Want to build new solutions for the clean energy industry with a chance at $10,000 in cash prizes? Powerhouse, a co-working space and venture fund in Oakland, is hosting their annual hackathon on May 17-18 in Downtown Oakland. SunCode is your opportunity to come together with over 150 developers, designers, and clean energy professionals to create solutions that will accelerate clean energy around the world.

Startups that have resulted from SunCode include UtilityAPI, Nanogrid, and OhmConnect. Check out last year's DevPost page to see more innovative submissions. You don't have to know anything about clean energy to participate!

Important Links

Friday Evening, May 17

6:00-7:00pm - Happy Hour and Dinner 
7:00-8:30pm - Team Formation
9:00pm - Doors Close 

Saturday, May 18

8:00am - Doors Open and Breakfast 
8:00am-12:00pm - Hacking
12:00-1:00pm - Lunch
1:00-6:00pm - Hacking
6:00pm - Deadline to upload to DevPost (takes 15+ min)
6:00-6:30pm - Dinner 
6:30-8:45pm - Pitches to Judges - 3 min per team and 30 sec Q&A
8:45-9:15pm - Happy Hour (Judges Deliberate)
9:15-9:30pm - Winners Announced

Mentors

10am:

Arwed Niestroj, CEO @ Mercedes-Benz Research and Development North America

Maud Texier, Senior Manager, Product Management @ Tesla Energy Products

Don Eckert, Director of Admin and Finance @ Silicon Valley Clean Energy

Poonum Agrawal, Senior Regulatory Analyst @ Silicon Valley Clean Energy

TBA, Nissan Renault Mitsubishi

1pm:

Aimee Gotway Bailey, Director, Decarbonization and Grid Innovation @ Silicon Valley Clean Energy

Challenges (please see the Google Drive packet for datasets)

SunPower: Estimate storage savings via API

Context: Estimating backup from batteries is relatively straightforward; however, modeling financial savings from adding a battery to a solar power system is more complex.

Challenge: Provide homeowners the ability to calculate their financial savings from adding a battery to their home solar power system. Ideally, the calculation engine would be delivered as an API that can be used across software platforms.

• The math must account for optimal charge and discharge, estimated homeowner usage, estimated solar production, and local utility rates.
• Maximize the savings of a homeowner by arbitraging his or her energy usage to purchase power from the utility at off-peak hours, using the delta of price per kWh for solar and price per kWh for their utility rate

Bonus: Create a machine learning model to optimize storage discharge based on changes in customers’ energy usage patterns.

SVCE: Electrifying the Built Environment: Retrofitting Single Family Homes to All-Electric

Context

In California, buildings are responsible for more than a quarter of statewide greenhouse gas emissions. The overwhelming scientific consensus is that we must electrify the building sector to achieve deep decarbonization. Electrification in buildings entails switching from a natural gas appliances to high efficiency electric alternatives powered by low-carbon electricity.

In SVCE service territory alone, there are approximately 145,000 single-family homes, which each emit roughly 2.2 metric tons of CO2e annually from on-site natural gas consumption. Approximately 1 metric ton comes from space heating, 0.5 metric tons come from water heating and the remainder is from other end uses such as clothes drying, pool heating and gas fireplaces. By the end of 2025, an aspirational goal consistent with statewide climate targets is to have retrofitted at least one quarter of this existing, single-family housing building stock to all-electric.

Challenge Statement

Retrofitting existing buildings to all-electric face a variety of barriers. The objective of this challenge is to propose solutions to accelerate electrifying existing single-family homes by addressing one or more of the key barriers. Successful solutions will likely take a multidisciplinary approach and may focus on education, marketing, finance, data analysis, and/or to application development.  Key barriers include:

• Lack of general awareness and understanding of the benefits of efficient electric appliances (quality, efficiency, costs, safety, performance)
• Costs associated with upgrading and installing electric systems, potential costs associated with electricity use during peak time periods, and indirect costs associated with service interruptions during the switch
• Difficulty for low-income communities and renters to electrify due to high upfront costs and split incentives between the renter and the landlord
• SVCE has limited visibility into appliance types in customer homes, which impacts the ability to target messages/programs

Data:

SVCE is providing a data set that is an anonymized, random subset of single-family residential household accounts in its service territory and includes the following information. You can download the dataset here.

