Self-adaptation-based dynamic coalition formation in a distributed agent network: a mechanism and a brief survey

In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies

The simultaneous mitigation of slow and fast voltage fluctuations caused by rooftop solar PV by controlling the charging/discharging of an integrated battery energy storage system

Both slow and fast voltage fluctuations in the connected low voltage (LV) distribution feeder are caused by intermittent variations in solar PV power output, in addition to the variations in load demand where rooftop solar photo-voltaic (PV) unit penetration is higher. A single energy storage system integrated with the solar PV unit can mitigate these fluctuations in voltage profile. A novel analytical approach to mitigate both slow and fast voltage fluctuations simultaneously in the connected LV distribution feeder is proposed, which has not explicitly been addressed in the literature. Integrated battery energy storage systems will be dynamically charged during mid-day to alleviate the voltage rise and discharged during the evening peak hours to alleviate the voltage drop, while simultaneously controlling the fast fluctuations of the PV inverter output to a specified value. The proposed control strategy has been validated using a hypothetical distribution feeder system and results have demonstrated that both the slow and the fast voltage fluctuations in the voltage profile can effectively be alleviated, if the proposed strategy is implemented

The state of the art of battery charging infrastructure for electrical vehicles: Topologies, power control strategies, and future trend

Electric vehicle battery (EVB) charger topologies play a vital role to increase the penetration of EVs. This paper reviews the status quo of EV battery (EVB) chargers in term of converter topologies, operation modes, and power control strategies for EVs. EVB Chargers are classified based on their power levels and power flow direction. Referring to power ratings, EV chargers can be divided into Level 1, Level 2 and Level 3. Level 1 and Level 2 are normally compatible with on-board chargers while Level 3 is used for an off-board charger. Unidirectional/bidirectional power flow can be obtained at all power levels. However, bidirectional power flow is usually designed for Level 3 chargers as it can provide the huge benefit of transferring power back to grid when needed. Moreover, the different operation modes of an EVB charger are also presented. There are two main modes: Grid-to-Vehicle (V1G or G2V) and Vehicle-to-Grid (V2G). The V2G mode helps bring EV batteries to become active distributed sources in smart grids and is the crucial solution for a high EV penetration. Future trend and authors\u27 recommendations with preliminary simulation and experimental results are demonstrated in this paper

Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency

Range anxiety is an obstacle to the acceptance of electric vehicles (EVs), caused by drivers\u27 uncertainty regarding their vehicle\u27s state of charge (SoC) and the energy required to reach their destination. Most estimation methods for these variables use simplified models with many assumptions that can result in significant error, particularly if dynamic and environmental conditions are not considered. For example, the combined efficiency of the inverter drive and electric motor varies throughout the route and is not constant as assumed in most range estimation methods. This study proposes an improved method for SoC and range estimation by taking into account location-dependent environmental conditions and time-varying drive system losses. To validate the method, an EV was driven along a selected route and the measured EV battery SoC at the destination was compared with that predicted by the algorithm. The results demonstrated excellent accuracy in the SoC and range estimation, which should help alleviate range anxiety

Energy Storage: from Chemicals to Materials and More

This Special Issue of Energies aims to contribute to the energy storage agenda through a combination of multidisciplinary and state-of-the-art scientific knowledge, to improve energy availability, security, and the performance and competitiveness of current or future renewable energy generation systemsinfo:eu-repo/semantics/publishedVersio