A Survey of Energy, Water, and Environment Complex Networks

Present By: Eric Klukovich

Date: 10/21/14

Overview

Complex Networks

Nevada Solar Nexus

Studies in Energy Complex Networks

Studies in Water Complex Networks

Studies in Environmental Complex Networks

Conclusion

Complex Networks

Relatively new concept and is being actively researched to understand their full potential.

Complex networks are based on graph theory. Model real-world data in a much more accurate way. Analyzed from a different point of view. Shows different trends and features within the data.

Can apply different metrics Degree distribution Closeness/betweenness Clustering coefficients

Uses for Complex Networks

Can help solve real-world problems such as improving resiliency and robustness in a network.

Can model energy, water, and environmental data. Used to see how reliable and efficient the current energy

and water distribution systems are.

Can show the impact if a power outage occurred in different regions.

Rainfall, climate, and seismic data have been modeled to identify patterns in the topology and dynamics.

Nevada Solar Nexus

Creating renewable energy resources has become a national priority. Nevada is focusing its research into this area

Currently three-quarters of all energy production is from fossil fuels. Increased dependency on global markets

Creates greenhouse gases

Focus in these areas: Solar Energy Water Environment

Solar Energy Goals

Create renewable solar energy generation in Nevada. One of the best locations for solar energy generation in

the world.

Has the potential to diversify the economy of the state.

Solar energy generation should have a small impact on water resources and the desert environment.

The relationship between solar, water, and environment should be understood for renewable energy to be beneficial.

Water Goals

Ideal locations for solar energy generation is in arid lands. Water resources will be limited.

Need to maximize limited water use at the facilities.

Explore the use of lesser quality water in solar energy development.

Minimize the impact of moving water/wastewater to and from the facilities. Extraction, treatment, distribution and disposal require

energy and impacts the environment

Environment Goals

Study the impact of the solar facilities on the environment Minimize construction, operation, and decommission

impacts

Study the impacts on organism populations

Microclimate change in planet communities

Impact of solar arrays on the balance of desert soil

Impact on landscape patterns

Before, during, and after construction

Energy Complex Networks

Energy distribution affects large amounts of people on a regular basis Electricity

Natural Gas

Oil

Transferring energy is usually done through wires or pipes in a grid configuration. Can grow to be very complex and difficult to analyze.

Complex networks can find patterns and help solve problems in the current system.

Modeling the Power Grid There have been many major blackouts in North

America within the last few decades. Difficult to determine what happened with the all the

interconnections within the grid.

Can show where power grids are vulnerable to blackouts or outages. A few papers have modeled the power grid and show

how well the grid can function if generators shut down.

Power Distribution

Study 1 - Power Grid Vulnerability

Modeled the North American power grid using data in the POWERMap mapping system. Modeled every major substation and 115 − 765 kV power

lines.

14,099 nodes (substations)

19,657 edges (transmission lines)

Three main types of substations Power generators

Transmission stations

Distribution stations

Study 1 - Power Grid Vulnerability

Power generation vulnerability was tested: Nodes were removed by their degree and randomly. It was found that the loss of connectivity when removing power

generation nodes did not alter the overall connectivity of the grid.

There is a high level of redundancy at the generating subsystem level.

Study 1 - Power Grid Vulnerability

Transmission substation vulnerability was tested: Removed nodes randomly, highest degree, highest load, and

removing the top 10 highest loads. When the nodes were selected randomly, then the loss of power

was proportionate to the number of nodes lost. The degree and load based removal showed a higher increase of

connectivity loss.

Study 1 - Power Grid Vulnerability

Issues with the study

The data set only identified the generator nodes and the rest were identified based on criterion.

This may or may not accurately model the power grid and could lead to different results.

The authors assumed that each distribution station only had one transmission line going to it.

There could be more than one transmission line going to each distribution station, changing the degree distribution.

Therefore if it failed it could lead to a greater loss of power.

Study 2 - Power Grid Reliability Modeled the North American power grid using

data for the western and eastern power grids. Western Electricity Coordinating Council (WECC)

North American Electric Reliability Council (NERC)

Western - 78,216 nodes

Eastern - 235,907 nodes.

Uses the Barabási-Albert Network Model to find quantify the grid’s resilience. Data sets are very accurate.

Contains all data needed to accurately test resilience.

Study 2 - Power Grid Reliability Uses loss of load probability to create a failure

propagation model. Power flows to node from an edge (propagation unlikely)

Power flows from the node to an edge (propagation likely)

Calculates the probably of removing edges or nodes.

The Eastern and Western power grids were scale free.

The loss of load probability for was found to be 0.026. This value was compared to the loss of load probability of the

Bonneville Power Administration’s region of the western grid (0.027).

