ski is also masters of the environment student who's also specializing in renewable and sustainable energy he spent five years operating a nuclear reactor on a submarine for the US Navy shortly after high school he then went on to receive his bas in project management from Columbia Basin College and then worked as a port control planner at the nuclear cleanup Hanford Superfund site for three years then last but certainly not least Raquel Soto is a graduate student at the University of Colorado Boulder as well she is also in the invest masters of the environment programs specializing in renewable and sustainable energy she is currently a research analyst at Navigant Consulting which is a guide house company where she leads Navigant research division on evey research service Cal holds a BA in political science from Emory University as well don't let them take it away good afternoon everyone we that is Andy Bingle Caroline Cox Raquel so to myself hope everyone is doing well and staying healthy during these great times our project is looking at commercial solar financing incentive analysis in the state of Colorado specifically commercial property assessed clean energy known as C pace financing which we explain more in a bit we're lucky to be working with Maya Resnick a commercial solar consultant with IPS independent power systems as our capstone partner additionally we're helping the C phase director Tracy Phillips further develop the C phase program within Colorado while we investigate solar adoption in the commercial and industrial sector as we know climate change is a worldwide issue here in Colorado 29% of greenhouse gas emissions come from electricity generation with the commercial industrial sector accounting for about 65 percent of total consumption these numbers are reported every five years in Colorado but are projected to be the same in 2020 when the new report comes out this summer in 2018 governor polish Poulos pictured Center in Hana Hana bull platform of achieving 100 cent renewable energy by 2040 upon winning the election the Colorado government really went to work passing bills addressing the issue of climate change surely after taking office House bill 19 1261 passed for the 13 to 0 vote setting greenhouse gas emission reduction goals moving forward culminating in a 90% reduction of 2005 levels by 2050 included in this is the goal of a hundred percent renewable electricity generation by 2040 so you may have noticed large industrial and commercial buildings right off the highways and roads as you drive around Colorado I don't know if you know this but these buildings all have some in common large roofs this is prime real estate for rooftop solar only few have them installed for example the Auraria library in downtown Denver's shown here commercial and industrial buildings have large relative electricity loads and we know solar can offset these providing financial returns so I would not seem more solar on these buildings next Carolina and Andy will walk us through an example to help better illustrate the issue so it was Dan mentioned there's a lot of support for more widespread renewable adoption like solar even from the state's
administration and a path has been paved to go completely fossil fuel free in the near future but there's a problem that interferes with large-scale adoption of solar especially on commercial buildings and that is because the costs and benefits of installing solar on commercial buildings are traditionally disjointed there are often high upfront costs and long payback periods for cni building owners to install solar and but the tenants who typically pay the energy bills are the ones who are seeing the monthly savings and tenants who experience the monthly savings in their energy bill and have a financial incentive towards solar often don't know that the own the building and don't have the authority to install panels even if they wanted to so to help explain the financial landscape I want to introduce you to two people that might help make it a little bit clearer meet Betty a commercial building owner she owns a shopping center that is home to a grocery store a brewery a climbing gym and an alteration studio tessa the owner of the alteration shop pays Betty EV rent every month for her commercial space and really wants her business to have as little of an environmental footprint as possible under traditional financing the way that this would work is that Betty the building owner would have would likely have to take out a loan from a bake or other another form of lender once she secures the loan she could hire a solar contractor like IPS to install solar panels on her commercial building and then her tenants like Tessa could see monthly cost savings from Xcel as less of her energy was derived from the bulk grid market and more from renewable solar but where does Betty stand in all of this sure she's able to provide her tenants with buildings that save them money and have less a minute of an environmental footprint but where's the financial incentive for her she's left spending more money every month simply paying off her loan at an interest rate without ever seeing the financial benefits of her investment there's a disconnect here the value chain is disjointed sirs caroline has said Betty doesn't really have much of an incentive to put solar on her commercial industrial building so if you're governor pulse or maybe a local government representative what do you do about this well you can do you can pass policy one policy is trying to help put solar on commercial industrial buildings is city ordinances or building codes so an example of one of the building codes that's trying to help this is the Denver Green Building ordinance which states that if you are have a new commercial industrial building that's over 25,000 feet 25,000 square feet in size or if you are doing a big renovation to a commercial building you have to either put a green roof or solar panels on your buildings now that doesn't help the finances of building owners and asset managers to actually develop these so there's also financial incentives that come into place we're all pretty all of us here at BRS probably have heard of the investment tax credit that we have on solar that is slowly declining here but there's another one that there's a new person in town here a new incentive in town called C pace and C pace financing stands for commercial property assessed clean energy financing this is been in Colorado since 2016 however it started in California in 2007 and there's been about 35 states that have adopted C pace programs and they're booming in other states but in Colorado just they're just starting to get off of the ground and this is where our project focuses on is on the C pace financing so before we move forward let's learn about C pace financing by looking at that original financing structure so all of the orange arrows here are that is an example of the traditional financing structure the blue arrows that are going to pop up are now the C pace financing structure we'll see how this is a little different for Betty and Tessa so this beginning looks a lot like the finding of the regular financial