How to catch renewable energy portfolio risk factors early

Picture this: It’s a Monday at work, and you are tasked with characterizing risk for a new project portfolio you are managing. As your computer clicks on, you open your inbox to find several requests from colleagues and portfolio stakeholders: “Sorry for the delay, here is all the data I could find on that portfolio you asked about last week.” “I saw this headline over the weekend about energy prices — does this affect our risk?” “Will we be able to deliver on new risk numbers on time this week?” 

While your coffee gets cold, you roll up your sleeves and get to work combing through spreadsheets, emailing others for new data, and investigating a dizzying array of risk factors to try to make sense of this sporadic information. You hope you can synthesize it all in time to report to your executive team on risk factors and, when necessary, help your team to correct those risks before it’s too late for the projects and their investors.

Sound familiar? For too many portfolio managers responsible for risk assessment, this scattered approach is the reality of the daily legwork required to chase down the data they require to do their job, leaving many gaps in efficiency, accuracy, and timeliness. 

Successful portfolio performance rests on the ability to characterize risk accurately. As a professional responsible for risk assessment, you occupy a key function of portfolio management: identifying the likelihood of ongoing project success according to internal and external events. However, current analog portfolio management systems hinder proper risk assessment, further exposing funds to risk factors due to their inefficient, siloed processes. 

Luckily, there are several early warning signs that portfolio managers and risk assessors can keep tabs on to ensure their portfolio management strategy is airtight — and several digital process strategies you can implement to get there. 

Data storage matters for risk management

Before accounting for each critical type of risk in your asset management, you must ensure that your data systems are well connected, regularly updated, and easily accessible. 

Since the advent of spreadsheets, portfolio professionals have used programs like Microsoft Excel to store, organize, and analyze datasets. For all of the computational advancements this method has brought to financial institutions, these spreadsheets leave much to be desired in data management, especially in the age of big data. 

Risk managers’ ability to assess risk relies on the accuracy of the data they can access. Using spreadsheet databases — also called analog systems — creates information siloes that make it difficult for individuals across an organization to access data stored in disparate files. This analog approach adds significant time to risk assessors’ workflows and increases the likelihood of missing key data points. 

The last decade of data science and cloud computing has resulted in new data storage methods that make massive sets of information accessible and searchable to many users. Also known as digitization, this method solves the inefficiencies of analog systems and opens the door for better data access across organizations. For renewable energy project risk assessment, digitization provides quick, accurate, real-time data access and sets systems up to add valuable digital tools for even greater efficacy.

The types of critical renewable energy risk

Calculating risk for renewable energy project portfolios requires a specialized view into the context of each project. The following are the essential risk types that must be considered for renewable energy project financing: 

• Credit risk: The involvement of multiple parties in a project increases the risk that these offtakers, contractors, suppliers, and investors could face their own financial setbacks, thus risking the project’s financial standing.
• Market and pricing risk: Energy from renewable projects is subject to energy pricing fluctuations and faces particular risk to revenue when a project is locked into a long-term contract like a power purchase agreement, or PPA. 
• Production risk: The risk of renewable energy underperformance due to equipment failures, inefficiencies, or unexpected asset degradation can drastically reduce the energy production of project investments. 
• Weather-related risk: Tracking and predicting climate and weather patterns to predict the future risk of project-damaging weather events, including an increase in 100-year storms and natural disasters that add extra uncertainty.
• Regulatory and compliance risk: Renewable energy projects often rely on federal, state, and local incentives to remain profitable but face the risk of changing legislation and regulation, interrupting their viability.

The basis of accurate risk assessment in any of these categories is high-quality data pertaining to the project and portfolio. While current risk assessment professionals are experts in risk calculation, you can only be so accurate when forced to work in analog systems with imperfect data and models.

Without a standard for renewable energy project data storage and reporting, risk assessors must engage in long information searches to find the answers they require for each project. Not only is this time-consuming, but it also leaves room for error: Risk assessors cannot factor in information of which they are unaware. 

The high consequences of late risk detection

The combination of disparate data sources and manual risk calculation means a more difficult, time-consuming task for risk assessment professionals and a higher likelihood of inaccurate risk assessment. Without accurate risk assessment, renewable energy project financiers and their stakeholders are left in the dark on investment success. 

Poor risk assessment sets portfolios up for potential failures, like… 

• Higher risk exposure
• Uninformed, reactive decision making
• Inaction on risk factors
• Worse portfolio outcomes
• Less investor interest in renewable investments

Proactive risk management for renewable energy projects

Derisking your internal systems not only streamlines your day-to-day work processes but ensures portfolio risk more accurately reflects real portfolio outcomes. 

For renewable energy project financiers ready to calculate and avert risk responsibly, data system standardization is an excellent first step. Systems like Banyan Infrastructure digitize all project, portfolio, and fund data in a central software accessible to all internal and external stakeholders. For risk assessors, this means quick and reliable access to real-time data to easily generate risk assessments. Banyan Infrastructure then connects to additional software that further addresses specific risk types, like Also Energy or Enphase for onsite performance monitoring. Data pulled from other software, such as First Street or Climate X for weather-related risk monitoring, can also be incorporated into Banyan’s digital tools.  

With standardized, digitized project data, risk assessment professionals can stack rank risk for easily digestible reporting. Banyan Infrastructure is constructed to capture the unique risk appetite of the organization and sets thresholds that the organization does not want to exceed. Automatic alerts flag risk factors approaching these thresholds so portfolio managers can remain proactive in their risk management. Prefabricated reports ensure that risk assessments are generated quickly and are easy to understand for recipients. 

Navigating the changing world of risk assessment in renewable energy is difficult enough — removing the risk of poor internal systems is a streamlined way to ensure all factors are appropriately considered for standardized, proactive risk management. 

Are you ready to shore up your risk strategy? Reach out to Banyan Infrastructure today to learn more.