Renewable Capital Race: A Dynamic Model of Sustainable…

Overview of Renewable Capital Race

The Renewable Capital Race is an interactive simulation that models the competition between different renewable energy technologies—such as solar, wind, and battery storage—as they evolve over time. The simulation uses a simplified representation of capital flow, cost reduction, and capacity expansion to demonstrate how these technologies compete in a shared global investment pool.

By adjusting parameters like carbon price and feed-in tariff, users can observe the impact on the adoption rates and technological advancements of each renewable energy source.

Key Concepts: Capital Flow and Learning Curves

The simulation employs a simplified version of Wright's Law to model cost reductions in renewable technologies. Wright’s Law posits that the cost per unit decreases as cumulative production increases, following an exponential relationship. This law is crucial for understanding how technological advancements can drive down costs over time.

Capital flow within the simulation represents the investment into different renewable energy sources. The amount of capital allocated to each technology affects its growth and competitiveness in the market.

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Policy Levers: Carbon Price and Feed-in Tariff

The carbon price acts as a financial incentive for reducing emissions, making cleaner energy sources more attractive. Higher carbon prices can accelerate the adoption of renewable technologies by increasing their relative cost-effectiveness compared to fossil fuels.

Feed-in tariffs are policies that guarantee a fixed payment per unit of electricity generated from renewable sources. This policy lever encourages investment in renewable energy projects and helps stabilize returns for investors.

Dynamic Behavior and Learning Rate Sensitivity

The simulation allows users to adjust the learning rate sensitivity, which determines how quickly costs decrease as production increases. A higher learning rate means that cost reductions occur more rapidly with increased production, reflecting faster technological progress.

By manipulating these parameters, users can observe how different policies and market conditions influence the dynamics of renewable energy adoption and technological advancement.

Frequently asked questions

How does the carbon price affect the simulation?

A higher carbon price increases the cost of emitting greenhouse gases, making clean energy sources more economically attractive. This can accelerate the transition to renewable technologies by reducing their relative costs compared to fossil fuels.

What is Wright's Law and how does it apply here?

Wright’s Law states that the cost per unit of a technology decreases exponentially as cumulative production increases. In this simulation, it models how technological advancements lead to cost reductions in renewable energy technologies over time.

Can different policies affect the outcome of the Renewable Capital Race?

Yes, varying policies such as carbon prices and feed-in tariffs can significantly influence which renewable technologies become dominant. Policies that support clean energy can accelerate technological progress and adoption rates.

How does adjusting the learning rate sensitivity impact the simulation?

Adjusting the learning rate sensitivity changes how quickly costs decrease with increased production, reflecting different rates of technological advancement. A higher learning rate means faster cost reductions and potentially quicker adoption of new technologies.

Try it live

Everything above runs in your browser — open Renewable Capital Race (2D) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Renewable Capital Race (2D) simulation

Renewable Capital Race: A Dynamic Model of Sustainable Energy Transition