Abstract
The problem of wind power curtailment (WPC) during winter heating periods in China’s “Three-North regions” is becoming worse. Wind power heating, though being an effective way to increase wind power consumptions, is constrained by high electric heating costs under a peak-to-valley electricity price pattern. This study develops a real-time price (RTP) decision model which adjusts the time-varying RTPs within an acceptable range of heating users based on the WPC distribution over a particular dispatch day. The lower RTPs accompanying the higher WPC can guide the electric heating user side equipped with regenerative electric boilers (REBs) to actively increase REB imports to absorb additional wind generation. Then, the demand side response using REBs under the RTP scheme is optimized to minimize the total heating cost met by electric heating users while assisting in the large-scale wind generation accommodation. The total heating costs and WPC reductions under different heating scenarios are compared and discussed alongside the effectiveness of the RTP-based demand side management in terms of reducing the WPC and heating costs and increasing the feasibility of wind power heating during winter heating periods.
Highlights
By the end of 2019, the cumulative installed capacity of wind power in China reached 210 GW, and the annual national wind generation was about 405.7 TWh (National Energy Administration, 2020)
This article is structured as follows: Theoretical Basis of real-time price (RTP) Decision Model for demand side management (DSM) section introduces the theoretical basis of the RTP decision model for DSM in the wind power heating mode; RTP Decision Model Based on wind power curtailment (WPC) Distribution section describes the RTP adjustment based on WPC distributions; demand side response (DSR) Model Under RTP Scheme for Wind Power Heating section develops a DSR model using regenerative electric boilers (REBs) under the RTP scheme; Results and Model Validation section evaluates the performance of the proposed models in reducing the WPC and heating costs; and Conclusion and Future Work section presents conclusions and recommendations for further work
Scenario 1: Conventional heating with a constant conventional heating price; Scenario 2: Wind power heating under peak-to-valley electricity price scheme with REB importing at off-peak time; Scenario 3: Wind power heating under peak-to-valley electricity price scheme with REB tracking WPC; Scenario 4: Wind power heating under the RTP scheme with REB tracking WPC
Summary
By the end of 2019, the cumulative installed capacity of wind power in China reached 210 GW, and the annual national wind generation was about 405.7 TWh (National Energy Administration, 2020). With the deployment of electric boiler and thermal energy storage, a coordinated wind power accommodating dispatch model was developed in (Cui et al, 2016) to improve the operational flexibility of CHP units. Encouraging users to actively switch to the wind power heating mode is expected to be a practical and direct way to increase wind power consumptions during winter heating periods. This can be realized by reducing real-time electricity prices (RTPs) in response to WPC signals, which guides the users to actively increase the electric heating demands to consume the additional wind generation. This article is structured as follows: Theoretical Basis of RTP Decision Model for DSM section introduces the theoretical basis of the RTP decision model for DSM in the wind power heating mode; RTP Decision Model Based on WPC Distribution section describes the RTP adjustment based on WPC distributions; DSR Model Under RTP Scheme for Wind Power Heating section develops a DSR model using REBs under the RTP scheme;
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