Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed

Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic

Accurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area