近年來，由於環保意識的提升，核能發電廠漸漸的被除役，使得台灣備用容量率也逐年降低，於是台電在民國104年提出了需量競價的需求面管理措施來降低尖峰用電量，而台電也指出低壓用戶用電量佔整個尖峰時段用電量的51%，因此，本研究建置一個住宅型的電能管理系統，目的是未來可以配合用戶群代表(Aggregator)的要求，使單一低壓用電戶匯集成群體用電戶，一起參加台電的需量競價措施，提高一般住宅用戶參與需量競價的可行度，使電能可以更有效率的使用。
本研究以實驗室用電情形來模擬一般住宅用電戶的環境，透過儲能設備與卸載策略之管理的方式，有效的控制實驗室用電量。儲能設備以模擬的方式進行充放電，對負載用電量進行削峰填谷的動作，目的在於提高負載因數。為了在特定時段抑低用電量，本文擷取實驗室即時用電量，並建立成歷史資料庫，在參與需量競價日時，從歷史資料做負載預測，透過負載截峰線進行電池充放電，以及使用動態規劃法進行事前負載規劃，最後配合線上負載的排程，以確保能將用電量抑低至限制值。
本研究藉由人機介面輸入需量競價參數後，實際以實驗室來試驗。儲能設備能有效達到削峰填谷的作用，且儲能設備充放電皆限制條件內，而參加需量競價時，無論限制值是固定型還是變化型，在事前規劃與線上排程都可以在抑低時段內將用電量抑低至限制值以下，達到需量競價的要求。

In recent years, due to the awareness of environmental protection, nuclear power plants gradually being decommissioned make Taiwan's reserve capacity rate be decreasing year by year. To reduce spikes electricity consumption, Taipower proposed demand bidding of the demand side management strategy and Taipower also pointed out that low-voltage users account for 51% of the total peak power consumption. So, this study build a residential energy management system. The purpose is to cooperate the requirements of the Aggregator, so that a single low-voltage households can be pooled into a group of electricity households to participate in Taipower’s demand bidding strategy. Improve the feasibility of general residential users to participate in demand bidding, so that electricity can be used more efficiently.
This study proposed the use of laboratory electricity to simulate the situation of general residential electricity users. Through the energy storage equipment and load shedding strategy, laboratory electricity consumption be controlled effectively. Our charge/discharge processes of the energy storage equipment is performed by simulation. We shift the peak load for the electricity consumption, which is to improve the load factor. In order to limit electricity consumption at a particular time, we capture the laboratory real-time electricity consumption, and established into a historical database. In the participation of the demand bidding day, we forecast the electricity consumption from the historical data, and energy storage equipment charges and discharges by the load peak line, and then use the dynamic programing method for load planning in advance. Finally, we use the online load of the schedule to ensure that the electricity consumption can be reduced to the limit.
This study uses the human machine interface to enter the demand bidding parameters to experiment. Energy storage equipment can effectively achieve to shift the peak load, and energy storage equipment’s charge and discharge doesn’t exceed constraints. When we participate in the demand bidding, on the pre-planning and online scheduling, electricity consumption can be reduced to below the limit value in the period of limit. Regardless of the limit value is fixed or variety type. The experiment result shows that it can satisfy the requirements of the demand bidding.

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