This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

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.

This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.

Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.

The Energy Service Interface (ESI), sitting at the boundary of a customer facility, plays a communication gateway role - interconnecting internal customer energy resources to external systems. A number of customer energy services are realized over the interconnected communications, which then contributes to smart grid interoperation eventually. In this paper, we examine the design issues of the ESI. To facilitate bi-directional customer energy services, the ESI must serve as both a service consumer and a service provider. At the same time, it must protect the customer energy resources from external threats and maximize the interoperation. To verify the issues, we build and deploy two ESI testbeds. Throughout experiments with a couple of energy service scenarios, we verify that the ESI plays the service "prosumer" in an interoperable manner. We also evaluate the performance of the security mechanism applied to the ESI and examine the potential of exploiting the Cloud technology for the ESI deployment. To the best of authors' knowledge, this is the first deployment of the ESI that addresses the fundamental, functional requirements.

This special issue explores the state of the art across the RFID technology landscape from hardware, to systems, as well as applications and support for innovative business models.

Radio-frequency identification (RFID) has a great number of unfulfilled prospects. Part of the problem until now has been the value proposition behind the technology-it has been marketed as a replacement technique for the barcode when the reality is that it has far greater capability than simply non-line-of-sight identification, towards decision making in strategic management and reengineered business processes. The vision of the internet of things (IOT) has not eventuated but a world in which every object you can see around you carries the possibility of being connected to the internet is still within the realm of possibility. However incremental innovations may see RFID being sold as a service (much like photocopiers are maintained today) than a suite of technologies within a system that are sold as individual or bundled packaged components. This paper outlines the vision for such a product service system, what kinds of smart applications we are likely to see in the future as a result, and the importance of data management capabilities in planetary-scale systems.

A multi-level hierarchical content organization and on-demand delivery framework is presented in this paper. This framework enables engineering content representation and exchange in mobile computing environment. Engineering documents generated by CAD/EDA software are transformed into XML based vector graphics format, decomposed and organized into a semantically grouped hierarchical structure. The client selectively can download and render the decomposed contents based on various rules. The evaluation shows that the proposed framework increases the reliability of content exchange and improves the user-application experience.

Volume decomposition is a fundamental issue that draws attention from researchers in different disciplines. In many contexts, a solid model that contains shape information needs to be partitioned into smaller simpler or more meaningful sub-models in order to facilitate efficient or effective analysis. Much progress has been made so far on convex decomposition of polyhedra and relatively difficult geometries; general decomposition on a CAD model with arbitrary shapes to produce intelligent partitions is still highly challenging. The constrained and aggregated half-space volume decomposition approach is aimed at improving the efficiency and intelligence of decomposition by trimming and aggregating cutting surfaces. In this paper, two kinds of extending strategies ("internal" extending and "external" extending) are identified and the algorithms for them are demonstrated.

Disassembling a subset of components from an assembly, defined as selective disassembly, often occurs during product de-manufacturing (recycling, reuse and maintenance). Selective disassembly analysis of a product enables quick assessment of the design for de-manufacturing. This paper presents an approach to solve the following selective disassembly problem: given an assembly of n components and s (

Shape feature information about a part is required to analyze the part for downstream issues such as manufacturability and assemblability. One method of obtaining the feature information is feature recognition from the geometric model. The current approach called Curvature Region (CR) approach for feature determination from solid models categorizes features into two primitive shape classes: protrusions and depressions. In the first step, these primitive shape classes are recognized from the solid model. In the next step, the primitive shape classes are matched with feature definitions to obtain features.

Disassembling a selected component from an assembly, defined as selective disassembly, is important for applications such as maintenance, recycling and reuse. This paper presents a geometric algorithm to solve the following selective disassembly problem: given an assembly and a selected component to be disassembled, determine the disassembly sequence with minimum component removals (motions), defined as an optimum sequence. We propose an abstraction “wave propagation” that analyzes the assembly from the selected component outwards, and orders the components for selective disassembly. The main contributions of this research are: (1) determining an optimal disassembly sequence; (2) reducing the search space by analyzing a subset of components in the assembly; and (3) providing a polynomial average complexity algorithm. The proposed selective disassembly approach applies to both two dimensional and three-dimensional assemblies.

Generation of geometric shapes called "geometric concept designs" via the multimodal user interface of a virtual reality (VR) based system motivates the current research. In the VR system, geometric designs can be more effectively input into the computer in a physically intuitive way (using voice, hand motions, and gestures-just as a person would communicate ideas to another person). Although the dimensions inputted may be imprecise, this approach is useful for concept shape generation since at this stage dimensions need not be exact. The paper focuses on determining the requirements for the multimodal user interface and assessing the applications of different input and output mechanisms in the virtual environment (VE). Based on the results of these investigations, a VR-based computer-aided design (CAD) system for the conceptual shape design will be implemented in the current research. Once designers use the system that we are developing, the interface requirements determined in the current paper may be verified or refined. The objectives of the research are to expand the frontiers of product design and establish a new paradigm for the VR-based conceptual shape design system.