*2.1. E*ffi*ciency Solutions Based on the Driving Style Management and Timetable Optimization*

Energy-efficient driving management coupled with the optimization of timetable allows finding optimal train speed profiles and departures to minimize the energy consumption and get the optimal braking trend due to time delay, so as to maximize the recoverable energy in line. Researches start from 1960, with timetable optimization and energy-efficient driving from the first suggested optimal control model in 1968 made by Ishikawa [10].

Several timetable optimization methods have been proposed in recent years [10]. Albrecht in [12] developed a new method based on dynamic programming, to manage running time of trains using an optimal combination of headway and synchronization time, with the task of reducing power peaks and energy consumption. Chen et al. [13] applied the genetic algorithm to optimize train scheduling; particularly the goal was to reduce power consumptions, preventing the synchronous acceleration of many trains. Ramos et al. [14] presented a method to maximize the braking energy recovery, during off-peak hours maximizing the overlapping time between acceleration and braking of the trains. Kim et al. [15] carried out a multi-criteria mixed integer programming, coordinating the train departure times at the starting stations, so as to minimize the peak energy and maximize regenerative energy utilization. Peña-Alcaraz et al. in [16] tested methods to synchronize the movement of trains in the Madrid Metro Line 3 reaching a 3.52% energy saving. In [17,18] a comparison between to real metro-lines in Italy and Spain is made in terms of energy savings that can be obtained by the recovering of the braking energy of the trains. Yang et al. with several studies suggested a cooperative scheduling model to schedule the accelerating and braking phases of nearby trains. In [19] a simulation performed on real data obtained from the Beijing Metro Yizhuang Line shows a great improvement in the overlapping time of around 22%. In [20] a stochastic cooperative scheduling model taking into consideration the randomness of departure delay, for trains considering busy stations, shows a percentage of save energy around 8%, compared with the cooperative scheduling approach reported in [19]. In [21] the same authors offer a model to optimize the timetable, coordinating trains at the same station to maximize the utilisation of recovery energy and reduce waiting time for the passengers. The model reached an 8.86% energy saving, with a waiting time of 3.22% relevant to the current timetable. In [22] a scheduling approach regarding effective speed profiles, to arrange arrivals and departures of all trains, reaches a 6.97% reduction in energy consumption, in comparison with the current timetable.

In [23] a model, based on a multidimensional state vector subspace for train operation, is presented. A smart scheduling methodology useful for multi-train energy saving operation and an optimization procedure based on a genetic algorithm and regenerative kinetic energy, to lowest total energy consumption, is proposed in [23,24].

In [25] the train trajectory optimization is carried out, in order to define a better train target speed profile, to minimize a cost function, including energy consumption and trains arriving on time for all trains. The minimum energy consumption, under different departure headways, is calculated, by using a heuristic algorithm in [26], reaching a reduction in energy consumption up to 19.2%. Furthermore, several studies on driving style are carried out, to define eco-energy driving profile strategies of the trains to couple with timetable optimisation. The motion stage of a train consists in acceleration, cruising, coasting, and braking management. Generally, the speed profile of trains with short travel distance or close intermediate stops, like in tram and metro systems, could not contain the cruising phase.

In [27] a dual speed-curve optimization for energy-saving operation of high-speed trains is proposed using two optimizations. An offline global and online local optimization, demonstrating the increase in energy saving, compared with other well know existing methods that use one-time optimization processes. The main structures of the dual optimization method proposed include: a global optimization to obtain better driving style; after, the speed trajectory is adjusted in real time by local optimization. Additionally, regarding rolling optimization, a closed loop control is integrated with a consistent optimization process that continuously corrects; at least global optimization is reachable using a genetic algorithm, with characteristics and predictive control, with the local optimization characteristics, to compensate for the limitations of a single optimization process [27].

In [28] the authors propose an integrated approach, consisting of both offline and online techniques. The projected framework generates throttle sequences that lead to energy saving under the constraints of trip time and computation time. This work leverages the fast-growing machine learning techniques, so to extract the optimized driving behaviours of human drivers and encode the learned knowledge into a parameter decision tree for fast online optimization. A case study on a given locomotive proved the effectiveness of the proposed framework and an energy saving of 9.84% on different running conditions can be achieved.

