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1. Investing in Renewable Energy: Reconciling Regional Policy With Renewable Energy Growth

   Ogunrinde, O ; Shittu, E ; Dhanda, K. K. ( November 2018 , IEEE engineering management review)

   To address the effects of climate change, it is imperative for economies to proactively invest in, and deploy, low carbon energy technologies to meet current energy demands. To this effect, several states in the U.S. have implemented policies to incentivize the growth of renewable energy technologies. One of these policies is the renewable portfolio standards (RPS), which mandates that a certain percentage of the total electricity sales of utilities be sourced from renewable energy sources. This paper examines the effectiveness of these policies in driving the growth of specific renewable technologies across different regional transmission organizations (RTOs). It evaluates the adoption of renewable energy technologies across these RTOs to provide insights on the varying successes of these policies. The paper develops a ranking system for the correlations between the strength of RPS and renewable energy capacity growth across the RTOs. Two central observations emerge. First, despite the presence of positive correlations between RPS and renewable energy capacity additions, the capacity growth of renewable energy is not monotonic in time as technological differences characterize regional attributes. Second, the technology returns on RPS mandates are location-specific. 

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2. Invited paper: An Efficient Energy Management Solution for Renewable Energy Based IoT Devices

   https://doi.org/10.1145/3571306.3571387  

   Liri, Elizabeth ; Ramakrishnan, K. K. ; Kar, Koushik ; Lyon, Geoff ; Sharma, Puneet ( January 2023 , ICDCN '23: Proceedings of the 24th International Conference on Distributed Computing and Networking)

   Multi-sensor IoT devices enable the monitoring of different phenomena using a single device. Often deployed over large areas, these devices have to depend on batteries and renewable energy sources for power. Therefore, efficient energy management solutions that maximize device lifetime and information utility are important. We present SEMA, a smart energy management solution for IoT applications that uses a Model Predictive Control (MPC) approach to optimize IoT energy use and maximize information utility by dynamically determining task values to be used by the IoT device’s sensors. Our solution uses the current device battery state, predicted available solar energy over the short-term, and task energy and utility models to meet the device energy goals while providing sufficient monitoring data to the IoT applications. To avoid the need for executing the MPC optimization at a centralized sink (from which the task values are downloaded to the SEMA devices), we propose SEMA-Approximation (SEMA-A), which uses an efficient MPC Approximation that is simple enough to be run on the IoT device itself. SEMA-A decomposes the MPC optimization problem into two levels: an energy allocation problem across the time epochs, and task-dependent sensor scheduling problem, and finds efficient algorithms for solving both problems. Experimental results show that SEMA is able to adapt the task values based on the available energy, and that SEMA-A closely approximates SEMA in sensing performance. 

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3. Consumers’ Attitudes towards Surcharges on Distributed Renewable Energy Generation and Energy Efficiency Programs

   https://doi.org/10.3390/su9081475  

   Liang, Jing ; Qiu, Yueming ; Padmanabhan, Poornima ( August 2017 , Sustainability)
4. The synergistic role of renewable energy integration into the unit commitment of the energy water nexus

   https://doi.org/10.1016/j.renene.2017.02.063  

   Hickman, William ; Muzhikyan, Aramazd ; Farid, Amro M. ( August 2017 , Renewable Energy)

   null (Ed.)
5. MicroFEWs: A Food–Energy–Water Systems Approach to Renewable Energy Decisions in Islanded Microgrid Communities in Rural Alaska

   https://doi.org/10.1089/ees.2019.0055  

   Whitney, Erin ; Schnabel, William E. ; Aggarwal, Srijan ; Huang, Daisy ; Wies, Richard W. ; Karenzi, Justus ; Huntington, Henry P. ; Schmidt, Jennifer I. ; Dotson, Aaron D. ( July 2019 , Environmental Engineering Science)