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This paper proposes a multiagent-based energy market for multimicrogrid systems using game-theoretic and hierarchical optimization approaches. The proposed method is tailored to achieve the optimal operation of smart microgrids in distribution systems. Because of rapid load variations in distribution systems, it is necessary to develop fast optimization algorithms which minimize the power mismatch in and among microgrids. In this paper, a three-level market framework is proposed. The first level comprises a game-theoretic double-auction mechanism for the day-ahead market while the next two levels are optimal rescheduling and intermicrogrid reverse auction model for the hour-ahead and real-time markets, respectively. Using the hierarchical optimization algorithm in a multiagent-based area, it is anticipated to not only minimize the optimization solution time, but also reduce the dependency on the network in grid-connected mode or load shedding in islanded mode. Using this approach, load demand response capabilities along with rescheduling of Distributed Energy Storage Systems and distributed generations could be utilized in all market levels, which will lead to optimal operation of multimicrogrid systems. Agents are developed in DIgSILENT PowerFactory and dynamic data exchange is activated for communication among agents communicating through a data distribution service which utilizes the real-time publish-subscribe communication protocol. The developed framework is applied to the modified 37-bus IEEE distribution test feeder system to validate the effectiveness of this market structure.

A good magnetic design of a wireless electric vehicle (EV) charging system requires accurate characterization of the equivalent circuit parameters. This is while considering the effect of the magnetic core and the conductive shielding layers. In the literature, such an analysis is done using 3-D finite element analysis, which makes the design and optimization process time-consuming and computationally expensive. In this paper, an integrated analytical model for the characterization of a circular wireless power transfer EV pads is utilized. This analytical model provides a valuable tool to optimize the design process. This model is utilized when evaluating the objective function for each individual in the multiobjective Pareto Optimality optimization algorithm. The objective is optimizing the pads with three decision variables and four constraints.

With the high penetration of Electric Vehicles (EVs), the number of public EV Charging Stations (EVCS) is also increasing significantly. Managing EVs, which are also known as mobile loads, is a critical issue from the power system point of view. This paper introduces a real-time power system optimization model for redistributing EVs onto the nearby EVCSs with two formulated objective functions. Those are reducing the active power losses in normal operation and mitigating transmission line congestions in contingency conditions. For this purpose, a service called the Collective Distribution of Mobile Loads (CDML) is introduced. This method comprises a power system optimization model that determines the optimal distribution of EVs, while considering the system condition, and a multiagent system for performing the CDML concept. To demonstrate the effectiveness of this method in terms of reductions in active power losses and in load shedding in both normal and contingency conditions, numerical results are obtained from a modified IEEE 14-bus system and from an actual smart grid testbed, reported, and analyzed.

This paper presents an electromagnetic-artificial intelligence design optimization approach of a wireless power transfer link (WPTL). Electromagnetic solutions from a 3-DFE model of the WPTL were utilized to minimize the field signature around each design while maintaining high efficiency. The reduced field signatures around the WPTL would enable the achievement of designs that are in accordance with international electromagnetic compatibility (EMC) standards such as international commission on non-ionizing radiation protection guidelines with respect to public exposure levels of EM signatures below 27 $\mu \text{T}$ . As the optimization process involved utilizing a large number of fitness function evaluations, numerous 3-DFE solutions were required increasing the computational burden. Artificial neural networks were developed, trained, and used to produce the required equivalent 3-DFE solutions to evaluate the fitness function. As a result, this process significantly reduced the computational time by nearly 90%. The genetic algorithm-based optimization process yielded the desired results of electromagnetically compatible WPTL designs at the early development stage satisfying EMC standards.

This paper proposes a Game-theory-based real-time reverse auction model for inter-microgrid market in a multi-microgrid system using a hierarchical optimization algorithm to address the optimal operation of microgrids in distribution systems. This market is organized to optimally assign the net power mismatch inside an area to microgrids’ market agents aiming at minimizing both the dependency on the utility and the market clearing cost. To implement the proposed framework in a Multi-Agent System (MAS), a fast and reliable communication middleware is required. Therefore, the data distribution service (DDS) which is a data-centric communications middleware based on the real-time publish-subscribe protocol is deployed for this purpose, where each agent is developed as a domain participant combining a publisher and subscriber. To validate the effectiveness of the proposed market framework, it is applied on the modified 37-bus IEEE distribution test feeder system, and the numerical results are obtained and presented.