In [29] the authors includes a new method for speed curve definition and tracking control, based on a random reinforcement genetic algorithm (GA) to avoid the local optimum and a sliding mode controller developed for speed curve tracking with bounded disturbance.

An improved chicken swarm optimization algorithm for energy-saving for a train, by taking minimum energy-consumption, accurate stopping and punctuality as optimization objectives is in [30] without changing the existing equipment and infrastructure. Chicken swarm optimization is a global optimization algorithm, which integrates the advantages of genetic, particle swarm and bat algorithms.

In [31], the authors introduce an optimization of train speed curve applied in a real case study of the Taipei Mass Rapid Transit System for journeys from "Dingpu Station" to "Yongning Station", showing that operational energy consumption could be reduced up to approximately 58%. A real driving method to reduce the traction energy demand is presented in [32]. In this case the authors carry out theoretical optimal driving solutions thanks to a train simulation using an enhanced Brute Force searching algorithm. A driver practical training system (DPTS) is created to help drivers practice energy-efficient driving controls. A train speed trajectory optimization method associated with a driver practical training system (DPTS) is the main goal. Thanks to the DPTS, traction energy consumption is reduced by around 15%.

The authors in [33] propose a methodology that includes an objective function using cardinality and square of the Euclidean norm functions. The optimization model proposed, allows defining properly the utilization of the regenerative energy. To solve the convex relaxation counterpart of the original NP-hard problem, a two-stage alternating direction method of multipliers is designed. The procedure produces an energy-efficient timetable of trains.

Genetic algorithms have been used for a subway line in Milan and it is reported in [34]. The main goal is to fulfil the transition from a traditional system to a driverless one. It shows an energy saving increase equal to 32.89%.

### *2.2. E*ffi*ciency Solutions Based on Stationary and on Board Energy Storage Systems*

Many studies about on board and stationary energy storage systems have been developed, especially for DC railway systems, without a reversible substation, where it is not possible to drive the surplus of regenerated energy back to the main AC power supply. Consolidated energy saving solutions using reversible substation focused on different implementations are reported in [35–41]. The innovative technologies used to design energy storage systems are super-capacitor, battery, or flywheel and IEC 62924:2017 standard fixed requirements and test methods. The International Union of Railways (UIC) with the sub commission "Energy Efficiency", creates a database where all relevant railway energy-saving technologies should be analyzed, categorized, and evaluated [42,43].

Regarding on board energy storage systems, they are already in use by some rail transit companies. The main advantages are the reduction of peak power, the stabilization of voltage, the loss reduction and the possibility to operate catenary free [44]. Real applications of on-board storage systems are

the Brussels, Madrid metro and Mannheim tramway lines. The percentage of energy saving reported in [45–47] are 18.6% ÷ 35.8%, 24% and 19.4% ÷ 25.6%, respectively. To reach high integration with motor drive control, some research studies are focused on the optimal design, sizing and control of on board energy storage systems [48–52]. Focuses on stationary storage systems, the real implementation of wayside Energy Storage System (ESS), show an increase in energy savings of up to 30%. The percentage of energy saving by ESS moreover is influenced by system features and storage technologies. In [53] it is highlighted that auxiliary battery-based substations could represent a feasible solution to store the required energy for partly powering a train, supporting the electric substation during train accelerations and to compensate for voltage drops. Numerous commercially available stationary systems are available. Sitras SES (Static Energy Storage) system, marketed by Siemens, can reach up to 30% of energy saving using a super-capacitor technology that can offer 1 MW peak power for 20 ÷ 30 s, with 1400 A DC discharging current. This system is in Germany (Dresden, Cologne, Koln and Bochum), Spain (Madrid) and China (Beijing). The EnerGstor of Bombardier Company, based on supercaps, is able to reach 20% ÷ 30% of reduction of energy demand [54]. Another system super-caps based in Hong Kong and Warsaw metro systems [55] is developed, by Meiden and marketed by Envitech Energy, with scalability from 2.8 to 45 MJ of storable energy.

