在擁有可再生能源與理想電池的獨立型電力系統中之可緩供負載調度

Deferrable Load Scheduling in a Stand-alone Power System with Renewal Energy Sources and a Perfect Battery

徐于婷

指導教授 ： 張正尚

國立清華大學/電機資訊學院/通訊工程研究所/碩士(2014年)

https://doi.org/10.6843/NTHU.2014.00516

摘要

相對於傳統能源，可再生能源對環境的傷害較低，因此近來如何有效地使用可再生能源漸漸地受到重視。本研究中，考慮了擁有可再生能源和理想電池的獨立型電力系統，假設負載是可延期的以達到供需平衡。在這個系統中，調度策略(scheduling policies)的主要目的是有效地使用可再生能源，使得負載能夠盡可能快地被供應，並且使能量被浪費的量減到最小。對於這個目的，考慮三種調度策略：(i)無向前看策略(no lookahead policies)、(ii)一時期向前看策略(1-period lookahead policies)和(iii)一時期預測策略(1-period prediction policies)。研究發現：(i)一時期向前看策略總是比無向前看策略好，且這點可透過樣本路徑法(sample path argument)正式地被證明、(ii)增加電池容量對一時期向前看策略的表現影響不大，且這點也可透過樣本路徑法正式地被證明、(iii)增加電池容量對無向前看策略的表現有明顯有效的改善以及(iv)不需要增加電池容量，預測可以有效地改善系統的表現。

關鍵字

可再生能源 ； 獨立型電力系統 ； 調度策略

並列摘要

As renewable energy resources are more environmentally friendly than the conventional energy sources, the problem of effectively utilizing renewable energy sources has received a lot of attention lately. In this thesis, we consider a stand-alone power system with renewable energy sources and a perfect battery. We assume that the loads to the system are deferrable so that they can be scheduled to balance the supply and the demand. The main objective of a scheduling policy in such a system is to effectively utilize renewable energy sources so that the loads can be served as quickly as possible and the amount of wasted energy can be minimized. For such a purpose, we consider three classes of scheduling policies: (i) no lookahead policies, (ii) 1-period lookahead policies, and (iii) 1-period prediction policies. Our findings are (i) a maximal 1-period lookahead policy is always better than a maximal no lookahead policy, and this can be formally proved by using a sample path argument, (ii) increasing the battery capacity has little effect on the performance of a maximal 1-period lookahead policy, and this can also be proved by using a sample path argument, (iii) increasing the battery capacity is very effective for improving the performance of a maximal no lookahead policy, and (iv) prediction can be very effective in improving the system performance without the need of increasing battery capacity.

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