The distinction between renewable energy and fossil fuels is clear. Less obvious is how comparatively complex it is to produce and deploy renewable energy.
Dozens of factors go into meeting a renewable energy project’s sustainability and business goals: grid limitations, energy demand, biodiversity impact, wind speed, solar irradiance, geothermal capacity and more. Very often, these calculations need to happen quickly to take advantage of fast-changing opportunities, such as government incentives or competitive bids.
Meanwhile, fossil fuels like oil and coal can be transported and deployed in energy plants that use the same technology worldwide, resulting in stable energy outflows. But because renewable energies come sourced from natural resources, their conversion capacity varies throughout the year based on seasonal changes and—crucially—their geographical location. After all, you wouldn’t put solar panels in the Arctic or a biomass plant—which relies on agricultural and wood residues—in the middle of a desert.
Many other location-based factors can spell success or failure for a renewable energy strategy, including:
- The emergence of energy grid overloads in some locations, especially as household solar panels present a significant surplus to the distribution grid.
- The trends, demand forecasts and grid limitations of a particular location that can adversely impact grid planning and renewable adoption.
The stakes are high: Pursuing a renewable energy project in the wrong place or time—or without regard to factors like grid capacity and balance of supply and demand—can lead to project delays, lost revenue and failure to meet sustainability commitments. In the UK, windfarms, solar arrays and battery projects are facing decade-long delays because of insufficient grid infrastructure, which could derail Britain’s progress toward legally binding climate targets.
It’s fast becoming clear that AI is essential for quickly determining the best location, timing and deployment strategy for renewable energy projects. The rapid analysis and data processing provided by AI allows energy producers, governments and policy makers to quickly analyze the multitude of variables and deployment scenarios that will determine success.
In this blog, we’ll explore three areas where renewable energy production and delivery poses immensely complex challenges and how an AI platform for renewable energy deployment can help.
1. Balancing demand and supply
Legacy energy grids can severely impact the ability to meet energy demand. In fact, once energy grids start displaying limitations, it is already too late to promote change.
Further, researchers agree that accurately matching grid demand and supply is key to determining efficiency. This is difficult to do in the face of all the variables that come into play, such as seasonality, daily fluctuations and overlaps in peak production.
In many cases, focusing on a single source of renewable energy can become a demand-supply limitation within itself because renewable generation tends to peak when demand is relatively low, which requires battery installation to support the energy grid. Recent studies have shown that combining solar and wind energy can reduce peak fluctuations and add stability to supply.
Machine learning systems can also help balance energy loads. Smart grid solutions that prompt wind turbines to generate energy only when solar panels are not meeting demand can reduce operational costs, reduce grid overload and speed ROI.
2. Managing renewables adoption
Investment in solar energy has been peaking recently as production costs have fallen and generation efficiency has improved. In addition to industry adoption, households are seeing up to 30% greater adoption of rooftop solar panels. This massive increase in solar adoption creates a surplus of energy that leads to grid over-congestion, which can cause unreliable energy supply and, eventually, power outages.
In fact, a recent report from the European Transmission System Operation (TSOs) shows that the grids in 19 of the 35 countries in Europe have underestimated the increase of solar energy adoption—representing over 200 GW of solar energy capacity that is not accounted for. In total, Eureletrics estimates that €67 billion will need to be invested to improve distribution grids in the EU from 2025 to 2040.
By 2030, according to the International Energy Agency, renewables’ share of global electricity will reach nearly 50%, up from around 30% today. As investments and projects get started, energy distributors are urging households to focus their highest energy consumption on periods of higher production (e.g., charging electric cars, doing laundry and other high energy demand activities when the sun is shining).
3. Understanding which data counts
Renewable energy adoption introduces the need for new datasets to determine optimal deployment conditions. Wind speed, solar irradiance, geothermal capacity, biomass availability and other variables can make or break project success.
In addition to these variables being important for determining production capacity, it is also vital to understand their fluctuations within daily and seasonal changes. For example, researchers have been focusing on the risk of energy shortages associated with “Dunkelflauten” events, which are periods of time when solar and wind production are low.
These new systems require complex modeling and continuous updates on weather forecasting, grid management, battery storage capacity and more. This increases the need for compute power to automate processes and reduce human-associated errors.
Amid all this complexity, grid managers will benefit from automation and AI-supported decision-making processes that can optimize energy management.
AI platforms for renewable energy deployment
With the daunting complexity of making these determinations and the continuously changing variables, it’s essentially impossible to do this work manually in the time needed to take advantage of fast-moving opportunities and ensure business success. It’s critically important to manage energy adequately to realize the promise of low OpEx for these high CapEx projects.
With an AI-driven platform, however, energy providers can ingest all the needed information, using machine-learning (ML) algorithms to churn through the data and reveal fast insights.
For instance, Midcontinent Independent System Operator (MISO)—which serves 15 US states and areas of Canada—applied ML to daily grid planning calculations that are carried out multiple times a day. MISO reduced calculation times from 10 minutes to 60 seconds, which helped the organization greatly optimize the time spent administrating energy grids in the area.
AI-based platforms are also useful for creating alternative scenarios that support best-fit solutioning. While certain locations might seem suitable in the short term, AI can analyze variables like technological development, policies and incentives, population growth, climate change, weather forecasting and estimated grid demand to best estimate where energy supply is needed and where restrictions will be encountered.
These long-term models are an essential support for policy planners when determining the optimal combination of renewable energy sources, grid management strategy and focus areas for grid improvements.
Renewable energy deployment with AI
Everyone involved with renewable energy—energy producers, governments and policy makers—needs an accurate and time-efficient way to determine where, when and how to pursue their renewable energy goals.
By interconnecting scientific, commercial and AI ML models, they can define the best location, conditions and capacity to deploy renewable energy in global markets. An AI platform can help stakeholders achieve the most reliable performance by ensuring all relevant models and variables are considered when dimensioning new energy systems, supplies and investments.
The ability to integrate, overlay and understand complex datasets makes AI a timely tool to support business and policy makers transition to greener energy supplies without harming the grid and compromising local energy capacity.
Author Lila Pupo is a senior sustainability advisor at Cognizant. Related: Read more Cognizant guest blogs.
