- 學位論文
自備發電機組用戶之最佳契約容量訂定
Optimal Contracted Capacity of Power Consumer with Self-Owned Generating Units
摘要
國際產業競爭下，伴隨著高科技產業迅速發展與製造業生產結構轉型，電力需求可說倍數成長，加上節能減碳意識抬頭，穩定電能供應與其使用效率，對產業運作極為重要。為降低電力事故等造成之龐大損失，且在台電供電無法絕對穩定的情況下，大型電力用戶大多採用自備發電機組，提升電力可靠度與電力品質。在考量電費結構及自備發電機組發電成本下，電費支出及自備發電機組運轉模式與所訂定之契約容量息息相關。有鑑於此，本文研究主旨乃在建立最佳化規劃系統，制定最佳契約容量，配合自備發電機組運轉，達用戶最低總用電成本。 本文建立一最佳化規劃系統，可藉由本文分別所提改良型田口方法與文化差值演算法為基礎，透過演化分析求解最佳契約容量，達到節省總用電成本之目的。改良型田口方法結合傳統田口法與粒子群體最佳化法，透過粒子群體經驗法則，搜尋演化以田口法則建立之正交矩陣，進一步求取最佳解；文化差值演算法係由差值演算法進行突變、交配與選擇運算，配合文化演算法擷取各階段個體之專門知識或問題特性進行分析演化，使得解之搜尋與效率。 為驗證所提方法之可行性，本文將實際運算分析於某光電廠實際用電數據資料，其中包括台電用電度數、自備發電機組容量與燃料及運轉成本函數，以及欲制訂契約容量月份的負載需求預測。實際系統結果分析比較改良型田口方法、文化差值演算法及該廠現行方法之總用電成本節省成效，本文所提方法和其他現有最佳化演算法比較，其運用於自備發電機組契約容量訂定之成本節省約14.56%，亦具有最佳化之效果。
並列摘要
Following rapid development of high-technology industry and manufacturing restructuring for international industrial competition, electric power demand is growing rapidly. On the other side, with the trend of energy conservation and carbon reduction, reliable power supply and its efficiency are quite important. To reduce the impact of a severe power event that may cause huge losses and to avoid the possible interrupted power supply of the utilities, many customers have their self-owned generating units (SOGUs) in attempt to improve the power reliability and quality. With the power tariff structure of the utilities and the cost functions of self-owned generating units considered at the same time, expenses due to the utility power consumed and the operation of self-owned generating units are highly related to the contracted capacity. By taking into account the corresponding operations of self-owned generating units, the dissertation focuses on developing an optimization planning system that may determine the optimal contracted capacity with the utilities to obtain the lowest total power expenditure. The dissertation proposes the improved Taguchi method and the cultural differential computation algorithm (CDCA) for the optimal contract capacity determination through evolution analysis to achieve savings of total electrical power expenses. The improved Taguchi method, combining existing Taguchi method and particle swarm optimization (PSO) algorithm, searches the optimal solution through the quality analysis in orthogonal matrices, which is based on the PSO searching experiences during the evolution process. The CDCA determines the optimal contracted capacity by differential evolution (DE) that uses the arithmetic operators, such as mutation, crossover, and selection and by cultural algorithm (CA) that extracts and saves the domain knowledge or problem properties during the evolution process. To verify feasibility of the proposed methods, the dissertation employs the real data obtained from an optoelectronics factory in Taiwan, the data which include the amounts of power consumption from the utilities, capacities and cost functions of self-owned generating units, and load demand forecasting in the months of planning period. It shows from the simulation results that 14.56% of electrical power expenses can be saved from the proposed improved Taguchi method as well as CDCA method, as compared with the method currently adopted by the factory. Also, in comparison with the other optimization methods, the proposed approaches have superior results as revealed in the numerical results.
參考文獻
延伸閱讀
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