Search

Renewable energy source integration in buildings can contribute to achieving sustainable energy use and reducing fossil fuel dependence. However, the intermittent and random nature of these energies, mainly photovoltaic and wind energy, make necessary the development of tools and strategies as forecasting to get a correct balance between demand and generation. In this paper, an energy consumption module based on the SARIMA statistical model is presented. This module is used to forecast the energy consumption of a green-elevator, which integrates photovoltaic and batteries, in different short-term time horizons. Genetic algorithms were used to speed up the optimal SARIMA model parametrization, and the chosen model was applied in a new and independent data set to test its effectiveness. Finally, the results were compared with the ANN technique and the GAM models. The SARIMA approach performed slightly better, and the best results were obtained with the time horizon of 1 h. [ABSTRACT FROM AUTHOR]

This paper presents an advanced market bidding and operation strategy for the joint participation of a solar plant with storage in Energy and secondary reserve markets (SRMs). A linear optimization is applied in order to calculate the optimal day-ahead and intraday market bids through a model predictive control (MPC) approach, considering solar generation forecast, hourly market prices as well as battery acquisition, operating and degradation costs. Moreover, several rule-based strategies are selected to participate in secondary reserve markets, focused on more profitable and reliable hours through the estimation of market prices and the expected operation of the battery. Finally, the real-time operation tackles all the uncertain parameters, such as the reserve requirements and solar forecast errors, with the objective of achieving an optimal tradeoff between market profits, battery costs and market technical fulfillments. Several market strategies have been compared in techno-economic terms with annual real market data. Regarding the most advanced strategy, market benefits are improved 6.42% from base case and non-fulfillment level of SRM is reduced up to 1.49%. [ABSTRACT FROM AUTHOR]

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 management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMSs), before being integrated in a real system, need to be experimentally validated in test-bench platforms in order to identify the possible deviations from the expected simulation-based performance while minimizing the implementation time and field-test on the real application. An economical and flexible mean of validating these strategies is the hardware-in-the-loop simulation. Therefore, this work aims to present the design approach and comparison, by means of experimental tests, of two optimized (simulation-based) EMSs proposed for a hybrid electric bus (HEB) focusing on the real-time operational performance. Both EMSs handle the proper power split behavior of the vehicle demand among a genset (internal combustion engine + electric generator) and a hybrid energy storage system (combining Li-ion batteries with supercapacitors). The experimental platform consists of a scaled test bench emulating the electrical dc grid of a HEB. This test bench, combined with software models to control the power electronic devices, allows us to emulate the real behavior of the genset, battery, supercapacitor, traction, and auxiliary loads. [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]

In this paper, an optimal energy management strategy (EMS) for a light rail vehicle with an onboard energy storage system (ESS) combining batteries (BT) and supercapacitors (SC) is presented. The optimal operating targets for the proposed EMS and ESS sizing (BT+SC) are obtained by multiobjective (MO) optimization with genetic algorithms. The MO fitness function is expressed in economic terms and includes the cost of the energy absorbed from the catenary, as well as the operating cost of the BT and SC pack (initial investment and cycling cost). The case study selected is the tramway of Seville. The aim is to minimize the daily operating cost of the tramway, taking into account the fulfillment of the tramway performance in the catenary-less zone and the BT-SC degradation by cycling during operation. Furthermore, a sizing analysis is done by comparing the current scenario (SC-based) with the proposed approach (optimal EMS and sizing) in terms of daily operating cost reduction. [ABSTRACT FROM PUBLISHER]

With growing global commitment to renewable energy generation, the role of energy storage systems has become a central issue in traction power applications, such as electric vehicles, trains, and elevators. To achieve the optimal integration of batteries in such applications, without unnecessary oversizing, improvements in the process of battery selection are needed. Specifically, it is necessary to develop models able to predict battery performance for each particular application. In this paper, a methodology for the parametrization of a battery equivalent circuit model (ECM) based on capacity and pulse tests is presented. The model can be extrapolated to different battery technologies, and was validated by comparing simulations and experimental tests with lead-acid and lithium-ion batteries. [ABSTRACT FROM AUTHOR]