• City
• Whether the household has basic electricity service or all-electric/space heating adjustment
• Retail electricity rate by month for 2018
• Electricity consumption by month for 2018
• Whether the household has on-site solar PV
• Number of bedrooms (value is shown as “5” for any household with 5 or more bedrooms)
• Whether the household has a garage
• Whether the household has heating (H), air conditioning (A), both (B), or neither (N)
• Total square footage of home rounded to nearest 250 (e.g. 2000 means the home square footage is between 2000-2250; value is shown as “5000” for any household with a square footage of 5000 or greater)
• Total number of rooms in the home (value is shown as “8” for any household with 8 or more rooms)
• Decade that the home was built (e.g. 1900 means the home was built between 1900-1909)
• Retail gas rate by month for 2018
• Gas consumption by month for 2018
• If there is a hybrid gas, electric (BEV) or plug-in hybrid (PHEV) vehicle registered at the address

 Please see Google Drive for datasets.

EDF: Renewable energy prospecting tool to gauge local community support when siting a new project

Context: When prospecting new renewable energy projects EDF takes into account “hard” data (e.g. resource availability, market conditions, etc.) and “soft” data (e.g. local community support). There are no good datasets available for the public support of a project. To judge local community support, indirect proxies may be used such as census data and other publicly available data. However, these are not great predictors.

Challenge:

• Create a solution to gauge or predict local support for renewable energy projects before a renewable energy company has to do due diligence in developing a project.
• Would land owners be interested in having a renewable energy site on their property?
• Would residents object to their neighbors having a renewable energy site on their property?
• Do local administrations support a renewable energy site in their community?

Datasets Available:

• EIA - All generating assets greater than 1 MW
• https://www.eia.gov/electricity/data/eia860/
• NREL Wind Prospector – estimated wind energy capacity across US
• https://maps.nrel.gov/wind-prospector/#/?aL=kM6jR-%255Bv%255D%3Dt%26qCw3hR%255Bv%255D%3Dt%26qCw3hR%255Bd%255D%3D1&bL=groad&cE=0&lR=0&mC=40.21244%2C-91.625976&zL=4
• NREL System Advisor Model – estimated solar energy capacity across US
• https://nsrdb.nrel.gov/api-instructions
• https://sam.nrel.gov/
• https://rredc.nrel.gov/solar/old_data/nsrdb/

TOTAL: Optimizing EV Parking and Charging

Context: There is currently no easy way to manage the utilization of workplace or public EV charging, especially where there are more EV-designated parking spots than there are charging points. For example, consider twelve charging points which can reach twenty designated EV-only parking spots. In order to ensure the availability of a parking spot and to make sure that each car receives a charge at some point during the day, drivers need to be able to reserve a location and timeframe to park as well as a timeframe to charge -- which may be different.

Challenge:

• Create a solution to optimize the customer experience of reserving EV parking spots and charging stations while maximizing charge point utilization.
• The solution should handle all cases, including reserving and releasing parking spots and charging points (both simultaneously and separately), prioritizing and plugging in cars in the queue, and dealing with “stallnapping” users.
• The solution may utilize one of more communications channels to the customer - feel free to get creative here to create a great customer experience. Demonstrate your communications and user experience over the course of a typical day.

Additional (recommended) considerations:

• Releasing the charger but not the parking spot, and plugging in the next driver in queue.
• Ensuring that the correct vehicle is plugged in & session initiated on behalf of the next driver.
• Releasing (leaving) a parking spot, with the charger previously released.
• Appropriately prioritizing users that are physically parked, but not yet charging, within the charging queue
• Appropriately handling users that are ‘stallnapping’ excessively – parked and fully charged for an extended period
• Addressing the time difference between confirming a queue position and physically taking the spot or charger
• ‘Retiring’ aged positions
• Handling periods where a driver is unavailable to move their car

Assumptions:

• You may assume that all users are registered, and any driver that is currently in a charge session is known. This includes vehicle attributes like the make / model / color / license plate number, and which charge point they are connected to (but specifically not which parking spot they are in!).
• Furthermore, assume that the occupancy status of all parking spots is known. Assume that all vehicles parked do intend to charge at some point (that is - you don’t have to address “ICEing”).

Data:

• A representative set of vehicle ‘personas’ and ‘day in the life’ schedule including vehicle attributes, reservation time, arrival time, departure time, and required charging duration.
• A map of parking spots and charge points, including the parking spots each charge point can serve.

 Please see Google Drive for datasets.

Renault Nissan Mitsubishi: EV Energy Gateway Usages

Context: The EV market will continue to grow in the coming years as costs sink and charging infrastructure evolves. Owning an electric vehicle is not just another driving experience, it’s also a new approach of energy consumption. Multiple usages of EVs as a mobile energy storage unit are possible, but right now V2G (Vehicle-to-Grid) and V2H (Vehicle-to-Home) are still not widespread.

