Decentralized water systems adoption’s multi-objective spatial optimization in pursuit of sustainability and resiliency
Abstract
Decentralized water systems adoption decisions, such as installing rainwater harvesting and graywater recycling systems, can be made on an individual level by multiple agents, but the outcomes of such decisions are critically interconnected in a complex system with emergent sustainability and resilience properties. We employ a cross-scale optimization approach which searches through many spatial configurations of possible decentralized system adoption choices to characterize the possible best-case outcomes. Each such spatially explicit adoption configuration is evaluated using a holistic water-energy system dynamics model that considers the interactions between centralized and decentralized water systems for the city of Boston, MA. In complex systems such as the one we are tackling, there is no single configuration which maximizes all relevant metrics or objectives. To address this multi-objective optimization problem, we use a Genetic Algorithm (GA) approach to find multiple Pareto optimal solutions that characterize non-dominated tradeoffs between the metrics of interest. It is critical to understand and characterize the possible best-case outcomes (adoption patterns), especially because they might not align with decisions guided by current individual interests. The values achieved for our resilience and sustainability objectives in these optimized adoption scenarios will help support future designs of policy and incentive programs.
Author(s)
| Name |
Affiliation |
|
Masoumeh Khalkhali
|
University of Southern California |
|
Bistra Dilkina |
Associate Professor, Computer Science Department, University of Southern California |
|
Weiwei Mo
|
University of New Hampshire |
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