### **3. E**ffi**ciency in Electrical Vehicles Transportation Sector**

The growing awareness of environmental issues, social pressures towards a solution to climate change that lead to a progressive disinvestment in fossil fuels [56] and the continuous technological improvements of storage batteries, have now led car manufacturers all over the world [57–59] to invest in new platforms for the construction of electric vehicles (EV). Electric vehicles, unlike those with an internal combustion engine (ICE), have the advantage of avoiding local emissions of greenhouse gases, or eliminating them if powered by renewable sources [60]. In fact, global emissions from electric vehicles vary according to the power generation mode. If coal plants produce the energy, they produce substantial global emissions that determine only local benefits of the use of electric vehicles [60]. Conversely, the use of alternative sources such as wind or photovoltaic allows a significant reduction in global emissions, given their lower carbon intensity [60–64]. Nevertheless, even with electricity generated from coal-fired power plants, the global emissions of an electric vehicle in the well-to-wheel cycle are lower than those ones generated by an ICE vehicle [60]. Electric vehicles are nowadays supported and encouraged by various governments around the world [65], also through measures aimed at reducing the tax burden, setting up free parking lots dedicated to them and equipped with charging infrastructures, the use of preferential lanes, access limited traffic areas, etc. [65]. An electric vehicle, unlike an ICE vehicle, allows it to be recharged from the mains, now available everywhere. However, ICE vehicles can refuel with a method dating back to the early 20th century. This refueling process started from a pharmacy that sold petrol tanks, and it was done completely manually in seconds. Electric vehicles were suffering from much longer charging times and the absence of public infrastructure, which led to their substantial disappearance to the present day [66]. In modern electric vehicles, the recharging process takes place mainly via an on-board charger; this mode requires a very long charging time, which can reach several hours [66]. Most models of electric vehicles nowadays support fast charging through dedicated infrastructures that allow, on average, to reach 80% of the state of charge (SOC) in about 30 min [67]. Nevertheless, the charging power is still not comparable to the refueling of a traditional ICE vehicle. Besides, fast charging, in addition to being more expensive, leads to faster degradation of batteries if used frequently [67].

The impact of electric vehicles on electric transmission and distribution grids is still negligible due to their low diffusion [61,68]. Their growing diffusion will inevitably cause an increase in the demand for electricity which may both have a negative impact, but also have a beneficial effect on the electricity system if well integrated [61,68]. Indeed, a further increase in the demand for electricity at peak times, because it is not restricted [68], could lead to an overload of the electric system and the underutilization of renewable sources [61,68]. It follows that leaving the decision on when to recharge without any coordination to individual users will inevitably lead to the need for further repowering of the transmission and distribution grids, premature aging of the devices and a lower quality of the energy supplied to the users [67,68].

Conversely, if recharges are coordinated among themselves with intelligent logic that also consider the actual production from renewable sources, then the electrical system it will benefit by reducing the percentage of fossil fuels in end uses [61,67,68].

To address these issues, new regulatory rules and management strategies are needed close to new technological advancements for a suitable integration of electric vehicles in the current transmission and distribution grids [69,70]. Some studies, but also common practice, have shown that on average a vehicle is parked for 95% of its life and that the weekly trips are often just the journey homework. It is from these considerations that the EVs can be seen not only as means of transport, but as active elements able to play a role in the management of power lines. The EV, once connected to the electric network, is therefore seen as an integral part of the system capable of supplying energy when demand is high (by discharging the battery) and absorbing the surplus of energy produced when demand is lower (by charging the battery) [71]. This practice is named Vehicle-to-Grid (V2G) and is based on the bidirectional power flow, from the grid to the vehicle but also from the vehicle to the grid [72,73]. When operational and integrated with the network, it can bring significant benefits such as [74]:


All this, however, is achievable only with the introduction of the smart grid concept. This requires modernization of the transmission and distribution grids and the introduction of tools for measurement and communication. The definition of a business model, clearly defining the players involved in the value chain and fairly compensating the exploitation of EV batteries, is another crucial factor for the diffusion of such a paradigm.

