Power price scenario swarms: evaluating a renewable energy plant

We explore how power price scenario swarms can help optimise the evaluation of renewable projects and improve long-term outcomes.

What is Swarm Modelling?

In today’s volatile electricity markets, optimising revenue from renewable energy assets is more crucial than ever. With fluctuating power prices driven by changes in commodity prices, electricity demand, and renewable energy production, now is the ideal time to explore ways to maximise returns. This article explores how you can leverage Scenario swarms and power price scenarios modelling to make informed decisions and secure the best market value for your assets. Our aim is to help you assess the profitability of renewable energy investments and navigate the challenges posed by highly variable power prices. Additionally, scenario swarms can assist in evaluating Power Purchase Agreements (PPAs) by accounting for different production volumes. 

Context: Evaluating Renewable Energy Assets Amidst Power Price Volatility 

Evaluating a wind turbine or solar power plant under fluctuating power prices presents a unique challenge in making realistic and quantitatively reliable assessments. Given the high price volatility in electricity markets, making the right decision can feel daunting. This is where scenario swarms and power price scenarios, based on your energy market modelling tool, Power2Sim, come into play. These tools are invaluable for the evaluation of renewable energy assets in volatile markets. 

Approach: Leveraging Power price scenarios for Strategic Decisions  

To navigate the complexities of the electricity market, we use our fundamental model, Power2Sim, to create consistent scenarios that forecast how the key drivers of power prices will evolve. However, as seen in the past, exogenous shocks—such as a sudden drop in electricity demand, sharp increases in commodity prices like the gas price spike in 2021/2022, or even significant changes in weather conditions—can drastically influence power prices. These unpredictable external shocks are difficult to model using traditional fundamental approaches. 

This is where the new "Swarm Modelling" comes in, enabling the incorporation of these exogenous factors into renewable energy asset valuation. The Swarm Approach simulates various scenarios by considering different weather patterns, economic conditions, and commodity price effects. Here's how it works: 

Scenario Analysis:

Power2Sim runs over 1,000 simulations with varying input factors. It uses a method similar to a Monte Carlo simulation to generate a probability distribution of power prices. 

Data Inputs:

The model incorporates weather data, economic forecasts, and commodity price trends.  

Probability Distribution:

The output is a probability distribution of base prices and the market value (capture price) of wind and solar energy for the selected period. P-values play a key role in this analysis, indicating the likelihood of certain outcomes. Depending on the chosen P-value, the results can range from conservative to optimistic estimates. 

This robust approach provides a detailed analysis of potential market conditions and their impact on asset revenue and value. 

Results: Making Data-Driven Decisions  

The probability distribution generated through the Power Price Scenarios is invaluable for addressing critical questions, such as:  

• Price Projections: How likely is it that the market value of wind and solar, or the base price, will fall below a certain level in the coming months or years?  

• Timing of Sales: Should I lock in current prices today, or is it better to wait?  

When minimising risk through power price forecasting, different P-values can represent varying probabilities of occurrence: 

• P(5) -Optimistic evaluation: The P-5 value refers to prices (base or capture) that are higher than 95% of all prices in the respective scenario year or observation period, providing an optimistic perspective. 

• P(50) – “Realistic” evaluation: The P-50 value represents the median of the price distribution and indicates the most likely power price development (base or capture).  

• P(95) – Pessimistic evaluation: The P-95 value consists of prices (base or capture) that are higher than only 5% of all prices in the respective scenario year or observation period, reflecting a more conservative outlook.  

By applying different P-values in your evaluation, you can develop a range of assessments, from pessimistic to optimistic, allowing you to explore various potential strategies and adapt accordingly. Understanding these probabilities enables more informed decisions about future revenues and ensures a well-grounded evaluation of your renewable energy assets. 

Conclusion: Maximising Renewable Energy Returns Through Power Price Scenario Analysis:    

Optimising the evaluation of your renewable energy assets in today’s volatile energy market requires a strategic approach. By leveraging power price scenario swarms and the Power2Sim model, you can effectively navigate market complexities and make more informed decisions. This approach allows you to evaluate a range of outcomes, from conservative to optimistic, and tailor strategies accordingly to maximise returns.