As the US cities gradually proceed towards all-electric-vehicle occupied roads, they should be ready to handle this accelerating domination of those roaming mobile loads and the accompanying expand in charging stations’ infrastructure. This paper contributes to the ongoing research in smart mobility of the electrified transportation sector by presenting a novel intelligent price-based real-time strategy to reroute the electric vehicles aiming at meeting preset power load distribution on dispersed electric vehicle charging stations. The strategy involves a pre-cognitive feature that helps to optimize both the power system operation and the electric vehicles’ benefits. An case study with dispersed electric vehicles and electric vehicle charging stations in a geographical block is presented and proves the robustness of this strategy in meeting the preset power objectives by managing the electric vehicles pre-connecting to the grid.

This paper proposes an autonomous controller for the charging of EVs in a microgrid. The proposed controller has three objectives: avoiding under- and over-voltages at the point of charging, ensuring fair charging among the EVs in the microgrid, and preserving the lifetime of the battery by reducing the charging rate at high state of charge. The benefits of the proposed controller are highlighted by comparing it to a proportional controller that decides on the charging reference based on the difference between the voltages at the point of connection and a reference value. The results illustrated the advantage of the use of the proposed controller over the conventional one in achieving fair charging and increasing the life time of the battery.

Being energy producers and consumers (prosumers), electric vehicles introduce new challenges to the operation of the electricity grid. With the anticipated increase of the market for electric vehicles in the near future, systems that set rules to manage and organize their large-scale penetration into the power grid are needed. As the Internet of Things (IoT) vision evolves, the Internet of Electric Vehicles (IoEV) shall be a stepping-stone for establishing a link between the well-known hierarchical smart grid control architecture (primary, secondary, and tertiary control) and the emerging layer of the EVs as dynamic and mobile energy prosumers. Against this background, this paper proposes a decentralized multi-agent system designed for the management of en route connected electric vehicles. In this framework, each electric vehicle and electric vehicle park is equipped with an intelligent agent. These agents interact with a higher-level agent responsible for managing charging requests within their geographical area. The importance of this multiagent framework is demonstrated in a case study, where electric vehicles are routed to the most suitable electric vehicle charging parks in their vicinity in a manner that satisfies the charging preferences of customers, with the lowest cost possible, while ensuring that the generation-load balance at the EV parks is not violated.

Accurate performance characterization is critical in a design optimization environment. The electromagnetic (EM) design optimization of an ocean wave linear generator buoy or wave activated device (WAD) requires the use of large number of finite element-state space, design evaluations due to the stochastic behavior of the wave and the nonlinear nature of the device. This paper proposes the utilization of Taguchi orthogonal arrays method to reduce the EM, computational time needed to accurately characterize a WAD performance corresponding to a sea wave profile and for use in a design optimization environment.

Power oscillations can result from the expansion of large-scale grid-connected Electric Vehicles (EV) parks fed from photovoltaic (PV) arrays which raising concerns about grid stability. This paper addresses the optimal sizing for the energy storage battery, its corresponding DC-DC power converter and the grid tie inverter to satisfy grid requirements and maximize the profit of a EV park owner under the utility constraints. This was achieved through two stages: probabilistic forecasting of the EV load pattern and optimal sizing of the battery and power converters by the aid of linear programming (LP). Three types of batteries were studied to support the EV park: Sodium Sulfur (NaS), Lead Acid (LA) and Vanadium-Redox (VR). Moreover, a sensitivity analysis was done by investigating the effect of changing the number of grid tie inverters. Results showed the effectiveness of the proposed solution in limiting the power oscillations at the point of common coupling with the grid and maximizing the EV park's owner profit. Results also revealed the superiority of the VR battery for a low number of grid tie inverters while LA was preferable for implementing higher numbers of grid tie inverters on the host grid while limiting the power fluctuations at the grid side.

