需量反應中顧客排程用電最佳化之研究

The Research of Optimal Customer Electrical Consumption Scheduling in Demand Response

藍明仁(Ming-Jen Lan)

指導教授 ： 陳昭榮

國立臺北科技大學/電資學院/電機工程系所/碩士(2013年)

https://doi.org/10.6841/NTUT.2013.00613

摘要

電力公司為了減緩興建電廠的迫切性及減輕供電壓力，可透過推行需量反應來達成目標。需量反應是顧客為反應變動電價，從正常的用電型態中改變用電量，並按照電價做適當的排程用電，讓能源使用的效率提高，使供需雙方都能達到最大的利益。
本論文提出運用基因演算法或內點法於顧客24小時排程用電問題。基因演算法主要是以多點方式搜尋，可避免如傳統演算法陷入局部最佳解。電力系統中應用內點法求解問題，已被多位專家學者証實可有效率的搜尋出答案。配電公司公佈電費加權曲線讓顧客決定彈性用電最佳使用時段，達到電費最小化的目標。研究中並使用電池組於排程用電，電價低時充電，電價高時放電。使用電池組的優點除了可作為備用電源，避免因為事故停電造成損失，更可帶來電費降低的附加效果。另外，實際考慮電池組操作耗損地成本，使其符合真實使用情形。
本研究模擬顧客排程用電時，在是否有利用需量反應的策略下，排程及電費的差異。結果顯示，顧客排程用電時透過執行需量反應策略並加入電池組設備較沒有使用需量反應策略省了近130美元(1.4%)，證明確實可達到降低電費的目標。

關鍵字

需量反應 ； 基因演算法 ； 內點法 ； 電池組

並列摘要

In order to retard the construction of power plants and urgency of supply pressure, power companies can achieve their goals through the implementation of demand response. Demand response is customers from their normal consumption pattern in response to change in the price of electricity, and make the appropriate electricity of scheduling in response to electricity price, improving the efficiency of energy usage, both supply and demand can reap maximum benefits.
This paper presents use genetic algorithm or interior point method for customer 24 hour electricity of scheduling problem. Genetic algorithm is mainly search with multi-mode method, avoiding falling into local optimal solution like traditional algorithm. Apply interior point method to solve problem in power system have been proved can search answer efficiency by most researcher. Utility companies proclaim electricity bill weighting curve let customer decide optimal elasticity of electricity scheduling for each time period, achieving minimize the goals of electric bill. This research also use battery pack to schedule consumption of electricity, discharging during low price, charging during high price, the advantages of use battery pack can as a backup power, avoiding cause loss since power outage, also bring the additional effect of reducing electric bill. Besides, think about battery pack maintenance cost in practical, to match actual usage.
This study simulate customers whether use demand response strategy to schedule consumption of electricity, scheduling and electric bill difference. The results show that customers according to demand response to schedule consumption of electricity and add battery pack is cheaper US$130(1.4%) than without use demand response strategy, to prove can achieve the goals of reducing electric bill.

並列關鍵字

Demand Response ； Genetic Algorithm ； Interior Point Method ； Battery Pack

參考文獻

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