GREEN ENERGY MODELING

Ankit Naik & Jan Brugård, Wolfram MathCore

Join the Conversation #WolframTechConf

ABSTRACT

The ongoing shift to renewable energy leads to new requirements on the entire electrical system. Large scale power plants that produce stable and controllable power are often replaced by smaller, distributed, and weather dependent sources.

Therefore, there’s a need to investigate novel concepts for power production, understand energy markets, and balance the grid. This can all be modeled and simulated in System Modeler.

In this presentation we will use a simplified model of Sweden's power system to illustrate how the electrical grid is balanced.

European Electricity Market

Image source: https://fsr.eui.eu/electricity-markets-in-the-eu/

Varying price between zones

Limited transmission capacity

Demand and supply should always be balanced

Electricity can’t be stored economically for long time (yet)

Perfect Storm

Image source: https://www.reddit.com/r/MapPorn/comments/x6ksnj/electricity_price_inflation_in_europe/

Gas deliveries from Russia disrupted

Maintenance and shutdown of nuclear power plants

Limited wind

Sweden’s Power System

Image Source: https://www.energimarknadsbyran.se/el/elmarknaden/elomraden/

Sweden is

Part of the European market

In itself divided into four separate electricity zones

Self-sufficient and can export power to other markets

Mix of electricity sources

Mostly powered by renewables and nuclear

Unbalanced (surplus in north and deficit in south)

System Modeler Model

Power sources:

Nuclear (Forsmark)

Hydro (Harsprånget)

Wind (Mönsterås)

Consumers:

Luleå

Sundsvall

Stockholm

Malmö

Dispatcher:

Manages power distribution among power sources

Balancing the System

Priority is given to wind power by the dispatcher.

Hydro power profile is assumed constant. The values for power surges are hard coded in the load dispatcher.

Nuclear is used to balance the system hence the fluctuating nature.

model="GreenEnergy.SwedenPower";
SystemModeler[model,"Diagram"]

Power sources

Wind

sim = SystemModelSimulate[model];
SystemModelPlot[sim,"wind power profile per wind turbine",PlotRange->All]

Hydro

SystemModelPlot[sim,"hydro power profile",PlotRange->All]

Nuclear

SystemModelPlot[sim,"nuclear power profile",PlotRange->All]

Transmission lines

SystemModelPlot[sim,"sundsvall transmission"]

Comparing with Real Numbers

SystemModelPlot[model,PlotRange->All]

Sweden’s power production by source on 4th October 2022

Image source: https://www.svk.se/en/national-grid/the-control-room/

Example: Sweden’s Power System

Approximation of emissions

SystemModelPlot[model,]

CO2 equivalent (gCO2 per kWh)

Hydro: 		24

Nuclear: 	12

Wind: 		12

Traditional vs Smart Grid

Traditional

Image source: 	https://www.eia.gov/energyexplained/electricity/delivery-to-consumers.php

One-way interaction: Power plant to consumer

Absence of monitoring systems: Vulnerable to failures (failed circuit breaker, unpruned foliage etc)

Rigid: Unable to meet sudden changes in power demand

Smart

Image source: https://www.enisa.europa.eu/news/enisa-news/smart-grid-security-certification-in-europe-challenges-and-recommendations

Two-way interaction: Consumers can also be producers

Continuous monitoring: Use of nodes to keep track of production and consumption

Flexible: Storage systems like batteries, thermal storages etc could cater to sudden demand fluctuations

Use of renewables

Image source: https://www.energy.gov/articles/getting-most-out-solar-panels

Highly location dependent:

Power output

Sizing of batteries

Other power equipments

Variable vs constant power generation to grid

Widely distributed generation sites

Controller design

Image source: https://cleantechnica.com/2021/10/26/high-renewables-systems-are-scalable-resilient-secure-with-communication-less-controls/

Equipment controllers

Orientation of solar panels and wind turbines

Cut-in and cut-off speed need to be monitored

Plant controllers

Combination of equipments with storage to deliver constant power to grid

Grid controllers

Manage supply and demand taking into account

variable power consumption from consumers

power input from renewables

Customer Story

Azelio

Concentrated Solar Power Systems

Image source: Azelio

Model

Thermodynamics

Mechanical

Electrical

Simulation results

What would happen if we moved a system from Australia to Dubai?

Accurate predictions makes it possible to test, analyze, and improve

System Modeler at WTC

System Modeler agenda at WTC 2022. All times in CST

Wednesday

10:30 - 10:55 am Green Energy Modeling

10:55 - 11:20 am Aircraft Design in System Modeler

11:00 - 12:00 pm Meetup: Engineering Education

02:45 - 03:45 pm Office Hour: System Modeling

04:30 - 05:00 pm Model Transformation for Smart Grid Applications

Thursday

09:00 - 09:30 am Extreme Citizen Science—Learning Enabled with Computational Essays

01:00 - 01:25 am New in System Modeler

01:25 - 01:50 am Model Predictive Control

01:50 - 02:15 am Neural Networks in System Models

Friday

09:30 - 10:30 am Meetup: Virtual Labs in Education

05:00 - 05:30 pm Cambodia Wolfram Student Stars! Mathematica, Arduino, Raspberry Pi