Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks
With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefore, a new demand response method considering multiple flexible loads is proposed in this paper to character the integrated demand response (IDR) resources. Firstly, a physical process analytical deduction (PPAD) model is proposed to improve the classification of flexible loads in industrial parks. Scenario generation, data point augmentation, and smooth curves under various operating conditions are considered to enhance the applicability of the model. Secondly, in view of the strong volatility and poor modeling effect of Wasserstein-generative adversarial networks (WGAN), an improved WGAN-gradient penalty (IWGAN-GP) model is developed to get a faster convergence speed than traditional WGAN and generate a higher quality samples. Finally, the PPAD and IWGAN-GP models are jointly implemented to reveal the degree of correlation between flexible loads. Meanwhile, an intelligent offline database is built to deal with the impact of nonlinear factors in different response scenarios. Numerical examples have been performed with the results proving that the proposed method is significantly better than the existing technologies in reducing load modeling deviation and improving the responsiveness of park loads.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism
In the operation of the integrated energy system (IES), considering further reducing carbon emissions, improving its energy utilization rate, and optimizing and improving the overall operation of IES, an optimal dispatch…
SchedulingOptimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load
In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation opti…
SchedulingDeep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
As an efficient way to integrate multiple distributed energy resources and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Data-Driven Domestic Flexible Demand: Observations from experiments in cold climate
In this chapter, we report on our experience with domestic flexible electric energy demand based on a regular commercial (HVAC)-based heating system in a house. Our focus is on investigating the predictability of the ene…
Targeted demand response for flexible energy communities using clustering techniques
The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed e…
ClusteringDynamic Time Warping