Sliding Tabs<\/title>\n <style>\n body {\n font-family: Arial, sans-serif;\n }\n\n .tab {\n margin: 10px 0;\n border: 1px solid #ccc;\n border-radius: 5px;\n box-shadow: 3px 3px 5px rgba(0, 0, 0, 0.2);\n }\n\n .tab-header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n background-color: #f1f1f1;\n padding: 10px;\n cursor: pointer;\n user-select: none;\n transition: background-color 0.3s ease, box-shadow 0.3s ease;\n border-bottom: 1px solid #ccc;\n }\n\n .tab-header:hover {\n background-color: #ddd;\n box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);\n }\n\n .tab-content {\n display: none;\n padding: 10px;\n border-top: 1px solid #ccc;\n background-color: #fafafa;\n box-shadow: inset 1px 1px 3px rgba(0, 0, 0, 0.1);\n }\n\n .tab-content p {\n margin: 0 0 10px;\n }\n\n .tab-content figure {\n text-align: center;\n margin: 0;\n }\n\n .tab-content img {\n max-width: 70%;\n height: auto;\n }\n\n .tab-content figcaption {\n text-align: center;\n font-size: 0.9em;\n color: #555;\n }\n\n .expand-text {\n font-size: 0.9em;\n color: #666;\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(1)\">\n Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-1\">\n <p>With widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid’s demand. Here we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers’ demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90% EVs, this shifting reduces total on-peak charging demand by 61%, which could require over 18 thousand additional Level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/05\/p1-scaled.jpg\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div> \n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(2)\">\n Planning the Electric Vehicle Transition by Integrating Spatial Information and Social Networks\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-2\">\n <p>The transition from gasoline-powered vehicles to electric vehicles (EVs) presents a promising avenue for reducing greenhouse gas emissions. Spatial forecasts of EV adoption are essential to facilitate this shift as they enable preparation for power grid adaptation. However, forecasting is hindered by the limited data availability at this early stage of adoption. Multiple model calibrations can match current adoption trends but yield divergent forecasts. By leveraging empirical data from places with leading adopters in the US, this study shows that taking into account the spatial and social structure linking potential EV adopters leads to forecasts of only 25\\% of the current predictions for 2050. Additionally, spatial social networks reproduce the temporal evolution of the empirical spatial auto-correlations over the last twelve years. At last, the study evaluates the potential impact of various EV marketing campaigns under prevailing uncertainties, emphasizing the need to tailor strategies to network dynamics for effective EV promotion.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/05\/p2.png\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div>\n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(3)\">\n Planning for electric vehicle needs by coupling charging profiles with urban mobility\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-3\">\n <p>Given the increasing ubiquity of plug-in electric vehicles (PEVs) in the Bay Area, this research aims to assist planning decisions by providing timing recommendations and assigning monetary values to modulations of PEV start and end charging times. Using cell phone activity of a large sample of Bay Area residents, charging session data from electric vehicles, surveys, and census data, we observed that PEV charging times peak between 8 and 10 a.m. following morning commutes. Because many tech employers in the Bay Area Peninsula provide free vehicle charging a concentrated charging density was observed in Cupertino and surrounding neighborhoods. The research recommends altering the PEV charging start and end times to limit the energy drain and potential power grid instability following commutes. The paper calculates the monetary gains achieved by slight shifts in charging start times. We believe monetary incentivizes could be offered to increase the adoption of charging PEVs at slightly off-peak times. We hope these results can inform planning decisions in the Bay Area and serve as a model to other cities by accommodating mobility needs while decreasing energy costs and minimizing the impact on commute duration.