以價格基礎為導向考慮風力之機組排程與風險評估

Considered the Wind Power Price-Based Units Commitment and Risk Assessment

徐亞信(Ya-Hsin Hsu)

指導教授 ： 陳昭榮

國立臺北科技大學/電資學院/電機工程系研究所/碩士(2014年)

摘要

在解制後的電力市場，對電力市場的各參與者創造了更多的競爭機會與更公平的交易機制。在電力市場中，電力供應業者將在預定的負載需求，透過競價的運作模式以求取最大的利潤。發電業者執行機組排程的目的是為了獲取本身最大利潤，為一種價格為導向的機組排程，傳統上滿足負載需求再也不是發電業者的主要目的。而電力市場中，發電業者必需承受各項的風險，諸如電價變動風險、事故風險等等，因此在不確定性電力市場中。發電業者在風險管理之下，尋求市場最終交易的排程，已成為重要的研究課題。
而由於世界能源的短缺問題，許多國家意識到再生能源的重要性，而再生能源技術中以風力發電為最成熟、具有商業化發展前景的新能源技術。雖然已有許多文獻能以很低的誤差預測風速，但是還是無法與實際情形完美結合，而風速的誤差正是本文探討的風險所在。本文考慮風力的可能誤差，以基因演算法分別討論傳統機組排程與最大利潤機組排程，最後考慮風險，得到一個兼具風險與最大利潤的結果。透過負面風險整合出的風險管理策略，可獲得最小的負面風險及提升永續經營的能力。

關鍵字

風險評估 ； 基因演算法 ； 機組排程 ； 經濟調度 ； 風力發電

並列摘要

For the power market participators, there are competition and more trade opportunities in the power industry under the deregulation. In the electricity market, the bidding model is adopted instead of the cost model. GenCos try to maximize the profit under bidding model according to the power demand. The purpose of power generation units were scheduled to perform in order to obtain the maximum profit in themself. It is a price-base unit commitment. To meet the load demand is traditionally not the main purpose of power generation industry. In the electricity market, GenCos necessarily exposure to the industry, such as changes in price risk, the risk of accidents. It has become an important research topic including the uncertainty in the electricity market, power generation industry under risk management, scheduling market seeking a final deal.
Because of the shortage for energy in the world, many countries realized the importance of the renewable energy. With the commercialization of promising new energy technologies, the wind energy is the most mature among the technologies in the renewable energy. In many papers, the predictive wind speed can be with low errors. However, it still cannot perfectly compare with the actual situation. The risk is the errors in the wind speed. This paper considers the possibility errors in the wind energy. It is discussed about the traditional unit commitment and the price-base unit commitment with genetic algorithms. With downside risks different cases of different cases, the risk assessment of the wind energy, loads, and the energy balance is considered. The obvious risk assessment results can therefore get an accurate risk management strategies for maximum profits and enhance capacity for sustainable development.

並列關鍵字

Unit commitment ； Risk assessment ； Wind power ； Genetic Algorithm

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