Multi-market joint optimization bidding model considering risk preference under limit price constraint
限界価格制約下でのリスク選好を考慮した複数市場共同最適化入札モデル (AI 翻訳)
Xin Zhao
🤖 gxceed AI 要約
日本語
本論文は、電力市場における複数市場（前日・リアルタイム、予備力・周波数調整）での共同入札モデルを提案する。リスク選好を考慮し、価格制約下で収益の安定性を維持する。17,520時間の実データを用いた実験では、従来手法と比較して運用コスト11.8%削減、入札利益9.6%向上、CVaRベースの下方リスク13.4%低減を達成した。計算時間は1ラウンド平均0.41秒で実運用に適用可能。
English
This paper proposes a joint bidding model for multiple electricity markets (day-ahead, real-time, reserve, frequency regulation) under price limits, incorporating risk preferences. Using 17,520 hours of data from 4 trading zones, the method reduces expected operating cost by 11.8%, increases average bidding profit by 9.6%, and reduces downside risk (CVaR) by 13.4%. The model improves bidding feasibility from 91.2% to 97.8% with an average computation time of 0.41 seconds per round, supporting computer-driven bidding systems.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では電力市場の自由化と再エネ導入拡大に伴い、入札戦略の高度化が求められている。本モデルはリスク選好を考慮した入札最適化を提供し、日本の電力市場運営にも応用可能な知見を示す。
In the global GX context
This paper addresses multi-market bidding optimization, crucial for integrating variable renewable energy globally. The joint optimization with risk preference under price constraints is relevant for market operators in liberalized electricity markets.
👥 読者別の含意
🔬研究者:This paper presents a novel optimization model for multi-market bidding that integrates risk preference and price constraints, offering improvements in profit and feasibility for energy market design researchers.
🏢実務担当者:Traders and bidding system developers can apply this model to improve bidding performance and reduce financial risk in electricity markets with multiple sub-markets.
🏛政策担当者:Policymakers designing electricity market rules may consider incorporating risk preferences and price limits to enhance market efficiency and revenue stability.
📄 Abstract（原文）
In order to support intelligent bidding in multi-market, this paper proposes a joint bidding model of risk preference modeling under price limit constraint. The electric energy market is divided into day-ahead and real-time sub-markets, and the auxiliary services are divided into standby and frequency regulation sub-markets. The framework integrates temporal state encoding, preference utility estimation, feasible region modification and iterative policy search to generate quotes based on historical prices, renewable forecasts, load trajectories and settlement signals. The experimental results on 17,520 hours of samples collected from 4 trading zones show that compared with the three benchmark strategies, the expected operating cost of the proposed method is reduced by 11.8%, the average bidding profit is increased by 9.6%, the downside risk based on CVaR is reduced by 13.4%, the average absolute bidding deviation is 2.7%, and the average computation time per round is 0.41 s. The results show that the proposed model maintains the stability of revenue under the limit price constraint, and improves the bidding feasibility from 91.2% to 97.8%, which can provide decision support for the computer-driven bidding system in the actual operation scenario of the electricity market.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.65102/is20261040first seen 2026-07-24 06:44:04
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