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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]