Reasons for the slow arrival of V2G / V2H slow arrival are multiple, including system cost and size, fixed installation, low standardization, sparse use cases, regulatory constraints, market diversity, and battery life impacts. Most of these issues don’t exist in non-grid solutions, so called ‘Vehicle to Load’ or V2L.

Challenge Statement: The Alliance Silicon Valley Lab has designed connected energy gateways to interact with the EV Energy in a safe, standardized and user-friendly way. This is a device that allows for V2L, which is essentially the idea of interacting with electrical energy sinks or sources without using a fixed installation.

In this challenge, you’ll:

• Create innovative solutions that bring value to consumers, companies, or any targeted population, making use of an EV V2L capability
• Adopt a lean / low cost approach to minimize or remove the barriers cited above
• Show the business value (cost versus revenues or benefits of the solution) by simulating operations and usage behavior
• Prototype the user experience or management system (B2B use case)

Additional Considerations:

• EVs considered can be passenger cars or Light Commercial Vehicles like the Renault Master ZE or the Nissan e-NV 200 EV
• The energy gateway can work while the car is driving
• The storable energy in an EV is assumed to be 40 to 60 kWh consumption while driving 140 Wh/km, gateway efficiency is typically 97% for 7 kW for a 48V DC output. Gateway building cost can be assumed to be 500 USD.
• Any market can be considered (not only US, but also China, Africa, Europe, Asia, etc.)

Schneider Electric

Context: As time-of-use is introduced as a new tariff mechanism, the average price of energy will increase for the average consumer. This cost increase will accelerate adoption of new energy-saving devices like smart thermostats, smart water heaters, solar panels, and energy storage.

Challenge: Propose and prototype a marketplace that offers consumers a convenient way to choose and purchase energy-saving devices. The marketplace should include:

1. Choosing a product
2. Projected energy savings
3. Installation
4. Financing

Mosaic

Context: Here’s an uncomfortable truth: Going solar is not simple. It is a complex decision making process for the homeowner and a complicated construction process for the installer. Over the last decade the industry has made great strides in streamlining, automating and advocating for a simpler solar journey. As a result, 2 million homes have gone solar. But if we want to 10X than number there is more work to be done.

This is a challenge to simplify the process of verifying whether a residential system has interconnected with the grid. Obtaining formal permission to energize residential solar systems can be a slow, bureaucratic process in many US states. Proof of the formal interconnection is an important but probably unnecessary evidence that the PV system has successfully interconnected. Some jurisdictions provide no formal proof. Some jurisdictions make installers wait months for proof. Proof that the home is drawing less power from the grid (appropriately modeled) is all the evidence we need that the system is soaking up the sun!

Challenge: Find a way to determine with a level of confidence that a just installed residential PV system has been energized i.e., ‘turned on’. The ideal solution will not restrict an installer to specific hardware such as an inverter that can communicate via cellular or wifi.

In this challenge you will,

• Explore the use of smart meter data as a proxy for whether the solar system is energized.

• If a meter’s usage decreases in a discernible manner, how likely is it that this was because the PV system turned on? How confident are you in this assessment? For example: if meter usage is down at night, that doesn’t mean the PV system turned on. If the meter usage is down because the family was on vacation, that doesn’t mean the PV system turned on.

Additional Considerations:

• You may want to consider using UtilityAPI for this challenge. Sign up using this special Suncode link. Read the quickstart guide and list of test scenarios. Note: UtilityAPI’s test API will not stream ongoing interval data so you might need to get creative.
• Alternatively,
• You can collect Green Button data and plug into a home you know, with permission
• You could look for example load curves a.k.a “8760s” online
• Get creative!
• Assume a system size, zip code and time of day for your demonstration. For e.g., if a 7kW system is installed zip code 94102, it is probably producing XWh at 12:30pm on Oct. 1. So if you’re drawing at least XWh less from the grid at this time, your solar system has probably turned on.

$10,000 in prizes

1. Purchase a ticket

2. Register on DevPost

3. Come ready to hack on Friday, May 17 at 6:00pm in Downtown Oakland at the Citizen Engagement Lab!

• Mission
  How well does the solution address the goals defined for this challenge? (20%)
• Quality
  How creative, innovative, interesting, and unique is the solution in meeting contest requirements? (20%)
• Implementation
  How well is the idea executed by the team and how well is the solution integrated with potential customers or systems? (20%)
• User Experience
  How successful is the design, user functionality, graphics, typography, ease of use and visual aesthetic? (20%)
• Potential Impact
  To what extent will the submission impact the clean energy industry? How scaleable is the submission? (20%)