Abstract
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.
Highlights
The power consumption in the distribution network is predicted to significantly increase owing to the electrification of transport and heating [1]
We propose a bi-level optimal pricing model for the electric utility companies (EUCs) to reduce the peak load of two types of load curves using the experimental data-driven model of an HVAC system in commercial buildings
This paper proposed an optimal retail pricing scheme to support the EUC and building operators in determining the day-ahead optimal retail price and the corresponding input power of price-elastic
Summary
The power consumption in the distribution network is predicted to significantly increase owing to the electrification of transport and heating [1]. We propose a bi-level optimal pricing model for the EUC to reduce the peak load of two types of load curves using the experimental data-driven model of an HVAC system in commercial buildings. While in the lower level, building end-users schedule the optimal power inputs of HVAC systems to minimize the electricity bills according to the optimal retail rates This allows the EUC to obtain more accurate and useful insights into the load shifting or curtailment capacities of HVAC systems, when the proposed pricing strategy is applied to DR programs in practice. This enables building managers to ensure the thermal comfort of occupants and, participate more fully in DR programs due to improved comprehension of the inherent thermal energy storage capacity in their building structures. Pricing Strategy Using the Thermal Response of HVAC Systems in Commercial Buildings
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