Locational Marginal Burden
Energy burden—the fraction of income spent on electricity—varies across communities due to how power systems are operated. This visualization demonstrates the Locational Marginal Burden (LMB) framework, which reveals how transmission congestion can create price disparities that disproportionately affect low-income communities.
Key Insight
When transmission lines become congested, some communities lose access to cheap power and must buy from expensive local generators. Low-income areas can end up paying a higher percentage of their income for electricity than wealthy neighbors—even though both receive the same kilowatt-hours.
The Three Communities
Consider a simple power network with three communities:
| Community | Description | Income | Generator |
|---|---|---|---|
| Riverside | Wealthy suburb | $125,000 | Expensive backup generator |
| Eastside | Low-income neighborhood | $20,000 | None |
| Industrial Park | Power plant | N/A | Cheap main generator |
When the transmission line between Riverside and Eastside becomes congested, Eastside is cut off from the cheap Industrial Park power. They must buy electricity from Riverside's expensive generator—causing their electricity prices to spike.
Interactive Visualization
Adjust the sliders below to explore how transmission capacity and demand affect electricity prices and energy burden.
The Mathematics
Energy Burden
Energy burden at location is defined as:
where:
= locational marginal price (LMP) at bus ($/MWh)
= electricity demand at bus (MW)
= household income at bus ($/year)
Locational Marginal Burden (LMB)
The LMB matrix captures how changes in demand at one location affect energy burden everywhere:
Diagonal entries (): Self-sensitivity—how your own demand affects your burden
Off-diagonal entries (, ): Spillover effects—how demand at location affects burden at location through network congestion
The off-diagonal terms are what make LMB different from simply looking at prices. They capture the network effects that propagate through congestion constraints.
What the Visualization Shows
| Component | Description |
|---|---|
| Network Diagram | The three-bus triangle with power flows. Red pulsing indicates congestion. |
| Controls | Adjust line capacity and demand at each community. |
| Metrics | LMP ($/MWh), energy burden (%), and system cost. |
| LMP Chart | How prices vary as transmission capacity changes. |
| Burden Chart | How energy burden varies with capacity. Notice the disparity under congestion! |
| LMB Matrix | The 2×2 sensitivity matrix (for the two communities with income). Off-diagonal entries show spillover effects. |
Citation
If you use this visualization or the LMB framework, please cite:
@inproceedings{talkington_west_haider_2024,
author = {Talkington, Samuel and West, Amanda and Haider, Rabab},
title = {Locational marginal burden: Quantifying the equity of optimal power flow solutions},
year = {2024},
isbn = {9798400704802},
publisher = {Association for Computing Machinery},
doi = {10.1145/3632775.3661947},
booktitle = {Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems},
pages = {97–107},
numpages = {11},
location = {Singapore, Singapore},
series = {e-Energy '24}
}
Acknowledgments
This research was supported by the NSF Graduate Research Fellowship, NSF AI Institute for Advances in Optimization (AI4OPT), and the Georgia Tech Strategic Energy Institute.
Technical Note: This visualization runs entirely in your browser. The DC optimal power flow is solved using a QP algorithm in JavaScript. LMB is computed via closed-form implicit differentiation of the KKT conditions.