Search

Simulation and performance evaluation of concurrent Java program execution have been difficult due to the lack of proper model and tools. Previous modeling and simulation approaches cannot simultaneously achieve three tasks: (1) support the direct simulation of unmodified multi-thread Java programs; (2) guarantee deterministic simulation results; and (3) offer low-overhead and scalable simulation. This paper first presents a novel simulation model based on the Java memory model. The model axiomatically defines action ordering, relationships, and constraints to ensure the well-formedness and determinism of a simulated execution. Then, based on the model, we implement the DecompositionJ simulation framework (deterministic, concurrent multi-processsing simulation for Java programs) which enables the direct execution simulation of target program by using compiler-based source-to-source transformation and a purposely designed runtime library. The framework is compatible with any JVM that complies with the Java specifications, and does not require manual modifications on the target program code. The performance of the framework has been evaluated with the Grande Java concurrency benchmark suite, results have shown a geometric mean of 98.9% overhead over all cases, which significantly outperforms full-system simulation techniques.

The use of distributed computation and control is pervasive for a wide range of smart grid research topics. However, recent developments of smart grid co-simulation platforms have not been able to provide effective support for the modeling and simulation of distributed software systems. In particular, cosimulation literatures only focused on the integration of electrical and communication network simulators, and the responsibility for modeling distributed software is often delegated to one of these two simulators. Since these domain specific simulators are not designed for this purpose, such delegation incurs many limitations that prevent convenient, effective, and accurate modeling of software behaviors. To mitigate the problem, this paper presents, to our knowledge, the first co-simulation integration of direct-execution simulators to provide dedicated support for distributed smart grid software. We first present the development of the novel DecompositionJ framework (DEterministic, COncurrent Multi-PrOcessing SImulaTION for Java programs), which is a compiler-based code analyzer and transformer to automatically convert multi-thread Java programs into direct-execution simulators, eliminating the need for manual code or model development. Next, we apply DecompositionJ framework to generate simulators for a popular multi-agent platform JADE. The JADE simulators are then integrated with electromagnetic transient simulator (PSCAD) and packet-level network simulator (OPNET) using standardized co-simulation runtime infrastructure. At last, we conduct a case study on agent-based smart grid restoration using this novel co-simulation platform. Through the analysis of simulation results, it is shown that the proposed direct-execution simulation framework is able to facilitate the understanding, evaluation, and debugging of distributed smart grid software.

Electric vehicle (EV) becomes a popular choice for its zero air pollutant and high energy efficiency. Nevertheless, the massive penetration of EVs can cause problems, including voltage drop and peak amplification. The charging pattern of EVs also poses a challenge to the reconfiguration work of the power system when failure occurs. Therefore, an EV schedule-control-based strategy is designed to address these issues in order to achieve voltage regulation and load shifting under both normal operation and failure scenarios. The framework involves two agents: a two-stage voltage control agent schedules EVs to perform load shifting and voltage regulation under normal condition and a fault control agent deals with line failure scenarios to recover the power supply of out-of-service basic and EV loads. A three-level queue table mechanism is designed to collaboratively perform EV scheduling. The influence of EV charging locations on the voltage variations of other nodes is considered and alleviated through a voltage sensitivity analysis method. Moreover, graph theory is employed to perform the network reconfiguration process to deal with line failure situations. The effectiveness of the scheme to restore the power supply while maintaining reliable system voltage level has been verified with the simulation results based on a modified IEEE 30 nodes test feeder.

A high accuracy localization technique using Long Term Evolution (LTE) based on a new and accurate multiple carrier noise model has been developed. In the noise consideration, the LTE multiple carriers phase noise has been incorporated so that a new and accurate noise model is achieved. An experiment was performed to characterize the phase noise of carriers at 2 GHz. The developed noise model was incorporated into LTE localization analysis in a high traffic area in Hong Kong to evaluate the accuracy of localization. The evaluation and analysis reveals that the new localization method achieves an improvement of about 10% accuracy comparing to existing widely adopted schemes.