Study on Flexible Loads Control Method for rural Community Heating in Severe Cold Region

In order to solve the problem of centralized demand response in severe cold region for flexible heating loads during low-price periods, an automated demand response (ADR) control method based on elastic load limit and nonlinear price response aggregator is used to achieve hybrid control of flexible loads. By penalizing the load flexibility with technology solution enablers, the peak demand and its period is curtailed, and the smoothness of the load demand curve is improved, ensuring reliability of supply without restructuring and development of infrastructure. Firstly, the nonlinear price parameter α is used as the coefficient of the higher-order term of the load demand curve, thereby limiting the flexibility of the curve by adjusting α and the order n. Then, based on the PSO (Particle Swarm Optimization) algorithm, the parameters α and n are optimized. Then, an elastic load limit interval is set in the technology solution enablers to balance the grid and incentivize user participation. Based on the technique for order preference by similarity to ideal solution (TOPSIS), a statistical analysis of user electricity consumption behavior is conducted to quantify user preferences. An electric heating load response experiment with 1032 households was conducted in a northeastern community of China, verifying the effectiveness of the proposed method. The experimental results show that the flexible load control method proposed in this paper can effectively suppress the peak load demand during the low-price period. Moreover, compared with the traditional method of using hard and linear parameters for load control, the proposed method can improve the smoothness of the load demand curve by 39.6% and For Class IV users, control error E<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> and load peak times f<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> can be reduced from 39.2% and 200 times respectively to 14.4% and 20 times. In addition, the experimental results also prove that the elastic load limit interval will be widened with the increase of load demand, and will be sharply narrowed during the price demarcation period to ensure the load response speed. The higher the sensitivity of users to TOU, the more obvious the accuracy improvement and demand peak suppression brought by the new hybrid method proposed in this paper.