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2020\/02\/Screen-Shot-2020-02-17-at-1.15.04-PM.png\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div>\n <script>\n function toggleTabContent(tabIndex) {\n const content = document.getElementById(`tab-content-${tabIndex}`);\n const isVisible = content.style.display === 'block';\n\n \/\/ Hide all tab contents\n document.querySelectorAll('.tab-content').forEach(tabContent => {\n tabContent.style.display = 'none';\n });\n\n \/\/ Toggle the clicked tab's content\n if (!isVisible) {\n content.style.display = 'block';\n }\n }\n <\/script>\n<\/body>\n<\/html>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Publication<\/strong>: <br>Wu, J., Powell, S., Xu, Y., Rajagopal, R., & Gonzalez, M. C. (2024). Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns. <em>Cell Reports Sustainability,<\/em> 1(1). [<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S294979062300006X\">Paper<\/a>][<a href=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/02\/JiamanWu_CellRepSus_EVs.pdf\">PDF<\/a>][<a href=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/02\/JiamanWu_CellRepSus_EVs_SI.pdf\">Supplementary Materials<\/a>][<a href=\"https:\/\/github.com\/humnetlab\/Planning-for-2050-Charging-Stations\">Code<\/a>]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Xu, Y., \u00c7olak, S., Kara, E. C., Moura, S. J., & Gonz\u00e1lez, M. C. (2018). Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nature Energy, 3(6), 484-493. [<a href=\"\/wp-content\/uploads\/2020\/02\/planningEVs_NatEn.pdf\">PDF<\/a>]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research Members: Jiaman Wu, Ayse Tugba Ozturk Sliding Tabs Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns \u25bc Click to expand With widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid’s…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":100,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-625","page","type-page","status-publish","hentry"],"yoast_head":"\n<title>Planning EVs - HuMNet Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/humnetlab.berkeley.edu\/index.php\/projects\/planning-evs\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Planning EVs - 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{"id":625,"date":"2020-02-17T05:16:13","date_gmt":"2020-02-17T05:16:13","guid":{"rendered":"http:\/\/coehumnet.wpengine.com\/?page_id=625"},"modified":"2024-05-28T05:16:39","modified_gmt":"2024-05-28T05:16:39","slug":"planning-evs","status":"publish","type":"page","link":"https:\/\/humnetlab.berkeley.edu\/index.php\/projects\/planning-evs\/","title":{"rendered":"Planning EVs"},"content":{"rendered":"\n

Research Members<\/strong>: Jiaman Wu<\/a>, Ayse Tugba Ozturk<\/a><\/p>\n\n\n\n\n\n\n \n \n Sliding Tabs<\/title>\n <style>\n body {\n font-family: Arial, sans-serif;\n }\n\n .tab {\n margin: 10px 0;\n border: 1px solid #ccc;\n border-radius: 5px;\n box-shadow: 3px 3px 5px rgba(0, 0, 0, 0.2);\n }\n\n .tab-header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n background-color: #f1f1f1;\n padding: 10px;\n cursor: pointer;\n user-select: none;\n transition: background-color 0.3s ease, box-shadow 0.3s ease;\n border-bottom: 1px solid #ccc;\n }\n\n .tab-header:hover {\n background-color: #ddd;\n box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);\n }\n\n .tab-content {\n display: none;\n padding: 10px;\n border-top: 1px solid #ccc;\n background-color: #fafafa;\n box-shadow: inset 1px 1px 3px rgba(0, 0, 0, 0.1);\n }\n\n .tab-content p {\n margin: 0 0 10px;\n }\n\n .tab-content figure {\n text-align: center;\n margin: 0;\n }\n\n .tab-content img {\n max-width: 70%;\n height: auto;\n }\n\n .tab-content figcaption {\n text-align: center;\n font-size: 0.9em;\n color: #555;\n }\n\n .expand-text {\n font-size: 0.9em;\n color: #666;\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(1)\">\n Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-1\">\n <p>With widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid’s demand. Here we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers’ demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90% EVs, this shifting reduces total on-peak charging demand by 61%, which could require over 18 thousand additional Level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/05\/p1-scaled.jpg\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div> \n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(2)\">\n Planning the Electric Vehicle Transition by Integrating Spatial Information and Social Networks\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-2\">\n <p>The transition from gasoline-powered vehicles to electric vehicles (EVs) presents a promising avenue for reducing greenhouse gas emissions. Spatial forecasts of EV adoption are essential to facilitate this shift as they enable preparation for power grid adaptation. However, forecasting is hindered by the limited