Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

Dependence Analysis Based on Dynamic Slicing for Debugging

Dynamic program slicing is an effective technique for narrowing the errors to the relevant parts of a program when debugging. Given a slicing criterion, the dynamic slice contains only those statements that actually affect the variables in the slicing criterion. This paper proposes a dynamic slicing method based on static dependence analysis. It uses the program dependence graph and other static information to reduce the information needed to be traced during program execution. Thus, the efficiency is dramatically improved while the precision is not depressed. The slicing criterion is modified to fit for debugging. It consists of file name and the line number at which the statement is.

Web Application Comprehension Based on Dependence Analysis

Many research indicate a lot of money and time are spent on maintaining and modifying program delivered. So the policies to support program comprehension are very important. Program comprehension is a crucial and difficult task. Insufficient design, illogical code structure, short documents will enhance the comprehensive difficulty. Developing Web application is usually a process with quick implementation and delivery. In addition, generally a Web application is coded by combining mark language statements with some embedded applets. Such programming mode affects comprehension of Web applications disadvantageously. This paper proposes a method to improving understanding Web by dependence analysis and slice technology. Key words Web application comprehension - program dependence - hyper graph - pogram slicing CLC number TP 311 Foundation item: Supported in part by the Young Scientist’s Fund of NSFC (60373066, 60303024). National Grand Fundamental Research 973 Program of China (2002CB312000) and National Research Foundation for the Doctoral Program of Higher Education of ChinaBiography: WU Jun-hua (1965-), female, Ph. D. research direction: software engineering.

THE Z SPECIFICATION DEPENDENCE GRAPH

The lack of existing solutions makes it really hard to understand formal specification languages since the application domain for representations is useful for the purpose of carrying out certain software engineering operations such as slicing and the computation of program metrics.A Z specification dependence graph is presented in this letter. It draws on the strengths of a range of earlier works and adapts them, if necessary, to the Z language.

Fuzzy Economic Order Quantity Inventory Models Without Backordering

In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent chance programming （DCP） model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level. Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization （PSO） algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm.