Energy Resource Dynamics

In this example, real data on the international fuel market and regional demand has been incorporated into a model of a 5.6 GW electricity producing plant, consisting of 14 oil-burning gas turbines with a maximal capacity of 400 MW each. Model predictions are then used to answer the questions: (1) How long will the plant be able to meet customer demand?; and (2) How is the electricity production cost affected by using either Combined Cycle Gas Turbines (CCGT) or Open Cycle Gas Turbines (OCGT)?

Create custom components

Use SystemModeler's built-in tools to custom design modules for the electricity plant.

Use accurate, curated data in your models

Get historic spot oil price data for the Oil Market module using Wolfram|Alpha.

Compare plant designs

Simulate and compare different plant designs in terms of production costs and fuel consumption.

The top-level model with its three main modules—Demand, Production, and Market. The Demand module is based on curated Wolfram|Alpha data.
The hierarchy of the oil power plant with SystemDynamics components visible at the lowest level.
The spot oil price data that the Oil Market module is based on can be retrieved from Wolfram|Alpha with just a few lines of Mathematica code.
The supply/demand ratio and production level of the plant for a 12-year scenario, as predicted by the model. Around the years 2011–2012, the plant reaches its maximum production level, and the electricity demand surpasses the supply.
Compare a CCGT scenario with a plant design using the simpler OCGT design. Although the CCGT design requires a larger initial investment, production cost can be reduced significantly. Both fuel consumption and cost per produced amount of electricity are higher when using the less efficient OCGT design.

Directly model mass and information flows

Use components from the SystemDynamics library to model things like fuel burn rates, oil consumption, and oil levels in the oil power plant.

Predict market fluctuations

Use the model to predict the variations in supply and demand of electricity.

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