Success in an infrastructure project depends as much on local support and participation as it does on coordination between politicians, financiers and utilities (Tufte and Mefalopulos 2009). We wanted to create a freely accessible tool for non-technical people to experience the infrastructure planning process and see the impact of different decisions on the community. By experimenting first-hand with web-based software, a user can understand on a map how changes in population, pricing and fiscal policy influence where infrastructure is built, who will get access and why different technologies such as off-grid solar, mini-grid diesel or bio-diesel and grid affect cost.

Our lab combined technical expertise from remote sensing, operations research and electrical engineering into an easy-to-use system so that local leaders, politicians, financiers and utility owners can focus on communicating visually and negotiating between different electrification scenarios.

• Local leaders: Will the construction affect my community?
• Politicians: How many businesses and households in my district will get access?
• Financiers: What are the risk and return on investment?
• Utility owners: Is it technically and financially feasible?

Where do people live?
The remote sensing component finds where people live using image recognition on satellite imagery.

Results from the remote sensing component provide a spatial census, enabling us to estimate population density and spread with reasonable accuracy. Different settlement patterns hint at different electrification strategies: sparsely distributed clusters as in Ghana or Mali are good candidates for off-grid technologies such as solar, while larger clusters as in Tanzania justify diesel mini-grids and densely packed Kenya find grid electrification to be cost-effective (Zvoleff, Kocaman, Huh, Modi 2009).

The command-line software to perform remote sensing was completed in 2008. We are in the process of optimizing the software to run on graphical processing units (GPUs) to make the system accessible via web.

How can we provide access to electricity?
The econometric and operations research component uses demographics and pricing models to project electricity demand, cost and placement (Parshall, Pillai, Mohan, Sanoh, Modi 2009). Users can freely explore what-if scenarios by changing the many parameters and see on a map what technology makes sense for each community, where and how much it will cost.

We have created a web-based prototype of the infrastructure planning component that is being used this semester by students of the Master’s in Development Practice program at Columbia University. We are currently improving the map-based user interface and you will be able to use it through your browser soon.

The power of open source software
Both systems are built entirely using open source components such as Python, GDAL, GEOS, Lush, OpenLayers and AMQP.

References

Alex Zvoleff, Ayse Selin Kocaman, Tim Huh, Vijay Modi. (2009) “The impact of geography on energy infrastructure cost.” Energy Policy, 37, 4066-4078. [link]

Lily Parshall, Dana Pillai, Shashank Mohan, Aly Sanoh, Vijay Modi (2009) “National electricity planning in settings with low pre-existing grid coverage: development of a spatial model and case study of Kenya.” Energy Policy, 37, 2395-2410. [link]

Pedro Sanchez et al. (2007) “The African Millennium Villages.” Proceedings of the National Academy of Sciences 104 (43). [link]

Thomas Tufte, Paolo Mefalopulos (2009) Participatory communication – a practical guide. World Bank Working Paper. [link]

Talks

How Python is guiding infrastructure construction in Africa
PyCon Atlanta
February 20, 2010 [video]

The impact of geography on energy infrastructure cost
Millennium Villages Student Research Showcase
February 18, 2009 [video]

Automatically finding houses in rural satellite images with multiband convolutional neural networks
Millennium Villages Student Research Showcase
February 18, 2009 [video]

Finding and connecting people in Africa to infrastructure using remote sensing and geospatial optimization
O’Reilly Where 2.0 Conference
March 31, 2010

Credits

Abstract

If solar power is to provide substantial portions of our electricity needs, it will first become cost effective when it provides peak power in the daytime, without the need for storing the energy.   Indeed since human electricity consumption is frequently small at night and larger when the sun is shining, there is already a natural correlation.  Existing power systems are currently geared to provide this variable demand, with baseload plants cheaply providing a constant level of power, and dispatchable plants dynamically (and more expensively) supplying the rest.  This leads to the frequent suggestion that one can exploit the correlation between sunlight and electricity by using energy from solar panels during the day to offset some of the load previously generated by dispatchable plants.

This paper addresses the question of how much of the load can be substituted by the solar electricity, without leaving the solar power plant substantially idle or requiring the solar power to be stored.  It uses historical sunlight and electrical load data from 32 regions of the United States to determine the photovoltaic (PV) power generation capacity that could be installed such that “almost all” of its energy output would occur at times of high demand. Specifically, what is the maximum deployment that permits 95% of the annual output from PV to be utilized without reducing the output of the baseload plants?

Our results for these 32 regions are that 7.8% of the total annual electricity demand could be met by installing 59 GW of PV panels.  This represents about a fourth of the present electrical energy supplied by dispatchable plants.  If solar power were equally effective in the rest of the United States, nearly 200 GW of PV capacity could be put to use without any energy storage.  Thus, in the near term, there is enormous room for expanding the roughly 1 GW installed base of PV power without investing in night-time energy storage.  The paper also provides insight into how year to year variability of sunlight and demand impact the results.

Download Article

• Solar Power Without Storage