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Hybrid energy storage systems (HESSs) have become an effective solution for smoothing the active power variations of photovoltaic (PV). In order to reduce the required capacities and costs of the HESS, a coordinated control scheme is developed to mitigate the power variations of a PV plant by using the HESS and the active power curtailment (APC) of PV. Furthermore, a multi-objective optimization model is established to dispatch the output power of batteries and supercapacitors, considering the overall losses and the state-of-charge (SOC) deviation of the supercapacitor. Based on the proposed smoothing strategy, an allocation model is developed to optimize the energy and power capacities of the HESS with the aim of maximizing the annual net income of the PV and HESS plant. The numerous simulations are carried out to verify the effectiveness of the proposed smoothing and allocation methods by using the real data of a PV plant. In addition, we also discuss the impacts of the different dispatching strategies of the HESS, grid requirements of power variations, and solution methods on the HESS allocation results. INDEX TERMS Hybrid energy storage system (HESS), photovoltaic (PV) power fluctuation, capacity allocation, active power curtailment.

Key characteristics (KCs) play a significant role in product lifecycle management (PLM) and in collaborative and global product development. Over the last decade, KCs methodologies and tools have been studied and practiced in several domains of the product lifecycle, and many world-class companies have introduced KCs considerations into their product development practices. However, there has been no systematic survey of KCs techniques, methodologies, and practices in this respect. This paper aims to give a comprehensive survey of KCs methodologies, and practices from the perspective of enterprise integration and PLM. The paper firstly presents a holistic framework of KCs methodologies and practices through the product lifecycle, and summarizes the fundamentals of KCs including their definition and classification, KC flowdown, and the identification and selection of KCs. A review of the KCs methods and practices in the product lifecycle is then presented, particularly in engineering design, manufacturing planning, production and testing as well as information and knowledge management respectively. Finally, the problems and challenges for future research on KCs techniques are discussed.

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