Jidong Wang

Robust-Index Method for Household Load Scheduling Considering Uncertainties of Customer Behavior

Robust-index method is proposed to tackle the challenges of uncertainties caused by customer behavior in order to minimize comfort violation in household load scheduling. Robust indexes for different kinds of loads are developed and integrated into the load scheduling optimization problems in the form of additional constraints, and thus the obtained load schedules are able to reach the expected robust level. The robust-index method is simple in modeling, independent of historical data, and with low additional computational burden. Simulation results verified the validity of the proposed method and demonstrated their application in load scheduling.

Intelligent Under Frequency and Under Voltage Load Shedding Method Based on the Active Participation of Smart Appliances

Under frequency load shedding (UFLS) and under voltage load shedding (UVLS) are effective methods to maintain the power balance of and prevent frequency or voltage collapse of power systems. With the construction of wide area measurement systems, the development of demand response technologies and the application of smart appliances, a new direction for load shedding method emerges under the background of smart grid. In this paper, an intelligent UFLS/UVLS method based on the active participation of smart appliances is proposed. First, this paper takes refrigerators and water heaters as research objects, establishes their practical models and proposes a suitable control method for intelligent load shedding. Then an intelligent UFLS/UVLS method is proposed and simulated in IEEE 39-bus system. Simulation results prove that this strategy can work well to suppress the frequency and voltage fall or even collapse, and reduce the amount of load shedding and economic losses.

Wind Power Interval Prediction Based on Improved PSO and BP Neural Network

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

Short-term wind power forecasting based on support vector machine

Wind power prediction, especially short-term forecasting is very significant for the security, stability and economy of power grid. Besides, it plays an important role in a micro-grid for load balancing and capacity planning. Precise prediction of wind power of micro-grid is a complex problem due to its strong randomness and little training data. Compared with traditional methods, Support Vector Machine (SVM) based on Structure Risk Minimization principle, plays a better performance on nonlinear and small sample problems. The method of SVM for short-term wind power prediction is proposed in this paper. An improved pattern search algorithm, which takes use of Lagrange interpolation to obtain the initial points, is used to optimize the parameters of SVM prediction model. The simulation results indicate that the method proposed in this paper can realize short-term wind speed prediction effectively. This paper presents some promising patents on prediction of wind power.

A research for the interactive behavior between Electric Vehicle and Residential Energy Management System based on probability distribution

With the human living environment getting rapid deterioration, Electric Vehicle(EV) is gaining more and more attention as a kind of potential new energy vehicle. Besides, Electric Vehicle plays a significant role in the smart home of the future, the strategies of its changing and discharging and intelligent access to the Residential Energy Management System are under pretty hot study. This paper, based on peak-valley electricity price, builds a residential energy management system which includes the renewable energy like solar energy and electric vehicle, makes the most optimal schedule for users through optimal algorithm. Specially, this paper studies the probability distribution of independent variables like daily trip miles, return time and the spending time in the charging behavior. Then, this paper adapts this result of probability distribution to whole energy management system. In addition, the influence brought by the charging behavior has been considered into the energy system, the system can reschedule for the appliances and Photovoltaic system output.

A research of the strategy of electric vehicle ordered charging based on the demand side response

The rapid development of electric vehicle as well as photovoltaic generation leads measures to combine their advantages in urgent need. The appliance of the TOU price also brings about new challenge to the schedule of electric vehicle. This paper studies the method to minimize the electric cost of the users by the response to the photovoltaic generation and TOU price. Firstly, the model of the photovoltaic generation is proposed. Secondly, the model of optimization of electric vehicle is suggested. Lastly, optimization algorithm is utilized in this model. Simulation verifies the valid of the model.