Abstract
Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous .csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers.
Highlights
Introduction and Literature ReviewAccording to [1], it is estimated that by 2030, load flexibility will avoid new generation capacity (57%), lower energy costs by shifting the operation of controllable appliances from peak to off-peak hours (29%), allow new transmission and distribution capacity (12%), and provide frequency regulation of ancillary services regulation (2%)
We propose a data model to organize data in order to answer the following two questions: What is the potential size of the load flexibility resources? What are the savings that residential or commercial consumers can obtain? To achieve these goals, the paper is divided into five sections
The volume of data could expand to hundreds of thousands if the individual consumers were considered
Summary
Introduction and Literature ReviewAccording to [1], it is estimated that by 2030, load flexibility will avoid new generation capacity (57%), lower energy costs by shifting the operation of controllable appliances from peak to off-peak hours (29%), allow new transmission and distribution capacity (12%), and provide frequency regulation of ancillary services regulation (2%). Various demand response (DR) programs have been studied [2,3] as well as methods to assess the load flexibility of buildings [4,5,6]. The purpose of this study was to identify the impact and performance of demand response (DR) programs. Another residential consumption area was investigated in the Netherlands from the DR point of view. The responsiveness of residential demand to signal tariffs using home energy management systems that shift the flexible load from evening to midday hours and consume energy from local generation was proposed in [3]
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