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In this paper, a non-adaptive and a novel adaptive energy management strategy (EMS) are proposed for a series hybrid electric bus with a dual energy storage system (ESS) combining batteries and supercapacitors. The scenario considers the hybrid (genset+dual ESS) and full-electric (only dual ESS) operation during the daily journey. The main goal of this paper is to highlight the improvements that can be achieved either with an enhanced adaptive EMS (fuzzy-based) compared with the non-adaptive (rule-based) approach as well as with the co-optimization of ESS sizing and operation targets in economic, fuel saving, and efficiency terms. To do so, a multi-objective optimization methodology is proposed (considering economic and ageing models) to minimize the operation cost (cycling cost) of the dual ESS and its replacements along the vehicle lifetime as well as the fuel consumption, while fulfilling the performance of the bus in its daily journey. The simulation results showed that the adaptive EMS can reduce both the operation cost of the vehicle and the average fuel consumption (up to 15% and 19%, respectively) compared with the optimal solution obtained with the non-adaptive EMS. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) when integrated with large-scale photovoltaic (PV) plants, constituting a so-called Intelligent PV (IPV) power plant, are able to contribute to improve the economic viability of these power plants and to help them participate in the electricity markets as other traditional generators. In this paper, the sizing and control strategy co-optimization for an existing IPV power plant is proposed and implemented. A global linear programming (LP) optimization algorithm is developed, where the optimal components sizing is computed directly in the same optimization as the operating management of the storage system. In the IPV power plant design stage, the LP optimization is applied to obtain the optimal energy storage sizing parameters (capacity and power rate). In the operation stage, the same LP algorithm optimizes the control strategy to participate to electricity markets. This participation is based on the online model predictive control (MPC) to counteract PV forecast errors by participating in intraday markets. The proposed approach (optimal ESS sizing and online MPC) increases the daily economic benefits around 20%. [ABSTRACT FROM PUBLISHER]

Energy storage systems (ESS) integration with large-scale photovoltaic (PV) power plants, named intelligent PV (IPV) power plant, could contribute to improving the viability of these plants and to provide ancillary services to the main grid. The number and extension of the provided services depend not only on the size of the ESS, but also on the manner in which this storage system is managed. In this paper, the market participation based on different firming control strategies of an IPV power plant is proposed to optimize the economic exploitation based on the storage system management considering PV generation predictions. The most appropriate firming control strategy is selected to participate on the daily market, which is strengthened with an online model predictive control (MPC) to compensate the PV prediction errors participating in the intraday market. The real operation of the Iberian Peninsula market integration is also explained and analyzed. The development of this management strategy considers as a case study a real IPV plant located in Tudela (Navarre, Spain) and owned by Acciona Energía, which included a Lithium-ion based ESS from 2012 to 2013 in the framework of a European project. The results show that considering the MPC approach, an increase around 20% in the benefits is obtained by comparing with the daily market participation. [ABSTRACT FROM PUBLISHER]