مقایسه سیستم های کنترل برای بهینه سازی ذخیره سازی یخ در زمان واقعی محیط قیمت گذاری برق پویا
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|46876||2015||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Energy, Volume 149, 1 July 2015, Pages 392–403
The objective of this study was to assess the benefits of introducing a demand side management optimisation controller to a cold thermal storage ice bank. This controller consisted of an ice bank model, an air temperature forecast model and an optimisation algorithm. The financial and grid utilisation benefits produced by implementation of this controller over the current state of the art in ice bank load shifting control was tested in a day ahead real time electricity pricing forecast environment. This hypothetical real time electricity price was based on the cost of electricity in the Irish wholesale market. Multiple ice bank charge levels were simulated in order to quantify the performance of two control methods for varying operating conditions. First, the “standard controller” was based on the current modus operandi for ice bank systems where ice was generated for food cooling at night when the off-peak electricity tariff is available (00:00–08:00 h). Second, the “upgraded controller” was developed as a bespoke Demand Side Management control system for food refrigeration in a future electricity pricing environment. It consisted of a dual function load shifting optimisation algorithm, an ice bank model, and a predictive air temperature model. A preliminary study was also carried out to test the robustness of the controller’s performance in an uncertain real time electricity pricing forecast scenario. Both economic and grid management benefits were found by simulating the operation of the cold thermal storage load shifting controller in a forecasted day ahead real time electricity pricing environment. The energy savings achieved for 100% ice bank charge were low, but as the desired charge level was reduced to 75%, 50% and 25% the savings potential increased. The introduction of uncertain real time electricity pricing forecasts nullified any cost savings made by the load-shifting controller in comparison to the current state of the art.