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At the present time, trains in heavy haul operations are typically hauled by several diesel-electric locomotives coupled in a multiple unit. This paper studies the case of a typical consist of three Co–Co diesel-electric locomotives, and considers replacing one unit with an alternative version, with the same design parameters, except that the diesel-electric plant is replaced with flywheel energy storage equipment. The intelligent traction and energy control system installed in this unit is integrated into the multiple-unit control to allow redistribution of the power between all units. In order to verify the proposed design, a three-stage investigation has been performed as described in this paper. The initial stage studies a possible configuration of the flywheel energy storage system by detailed modelling of the proposed intelligent traction and energy control system. The second stage includes the investigation and estimation of possible energy flows using a longitudinal train dynamics simulation. The final stage compares the conventional and the proposed locomotive configurations considering two parameters: fuel efficiency and emissions reduction. [ABSTRACT FROM AUTHOR]

This paper presents a generic bulk energy storage dispatch model for production costing simulation, and assesses the performance and economics of storage under various system scenarios. The storage model presented captures the dynamic relationship between the reservoir energy status and the storage commitments in energy and ancillary co-optimization market, thereby enabling the dispatch of storage for both energy and cross-market arbitrage opportunities. The paper also presents a methodology to quantify the cycling costs incurred by conventional generating units due to frequent start–shut cycles and ancillary services. The bulk storage technology modeled is compressed air energy storage (CAES), and it is represented within the IEEE 24 bus reliability test system. Simulations are performed to quantify the impacts of bulk energy storage in terms of reduction in market prices, system production and cycling costs, and also to investigate the economic viability of storage projects in terms of payback periods under increasing wind penetration levels. [ABSTRACT FROM AUTHOR]

Highlights • Battery state of charge profiles are affected by the suppressed demand effect. • In small rural systems the suppressed demand effect impacts directly to reliability. • Considering the suppressed effect demand lead to more sustainable systems design. Abstract Rural electrification programs usually do not consider the impact that the increment of demand has on the reliability of off-grid photovoltaic (PV)/battery systems. Based on meteorological data and electricity consumption profiles from the highlands of Bolivian Altiplano , this paper presents a modelling and simulation framework for analysing the performance and reliability of such systems. Reliability, as loss of power supply probability (LPSP), and cost were calculated using simulated PV power output and battery state of charge profiles. The effect of increasing the suppressed demand (SD) by 20% and 50% was studied to determine how reliable and resilient the system designs are. Simulations were performed for three rural application scenarios: a household, a school, and a health centre. Results for the household and school scenarios indicate that, to overcome the SD effect, it is more cost-effective to increase the PV power rather than to increase the battery capacity. However, with an increased PV-size, the battery ageing rate would be higher since the cycles are performed at high state of charge (SOC). For the health centre application, on the other hand, an increase in battery capacity prevents the risk of electricity blackouts while increasing the energy reliability of the system. These results provide important insights for the application design of off-grid PV-battery systems in rural electrification projects, enabling a more efficient and reliable source of electricity. [ABSTRACT FROM AUTHOR]

Considering uncertain power outputs of distributed generations (DGs) and load fluctuations, energy storage system (ESS) represents a valuable asset to provide support for the smooth operation of active distribution networks. This paper proposes an affine arithmetic-based multi-objective optimization method for the optimal operation of ESSs in active distribution networks with uncertainties. Affine arithmetic is applied to the optimization model for handling uncertainties of DGs and loads. Two objectives are formulated with affine parameters including the minimization of total active power losses and the minimization of system voltage deviations. The affine arithmetic-based forward-backward sweep power flow is first improved by the proposed pruning strategy of noisy symbols. Then, the affine arithmetic-based non-dominated sorting genetic algorithm II (AA-NSGAII) is used to solve the multi-objective optimization problem for ESSs operation under uncertain environment. Furthermore, three types of indices with respect to convergence, diversity, and uncertainty are defined for performance analysis. Numerical studies on a modified IEEE 33-bus system with embedded DGs and ESSs show the effectiveness and superiority of the proposed method. The optimization results demonstrate that the obtained Pareto front has better convergence and lower conservativeness in comparison to the interval arithmetic-based NSGA-II. A multi-period case considering seasonality of DGs and loads is further simulated to show the applicability in real applications. [ABSTRACT FROM AUTHOR]

