Yinghua Han

Resident Plug-In Electric Vehicle Charging Modeling and Scheduling Mechanism in the Smart Grid

With the development of smart grid and the increase of global resident Plug-In Electric Vehicle (PEV) market in the near future, the interaction between limited distribution grid capacity and uncontrollable PEV charging loads can lead to violations of local grid restrictions. And the proper model charging scheduling mechanism is the key to assess and satisfy various resident charging requirements and help in optimizing utility utilization. In this paper, the distribution grid profile model with PEV charging power is firstly constructed for the purpose of studying resident PEV charging impact on the distribution grid. To better reflect the actual impact of PEVs, we use real data on driving behaviors, vehicle characteristics, and electricity loads to generate our model. Furthermore, an improved queuing-theory-based scheduling mechanism is proposed, the distribution grid communication structure and the algorithm are illustrated, and computer simulations are demonstrated to verify their performance. The results show that the proposed scheduling mechanism will enhance the distribution grid flexibility to meet various charging requirements while maximizing the grid capacity.

Novel WSN-based residential energy management scheme in smart grid

Smart grid integrates the latest advances in information and communication technologies to contribute to a more reliable and efficient electricity system. By realizing the two-way communication between the utility and the smart meters in the houses, smart grid enables a time-of-use tariff to reduce peak load through incenting residents to adopt a more efficient usage of domestic appliances. In this paper, the candidate communication techniques and the classification of appliances are discussed. Then we proposed a WSN-Based Residential Energy Management Scheme which wirelessly connects non-urgent appliances to the smart meter through the wireless sensor networks. The structure components and the algorithm shows that the scheme can response to the residents command with the economically suggestion, and help them shift their non-urgent appliances to the off-peak hours. Therefore, the high peak load is alleviated, the green-house gas emission is reduced, furthermore, through the two-way communication, the utility is able to decide the optimal generation plan to satisfy resident demands, and the total utility cost can be reduced.

Cognitive radio networks for smart grid communications

A critical component of smart grid is the integration of multiple communication technologies which can facilitate the development of smart grid. However, most of the traditional communication technologies can not satisfy the critical and complex requirements of smart grid, such as, efficiency, reliability, resilience, sustainability, and security. In this paper, the cognitive radio (CR) technology is leveraged to construct the communication infrastructure of smart grid. First, ISM bands and leased bands are introduced as backup bands to ensure the QoS of data communications in CR based networks, for which a rule that decides when to stop spectrum sensing and access the ISM bands is also provided. Furthermore, a proper communication scheme is proposed for distributed generation system based on the fact that different renewable energy source has different active period during a day. Some licensed bands whose idle time is in line with the active period of renewable energy sources are set aside exclusively for distributed generation system access. The two schemes improve both the efficiency of spectrum utilization and reliability of cognitive communications in smart grid.

Hierarchical Agent-Based Integrated Modelling Approach for Microgrids with Adoption of EVs and HRES

The large adoption of electric vehicles (EVs), hybrid renewable energy systems (HRESs), and the increasing of the loads shall bring significant challenges to the microgrid. The methodology to model microgrid with high EVs and HRESs penetrations is the key to EVs adoption assessment and optimized HRESs deployment. However, considering the complex interactions of the microgrid containing massive EVs and HRESs, any previous single modelling approaches are insufficient. Therefore in this paper, the methodology named Hierarchical Agent-based Integrated Modelling Approach (HAIMA) is proposed. With the effective integration of the agent-based modelling with other advanced modelling approaches, the proposed approach theoretically contributes to a new microgrid model hierarchically constituted by microgrid management layer, component layer, and event layer. Then the HAIMA further links the key parameters and interconnects them to achieve the interactions of the whole model. Furthermore, HAIMA practically contributes to a comprehensive microgrid operation system, through which the assessment of the proposed model and the impact of the EVs adoption are achieved. Simulations show that the proposed HAIMA methodology will be beneficial for the microgrid study and EV’s operation assessment and shall be further utilized for the energy management, electricity consumption prediction, the EV scheduling control, and HRES deployment optimization.

