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

Comparative Study on Impacts of Power Curve Model on Capacity Factor Estimation of Pitch-Regulated Turbines

The amount of energy produced by a turbine depends on the characteristics of both wind speed at the site under investigation and the turbine's power performance curve. The capacity factor (CF) of a wind turbine is commonly used to estimate the turbine's average energy production. This paper investigates the effect of the accuracy of the power curve model on CF estimation. The study considers three CF models. The first CF model is based on a power curve model that underestimates the turbine output throughout the ascending segment of the power curve. To compensate for the aforementioned discrepancy, the Weibull parameters, c and k, which are used to describe wind profile, are calculated based on cubic mean wind speed (CMWS). The second CF model is based on the most accurate generic power curve model available in open literature. The third CF model is based on a new model of power performance curve which mimics the behavior of a typical pitch-regulated turbine curve. As the coefficients of this power curve model are based on a general estimation of the turbine output at different wind speeds, they can be further tuned to provide a more accurate fit with turbine data from a certain manufacturer

Innovative User Experience Design and Customer Engagement Approaches for Residential Demand Response Programs

The increasing share of intermittent sources is making it more difficult to guarantee a real-time balance between demand and supply on the electricity grid. To decrease the dependency from fossil fuel generation, a change in paradigm is required: from supply following demand whenever it occurs to demand following generation when it is available. Demand response (DR) programs enclose all practices that allow demand to take part in actively managing the grid. According to this perspective, the residential sector hides a huge still unexploited flexibility resource. Therefore, utilities and aggregators need to address weak customer engagement and a lack of regulation in order to employ innovative business models for harnessing residential DR programs potential. Within this paper, some of these challenges are investigated, with the view to improve the design of an appropriate engagement strategy and an incentive scheme to involve residential customers. The innovation consists in the development of a questionnaire as a tool to understand customers’ behavior and preferences, so as to consequently design customized solutions. Finally, a first-order approximation techno-economic analysis is conducted to contextualize the actual incentives for the single customer

Data-driven modeling for energy consumption estimation

Energy consumption estimation for building energy management systems (BEMS) is one of the key factors in the success of energy saving measures in modern building operation, either residential buildings or commercial buildings. It provides a foundation for building owners to optimize not only the energy usage but also the operation to respond to the demand signals from smart grid. However, modeling energy consumption in traditional physical modeling techniques remains a challenge. To address this issue, we present a data mining-based methodology, as an alternative, for developing data-driven models to estimate energy consumption for BEMSs. Following the methodology, we developed data-driven models for estimating energy consumption for a chiller and a supply fan in an air handling unit (AHU) by using historic building operation data and weather forecast information. The models were evaluated with unseen data. The experimental results demonstrated that the data-driven models can estimate energy consumption for BEMS with promising accuracy