Diversity evaluation for power system operation based on weather forecast information considering uncertainty
DOI:
https://doi.org/10.51094/jxiv.606Keywords:
Optimal power flow, Power system operation, Uncertainty, Renewable energy, Prediction, Weather forecastAbstract
In recent years, a large scale of renewable energy (RE) such as photovoltaic (PV) power generation has been introduced to the power grid. As the output of RE fluctuates due to weather conditions, various difficulties occur in the operation of the power grid. One of them is grid congestion, in which transmission lines and transformers become overloaded. Thus the operation must be planned to avoid grid congestion based on the forecasted values of the RE output. However, the prediction of the RE output is usually accompanied by errors. Therefore, it is also important to operate the grid taking into account the uncertainty of the forecasted values. In addition, PV output has a strong correlation with solar radiation and it fluctuates depending on the amount of clouds in the atmosphere. Therefore, it is necessary to utilize meteorological forecast information. Recently, the Japan Meteorological Agency (JMA) has started to distribute the Meso-scale Ensemble Prediction System (MEPS) as weather forecast information that takes uncertainty into account. In this paper, we utilize the MEPS information to evaluate the diversity of grid operation by using optimal power flow considering the uncertainty.
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References
(1) M.B. Cain, R.P. O'neill and A. Castillo : “History of optimal power flow and formulations”, Federal Energy Regulatory Commission, Vol. 1, pp.1-36 (2012)
(2) H. Abdi, S. D. Beigvand, and M. La Scala : “A review of optimal power flow studies applied to smart grids and microgrids”, Renewable and Sustainable Energy Reviews, Vol. 71, pp.742-766 (2017)
(3) 経済産業省資源エネルギー庁:「第6次エネルギー基本計画」 (2021)
(4) 電力広域的運営推進機関 需給調整市場検討小委員会:「系統混雑を考慮した調整力確保の考え方について<混雑発生時の需給調整市場における課題と対応>」(2022)
(5) F. Capitanescu, J.M. Ramos, P. Panciatici, D. Kirschen, A.M. Marcolini, L. Platbrood and L. Wehenkel : “State-of-the-art, challenges, and future trends in security constrained optimal power flow”, Electric power systems research, Vol. 81, No.8, pp.1731-1741 (2011)
(6) F. Capitanescu, S. Fliscounakis, P. Panciatici, and L. Wehenkel : “Cautious operation planning under uncertainties.”, IEEE Transactions on Power Systems, Vol. 27, No.4, pp.1859-1869 (2012)
(7) F. Capitanescu : “Critical review of recent advances and further developments needed in AC optimal power flow”, Electric Power Systems Research, Vol. 136, pp.57-68 (2016)
(8) 益田泰輔・杉原英治・山口順之・宇野史睦・大竹秀明:「大規模連系系統における太陽光発電の出力抑制を考慮した最適潮流計算による経済負荷配分制御」,電気学会論文誌 B,Vol.139,No. 2,pp.74-83 (2019)
(9) 気象庁情報基盤部:「令和4年度数値予報解説資料集」(2023)
(10) 気象予報士試験受験支援会:「気象予報士かんたん合格テキスト 学科専門知識編」(2014)
(11) 気象庁予報部数値予報課:「数値予報60年誌 ~数値予報課60年(1959 – 2019)の歩み~ 」(2020)
(12) 気象庁情報基盤部:「配信資料に関する技術情報 第575号 ~メソ数値予報モデルGPVおよびMSMガイダンスの予報時間延長について~ 」(2021)
(13) 気 象 庁 予 報 部:「メソスケール気象予測の現状と展望」,数値予報課報告・別冊第66号 (2020)
(14) K. Ono, M. Kunii and Y. Honda : “The regional model‐based mesoscale ensemble prediction system, MEPS, at the Japan meteorological agency.", Quarterly Journal of the Royal Meteorological Society, Vol. 147, No.734, pp.465-484 (2021)
(15) 気象庁情報基盤部:「配信資料に関する技術情報 第505号 ~メソアンサンブル数値予報モデルGPVの提供開始について~」(2019)
(16) 大関崇・大竹秀明・高松尚宏・中島虹:「メソアンサンブル予報システム(MEPS)の3時間データの1時間値への補間による予測誤差評価」,日本太陽エネルギー学会講演論文集 2022 年度 (令和 4 年度) 研究発表会,Vol.,No. ,pp. 305-306 (2022)
(17) 電力系統標準モデルの普及・拡充調査専門委員会:「モデル拡充に関する報告書(マニュアル)」(2021)
(18) 東京電力パワーグリッド株式会社,:「275kV以上系統空容量マッピングの記載方法ならびに留意事項について」(2023),https://www.tepco.co.jp/pg/consignment/system/pdf_new/akiyouryou_kikan.pdf(2024/1/7アクセス)
(19) 東北電力ネットワーク株式会社:「電力系統図(1次系)」(2023),https://nw.tohoku-epco.co.jp/consignment/system/announcement/pdf/5001.pdf(2024/1/7アクセス)
(20) 経済産業省 資源エネルギー庁:「再生可能エネルギー電気の利用の促進に関する特別措置法 情報公表用ウェブサイト」,https://www.fit-portal.go.jp/PublicInfoSummary(2024/1/7アクセス)
(21) L. Thurner, A. Scheidler, F. Schäfer, J.H. Menke, J. Dollichon, F. Meier, S. Meinecke, M. Braun : “pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems”, IEEE Transactions on Power Systems, Vol. 33, No.6, pp.6510–6521 (2018)
(22) Met Office : “Cartopy: a cartographic python library with a Matplotlib interface", https://scitools.org.uk/cartopy (2010 - 2015) (2024/1/7アクセス)
(23) 日本気象株式会社,https://n-kishou.com/corp/(2024/1/7アクセス)
(24) 太陽光発電協会:「表示ガイドライン(2023年度)」 (2023)
(25) 東京電力パワーグリッド株式会社:「過去の電力使用実績データ」,https://www.tepco.co.jp/forecast/html/download-j.html(2024/1/7アクセス)
(26) 東北電力ネットワーク株式会社:「過去実績データのダウンロード」,https://setsuden.nw.tohoku-epco.co.jp/download.html(2024/1/7アクセス)
(27) 中田和秀:「主双対内点法」,オペレーションズ・リサーチ: 経営の科学,Vol.64,No. 4,pp.218-224 (2019)
(28) “Ipopt Documentation”, https://coin-or.github.io/Ipopt/(2024/1/7アクセス)
(29) “Welcome to the Matplotlib Basemap Toolkit documentation”, https://matplotlib.org/basemap/(2024/1/7アクセス)
(30) H. Ohtake, F. Uno, T. Oozeki, Y. Yamada, H. Takenaka, and T.Y. Nakajima : “Outlier events of solar forecasts for regional power grid in Japan using JMA mesoscale model.", Energies, Vol. 11, No.10, pp.2714 (2018)
(31) 気象庁:「過去の気象データ・ダウンロード」,
https://www.data.jma.go.jp/gmd/risk/obsdl/index.php(2024/1/7アクセス)
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Submitted: 2024-01-25 01:17:28 UTC
Published: 2024-01-26 08:12:54 UTC
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Tomohide Yamazaki
Ichiro Toyoshima
Naoya Inuzuka
Daiki Kato
Yusuke Mori
Shinji Wakao
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