data availability at this early stage of adoption. Multiple model calibrations can match current adoption trends but yield divergent forecasts. By leveraging empirical data from places with leading adopters in the US, this study shows that taking into account the spatial and social structure linking potential EV adopters leads to forecasts of only 25\\% of the current predictions for 2050. Additionally, spatial social networks reproduce the temporal evolution of the empirical spatial auto-correlations over the last twelve years. At last, the study evaluates the potential impact of various EV marketing campaigns under prevailing uncertainties, emphasizing the need to tailor strategies to network dynamics for effective EV promotion.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/05\/p2.png\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div>\n <div class=\"tab\">\n <div class=\"tab-header\" onclick=\"toggleTabContent(3)\">\n Planning for electric vehicle needs by coupling charging profiles with urban mobility\n <span class=\"expand-text\">\u25bc Click to expand<\/span>\n <\/div>\n <div class=\"tab-content\" id=\"tab-content-3\">\n <p>Given the increasing ubiquity of plug-in electric vehicles (PEVs) in the Bay Area, this research aims to assist planning decisions by providing timing recommendations and assigning monetary values to modulations of PEV start and end charging times. Using cell phone activity of a large sample of Bay Area residents, charging session data from electric vehicles, surveys, and census data, we observed that PEV charging times peak between 8 and 10 a.m. following morning commutes. Because many tech employers in the Bay Area Peninsula provide free vehicle charging a concentrated charging density was observed in Cupertino and surrounding neighborhoods. The research recommends altering the PEV charging start and end times to limit the energy drain and potential power grid instability following commutes. The paper calculates the monetary gains achieved by slight shifts in charging start times. We believe monetary incentivizes could be offered to increase the adoption of charging PEVs at slightly off-peak times. We hope these results can inform planning decisions in the Bay Area and serve as a model to other cities by accommodating mobility needs while decreasing energy costs and minimizing the impact on commute duration.<\/p>\n <figure>\n <img decoding=\"async\" src=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2020\/02\/Screen-Shot-2020-02-17-at-1.15.04-PM.png\" alt=\"Figure\">\n <\/figure>\n <\/div>\n <\/div>\n <script>\n function toggleTabContent(tabIndex) {\n const content = document.getElementById(`tab-content-${tabIndex}`);\n const isVisible = content.style.display === 'block';\n\n \/\/ Hide all tab contents\n document.querySelectorAll('.tab-content').forEach(tabContent => {\n tabContent.style.display = 'none';\n });\n\n \/\/ Toggle the clicked tab's content\n if (!isVisible) {\n content.style.display = 'block';\n }\n }\n <\/script>\n<\/body>\n<\/html>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Publication<\/strong>: <br>Wu, J., Powell, S., Xu, Y., Rajagopal, R., & Gonzalez, M. C. (2024). Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns. <em>Cell Reports Sustainability,<\/em> 1(1). [<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S294979062300006X\">Paper<\/a>][<a href=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/02\/JiamanWu_CellRepSus_EVs.pdf\">PDF<\/a>][<a href=\"http:\/\/humnetlab.berkeley.edu\/wp-content\/uploads\/2024\/02\/JiamanWu_CellRepSus_EVs_SI.pdf\">Supplementary Materials<\/a>][<a href=\"https:\/\/github.com\/humnetlab\/Planning-for-2050-Charging-Stations\">Code<\/a>]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Xu, Y., \u00c7olak, S., Kara, E. C., Moura, S. J., & Gonz\u00e1lez, M. C. (2018). Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nature Energy, 3(6), 484-493. [<a href=\"\/wp-content\/uploads\/2020\/02\/planningEVs_NatEn.pdf\">PDF<\/a>]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research Members: Jiaman Wu, Ayse Tugba Ozturk Sliding Tabs Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns \u25bc Click to expand With widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid’s…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":100,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-625","page","type-page","status-publish","hentry"],"yoast_head":"\n<title>Planning EVs - HuMNet Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/humnetlab.berkeley.edu\/index.php\/projects\/planning-evs\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Planning EVs - 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