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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
Incentive based Residential Demand Aggregation
From the beginning of the twenty-first century, the electrical power industry has moved from traditional power systems toward smart grids. However, with the increasing amount of renewable energy resources integrated into the grid, there is a significant challenge in power system operation due to the intermittency and variability of the renewables. Therefore, the utilization of flexible and controllable demand-side resources to maintain power system efficiency and stability has become a fundamental goal of smart grid initiatives.
Meanwhile, due to the development of communication and sensing technologies, intelligent demand-side management with automatic controls enables residential loads to participate in demand response programs. Therefore, the aggregate control of residential appliances is anticipated to be feasible technique in the near future, which will bring considerable benefits to both residential consumers and load-serving entities. Hence, this dissertation proposes a comprehensive optimal framework for incentive based residential demand aggregation. The contents of this dissertation include: 1) a hardware design of smart home energy management system, 2) a new model to assess the responsive residential demand to financial incentives, and 3) an online algorithm for scheduling residential appliances.
The proposed framework is expected to generate optimal control strategies over residential appliances enrolled in incentive based DR programs in real time. To residential consumers, this framework will 1) provide easy-to-use smart energy management solution, 2) distribute financial rewards by their quantified contribution in DR events, and 3) maintain residents’ comfort-level expectations based on their energy usage preferences. To LSEs, this framework can 1) aggregate residential demand to enhance system reliability, stability and efficiency, and 2) minimize the total reward costs for executing incentive based DR programs. Since this framework benefits both load serving entities and residents, it can stimulate the potential capability of residential appliances enrolled in incentive based DR programs. Eventually, with the growing number of DR participants, this framework has the potential to be one of the most vital parts in providing effective demand-side ancillary services for the entire power system
Achieving an optimal trade-off between revenue and energy peak within a smart grid environment
We consider an energy provider whose goal is to simultaneously set
revenue-maximizing prices and meet a peak load constraint. In our bilevel
setting, the provider acts as a leader (upper level) that takes into account a
smart grid (lower level) that minimizes the sum of users' disutilities. The
latter bases its decisions on the hourly prices set by the leader, as well as
the schedule preferences set by the users for each task. Considering both the
monopolistic and competitive situations, we illustrate numerically the validity
of the approach, which achieves an 'optimal' trade-off between three
objectives: revenue, user cost, and peak demand
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