ID:
publications-3819
Type:
article
Year:
2014
Authors:
Friedman, Kenneth and Friedman, Kenneth and Heaney, James P. and Heaney, James P. and Morales, Miguel and Morales, Miguel and Palenchar, John and Palenchar, John
Title:
Analytical optimization of demand management strategies across all urban water use sectors
Venue/Journal:
Water Resources Research
DOI:
10.1002/2013wr014261
Research type:
Water System:
Technical Focus:
Abstract:
An effective urban water demand management program can greatly influence both peak and average demand and therefore long-term water supply and infrastructure planning. Although a theoretical framework for evaluating residential indoor demand management has been well established, little has been done to evaluate other water use sectors such as residential irrigation in a compatible manner for integrating these results into an overall solution. This paper presents a systematic procedure to evaluate the optimal blend of single family residential irrigation demand management strategies to achieve a specified goal based on performance functions derived from parcel level tax assessor's data linked to customer level monthly water billing data. This framework is then generalized to apply to any urban water sector, as exponential functions can be fit to all resulting cumulative water savings functions. Two alternative formulations are presented: maximize net benefits, or minimize total costs subject to satisfying a target water savings. Explicit analytical solutions are presented for both formulations based on appropriate exponential best fits of performance functions. A direct result of this solution is the dual variable which represents the marginal cost of water saved at a specified target water savings goal. A case study of 16,303 single family irrigators in Gainesville Regional Utilities utilizing high quality tax assessor and monthly billing data along with parcel level GIS data provide an illustrative example of these techniques. Spatial clustering of targeted homes can be easily performed in GIS to identify priority demand management areas.
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