This paper develops techniques to improve the power quality of energy systems as a result of integrating large-scale Electric Vehicles (EV) parks. These parks are fed from photovoltaic (PV) arrays and the system is connected to the medium voltage (MV) grid. This was achieved through two stages; 1) probabilistic forecasting of the EV load pattern and 2) coordinating the power injected from the systems' inverters to support the voltage stability. The latter was done according to the Spanish grid code P.O.12.2, to enhance the power factor (pf), and support frequency stability even under pulsed load condition. The dynamics of the control loops were investigated in the in a software environment using PSCAD/EMTDC environment. The results proved the effectiveness of the coordinated control technique in supporting the voltage and frequency profiles and enhance the pf as well at the point of common coupling (PCC) with the host grid.

The fault current level is different in grid connected and islanded microgrid fault cases for the same relay due to large integration of distributed energy resources in the latter. Therefore, protection systems must be adaptive and assisted with extensive communication networks to respond to the dynamic changes in the microgrid configurations. However, the microgrid must be protected even under the conditions of a cyber-attack which will render the communication link insecure or down. This paper proposes an adaptive protection scheme abiding by IEC 61850 communication standard for microgrids making them capable of riding-through communication failures by the aid of Electric Vehicle Parks. The control of the Electric Vehicle Park can identify a fault in the AC side based on direct measurements of frequency and voltage without the need for a communication signal. The most critical case is when the microgrid is islanded and the communication is down. In this case, the control will adjust the operation of the connected Electric Vehicles to inject a high current into the microgrid as demanded by the fault, allowing the relays to operate properly. The effectiveness of the proposed protection scheme is evaluated through detailed simulation. The results demonstrate the successful operation of the protective devices during the various modes of operation.

A good magnetic design of wireless Electric Vehicle (EV) charging system requires accurate characterization of the equivalent circuit parameters. This is while taking into consideration the proximity effect of the magnetic core and the conductive shielding layers. In the literature, such analysis is done using 3D finite element analysis (3DFE), which makes the design process time consuming and computationally expensive. In this paper, an integrated analytical model for the characterization of a circular Wireless Power Transfer (WPT) EV pads is utilized. The results compare well with the results from an FE model as well as experimental test. This analytical model provides a valuable tool to optimize the design process and introduce an element of numerical verification.

Background: Bone is the most common organ of involvement in patients with Langerhans cell histiocytosis (LCH), which is often painful and associated with significant morbidity from pathological fractures. Current first-line treatments include chemotherapy and steroids that are effective but often associated with adverse effects, whereas the disease may reactivate despite an initial response to first-line agents. Bisphosphonates are osteoclast inhibitors that have shown to be helpful in treating bone lesions of LCH. To date, there are no large international studies to describe their role in treating bone lesions of LCH. Method: We conducted a multicenter retrospective review of 13 patients with histologically proven LCH, who had received bisphosphonates either at diagnosis or at disease reactivation. Results: Ten patients (77%) had a single system bone disease, and 3 (23%) had bone lesions as part of multisystem disease. Median follow-up time post-bisphosphonate therapy was 4.6 years (range, 0.8 to 8.2 years). Treatment with bisphosphonates was associated with significant pain relief in almost all patients. Twelve (92%) achieved resolution of active bone lesions, and 10 out of them had no active disease for a median of 3.5 years (range, 0.8 to 5 years). One patient did not respond. No major adverse effects were reported in this series. Conclusion: Bisphosphonates are well-tolerated drugs that can significantly improve bone pain and induce remission in active bone LCH. Future prospective studies evaluating the role of bisphosphonates in LCH are warranted. © 2016.

This paper presents a performance evaluation procedure for an inductive wireless power transfer (WPT) system with a parallel-parallel compensation network. The effect of the WPT system design parameters on its efficiency is investigated. The WPT system is modeled using a 3D electromagnetic field technique coupled with circuit simulation. The 3D finite element (3DFE) model is created and a MATLAB-FEA automated routine configures the model parameters. Moreover, the paper proposes a mathematical model for efficiency assessment purposes. The results from the 3DFE and the mathematical model are compared with the results of a Matlab Simulink model for the same system. The results show good agreement between all the models.