{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:35:56Z","timestamp":1793111756350,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This study examines how forecast accuracy and rolling schedule updates affect portfolio imbalance and gross trading margin in Japanese electricity markets. Prophet and vector autoregression (VAR) were compared using a mixed-integer scheduling model and 334 days of data from Kyushu. The portfolio comprised a 10 MW photovoltaic plant and a 10 MW\/40 MWh battery energy storage system. The model included day-ahead and intraday trading and ex-post imbalance settlement. Primary Reserve and Tertiary Reserve 2 prices were used in energy-equivalent proxy channels, without modeling \u0394kW capacity bidding or reserve activation. A fixed schedule was compared with forecasts and eligible decisions updated every 30 min. Prophet had lower root mean squared errors for all five day-ahead forecast series. With fixed schedules, Prophet achieved a gross trading margin of JPY 204.47 million and portfolio imbalance of 4.282 GWh, compared with JPY 209.05 million and 4.905 GWh for VAR. Rolling updates increased the margins to JPY 210.05 million and JPY 210.17 million, respectively, and reduced imbalance to 4.123 and 1.600 GWh. Prophet gained more in margin, whereas VAR achieved a greater imbalance reduction. A model-based sensitivity allowing a later final update further reduced imbalance, particularly with VAR. Increasing battery capacity from 10 to 40 MWh raised the margin; investment and degradation costs were excluded. Lower day-ahead forecast error did not yield a higher margin in the fixed-schedule case. Forecasting methods should therefore be evaluated using both forecast errors and scheduling outcomes.<\/jats:p>","DOI":"10.2139\/ssrn.7674819","type":"posted-content","created":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T13:55:00Z","timestamp":1793109300000},"source":"Crossref","is-referenced-by-count":0,"title":["Forecast Accuracy and Rolling Schedule Updates for a PV and Battery Portfolio in Japanese Electricity Markets"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4166-8916","authenticated-orcid":true,"given":"Kiyofumi","family":"Sato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshikuni","family":"Yoshida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","article-title":"Review of photovoltaic power forecasting","volume":"136","author":"J Antonanzas","year":"2016","journal-title":"Solar Energy"},{"issue":"3","key":"ref2","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1287\/mnsc.2018.3253","article-title":"From predictive to prescriptive analytics","volume":"66","author":"D Bertsimas","year":"2020","journal-title":"Management Science"},{"issue":"4","key":"ref3","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/oxrep\/grx041","article-title":"Electricity market design","volume":"33","author":"P Cramton","year":"2017","journal-title":"Oxford Review of Economic Policy"},{"issue":"3","key":"ref4","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/07350015.1995.10524599","article-title":"Comparing predictive accuracy","volume":"13","author":"F X Diebold","year":"1995","journal-title":"Journal of Business and Economic Statistics"},{"key":"ref5","year":"2026","journal-title":"Electric Power Reserve eXchange (EPRX), n.d. Trading information"},{"key":"ref6","author":"P Ellinas","year":"2024","journal-title":"Decision-focused learning under decision dependent uncertainty for power systems with price-responsive demand. Electric Power Systems Research 235"},{"issue":"1","key":"ref7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1287\/mnsc.2020.3922","article-title":"Smart \"predict, then optimize","volume":"68","author":"A N Elmachtoub","year":"2022","journal-title":"Management Science"},{"key":"ref8","article-title":"A market feedback framework for improved estimates of the arbitrage value of energy storage using price-taker models","volume":"310","author":"M I Emmanuel","year":"2022","journal-title":"Applied Energy"},{"key":"ref9","author":"Entso-E","year":"2024","journal-title":"ENTSO-E Balancing Report 2024. European Network of Transmission System Operators for Electricity"},{"issue":"2","key":"ref10","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.ejor.2021.11.010","article-title":"Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming","volume":"301","author":"B Finnah","year":"2022","journal-title":"European Journal of Operational Research"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121667","article-title":"Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: a rolling horizon case study","volume":"352","author":"E Finhold","year":"2023","journal-title":"Applied Energy"},{"issue":"477","key":"ref12","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1198\/016214506000001437","article-title":"Strictly proper scoring rules, prediction, and estimation","volume":"102","author":"T Gneiting","year":"2007","journal-title":"Journal of the American Statistical Association"},{"key":"ref13","year":"2026","journal-title":"Gurobi Optimizer Reference Manual"},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124753","article-title":"Advancing energy system optimization via data-centric task-oriented forecasting: an application in PV-battery operation","volume":"378","author":"X Huang","year":"2025","journal-title":"Applied Energy"},{"key":"ref15","author":"Iea","year":"2021","journal-title":"Japan 2021. International Energy Agency"},{"key":"ref16","author":"Japan Meteorological","year":"2026","journal-title":"Historical weather data download"},{"key":"ref17","year":"2026","journal-title":"Supply-demand balancing market"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116983","article-title":"Forecasting day-ahead electricity prices: a review of stateof-the-art algorithms, best practices and an open-access benchmark","volume":"293","author":"J Lago","year":"2021","journal-title":"Applied Energy"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-27752-1","author":"H L\u00fctkepohl","year":"2005","journal-title":"New Introduction to Multiple Time Series Analysis"},{"issue":"4","key":"ref20","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.ijforecast.2018.06.001","article-title":"The M4 competition: results, findings, conclusion and way forward","volume":"34","author":"S Makridakis","year":"2018","journal-title":"International Journal of Forecasting"},{"issue":"5","key":"ref21","doi-asserted-by":"crossref","first-page":"3333","DOI":"10.1109\/TPWRS.2021.3135334","article-title":"Mitigation of the inefficiency in imbalance settlement designs using day-ahead prices","volume":"37","author":"T Matsumoto","year":"2022","journal-title":"IEEE Transactions on Power Systems"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.rser.2017.05.234","article-title":"Recent advances in electricity price forecasting: a review of probabilistic forecasting","volume":"81","author":"J Nowotarski","year":"2018","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"ref23","article-title":"Organization for Cross-regional Coordination of Transmission Operators","author":"Occto","year":"2025","journal-title":"Operational Rules"},{"issue":"1","key":"ref24","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at scale","volume":"72","author":"S J Taylor","year":"2018","journal-title":"The American Statistician"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2025.108608","article-title":"Profitability of batteries in day-ahead and intraday electricity markets: assessment of operation strategies with endogenous prices","volume":"148","author":"A T Veenstra","year":"2025","journal-title":"Energy Economics"},{"issue":"4","key":"ref26","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijforecast.2014.08.008","article-title":"Electricity price forecasting: a review of the state-of-the-art with a look into the future","volume":"30","author":"R Weron","year":"2014","journal-title":"International Journal of Forecasting"}],"container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T13:56:16Z","timestamp":1793109376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7674819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":26,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7674819","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}