Parametric Wind Farm Insurance

Market

Wind energy market: (100B, 2022,    165B, 2030) 

The top wind energy companies: 
GE Wind, Vestas, Siemens Wind Power, Suzlon Group, Goldwind, United Power, Acciona, Nordex SE, Sinovel Wind Group, EDF Renewable Energy, ReGen Powertech, Vensys Energy, ABB Limited, NextEra Energy Inc., Northland Power Inc., DONG Energy

2018 leading wind turbine manufacturers market share: 
20.3% Vestas (Denmark), 13.8% Goldwind (China), 12.3% Gamesa (SGRE, Spain), 10% G.E Energy (US), 8.4% Envision (China)

Countries with the highest wind energy capacity in 2020:
China – 288.32 GW, United States – 122.32 GW, Germany – 62.85 GW, India – 38.63 GW, Spain – 27.24 GW

High barrier to entry…. = limited competition in the wind energy sector … its centralized almost worse than bitcoin mining in terms of manufacturers market share… or comparable at least lol … furthermore, in terms of countries wind power generation, China is more than double times the output than the 2nd highest country, and more than 10x the output of the 5th highest country. 

 

Problem

The result of the downtime ratio reflects the ratio of the amount of energy that could be produced in case the downtime did not occur (e.g 5% of energy production). The downtime cost is then estimated by multiplying the ratio (5%) by the amount of energy a WT can produce (in kWh) in one year.  It is worth to mention that the energy production of wind farms is not consistent throughout the year due to the changing wind speed. Therefore, the cost of downtime is subject to when the downtime occurs. In case it occurs during high wind speed, the cost is higher and vice versa….
Low wind speed events are defined as average hourly wind speed measurements less than 4 m s1 (14 km h1 , 9 mph), which is a common cut-in speed for modern wind turbines—note that this is a significantly higher criterion than that of calm conditions (or 0 m s1 ). High wind speed events are defined as average hourly wind speeds above 25 m s1 (90 km h1 , 56 mph), which is the wind speed at which many modern turbines commonly shut down as a safety measure. 

The Cost Of Downtime

At individual wind recording sites, periods of no generation are clearly apparent in the wind record—on average, turbines located at individual wind-recording sites will generate electricity for around 80% of all hours in a year. Low wind speed events are responsible for the vast majority of hours with no generation—approximately 99% of all no-generation hours are the result of low-wind conditions, with high-speed wind conditions accounting for the remaining 1% of hours without generation (excluding OPEXs) – (UK study)
One approach to structure the policy terms\conditions is based upon the threshold of expected downtime due to low wind events… and defining the trigger based on the expected no energy generation variable … For example, if the downtime is greater than 20% (the average expected downtime from low winds) then trigger a payout .. in terms of policy structure… (5% more) 25% downtime = 5% payout of the kWh the farm produces… (10% increase) 30% downtime = 10% payout of the kWh the farm produces.. and so on (daily, monthly, yearly)
without insurance it would be a much greater risk of being unsustainable for wind farm companies to operate year after year … insurance is what allows businesses especially such as wind farms that experience volatility to carry on operations without going insolvent (daily, monthly, yearly contract possibility)… wind farm owners can hedge against the economic losses created by sustained periods of low wind speeds. 

Contract Specs

The parametric cover for downtime against excess low wind events would likely be an add-on to existing policies which cover various perils\risks that wind energy farms try to circumvent. The risk model for calculating the premiums would be based upon probabilities gathered from historical data of the low wind speeds in a particular area .. in relation to the value being secured/Kwh output. 
There is an array of elements that wind farms hedge against through insurance… Serial losses (high value components), project schedule delays from adverse weather conditions, wind turbine failures (unscheduled maintenance), Engineering, procurement and construction (faulty construction, materials, production, defects, workmanship), turbine fires\catastrophe.. e.g. sub sea cables can become damaged by issues upon installation, external impacts, or manufacturer errors.. therefore offshore wind farms require coverage for new sub sea cables, & expensive offshore logistics. 
Factors that affect risk for turbine fires: Location, Deductibles, Claims history, Turbine manufacturer, Wildfire concerns, Quality of operations and maintenance, Age of turbines, Vegetation management, Fire Suppression, Ice protection system (IPS).. etc

Snowball Effect

There is a snowball effect around the first mover (or contracts with the largest amount of capital)…. e.g AAVE lending protocol… applies the network effect…. for best borrowing/lending rates because they have the most liquidity.. In the context of insurance, when the primary pool grows, the reinsurance premium drops… thus, the collective policy costs for the group are now lower ….

Hackathon Features

Before, clients had to pay the exact premium daily, now tho, we added a feature.. The client can either pay normally like before, e.g., exact premium in regular intervals, or they can prepay in advance. the contract knows and checks regularly whether the client is due to pay or not. and if they miss the payment, the policy ends. so they can save on gas fees by paying for their whole policy all at once if they want or in smaller chunks. paying every day does add up for gas. the option is a nice bit of value for client, and value was on the rubric for the judges.

Our contract is using Keepers, without coding it in. we hook up our insurance contract’s ABI to Chainlink and they call a function (update state) for us every 15 minutes. no keepers code needed, as its time based, not logic based. the payout logic is contained in the contract.. only thing keepers does is call updateState, and that pings a bunch of variables that determine if a payout is warranted, and calls it… keepers can’t really monitor off-chain… they can but you’d have to be pulling it off-chain every request, which is not-efficient, opposed to if the data was on-chain like the aggregated price feeds.. So for off-chain data, we would use backend web.js script that reads the weather conditions … essentially it would act as a proxy script to keep querying the state, that way we know when to look for it (the below 15 mph threshold) in our Chainlink keeper request, because we are aware of the data conditions off-chain.

Contract Trigger

Cont.

Overview

Overall it’s a pretty thorough contract that allows you to declare a lot of what you need for an insurance contract… some key functions: function getPolicyCoordinates(), function getLatestWindSpeed(), function payPremium(), function payoutFunction(), function fulfillLocationCurrentConditions(), function storeCurrentConditionsResult(),  function updateState()

Hackathon Requirements

Our smart contract uses a) API call, b) keepers …  for the 2nd weather source, we had the intention of adding the google weather oracle but it doesn’t fetch wind speed, and it’s not on goerli.  so we were unable to take a median average and make a multi-data source aggregation..  but that’s something we would add in a production version … as well, we would introduce an off-chain mechanism to monitor the API, so that we could know when to call updatestate to track the down-periods below 15 mph more accurately… instead of on a timed interval. cost efficiency is not great on time-based upkeep, also it does not account for any volatile periods if you are just calling it on a timed schedule, vs updating state every time it crosses a specified speed.

Chainlink Fall Hackathon 2022

https://devpost.com/software/parametric-wind-farm-insurance-r3tzhp