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.Comment: Submitted to Expert Systems with Application

Load shifting of a supplier-based demand response of multi-class subscribers in smart grid

We propose a demand response (DR) solution approach for a real-time pricing model with multi-class users to determine the electricity supply mix. The model aims to address the problem of power consumption overloading in peak hours using the real-time information obtained from the interaction between suppliers and users in a smart grid. The proposed DR algorithm allocates the overloaded demand assigned to a supplier to other electricity suppliers in order to satisfy all users’ demand while the supplier ensures to maximize the utility or reserved demand of users. Furthermore, a priority approach based on different user groups is developed for allocating the extra demand to other suppliers. Numerical experiments have been conducted to analyze the performance of the algorithm and compare the real-time electricity price with the fixed price

Intelligent flexibility management for prosumers

Emission of greenhouse gases and their effects on climate change have become a matter of serious concern all over the world. In addition, electricity demand is expected to increase in the upcoming years. This growth comprises the construction of new power plants, resulting in additional costs on the price of electricity. Due to what is been exposed before, a new way to manage and generate electricity is needed. Recent researches have provided the tools for modernizing the traditional electricity grid into a smart one, which main objective is to coordinate an ever-growing number of intelligent de- vices, each with their own objectives and value perspectives, into a resilient, secure, and ef- ficient system. Here is were the flexibility concept plays an important role in the upcoming energy transition, understanding flexibility as the ability to change certain previously defined parameters in order to fit new requirements. This Master Thesis focuses on the prosumer flexi- bility concept, quantifying his flexibility potential. This flexibility is used to minimize the total expected costs of each prosumer individually, thus reducing their electricity bills. The method- ology developed consists in a Home Energy Management System (HEMS) that will manage automatically that flexibility in order to benefit the end user by minimizing its electrical bill, where the comfort is also taken into account. The results show that it is possible to reduce the electricity invoice by managing optimally the flexibility from loads, batteries, photovoltaic generation and electric vehicles charging points

Residential Demand Side Management: Strategies for Increasing Electric Utility Profitability

Some electric utility leaders lack effective strategies to reduce demand billing charges. Utility leaders are concerned with lowering demand billing charges, as failure to do so can negatively affect profitability. Guided by the operations management theory, the purpose of this qualitative multiple case study was to explore strategies that distribution electric utility managers use to reduce demand billing chargers from their power providers. The participants were three electric utility leaders working in the Midwest United States who used successful residential demand-side management (DSM) strategies. Data were collected using semistructured interviews and utility documents to address the research question. The collected data were analyzed using Yin’s five-step data analysis, which included compiling, disassembling, reassembling, interpreting data, and concluding the findings. Three key themes emerged: residential demand billing is used to promote peak-shaving, education is used to make demand billing more acceptable to consumers, and incentive structures are tailored to ensure utility company costs are reduced or recouped. A key recommendation is for leaders of distribution electric utility companies to reduce demand billing charges from their power providers while making demand billing more acceptable to consumers and ensuring that utility company costs are reduced or recouped. The implications for positive social change include the potential to implement new strategies for improving DSM practices for benefiting residential applications. With the improvement of DSM, consumers\u27 total electric consumption can be decreased, which can increase disposable income for consumers