The Barabási-Albert model accurately predicts the reliability.

Water Complex Networks Water distribution is infrastructure that must

always be available.

Can analyze the efficiency, vulnerability, and create plans for alternative resources.

Rivers can also be modeled Monitor the water flow. Take protective action if the river is being depleted.

Study 1 - Water Distribution Analysis

Modeled four different water distribution networks East-Mersea, United Kingdom Colorado Springs, Colorado Kumasi Town, Ghana Richmond, Virginia

Nodes represented source, control, and storage/processing facilities.

Edges represented by pipes. The weight was the diameter of the pipes.

Study 1 - Water Distribution Analysis

East-Mersea Colorado Springs

Richmond Kumasi

Study 1 - Water Distribution Analysis

Each network’s density was calculated All networks were sparse and resemble the urban areas. The Colorado Springs network had many loops.

The degree distribution and central point dominance for each network was calculated. Determined which nodes were the most important. Found that large clustering was in the town’s center.

The efficiency of the water distribution was measured Topographic measurement for efficiency was not accurate. Construction and cost has a major factor on how the

network is created.

Study 1 - Water Distribution Analysis

Route factor is a better way to measure efficiency. Distance between the supply node and the demand

source The network was found to be highly efficient in the four

graphs.

The robustness was measured by random removal of nodes. 42% removal for Colorado Springs caused failure. 37% removal for Kumasi caused failure 32% removal for Richmond caused failure 22% removal for East-Mersea caused failure An extreme event would make water distribution

vulnerable.

Study 2 - Modeling River Networks

Modeled the Haihe Basin River network in China 565 nodes (319 natural and 246 engineered nodes)

Two types of nodes Natural – source, bifurcations, confluence, and outlet. Engineered - hydro power plants, reservoirs, pumping

stations, and transfer plants.

Edges Natural or artificial water channel that connected two

nodes. Directed – flow of the river.

Study 2 - Modeling River Networks

River network

River Node/Edge Example

Study 2 - Modeling River Networks

The degree distribution was calculated to categorize the different nodes. The river’s sources and outlets could be easily

determined. The nodes that can be used to regulate the flow were also

found.

This study acts as a foundation for more in depth studies in river networks. Could find potential sources of drinking water. Could model pollution spread in the water system. Find the impacts on surrounding communities if the river

dried up.

Environment Complex Networks

Complex networks can be used to analyze data and find new information and patterns.

There have been several studies in areas related to climate dynamics, rainfall and seismic activity. Spatial grid points as nodes The edges represent if an event occurred in both the

linked nodes.

Study 1 - Modeling Earthquakes

Modeled earthquake data for Southern California. Nodes – small cells that divide up the geographical region. Edge – seismic activity that occurred different cells. Loop – seismic activity that occurred in the same cell.

Found that the aftershocks of the earthquake tended to have a loop back to the original node.

Earthquake data is scale-free and is a small world network. The connectivity distribution follows the power law. The average path length is small and has a high

clustering coefficient.

Study 1 - Modeling Earthquakes

Degree Distribution

Earthquake Network

Study 2 - Modeling Precipitation

Modeled extreme rainfall in different areas of the world. South America South Asia Indian subcontinent Entire globe

Nodes were based on small divisions of geographical locations.

Edges were created between two nodes if the amount of precipitation met a certain threshold.

Study 2 - Modeling Precipitation

A highly accurate network was created and many different aspects were observed.

The degree distribution accurately placed the most arid places at the nodes with the lowest degree.

Islands are disconnected in the network and they form their own micro-networks.

The larger land mass does not affect the islands.

Rainfall patterns have changed rapidly more recently compared to previous decades.

Study 2 - Modeling Precipitation

Degree Centrality

Conclusion

Complex networks can be used to model realistic datasets in different domains.

Energy, water, and environmental data can be analyzed using complex network modeling and metrics.

Many studies have accurately modeled data in distribution system to determine how efficient, robust, or vulnerable the system is.

References

http://venturebeat.files.wordpress.com/2010/10/grid.jpg

http://regan.med.harvard.edu/pictures/Hier/3D_hier.jpg

“Structural Vulnerability of the North American Power Grid” http://arxiv.org/pdf/cond-mat/0401084.pdf.

“Evaluating North American Electric Grid Reliability Using the Barabási-Albert Network Model ” http://arxiv.org/ftp/nlin/papers/0408/0408052.pdf.

“Complex network analysis of water distribution systems” http://arxiv.org/pdf/1104.0121.pdf.

“Modelling and analysis of river networks based on complex networks theory”

“Small-world structure of earthquake network” http://arxiv.org/pdf/cond-mat/0308208.pdf

Questions