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2. LA100 Equity Strategies

Author

Anderson, Kate, primary, Day, Megan, additional, Romero Lankao, Patricia, additional, Berdahl, Sonja, additional, Rauser, Casandra, additional, Bowen, Thomas, additional, Fournier, Eric, additional, Heath, Garvin, additional, Hinojosa, Raul, additional, Ong, Paul, additional, Palmintier, Bryan, additional, Pierce, Gregory, additional, Pincetl, Stephanie, additional, Prasanna, Ashreeta, additional, Ravi, Vikram, additional, Reyna, Janet, additional, Lee, Dong-Yeon, additional, Rosner, Nicole, additional, Sandoval, Noah, additional, Sekar, Ashok, additional, Sheinberg, Rachel, additional, Simeone, Christina, additional, Stenger, Katelyn, additional, Sun, Bingrong, additional, Valenzuela, Abel, additional, Wilson, Alana, additional, Zhu, Yifang, additional, Abraham, Sherin Ann, additional, Blanco, Lis, additional, Bolla, Greg, additional, Bustamante, Leticia, additional, Coffee, Daniel, additional, Craer, Jennifer, additional, Das, Paritosh, additional, Denholm, Paul, additional, Duwadi, Kapil, additional, Fontanini, Anthony, additional, Gonzalez, Silvia, additional, Gu, Yu, additional, He, Yueshuai, additional, Hernandez, Ariana, additional, Horsey, Ry, additional, Katz, Sophie, additional, Krishnamoorthy, Gayathri, additional, Li, Yun, additional, Lin, Yun, additional, Liu, Lixi, additional, Lockshin, Jane, additional, Ma, Jiaqi, additional, Maguire, Jeff, additional, Marroquin, Isaias, additional, Mooney, Meghan, additional, Panda, Kinshuk, additional, Pleitez, Marcelo, additional, Robertson, Joe, additional, Rodriguez, Ruth, additional, Ruddick-Schulman, Saul, additional, Sanchez-Hall, Magali, additional, Sedzro, Kwami Senam, additional, Velasquez, Leslie, additional, Walzberg, Julien, additional, White, Philip, additional, Yu, Qiao, additional, and Zimny-Schmitt, Daniel, additional

Published
2023
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3. LA100 Equity Strategies. Executive Summary

Author

Anderson, Kate, primary, Day, Megan, additional, Romero-Lankao, Patricia, additional, Berdahl, Sonja, additional, Rauser, Casandra, additional, Bowen, Thomas, additional, Fournier, Eric Daniel, additional, Heath, Garvin, additional, Hinojosa, Raul, additional, Ong, Paul, additional, Palmintier, Bryan, additional, Pierce, Gregory, additional, Pincetl, Stephanie, additional, Prasanna, Ashreeta, additional, Ravi, Vikram, additional, Reyna, Janet, additional, Lee, Dong-Yeon, additional, Rosner, Nicole, additional, Sandoval, Noah, additional, Sekar, Ashok, additional, Sheinberg, Rachel, additional, Simeone, Christina, additional, Stenger, Katelyn, additional, Sun, Bingrong, additional, Valenzuela, Abel, additional, Wilson, Alana, additional, Zhu, Yifang, additional, Abraham, Sherin Ann, additional, Blanco, Lis, additional, Bolla, Greg, additional, Bustamante, Leticia, additional, Coffee, Daniel, additional, Craer, Jennifer, additional, Das, Paritosh, additional, Denholm, Paul, additional, Duwadi, Kapil, additional, Fontanini, Anthony, additional, Gonzalez, Silvia, additional, Gu, Yu, additional, He, Yueshuai, additional, Hernandez, Ariana, additional, Horsey, Ry, additional, Krishnamoorthy, Gayathri, additional, Katz, Sophie, additional, Li, Yun, additional, Lin, Yun, additional, Liu, Lixi, additional, Lockshin, Jane, additional, Ma, Jiaqi, additional, Maguire, Jeff, additional, Marroquin, Isaias, additional, Mooney, Meghan, additional, Panda, Kinshuk, additional, Pleitez, Marcelo, additional, Robertson, Joe, additional, Rodriguez, Ruth, additional, Ruddick-Schulman, Saul, additional, Sanchez-Hall, Magali, additional, Sedzro, Kwami Senam, additional, Velasquez, Leslie, additional, Walzberg, Julien, additional, White, Philip, additional, Yu, Qiao, additional, and Zimny-Schmitt, Daniel, additional

Published
2023
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7. Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method.

Author

Kabe, Moyème, Bokovi, Yao, Sedzro, Kwami Senam, Takouda, Pidéname, and Lare, Yendoubé

Subjects
LINEAR programming, CENTROID, K-means clustering, PYTHON programming language, INTEGER programming, MICROGRIDS, RENEWABLE energy sources
Abstract

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Evaluating the Curtailment Risk of Non-Firm Utility-Scale Solar Photovoltaic Plants under a Novel Last-In First-Out Principle of Access Interconnection Agreement

Author

Sedzro, Kwami Senam A., Horowitz, Kelsey, Jain, Akshay K., Ding, Fei, Palmintier, Bryan, Mather, Barry, Sedzro, Kwami Senam A., Horowitz, Kelsey, Jain, Akshay K., Ding, Fei, Palmintier, Bryan, and Mather, Barry

Abstract

With the increasing share of distributed energy resources on the electric grid, utility companies are facing significant decisions about infrastructure upgrades. An alternative to extensive and capital-intensive upgrades is to offer non-firm interconnection opportunities to distributed generators, via a coordinated operation of utility scale resources. This paper introduces a novel flexible interconnection option based on the last-in, first-out principles of access aimed at minimizing the unnecessary non-firm generation energy curtailment by balancing access rights and contribution to thermal overloads. Although we focus on solar photovoltaic (PV) plants in this work, the introduced flexible interconnection option applies to any distributed generation technology. The curtailment risk of individual non-firm PV units is evaluated across a range of PV penetration levels in a yearlong quasi-static time-series simulation on a real-world feeder. The results show the importance of the size of the curtailment zone in the curtailment risk distribution among flexible generation units as well as that of the “access right” defined by the order in which PV units connect to the grid. Case study results reveal that, with a proper selection of curtailment radius, utilities can reduce the total curtailment of flexible PV resources by up to more than 45%. Findings show that non-firm PV generators can effectively avoid all thermal limit-related upgrade costs.

Published
2021

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