Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model

Smart grids have introduced several key concepts, including demand response, prosumers—active consumers capable of producing, consuming, and storing both electrical and thermal energies—retail market, and local energy markets. Preserving data privacy in this emerging environment has raised concerns and challenges. The use of novel methods such as online learning is recommended to address these challenges through prediction of the behavior of market stakeholders. In particular, the challenge of predicting prosumers’ behavior in an interaction with retailers requires creating a dynamic environment for retailers to set their optimal pricing. An innovative model of retailer–prosumer interactions in a day-ahead market is presented in this paper. By forecasting the behavior of prosumers by using an online learning method, the retailer implements an optimal pricing scheme to maximize profits. Prosumers, however, seek to reduce energy costs to the greatest extent possible. It is possible for prosumers to participate in a price-based demand response program voluntarily and without the retailer’s interference, ensuring their privacy. A heuristic distributed approach is applied to solve the proposed problem in a fully distributed framework with minimum information exchange between retailers and prosumers. The case studies demonstrate that the proposed model effectively fulfills its objectives for both retailer and prosumer sides by adopting the distributed approach

Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model

Smart grids have introduced several key concepts, including demand response, prosumers—active consumers capable of producing, consuming, and storing both electrical and thermal energies—retail market, and local energy markets. Preserving data privacy in this emerging environment has raised concerns and challenges. The use of novel methods such as online learning is recommended to address these challenges through prediction of the behavior of market stakeholders. In particular, the challenge of predicting prosumers’ behavior in an interaction with retailers requires creating a dynamic environment for retailers to set their optimal pricing. An innovative model of retailer–prosumer interactions in a day-ahead market is presented in this paper. By forecasting the behavior of prosumers by using an online learning method, the retailer implements an optimal pricing scheme to maximize profits. Prosumers, however, seek to reduce energy costs to the greatest extent possible. It is possible for prosumers to participate in a price-based demand response program voluntarily and without the retailer’s interference, ensuring their privacy. A heuristic distributed approach is applied to solve the proposed problem in a fully distributed framework with minimum information exchange between retailers and prosumers. The case studies demonstrate that the proposed model effectively fulfills its objectives for both retailer and prosumer sides by adopting the distributed approach.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacultyGraduat

Application of finite-time control Lyapunov function in low-power PMSG wind energy conversion systems for sensorless MPPT

This paper discusses the problem of observer-based finite-time control (FTC) of a grid-connected wind turbine and a low-power permanent magnet synchronous generator (PMSG). An adaptive nonlinear observer is employed to estimate the mechanical variables. Maximum power point tracking (MPPT) is obtained using the estimation of rotor speed and torque from the adaptive observer, and excluding the wind speed sensor. Improvement of the MPPT technique through the designed FTC is investigated. The proposed controller stabilizes the WECS and tracks the reference trajectories in a short pre-known time alternative to common nonlinear controllers with large settling time. The suggested controller is also robust against uncertainties in WECS parameters. Parameters’ variations are compensated by robust control design. Finite time stability and robustness of the proposed WECS controller is mathematically proved. Moreover, the advanced performance of the suggested FTC is demonstrated by simulation and is compared to a conventional asymptotic convergent controller (ACC). The proposed FTC provides fast and robust rotor speed regulation and thus enhances the sensorless MPPT. The proposed FTC improves the WECS performance for tracking of ramp references and robustness against parameter uncertainties. Furthermore, advanced control of the grid-side converter yields improved resiliency and reliability

Influence of Socio-Cultural Attributes on Stigmatizing Public Transport in Saudi Arabia

Several factors over the years have contributed to stigma in public transport. Many studies have highlighted the need to make the transport system more equitable both from economic and gender perspectives. This study attempts to demonstrate how the perceptions of public transport users and non-users are stigmatized from social and cultural standpoints. Thus, it identifies the social and cultural stigma-induced barriers embedded with the use and people’s general perception about the public bus service, taking SAPTCO (Saudi Public Transport Company) as a case study. The study results suggest that privacy concern is the primary cause of stigma. Most of the users are unwilling to ride with their families as SAPTCO does not account for gender needs (e.g., privacy, travel convenience, safety, comfort, etc.). Moreover, people from the high-income classes are more stigmatized against this ridership. A fuzzy inference system (FIS) model is used to analyze the survey questionnaire responses and understand what stigma means for the public bus service. Expert opinions are employed to generate “if–then” rules of the FIS models. Sensitivity of the defined fuzzy model is conducted to different aspects of the ridership. The study results further suggest that “inconvenience” poses the highest impact while “feeling safe”, “privacy”, “fare”, “timing”, and “comfort” are found to be the medium impact-making variables for stigma. The stigma-defining variables would be critical for the public bus service to improve its service quality and help (re-)design the policies that would attract a high amount of ridership. Some solutions are suggested in the end that would complement, strengthen, and promote the current SAPTCO service. The demonstrated methodology of this study would be relevant and adaptive to any relevant context to improve public transportation service and pertaining policies.Applied Science, Faculty ofNon UBCEngineering, School of (Okanagan)ReviewedFacultyResearche