structure Betty's going to get a loan from a lender or bank she's gonna give the money to a contractor who's going to do the building upgrades and those Billy I'm kraid's could be solar panels or energy efficiency in this case we'll think about solar panels then Tessa gets those benefits of reduced electricity bills but now here is the difference Tessa instead of getting a free ride she is going to pay taxes over a long period of time it's a specialty property tax that's put on to the building so Tessa is going to pay this out of out of her pocket once every year that she is in the building but whenever C paced projects look really nice and why they're expanding across the United States is that usually the benefits are higher savings and what the taxes are and then that local municipality with those taxes collected are the ones that actually pay back the bank and Betty doesn't have to pay back the bank on her own so this C pace financing structure really helps out not only Betty because she doesn't need to repay the bank but also Tessa the tenant because she can say that her that her alteration shop is now a green green business and she's saving money through those benefits so in some CPAs can be can be described as trying to get those high upfront costs covered through long period of taxes up to 20 to 25 years that the tenant pays instead of the business owner pain so we're going to talk a little bit about what one of the issues though with see pace as we go forward here and this is what our project is really going to focus on yep so as you've heard there are two issues with traditional financing and you've also heard about the benefits of CPAs so far but to date there are limited building owners that have used the pace in Colorado last time we talked to Tracy Phillips at the Colorado CPAs office there have been only about 60 projects since the policy was adopted in the state in 2016 and only a handful of those included solar but this is where our project comes in and this problem can be broken into two subcategories the first which is a lack of stakeholders using DePace our project can be impactful by decreasing the barriers to entry into the market by increasing the proposals and energy assessments that IPS completes for CNI clients we are therefore helping to decrease those barriers to entry because in theory it would lead to more clients and more solar in Colorado and expand that awareness and knowledge of CPAs for the second subcategory there is a lack of knowledge about C pace in the market and we plan to tackle this problem with a stakeholder outreach and analysis campaign which may look a little bit different after COBIT but it will likely involve an in-depth survey and interview process and hopefully followed by an energy assessment from IPS for these clients we also plan to spread knowledge and awareness of CPAs as well as our project findings through industry events like this one and other conferences and webinars which will likely take place in the fall semester so in terms of work done to date we started our stakeholder outreach campaign back in March and have reached out to over 200 folks so far primarily being CNI owners and developers this has resulted in us receiving 17 survey responses which is about a nine percent response rate this is a little low we definitely prefer to see that closer to the 15% but given that the book will the current global climate in the pandemic we are happy with that response rate and we have also conducted three interviews with C&I owners and developers so in terms of next steps for the remainder of this month we will be conducting more cycle or outreach primarily within the CNI tenant segment as well as with current CPAC users we want to gain some insight from the folks in addition to those building owners and developers we're already hearing from June is where we will need to determine whether the stakeholder outreach has been thorough enough despite the impacts of kovat or if we need to incorporate other project components to gain these insights and we will be conducting a market landscape analysis and doing some case studies and these will be scaled up and down depending on the need for our project July will consist primarily of analysis both qualitative and quantitative and in August and September we will develop our deliverable for ips and these will include an update to their proposal process and a turnkey solution document that will help IPS increase their client base by marketing CPAs so in short we are working with IPS to determine what market factors are deterring the use of seat a store CNI clients and shaping these findings into an actionable plan the IPS can use to increase his client base so if you have any connections in this industry that you think would be valuable for our project we would definitely love to for you to pass along any introductions or contact information you may have and a few your sunflora C&I attendant or building owner web developer that would like to participate in our survey or interview process definitely feel free to drop your email in the chat to me or one of my colleagues and we can connect and with that we will open it up for questions well thank you big thanks to all three groups who presented today all righty so we're gonna hop into the Q&A section here so we have our first question in now this is a two-part question directed for Kaitlyn the question is will the algorithm learn from each home or is it an aggregate system that uses the averages of an area then the second part of the question is are you considering the human side of all this and will the application learn from human behavior in each home so right now for the first part of the question algorithm can anticipate the demand which would kind of have the it's like learning the expected demand or load that each home would have so that's in there but in terms of learning that's not incorporated into this work but I'm perhaps like people who are still working on this project might be considering that my my main focus was really just kind of how the uncertainty drives the results in a while that probably like didn't come through 100% in the presentation because I like about the spectrum of the homes and the different results at the time of use prices were really cool to highlight but yeah there was no kind of learning or fancy machine learning type predictions for the load going on I was just using an expected load profile and those were not averages those are from the residential building stock assessment data sets I'm pretty sure that's publicly available Thank You Caitlin yes any other questions we are light on questions tonight already if nobody has any other questions we will you know got a wrap-up here did want to thank everyone for coming and I did want to thank all the presenters for having some fantastic presentations tonight and thanks to everyone who is here tonight as well it really does mean a lot to have such a wonderful group of you guys who pick you to want to learn about renewable energy and attend our events again thank you all for being here tonight and be sure to stay safe or to see you guys as a person before too long [Music] you [Music]