Abstract: In this paper is presented an analysis of energy efficiency for the Multiport Power Converters (MPCs) used in Plug-in Fuel Cell Vehicles (PFCVs). A generic MPC architecture for PFCVs is proposed, which is analyzed for different operating modes of MPC in relation with PFCV operating regimes and the plug-in feature. The basic MPC architecture is described in relation with the PFCV operating regimes. Two MPC architectures are derived from the basic MPC architecture: (1) the MPC1 architecture, which is the MPC architecture without reverse power flow during regenerative braking process, and (2) the MPC2 architecture – MPC architecture without charging mode of Energy Storage System (ESS) from the FC system. Taking in account the imposed window for the ESS state-of-charge, the MPC can be connected to Plug-in Charging Stations (PCS) to exchange power with the Electric Power (EP) system, which will include renewable Distributed Generation (DG) systems. The Energy Management Unit (EMU) of MPC can communicate with the EP system to determine the moments that match the energy demand of plug-in vehicle with the supply availability of the EP system, stabilizing the EP system. The MPC features regarding its energy efficiency were shown by analytical computing performed and appropriate simulations presented in relation with the ESS that can be charged (discharged) from (to) the home/DG/EP system. [Copyright &y& Elsevier]

Abstract: In this paper, the efficiency of one full charging and discharging cycle of several adiabatic Compressed Air Energy Storage (CAES) configurations are analyzed with the help of an energy balance. In the second step main driving factors for the efficiency of the CAES configurations are examined with the help of sensitivity analysis. The results show that the efficiency of the polytropic configuration is about 60%, which is considerable lower than literature values of an adiabatic CAES of about 70%. The high value of 70% is only reached for the isentropic (ideal) configuration. Key element to improve the efficiency is to develop high temperature thermal storages (>600°C) and temperature resistant materials for compressors. The highest efficiency is delivered by the two-stage adiabatic CAES configuration. In this case the efficiency varies between 52% and 62%, depending on the cooling and heating demand. If the cooling is achieved by natural sources (such as a river), a realistic estimation of the efficiency of adiabatic Compressed Air Energy Storages (without any greenhouse gas emissions due to fuel consumption) is about 60%. [Copyright &y& Elsevier]

Abstract: This paper presents the details of a theoretical study of the economic advantages of using large-scale energy storage to complement a wind farm in a base-load dominated electricity grid. A computer model is developed which simulates the operation of several energy storage systems when used with the 190-MW Portland Wind Farm (PWF) located in Portland, Victoria, Australia. A variety of operating strategies are compared with the results of a dynamic programming model which finds the maximum possible revenue which a given system can generate for a set of input conditions. Three energy storage systems are modelled and costed: Pumped Seawater Hydro Storage (PSHS), Compressed Air Energy Storage (CAES), and Thermal Energy Storage (TES). It is found that CAES is the most profitable storage medium, requiring a capital expenditure of A$140M and generating a rate of return (ROR) of 15.4%. The ROR for PSHS was 9.6%, and for TES was 8.0%. Therefore, a significant investment opportunity exists for the installation of an energy storage system in this wind farm. It is therefore highly recommended that CAES is investigated further with the aim of introducing large-scale energy storage to PWF and other similar wind turbine installations. [Copyright &y& Elsevier]

Abstract: This paper outlines a simulation model for the cycling behavior of household refrigerators and, therefore, for predicting their energy consumption. The modeling methodology follows a quasi-steady approach, where the refrigeration system and also the refrigerated compartments are modeled following steady-state and transient approaches, respectively. The mass, energy and momentum conservation principles were used to put forward the equation set, whereas experimental data were collected and used to reduce the model closing parameters such as the thermal conductances and capacitances. The experiments were carried out using a controlled temperature and humidity environmental chamber. The model predictions were compared to experimental data, when it was found that the energy consumption is well predicted by the model with a maximum deviation of ±2%. A sensitivity analysis was also carried out to identify opportunities for energy savings. [Copyright &y& Elsevier]

Abstract: In this paper, we perform Simulated Annealing (SA) algorithm for optimizing size of a PV/wind integrated hybrid energy system with battery storage. The proposed methodology is a heuristic approach which uses a stochastic gradient search for the global optimization. In the study, the objective function is the minimization of the hybrid energy system total cost. And the decision variables are PV size, wind turbine rotor swept area and the battery capacity. The optimum result obtained by SA algorithm is compared with our former study’s result. Consequently, it is come up with that the SA algorithm gives better result than the Response Surface Methodology (RSM). The case study is realized for a campus area in Turkey. [Copyright &y& Elsevier]