Distributed demand side management via smart appliances contributing to frequency control

Nowadays renewable energy has become a trend for energy production but its variable nature has made balancing of demand and supply of the power grid difficult. Dynamic demand management using smart appliances is proposed to serve as a way that part of the regulation burden of balancing demand and supply is shifted to the demand side. However, if all appliances respond to the same frequency deviation, they may start to synchronize, causing large power overshoots and instability of the power grid. Therefore, the idea of implementing randomness into the frequency control of the appliances is proposed and this is what we call a stochastic approach. Simulators are built from scratch to model both scenarios. The effect of synchronization is analyzed and the parameters that can affect the synchronization are investigated. It has been found that the larger the contribution from the smart appliances to the power grid, the easier and faster the synchronization takes place. The stochastic approach solves the problem of synchronization and averages out the large power overshoot. However, the overall performance of stochastic operations is unacceptable due to the randomness in the operation though the mean and variance are as expected. More advanced feedback policies and schemes may be designed to achieve a better performance.

Multi-objective reconfigurable production line scheduling for smart home appliances

In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.

Internet of Things Cloud Framework for Smart Homes

Smart appliances and renewable energy resources are becoming an integral part of smart homes. Nowadays, home appliances are communicating with each other with home short-range home area gateways, using existing network communication protocols such as ZigBee, Bluetooth, RFID, and WiFi. A Gateway allows homeowners and utilities to communicate remotely with the appliances via long-range communication networks such as GPRS, WiMax, LTE, and power liner carrier. This paper utilizes the Internet of Things （IoT） concepts to monitor and control home appliances. Moreover, this paper proposes a framework that enables the integration and the coordination of Human-to-Appliance, Utility-to- Appliance, and Appliance-to-Appliance. Utilizing the concepts of Internet of Things leads to one standard communication protocols, TCP/IPV6, which overcomes the many diverse home area networks and neighborhood area networks protocols. This work proposes a cloud based framework that enables the IoTs integration and supports the coordination between devices, as well as with device-human interaction. A prototype is designed, implemented, and tested to validate the proposed solution.

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario,the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation.Demand side management(DSM)is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives.Consumers are expected to respond(demand response(DR))in various ways to attain these benefits.Nowadays,residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals.In this paper,the use of a smart residential energy management system(SREMS)is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances.Further,the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery(charging/floating/discharging)and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit(CCL).The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.