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These profitability metrics are shown to be more robust for evaluating technology competitiveness by keeping comparisons on an equivalent monetary basis.", "publicationTitle": "The Electricity Journal", "publisher": "", "place": "", "date": "2021-05-01", "volume": "34", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "106931", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "The Electricity Journal", "DOI": "10.1016/j.tej.2021.106931", "citationKey": "mowersEvaluationElectricitySystem2021a", "url": "https://www.sciencedirect.com/science/article/pii/S1040619021000221", "accessDate": "2026-04-09T20:28:26Z", "PMID": "", "PMCID": "", "ISSN": "1040-6190", "archive": "", "archiveLocation": "", "shortTitle": "An evaluation of electricity system technology competitiveness metrics", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Competitiveness metrics", "type": 1 }, { "tag": "Electricity technology competitiveness", "type": 1 }, { "tag": "Levelized cost of electricity", "type": 1 }, { "tag": "Profitability", "type": 1 }, { "tag": "Renewable energy", "type": 1 }, { "tag": "System value", "type": 1 } ], "collections": [ "9PTAQKGN" ], "relations": {}, "dateAdded": "2026-04-09T20:28:26Z", "dateModified": "2026-04-09T20:28:28Z" } }, { "key": "GYSWH66R", "version": 11979, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/GYSWH66R", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/GYSWH66R", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/700012/items/RGILPJLA", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "GYSWH66R", "version": 11979, "parentItem": "RGILPJLA", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-04-09T16:29:37Z", "url": "https://www.mdpi.com/1996-1073/16/3/1352/pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Jahangiri et al. - 2023 - A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Elec.pdf", "md5": "2c339604a217b2f66b889da6d60b3950", "mtime": 1775752177421, "tags": [], "relations": {}, "dateAdded": "2026-04-09T16:29:37Z", "dateModified": "2026-04-09T16:29:37Z" } }, { "key": "RGILPJLA", "version": 11977, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/RGILPJLA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/RGILPJLA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "creatorSummary": "Jahangiri et al.", "parsedDate": "2023-01-26", "numChildren": 1 }, "data": { "key": "RGILPJLA", "version": 11977, "itemType": "journalArticle", "title": "A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System", "creators": [ { "creatorType": "author", "firstName": "Zahra", "lastName": "Jahangiri" }, { "creatorType": "author", "firstName": "Mackenzie", "lastName": "Judson" }, { "creatorType": "author", "firstName": "Kwang Moo", "lastName": "Yi" }, { "creatorType": "author", "firstName": "Madeleine", "lastName": "McPherson" } ], "abstractNote": "Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastr...", "publicationTitle": "Energies", "publisher": "Multidisciplinary Digital Publishing Institute", "place": "", "date": "2023-01-26", "volume": "16", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3390/en16031352", "citationKey": "jahangiriDeepLearningApproach2023", "url": "https://www.mdpi.com/1996-1073/16/3/1352", "accessDate": "2026-04-09T16:29:30Z", "PMID": "", "PMCID": "", "ISSN": "1996-1073", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.mdpi.com", "callNumber": "", "rights": "http://creativecommons.org/licenses/by/3.0/", "extra": "", "tags": [ { "tag": "K-means clustering", "type": 1 }, { "tag": "decision making", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "energy decarbonization", "type": 1 }, { "tag": "energy planning", "type": 1 }, { "tag": "machine learning", "type": 1 }, { "tag": "power systems", "type": 1 }, { "tag": "residual neural networks", "type": 1 } ], "collections": [ "9PTAQKGN" ], "relations": {}, "dateAdded": "2026-04-09T16:29:30Z", "dateModified": "2026-04-09T16:29:32Z" } }, { "key": "F2IFH6LV", "version": 11976, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/F2IFH6LV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/F2IFH6LV", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/700012/items/NFZICAHN", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "F2IFH6LV", "version": 11976, "parentItem": "NFZICAHN", "itemType": "attachment", "linkMode": "imported_url", "title": "Available Version (via Google Scholar)", "accessDate": "2026-04-09T16:28:46Z", "url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sam.11419", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Bugbee et al. - 2019 - Enabling immersive engagement in energy system models with deep learning.pdf", "md5": "c6442f0d414ec83cbef5a07f591d89c2", "mtime": 1775752126190, "tags": [], "relations": {}, "dateAdded": "2026-04-09T16:28:46Z", "dateModified": "2026-04-09T16:28:46Z" } }, { "key": "NFZICAHN", "version": 11974, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/NFZICAHN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/NFZICAHN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "creatorSummary": "Bugbee et al.", "parsedDate": "2019", "numChildren": 1 }, "data": { "key": "NFZICAHN", "version": 11974, "itemType": "journalArticle", "title": "Enabling immersive engagement in energy system models with deep learning", "creators": [ { "creatorType": "author", "firstName": "Bruce", "lastName": "Bugbee" }, { "creatorType": "author", "firstName": "Brian W.", "lastName": "Bush" }, { "creatorType": "author", "firstName": "Kenny", "lastName": "Gruchalla" }, { "creatorType": "author", "firstName": "Kristin", "lastName": "Potter" }, { "creatorType": "author", "firstName": "Nicholas", "lastName": "Brunhart‐Lupo" }, { "creatorType": "author", "firstName": "Venkat", "lastName": "Krishnan" } ], "abstractNote": "Abstract\n Complex ensembles of energy simulation models have become significant components of renewable energy research in recent years. Often the significant computational cost, high‐dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have developed an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low‐dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.", "publicationTitle": "Statistical Analysis and Data Mining: The ASA Data Science Journal", "publisher": "", "place": "", "date": "08/2019", "volume": "12", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "325-337", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Statistical Analysis", "DOI": "10.1002/sam.11419", "citationKey": "bugbeeEnablingImmersiveEngagement2019", "url": "https://onlinelibrary.wiley.com/doi/10.1002/sam.11419", "accessDate": "2026-04-09T16:28:41Z", "PMID": "", "PMCID": "", "ISSN": "1932-1864, 1932-1872", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "DOI.org (Crossref)", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "9PTAQKGN" ], "relations": {}, "dateAdded": "2026-04-09T16:28:41Z", "dateModified": "2026-04-09T16:28:42Z" } }, { "key": "KSVL6NC2", "version": 11973, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/KSVL6NC2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/KSVL6NC2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2548262, "username": "patrickbrown4", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/patrickbrown4", "type": "text/html" } } }, "creatorSummary": "EIA", "parsedDate": "2025-11-10", "numChildren": 0 }, "data": { "key": "KSVL6NC2", "version": 11973, "itemType": "webpage", "title": "State Electricity Profiles", "creators": [ { "creatorType": "author", "name": "EIA" } ], "abstractNote": "", "websiteTitle": "", "websiteType": "", "date": "2025-11-10", "publisher": "", "place": "", "DOI": "", "citationKey": "eiaStateElectricityProfiles2025", "url": "https://www.eia.gov/electricity/state/", "accessDate": "", "shortTitle": "", "language": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-04-03T20:28:29Z", "dateModified": "2026-04-03T20:29:29Z" } }, { "key": "BGY3WL5L", "version": 11966, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/BGY3WL5L", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/BGY3WL5L", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/700012/items/PLAZP7FI", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "BGY3WL5L", "version": 11966, "parentItem": "PLAZP7FI", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-04-02T16:07:31Z", "url": "https://dspace.mit.edu/bitstream/1721.1/130589/1/Enhanced%20decision%20support%20for%20a%20changing%20electricity%20landscape.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Jenkins and Sepulveda - 2017 - Enhanced Decision Support for a Changing Electricity Landscape The GenX Configurable Electricity Re.pdf", "md5": "dd81c1451193fadf41886a97a8aa93e8", "mtime": 1775146051635, "tags": [], "relations": {}, "dateAdded": "2026-04-02T16:07:31Z", "dateModified": "2026-04-02T16:07:31Z" } }, { "key": "PLAZP7FI", "version": 11964, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/PLAZP7FI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/PLAZP7FI", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1036947, "username": "wcole", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/wcole", "type": "text/html" } } }, "creatorSummary": "Jenkins and Sepulveda", "parsedDate": "2017-11-27", "numChildren": 1 }, "data": { "key": "PLAZP7FI", "version": 11964, "itemType": "report", "title": "Enhanced Decision Support for a Changing Electricity Landscape: The GenX Configurable Electricity Resource Capacity Expansion Model", "creators": [ { "creatorType": "author", "firstName": "Jesse D.", "lastName": "Jenkins" }, { "creatorType": "author", "firstName": "Nestor A.", "lastName": "Sepulveda" } ], "abstractNote": "The electric power sector is currently undergoing several