Abstract
The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.
Highlights
Much has been written about the new role consumers can play in future smart grid (SG)
While most of the proposed models address this issue as a task scheduling problem using algorithm to make decisions on shifting, shedding or even disconnecting the load, this paper proposes heuristics algorithm to make decisions on shifting, shedding or even disconnecting the load, this a novel mixed-integer linear programming (MILP) model that uses the price-based demand response (DR) programs to paper proposes a novel mixed-integer linear programming (MILP) model that uses the price-based optimize the power consumption using the potential flexibility that thermostatically-controlled loads (TCL) provides to the demand
In the current context of increasing energy use in the residential environment, where most consumption comes from the smart appliances (SA) use, the employment of DR policies is essential to deal with this type of loads through a Direct Load Control (DLC) paradigm with the goal of reaching higher efficient management of the energy resources
Summary
Much has been written about the new role consumers can play in future smart grid (SG). The main contribution of this work is the development of a research test bench flexible enough to incorporate different tools of different origins such as weather forecasting APIs, DR providers from the utility and mathematical optimization features built on the basis of the LabVIEW systems (2015, National Instruments, Austin, USA) design platform and development environment for a visual programming language It can benefit from user-friendly and intuitive software as well as hardware such as powerful real-time processors, user-programmable field-programmable gate array (FPGA), and full I/O interfaces. The combination of the SG paradigm with IoT technologies and the will of consumers to actively participate in their energy control has enhanced the HEMS concept These are systems capable of monitoring home consumption at different levels and implementing automation or control mechanisms. As is evident from the most recent publications, the use of Internet technologies as a solution to optimization problems is becoming more and more common [19], as they tackle issues such as the diversity of household appliances, the simultaneous pursuit of several objectives in parallel, and the uncertainty in predicting conditions such as occupancy levels, energy consumption or weather conditions [20]
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