Analysis of the reliability of heat supply from hybrid energy source based on WPP and CHPP

The paper is devoted to the problem of the reliability analysis the functioning of the hybrid energy source based on the wind power plant (WPP) and the combined heat and power plant (CHPP). The main effect of the proposed technology is substituting a part of the fossil fuel used at CHPP (or other heat sources) for the production of electricity and thermal energy. At the same time, CHPP operates according to the heat schedule, i.e. in accordance with changes in the heat loads of consumers. In this regard, the attention in the study is focused on the problem of the reliability analysis of the heat supply (heating) from a hybrid energy source. To assess the reliability of heat supply, one of the main complex reliability index (RI) is used - the availability factor (AF), and the system of equations of a stationary markov process is used as a mathematical model of the states evolution of the studied object. Calculation of AF is made in accordance with seasonal changes in heat loads (annual heat load schedule); the heat storage effect of consumers (thermal inertia) and the factor of a possible decrease in wind speed are also additionally taken into account. Case study is carried out on a test circuit of the hybrid energy source (WPP+CHPP), as a result of which some analytical characteristics are obtained.

Dynamic Scheduling Based on Predicted Inventory Variation Rate for Public Bicycle System

To improve efficiency and reduce the total scheduling cost of the public bicycle system (PBS), dynamic scheduling based on the predicted inventory variation rate (DS-PIVR) is proposed. Regarding a station in the PBS as an inventory system, its inventory variation rate during the scheduling period and its inventory rate at the end of the scheduling period were predicted based on the stationary Markov process condition. A mixed integer programming (MIP) model, whose objective is to minimize the total scheduling distance, was established to describe the dynamic scheduling problem (DSP). Results from Boston and Washington D.C. PBSs show that, when compared to the dynamic scheduling based on the rolling horizon (DS-RH), the DS-PIVR method could at most shorten the routing distance by 62.25% (for Boston) and 74.7% (for Washington D.C.) among all scheduling areas, and could at most shorten the total routing distance for the whole PBS by 21.06% (for Boston) and 17.26% (for Washington D.C.). Moreover, the DS-PIVR method makes the repositioning vehicle journey only once and keeps the inventory rate of each station in balance during the scheduling period. Furthermore, the DS-PIVR method provides a promising reference to improve the operation efficiency by reducing the scheduling cost and the quality of service by satisfying the users’ demand in time during the rush hours for the PBS operators.

Data-Driven Tracking Based on Kalman Filter

A good model of the target will extract useful information about the target’s state from observations effectively. There are many models used to maneuvering target tracking, such as constant-velocity (CV) models, Singer acceleration model (zero-mean first-order Markov model) and “current” model (Mean-Adaptive Acceleration Model), etc. While due to the complexity of maneuvering target, to seek the target model which can get better performance is still a subject worthy of study. For the AR process, autocorrelation function is estimated by the random sampling points in this paper. We have the statistics relation between the autocorrelation function and variance based on a first-order stationary Markov process. Then the system parameters are obtained and a model is developed based on statistics relation, which needn’t set unknown parameter. Simulation shows the model developed can adaptively get the model parameter and obtain good performance.

Dynamics of large uncontrolled loss networks

This paper studies the connection between the dynamical and equilibrium behaviour of large uncontrolled loss networks. We consider the behaviour of the number of calls of each type in the network, and show that, under the limiting regime of Kelly (1986), all trajectories of the limiting dynamics converge to a single fixed point, which is necessarily that on which the limiting stationary distribution is concentrated. The approach uses Lyapunov techniques and involves the evolution of the transition rates of a stationary Markov process in such a way that it tends to reversibility.

Dynamics of large uncontrolled loss networks

This paper studies the connection between the dynamical and equilibrium behaviour of large uncontrolled loss networks. We consider the behaviour of the number of calls of each type in the network, and show that, under the limiting regime of Kelly (1986), all trajectories of the limiting dynamics converge to a single fixed point, which is necessarily that on which the limiting stationary distribution is concentrated. The approach uses Lyapunov techniques and involves the evolution of the transition rates of a stationary Markov process in such a way that it tends to reversibility.

COMPARISONS OF DEPENDENCE FOR STATIONARY MARKOV PROCESSES

Results and conditions which quantify the decrease in dependence with lag for a stationary Markov process and enable one to compare the dependence for two stationary Markov processes are obtained. The notions of dependence used in this article are the supermodular ordering and the concordance ordering. Both discrete-time and continuous-time Markov processes are considered. Some applications of the main results are given. In queueing theory, the monotonicity results of the waiting time of the nth customer as well as the stationary waiting time in an MR/GI/1 queue and the stationary workload in a Markov-modulated queue are established, thus strengthening previous results while simplifying their derivation. This article is a continuation of those by Fang et al. [7] and Hu and Joe [10].