{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:26:19Z","timestamp":1754155579147,"version":"3.41.2"},"reference-count":23,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2015,4,7]],"date-time":"2015-04-07T00:00:00Z","timestamp":1428364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,4,7]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 Following the deregulation of electricity markets in the USA, independent power producers operate as for-profit entities. Their profit depends on the price of electricity and an accurate forecast is critical in making bidding decisions on the electricity and reserve markets or engaging in bilateral contracts. Competing price forecasts have their accuracy expressed in statistical terms but producers need to determine the long-term value of using a given forecast. The purpose of this paper is to address this issue by presenting a method of electricity price forecast valuation which compares forecast models using financial rather than statistical measures. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 The objectives of this paper are achieved by mathematical modeling of thermal power plants and price forecast information available to market participants and simulating the operation of a thermal power plant using various price forecasts and perfect information (as a baseline). The operating profit calculated over a long period was used for ranking forecast models. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The framework can be used to estimate the value of a new price forecast as well as to determine if potential gains from developing or acquiring a new forecast will justify the expenses. The results show that an improvement in terms of statistical forecast accuracy measures does not guarantee increased profit. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 This paper presents a new method for comparing electricity price forecast models. It can be adapted to various types of thermal power plants that operate on liberalized electricity markets and utilize price-based dynamic economic dispatch models. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 This paper presents a simulation-based valuation framework for short-term electricity price. The approach described in this paper can be utilized by independent power producers for different types of generators, operating on deregulated electricity markets.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/k-08-2014-0174","type":"journal-article","created":{"date-parts":[[2015,6,8]],"date-time":"2015-06-08T08:55:27Z","timestamp":1433753727000},"page":"490-504","source":"Crossref","is-referenced-by-count":0,"title":["Price forecast valuation for the NYISO electricity market"],"prefix":"10.1108","volume":"44","author":[{"given":"Pawel","family":"Kalczynski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawit","family":"Zerom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122501445253700_b1","doi-asserted-by":"crossref","unstructured":"Allen, E.H.\n and \n Ilic, M.D.\n (2000), \u201cReserve markets for power systems reliability\u201d, \n IEEE Transactions on Power Systems\n , Vol. 15 No. 1, pp. 228-233.","DOI":"10.1109\/59.852126"},{"key":"key2020122501445253700_b2","doi-asserted-by":"crossref","unstructured":"Bunn, D.W.\n (2000), \u201cForecasting loads and prices in competetive power markets\u201d, \n Proceedings of IEEE\n , Vol. 88 No. 2, pp. 163-169.","DOI":"10.1109\/5.823996"},{"key":"key2020122501445253700_b3","doi-asserted-by":"crossref","unstructured":"Davison, M.\n , \n Anderson, C.L.\n , \n Marcus, B.\n and \n Anderson, K.\n (2002), \u201cDevelopment of a hybrid model for electrical power spot prices\u201d, \n IEEE Transactions on Power Systems\n , Vol. 17 No. 2, pp. 257-264.","DOI":"10.1109\/TPWRS.2002.1007890"},{"key":"key2020122501445253700_b4","doi-asserted-by":"crossref","unstructured":"De Jong, C.\n (2006), \u201cNature of power spikes : a regime-switch approach\u201d, \n Studies in Nonlinear Dynamics & Econometrics\n , Vol. 10 No. 3, pp. 1-28.","DOI":"10.2202\/1558-3708.1361"},{"key":"key2020122501445253700_b5","unstructured":"DOE\n (2014), \u201cNet generation by energy source: total (all sectors)\u201d, available at: www.eia.doe.gov\/cneaf\/electricity\/epm\/table1_1.html (accessed 15 July 2014)."},{"key":"key2020122501445253700_b6","unstructured":"EIA\n (2014), \u201cElectric power monthly\u201d, available at: www.eia.gov\/electricity\/monthly\/ (accessed 15 July 2014)."},{"key":"key2020122501445253700_b7","doi-asserted-by":"crossref","unstructured":"Geman, H.\n and \n Roncoroni, A.\n (2006), \u201cUnderstanding the fine structure of electricity prices\u201d, \n Journal of Business\n , Vol. 79 No. 79, pp. 1225-1261.","DOI":"10.1086\/500675"},{"key":"key2020122501445253700_b8","doi-asserted-by":"crossref","unstructured":"Kalczynski, P.J.\n (2012), \u201cA discrete model for optimal operation of fossil-fuel generators of electricity\u201d, \n European Journal of Operational Research\n , Vol. 216 No. 3, pp. 679-686.","DOI":"10.1016\/j.ejor.2011.08.003"},{"key":"key2020122501445253700_b9","unstructured":"Kalczynski, P.J.\n and \n Zerom, D.\n (2012), \u201cLocational price and load forecast \u2013 NYISO\u201d, available at: http:\/\/mcbe-bi1.fullerton.edu\/NYISO_Forecast\/ (accessed 15 July 2014)."},{"key":"key2020122501445253700_b10","doi-asserted-by":"crossref","unstructured":"Kamat, R.\n and \n Oren, S.S.\n (2004), \u201cTwo-settlement systems for electricity markets under network uncertainty and market power\u201d, \n Journal of Regulatory Economics\n , Vol. 25 No. 1, pp. 5-37.","DOI":"10.1023\/B:REGE.0000008653.08554.81"},{"key":"key2020122501445253700_b11","doi-asserted-by":"crossref","unstructured":"Limpaitoon, T.\n , \n Chen, Y.\n and \n Oren, S.S.\n (2011), \u201cThe impact of carbon cap and trade regulation on congested electricity market equilibrium\u201d, \n Journal of Regulatory Economics\n , Vol. 40 No. 3, pp. 237-260.","DOI":"10.1007\/s11149-011-9161-4"},{"key":"key2020122501445253700_b12","unstructured":"NYISO\n (2014a), \u201cMarket participant user\u2019s guide\u201d, available at: www.nyiso.com\/public\/webdocs\/documents\/guides\/mpug.pdf (accessed 15 July 2014)."},{"key":"key2020122501445253700_b13","unstructured":"NYISO\n (2014b), \u201cNew York independent system operator\u2019s website\u201d, available at: www.nyiso.com (accessed 15 July 2014)."},{"key":"key2020122501445253700_b14","doi-asserted-by":"crossref","unstructured":"Sanders, N.R.\n and \n Graman, G.A.\n (2009), \u201cQuantifying costs of forecast errors: a case study of the warehouse environment\u201d, \n Omega\n , Vol. 37 No. 1, pp. 116-125.","DOI":"10.1016\/j.omega.2006.10.004"},{"key":"key2020122501445253700_b15","doi-asserted-by":"crossref","unstructured":"Skantze, P.L.\n and \n Ilic, M.D.\n (2001), \n Valuation, Hedging and Speculation in Competetive Electricity Markets. 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