A Novel Coordinative Resident Electric Vehicle Charging Mechanism

With advanced Electric Vehicles (EVs) techniques being introduced in these decades, EVs are expected to be widely adopted as resident vehicles due to their low greenhouse gas emission and low operating costs. Meanwhile, the resident EV charging activities will be a challenging problem for the power grid and will cause severe voltage fluctuations, high Peak-to-Average Ratio (PAR) and increasing the blackout possibility. Therefore the random and unpredictable resident EV charging activities calls for an efficient and coordinative mechanism. In this paper, the general scenario of resident EV charging activities is studied and the charging activity is analyzed by queuing theory. Based on that, the novel mechanism is proposed to coordinate the charging load and the grid peak load through wireless communication as well as to set EVs a proper charging moment. Through this mechanism, all arriving EVs can be fully charged before a specific time, and the grid peak load shall not be largely increased. A comparative study is carried out by simulating the uncontrolled charging mechanism with the proposed EV charging mechanism. And the result indicates that the proposed mechanism works efficiently on reducing the grid peak load as well as the grid PAR.

An adaptive double thresholds scheme for spectrum sensing in cognitive radio networks

Spectrum sensing is a fundamental requirement in cognitive radio (CR) networks. In this paper, a novel spectrum sensing scheme based on energy detection is presented which enables a significant reduction in the number of samples. The sensing process involves several stages and each stage employs two thresholds that are adjusted according to the number of samples. Furthermore, an iterative algorithm is developed for obtaining the optimal number of samples at each sensing stage. As the proposed scheme requires fewer samples of signal, there will be less time for sensing and more time for data transmission which contributes to an improvement in the throughput of the CR networks. Numerical results are provided to show that the proposed sensing scheme has a clear advantage over the conventional energy detection.

Cognitive information communication network for smart grid

Two-way seamless communication is the key aspect of realizing the vision of smart grid. Reliable and real-time information becomes the key factor for reliable delivering of power from the generating units to the end-users. Given the smart grid use cases, this article is focused on proposing a cognitive based communication paradigm. The proposal is related to the hierarchical communication infrastructure, cognitive home area network, cognitive neighborhood area network and cognitive wide area network. The cognitive radio is mentioned to achieve dynamic spectrum access, interference avoidance, data throughput adaptive and act as a backup wireless communication technique in emergency situations for critical data transmission. And parallel acceleration processing techniques is proposed for data processing to solve the compute-intensive character of applications of smart grid.

A New Waveform Design Method for Cognitive Radar

In cognitive radar system, how the transmitted waveform adapts in response to information regarding the radar environment is an important problem. In this paper, the waveform design for cognitive radar is viewed as an optimization problem. Then a new waveform design method for cognitive radar is proposed, which uses IPM (interior-point method) to carry out the optimization task. The simulation results demonstrate the validity of our algorithm.

An efficient compressed sensing-based cross-layer congestion control scheme for Wireless Sensor Networks

Cross layer congestion control algorithm based on compressed sensing (CS) is developed and designed in order to relieve congestion in Wireless Sensor Networks (WSNs).The main idea of this paper is to reduce congestion of sensor nodes by compressed transmission signal and allocated channel. In order to make an ideal distribution for sensor node, original signal is compressed at the network bottleneck, prevented high levels of data flow. The channel weights and the maximum effective set of containing channel least are calculated, and we allocate the appropriate channel in order to avoid channel contention and balance the network loading among the sensor nodes. Simulation results indicate the superior performance of our proposed algorithm to strike the appropriate performance in the congestion control, energy consumption and network lifetime for the wireless sensor networks.

An Optimal Operating Strategy for Battery Life Cycle Costs in Electric Vehicles

Impact on petroleum based vehicles on the environment, cost, and availability of fuel has led to an increased interest in electric vehicle as a means of transportation. Battery is a major component in an electric vehicle. Economic viability of these vehicles depends on the availability of cost-effective batteries. This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization for battery. Assume that the deterioration of the battery is stochastic. Under the assumptions, the proposed operating strategy for battery is formulated as a nonlinear optimization problem considering reliability and failure number. And an explicit expression of the average cost rate is derived for battery lifetime. Results show that the proposed operating strategy enhances the availability and reliability at a low cost.