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Item type:Publication, Unsupervised learning for deploying smart charging public infrastructure for electric vehicles in sprawling cities(Elsevier, 2020-09-01)This paper presents a novel methodology to study the deployment of public smart charging stations (CS) of electric vehicles (EV) in a sprawling Latin American city. A relevant difference between developed and emerging economies is the reduced access to home charging in emerging economies, which is the case in Latin American cities. Thus, developing public charging stations represents a crucial factor in the mass adoption of EVs by road commuters. We develop herein a methodology for optimizing the deployment of smart charging stations under the sprawling phenomenon perspective. Our method comprises two steps. In step one, we applied principal component analysis (PCA) to facilitate the analysis of a sprawling city, and then we define candidates for potential locations from ‘demand clusters’ within an urbanized area, by K-means clustering analysis. In the second step, a stochastic programming model was employed to optimize the integration of infrastructural facilities with distributed energy resources (DERs) and EV charging stations using a collaborative strategy to minimize its energy consumption cost under demand uncertainty. We demonstrate the capabilities of this approach through a case study in the city of Lima. Experimental results reveal managerial insights for different stakeholders (i.e., government, industry, academia, and civil society) to promote policies, investment, and incentives. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics(Elsevier, 2020-05-01)A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decision-making process.