Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.
To achieve low-carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero-carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO 2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO 2 emission.
Electric vehicles (EV) are becoming increasingly popular due to their efficiency and potentials to reduce greenhouse gas emission. However, penetration of a very large number of EVs can have negative impacts on power systems. This study proposes optimal vehicle-to-grid (V2G) models to incorporate the EV penetration by minimizing multiple objectives including the peak demand, the variance of load profile, the battery degradation cost and the EV charging/discharging cost based on real-time pricing (RTP). The proposed models incorporate EV driving patterns including driving distance, driving periods, and charging/discharging levels and locations. A nonlinear battery degradation cost function is linearized and incorporated into the optimal models. In addition, a distributed control algorithm is developed to implement the optimal models. One-day simulation results show that the proposed approach can reduce the peak demand and the variance of the load profile by 7.8% and 81.9%, which can significantly improve power system stability and energy efficiency. In addition, the sum of EV charging/discharging cost and battery degradation cost is decreased from $251 to -$153. In fact, 100 EVs earn $153 in the day from the V2G program. The approaches can be used by a load aggregator or a utility to effectively incorporate EV penetration to power systems to unlock V2G opportunities and mitigate negative impacts.
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