Chi-Cheng Chu

Mobile DRM for multimedia content commerce in P2P networks

In this paper, the requirements for multimedia content sharing among Peer-to-Peer (P2P) networks are investigated and novel business models along with Digital Rights Management (DRM) solutions are proposed. In the current approach, to provide least intrusion and interference for end content consumers, content providers are not involved in the communications or peer transactions of P2P networks after the multimedia content are sold to the peer users. Peer users may legally trade or exchange their content within the P2P network via the proposed Ticket and Credit based Multimedia Commerce (T&C Commerce) system. The aim of this DRM research is to set new business models for multimedia content owners and retailers to benefit from the massive power of content distribution of P2P networks with least intrusion and interference to end consumer’s privacy and anonymity.

Integration of IEC 61850 into a Distributed Energy Resources system in a smart green building

A Distributed Energy Resources (DER) system, composed of distributed generation and storage units, has been proposed as a promising enhancement to the traditional power grids. One key challenge to implement a DER system, however, places on standardizing the communication network for seamless information exchange. As one approach, this paper focuses on integrating IEC 61850, which is an international unified standard for standardizing the communication network within a substation, into a DER system, using the UCLA SMERC building as test bed. To this end, we discuss a mathematical model of PV generation, present a representative demand profile, and develop a battery charging/discharging control algorithm. Moreover, we demonstrate a procedure to integrate IEC 61850 into the DER system step by step, with configuring the communication network and defining the data structure for the information exchange.

Predictive scheduling for Electric Vehicles considering uncertainty of load and user behaviors

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

Predictive Scheduling Framework for Electric Vehicles Considering Uncertainties of User Behaviors

The randomness of user behaviors plays a significant role in electric vehicle (EV) scheduling problems, especially when the power supply for EV supply equipment (EVSE) is limited. Existing EV scheduling methods do not consider this limitation and assume charging session parameters, such as stay duration and energy demand values, are perfectly known, which is not realistic in practice. In this paper, based on real-world implementations of networked EVSEs on University of California at Los Angeles campus, we developed a predictive scheduling framework, including a predictive control paradigm and a kernel-based session parameter estimator. Specifically, the scheduling service periodically computes for cost-efficient solutions, considering the predicted session parameters, by the adaptive kernel-based estimator with improved estimation accuracies. We also consider the power sharing strategy of existing EVSEs and formulate the virtual load constraint to handle the future EV arrivals with unexpected energy demand. To validate the proposed framework, 20-fold cross validation is performed on the historical dataset of charging behaviors for over one-year period. The simulation results demonstrate that average unit energy cost per kWh can be reduced by 29.42% with the proposed scheduling framework and 66.71% by further integrating solar generations with the given capacity, after the initial infrastructure investment. The effectiveness of kernel-based estimator, virtual load constraint, and event-based control scheme are also discussed in detail.

On the Utilization and Integration of RFID Data Into Enterprise Information Systems Via WinRFID

It is challengeable to utilize and integrate RFID data into current Enterprise Information Systems because high volume of RFID data is captured at high speed and the data are in heterogeneous formats. In this paper, we propose a “store and forward” and subscription-based integration approach in WinRFID Data Collector to solve the challenges. The Data Collector is a middleware which processes and delivers data to end application based on rules defined in subscription conditions. “Event Cycle” based data reporting defined in EPCglobal Application Level Event specification is analyzed. WinRFID subscription is proposed to provide real-time event notification. The data processing and integration capability of the Data Collector is measured and analyzed.Copyright

Two-tier prediction of solar power generation with limited sensing resource

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

Scalable vector graphics (SVG) based multi-level graphics representation for engineering rich-content exchange in mobile collaboration computing environments

A two-dimensional (2D) graphics hierarchical representation framework and an on-demand content delivery mechanism for facilitating mobile engineering collaboration are presented in this paper. Multi-level graphics content sub-division is utilized to transform large engineering graphics into multiple levels of Scalable Vector Graphics (SVG) content. The hierarchical structure of the SVG content that maintains the relationship between the sub-divided content is formed during the process of sub-division. The divided content is selectively delivered and rendered on the mobile devices in an on-demand fashion. A prototypical system of the proposed approach is implemented and the performance of the framework is evaluated.

Battery Energy Storage System Control for Intermittency Smoothing Using an Optimized Two-Stage Filter

A new method for the control of a battery energy storage system and its implementation on a 25 kW solar system to compensate solar power intermittency and improve distribution grid power quality is presented in this paper. The novelty of the proposed method is to provide a systematic way to optimize the size of the battery capacity for the desired level of solar power smoothing. This goal is achieved by designing a two-stage filter solution. The first stage is a fast response digital finite impulse response (FIR) filter that makes a trade-off between smoothing of the solar output and battery capacity. This paper proposes an optimal design of a minimum-length, low-group-delay FIR filter by employing convex optimization, discrete signal processing, and polynomial stabilization techniques. The new strategy proposed in this paper formulates the design of a length-

Optimal energy management for Microgrid with stationary and mobile storages

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Energy management for a commercial building microgrid with stationary and mobile battery storage

low-group-delay FIR filter as a convex second-order cone programming, which guarantees that all the filter zeros are inside the unit circle (minimum-phase). A quasi-convex optimization problem is formulated to minimize the length of the low-group-delay FIR filter. The second-stage filter is designed to level the battery charging load. The effectiveness and performance of the proposed approach is demonstrated by simulation results and also over a real-case implementation.