important transitions, which individually and collectively have the potential to transform the design, operation, and characteristics of electricity systems, including: decarbonization of electricity supplies; increased adoption of variable renewable energy and distributed energy resources; digitization of power systems; and electrification of greater shares of heating, transportation, and industry. In the face of these transformations, many conventional electricity resource capacity expansion models are no longer adequate for rigorous decision support and policy analysis. This working paper describes the formulation of “GenX,\" a highly-configurable electricity resource capacity expansion model that incorporates several state-of-the-art improvements in electricity system modeling to offer improved decision support for a changing electricity landscape. GenX is a constrained optimization model that determines the mix of electricity generation, storage, and demand-side resource investments and operational decisions to meet electricity demand in a future planning year at lowest cost subject to a variety of power system operational constraints and specified policy constraints, such as CO2 emissions limits. The appropriate level of model resolution with regards to chronological variability of electricity demand and renewable energy availability, power system operational detail and unit commitment constraints,\nand transmission and distribution network representation each vary for a given planning problem or policy question. As such, the GenX model is designed to be highly configurable, with several different degrees of resolution possible on each of these three key dimensions. The model is capable of representing a full range of conventional and novel electricity resources, including thermal generators, variable renewable resources (wind and solar), run-of-river, reservoir and pumped-storage hydroelectric generators, energy storage devices, demand-side \nflexibility, and several advanced technologies such as high temperature nuclear reactors and carbon capture and storage. Two optional modules also allow modeling of heat storage, industrial heat demand, and co-generation of heat and power; and distributed energy resources deployed at distribution voltages. The model has been implemented in Julia Language.", "reportNumber": "", "reportType": "Working Paper", "institution": "MIT Energy Initiative", "place": "", "date": "2017-11-27", "seriesTitle": "", "seriesNumber": "", "pages": "", "DOI": "", "ISBN": "", "citationKey": "jenkinsEnhancedDecisionSupport2017", "url": "https://dspace.mit.edu/handle/1721.1/130589", "accessDate": "2026-04-02T16:07:27Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Enhanced Decision Support for a Changing Electricity Landscape", "language": "en_US", "libraryCatalog": "dspace.mit.edu", "callNumber": "", "rights": "", "extra": "Accepted: 2021-05-13T17:24:21Z", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-04-02T16:07:27Z", "dateModified": "2026-04-02T16:07:28Z" } }, { "key": "AZUEBQQC", "version": 11961, "library": { "type": "group", "id": 700012, "name": "ReEDS", "links": { "alternate": { "href": "https://www.zotero.org/groups/reeds", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/700012/items/AZUEBQQC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/reeds/items/AZUEBQQC", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/700012/items/Y3FBR923", "type": "application/json" } }, "meta": { "createdByUser": { "id": 4257393, "username": "jjorgens", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/jjorgens", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "AZUEBQQC", "version": 11961, "parentItem": "Y3FBR923", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text", "accessDate": "2026-03-31T19:53:30Z", "url": "https://www.nature.com/articles/s41560-021-00810-z.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Wiser et al. - 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However, typically there are many near-optimal alternatives that stand out due to other attractive properties like social acceptance. Understanding features that persist across many cost-efficient al­ ternatives enhances policy advice and acknowledges structural model uncertainties. We apply the modeling-togenerate-alternatives (MGA) methodology to systematically explore the near-optimal feasible space of a com­ pletely renewable European electricity system model. While accounting for complex spatio-temporal patterns, we allow simultaneous capacity expansion of generation, storage and transmission infrastructure subject to linearized multi-period optimal power flow. Many similarly costly, but technologically diverse solutions exist. Already a cost deviation of 0.5% offers a large range of possible investments. 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