Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.
The pattern of electric demand needs to be analyzed to obtain a simple and precise Very Short-Term Load Forecast (VSTLF) for an office building because the electric demand of a small power system such as a building is difficult to express as a function. In order to develop an improved VSTLF, data from LG Electronics was analyzed. The proposed method is compared to the conventional method using a correlation between electric demand and temperature. The test results show that the proposed method based on a pattern ratio is better than the conventional method based on linear regression. MAPE of the proposed method is 9.0973%, while MAPE of the conventional method is 9.4533%.
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