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• Five propulsion systems and six energy carriers for regional trains are analyzed. • Dutch regional railway network with sixteen rail passenger services is considered. • Energy carrier production pathway is the main driver of energy use and emissions. • Battery-electric system running on green electricity offers net-zero emissions. • Hydrogen provides emissions reduction only if produced from green electricity. This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies. [ABSTRACT FROM AUTHOR]

This study presents a novel load estimation method for isolated communities that do not receive energy or only receive it for a limited time each day. These profiles have been used to determine the installed capacity of generating units for microgrid electrification projects. The social characteristics and lifestyles of isolated communities differ from those in urban areas; therefore, the load profiles of microgrids are sensitive to minor variations in generation and/or consumption. The proposed methodology for obtaining the residential profiles is based on clustering algorithms such as k-means, a self-organizing map (SOM) or others. In this work, SOM clustering is considered because it allows a better interpretation of results that can be contrasted with social aspects. The proposed methodology includes the following components. First, the inputs are processed based on surveys of residents that live in each socio-economic level of housing and the community. Second, family types are clustered using an SOM, from which relevant information is derived that distinguishes one family from another. Third, the load profiles of each cluster are selected from a database. Additionally, social aspects and relevant energy supply information from communities with similar characteristics are used to generate the required database. The SOM for the clustering of families of the community with available energy measurements is used as an initial guess for the clustering of the families in the community with unknown energy measurements. The methodology is applied and tested in the community of El Romeral, Chile, where a microgrid will be installed. The SOM technique compares favorably with a benchmark method that uses the average load profile of a community; furthermore, the SOM clustering algorithm for the methodology is favorably compared with the k-means algorithm because the results obtained by SOM are consistent with the social aspects. [ABSTRACT FROM AUTHOR]

• Optimal Lithium-ion battery sizing for hybrid diesel-electric train is presented. • Bi-level optimization approach integrates optimal sizing and control levels. • Two energy management strategies are developed and compared. • Trade-off between fuel savings and hybridization cost is analyzed. • Fuel savings and CO 2 emissions reduction of up to 34.5% is obtained. Hybridization of diesel multiple unit railway vehicles is an effective approach to reduce fuel consumption and related emissions in regional non-electrified networks. This paper is part of a bigger project realized in collaboration with Arriva, the largest regional railway undertaking in the Netherlands, to identify optimal solutions in improving trains' energy and environmental performance. A significant problem in vehicle hybridization is determining the optimal size for the energy storage system, while incorporating an energy management strategy as well as technical and operational requirements. With the primary requirement imposed by the railway undertaking to achieve emission-free and noise-free operation within railway stations, we formalize this as a bi-level multi-objective optimization problem, including vehicle performance, the trade-off between fuel savings and hybridization cost, influence of the energy management strategy, and other constraints. By deriving a Li-ion battery parameters at the cell level, a nested coordination framework is employed, where a brute force search finds the optimal battery size using dynamic programming for full controller optimization for each feasible solution. In this way, the global minimum for fuel consumption for each battery configuration is achieved. The results from a Dutch case study demonstrated fuel savings and CO 2 emission reduction of more than 34% compared to a standard vehicle. Additionally, benefits in terms of local pollutants (NO x and PM) emissions are observed. Using an alternative sub-optimal rule-based control demonstrated a significant impact of the energy management on the results, reflected in higher fuel consumption and increased battery size together with corresponding costs. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads. [ABSTRACT FROM AUTHOR]

This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers and was developed in the MATLAB/Simulink ® environment. A MPC (model predictive control) strategy established at a supervisory level is presented. The intent of the strategy is to regulate the steam pressure of the superheater of the ISCC plant. The combined use of the simulator and the supervisory control strategy allows for the quantification of the reduction in fuel consumption that can be achieved when integrated solar collectors are used in a combined cycle plant. The ISCC plant simulator is suitable for designing, evaluating and testing control strategies and for planning the integration of solar and combined cycle plants. [ABSTRACT FROM AUTHOR]