Topic
Load control switch
About: Load control switch is a research topic. Over the lifetime, 1616 publications have been published within this topic receiving 6750 citations.
Papers published on a yearly basis
1 / 2
Papers
TL;DR: A detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies.
Abstract: In this paper, a detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.
422 citations
TL;DR: In this article, the authors present the development of physical-based residential load models at the appliance level, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle.
Abstract: In order to support the growing interest in demand response (DR) modeling and analysis, there is a need for physical-based residential load models. The objective of this paper is to present the development of such load models at the appliance level. These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Validation of the appliance-level load models is carried out by comparing the models' output with the real electricity consumption data for the associated appliances. The appliance-level load models are aggregated to generate load profiles for a distribution circuit, which are validated against the load profiles of an actual distribution circuit. The DR-sensitive load models can be used to study changes in electricity consumption both at the household and the distribution circuit levels, given a set of customer behaviors and/or signals from a utility.
309 citations
Patent•
18 Aug 2011
TL;DR: In this article, a load control device is configured to connect or disconnect electrical power to the an attached air conditioner or heater, and the microprocessor is configurable to communicate over a network.
Abstract: Thermostatic HVAC and other energy management controls that are connected to a computer network. For instance, remotely managed load switches incorporating thermostatic controllers inform an energy management system, to provide enhanced efficiency, and to verify demand response with plug-in air conditioners and heaters. At least one load control device at a first location comprises a temperature sensor and a microprocessor. The load control device is configured to connect or disconnect electrical power to the an attached air conditioner or heater, and the microprocessor is configured to communicate over a network. In addition, the load control device is physically separate from an air conditioner or heater but located inside the space conditioned by the air conditioner or heater.
262 citations
TL;DR: In this paper, the authors proposed the use of a novel algorithm for smart-direct load control and load shedding to minimize power outages in sudden grid load changes and reduce the peak-to-average ratio.
Abstract: This paper proposes the use of a novel algorithm for smart-direct load control (S-DLC) and load shedding to minimize power outages in sudden grid load changes and reduce the peak-to-average ratio. The algorithm utilizes forecasting, shedding, and S-DLC. It also uses the Internet of Things and stream analytics to provide real-time load control, and generates a daily schedule for customers’ equipped with intelligent electronic devices based on their demands, thermal comfort, and the forecasted load model. The demand response techniques are utilized for real-time load control and optimization. To test the algorithm, a simulation system was developed, which takes into account 100 customers owning randomly selected appliances. The results indicated that load shedding using autoregressive integrated moving average time-series prediction model, and applying S-DLC and Internet of Things can significantly reduce customers’ power outage.
193 citations
29 Apr 2011
TL;DR: Simulation results indicate that for an appropriate target total power consumption, this scheme leads to a reduced peak demand for the home and produces a demand that is more level over time.
Abstract: This paper proposes a power scheduling-based communication protocol for in-home appliances connected over home area network and receiving real-time electricity prices via a smart meter. Specifically, a joint media access and appliance scheduling approach is developed to allow appliances to coordinate power usage so that total demand for the home is kept below a target value. Two types of appliances are considered: 1) “real-time” which consume power as they desire; and 2) “schedulable” which can be turned on at a later time. Simulation results indicate that for an appropriate target total power consumption, our scheme leads to a reduced peak demand for the home and produces a demand that is more level over time.
184 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 7 |
| 2020 | 44 |
| 2019 | 55 |
| 2018 | 95 |
| 2017 | 106 |
| 2016 | 110 |