Energy Management for a Residential Microgrid Using Wavelet Transform and Fuzzy Control Including a Vehicle-to-Grid System

This paper presents the design and implementation of an energy management system （EMS） with wavelet transform and fuzzy control for a residential micro-grid. The hybrid system in this paper consists of a wind turbine generator, photovoltaic （PV） panels, an electric vehicle （EV）, and a super capacitor （SC）, which is able to connect or disconnect to the main grid. The control strategy is responsible for compensating the difference between the generated power by the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into a smoothed component and a fast fluctuated component. The command approach used for fuzzy logic rules considers the state of charging （SOC） of EV, renewable production, and the load demand as parameters. Furthermore, the command rules are developed in order to ensure a reliable grid when taking into account the EV battery protection to decide the output power of the EV. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm

Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-

Energy Management System Design and Testing for Smart Buildings Under Uncertain Generation (Wind/Photovoltaic) and Demand

This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

A review of inexact optimization modeling and its application to integrated water resources management

Water is crucial in supporting people＇s daily life and the continual quest for socio-economic development. It is also a fundamental resource for ecosystems. Due to the associated complexities and uncertainties, as well as intensive competition over limited water resources between human beings and ecosystems, decision makers are facing increased pressure to respond effectively to various water-related issues and conflicts from an integrated point of view. This quandary requires a focused effort to resolve a wide range of issues related to water resources, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty that successfully address interactions, complexities, uncertainties, and changing conditions associated with water resources, human activities, and ecological conditions are desired, which requires a systematic investigation of the previous studies in relevant areas. Systems analysis and optimization modeling for integrated water resources management under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to stochastic, fuzzy, and interval mathematical optimization modeling are examined. Then, their applica- tions to integrated water resources management are presented. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns over uncertainty, necessity of post-modeling analysis, and the usefulness of the development of simulation techniques.