On a smaller sale, V2G concept can be applied also in the context of a building (vehicle-to-building (V2B)) or of a home (vehicle-to-home (V2H)) [75]; the objective is to benefit from the exploitation of the batteries of EVs when connected to a smart system. Back up and time-shift are two examples of the possible advantages that a bidirectional interaction with EVs can bring. The higher self-consumption rate of renewable energy production achievable thanks to the exploitation of batteries, along with their lower operating costs with respect to conventional cars, represent the main driver for the integration of EVs in smart home environments.

The number of electric vehicles is expected to grow sharply in the incoming decades and the potential impact on the electric grid could be substantial. Of course, this aspect is not related to generation and transmission, in which the effect is relatively small, but it affects the distribution network in a significant way [76]. For the distribution system operator (DSO) the fluctuation of the load due to the plug in of vehicles must be minimized in order to guarantee a good quality of service for the final user. Moreover, these technical problems have to be matched with additional problematics coming from the interaction between the EV user and the grid operator, since the service must be convenient for both the grid and the owner of the vehicle.

The first mention of the vehicle to grid service was proposed by Amory Lovins in 1995, and then developed by William Kempton [76]. The main idea is that the vehicle can be considered as a storage system able to provide energy to the distribution network when parked which can be charged and discharged according to the grid necessities and price of energy fluctuation.

V2G technology can offer a wide range of functions for the grid: load balancing, harmonics suppressing, power quality improvement, peak load shaving, voltage sags reduction and

interaction with Renewable Energy Sources (RES) [77]. The V2G concept is still at its first stages and is becoming more and more important as the diffusion of electric vehicles increases. From the literature [76], four key issue areas can be identified: a smart dispatching from the operator point of view, a smart charging management from the vehicle point of view, the bi-directional charger and the effect that the V2G service has on the vehicle's battery. V2G functions have a great potential for electric vehicles to become a tool for the electric grid, to manage power and energy storage applications. The challenge in developing these functions is that it is always mandatory to remember that vehicles have mobility as their primary mission and not storage system for the grid. This means that every time the vehicle's battery is used for providing services to the grid, the battery cannot be charged and discharged in a way that does not guarantee to the vehicle owner a full availability of its own car. Moreover, another critical aspect is the fact that using vehicle's batteries for providing power to and from the network contributes to the degradation of the battery, and so limitations on charge and discharge cycle must be set.

The V2G modelling has to move through the day, simulating hour by hour a real life situation, in which a certain number vehicle is generated according to a specific load profile and interacts with energy requests needed by the photovoltaic park managed by the same aggregator [78]. It is important that the fleet car has to be composed of many vehicles in order to aggregate a total power and energy storage capacity able to deal with the fluctuation of power production during the all day. The figure responsible of managing the vehicles fleet of this region is called aggregator [79].

To be more specific an aggregator is a market participant which aggregates in a unique offer the distributed generation of a certain zone. The reason for the existence of this market figure is that single distributed generation plants are usually of small size, which could be neglected in a big market like the energy one. The aggregator will be in charge of collecting both distributed generation plants and energy storage systems in a defined area. The aggregator can also be responsible for managing electric vehicles charging and discharging processes in order to guarantee to its customers a fully charged vehicle when the parking time is over while guaranteeing to its own aggregated photovoltaic plants the possibility of storing and delivering extra power when requested. The extra power, which could be managed using the fleet batteries, represents the variation in photovoltaic production, or other renewables, for the next hour with respect to the forecasted production of the day ahead.

The reason behind this definition is that the energy market is called Day-Ahead-Market (DAM), since each day the market trades the energy quantities that will be exchange during the next day. The problem is that renewable system production can be predicted but with high errors if we consider time windows of 24 h. From [79] it is possible to see that moving from 24 h ahead time windows to 1 h before the event the error on the prediction is reduced from 24% to 12%. In [80] it is shown that with an artificial neural network it is possible to predict, 24 h in advance, the irradiation during the day with an error as low as 28%. Moreover, being RES market price takers since they bid at 0 €/kWh, they will be always able to enter the market. However, the next day when the real production will change due to the unpredictability of the source there will be a lack or a surplus of energy [81,82]. This means that in the case of extra energy, part of it could be lost since the grid is already balanced thanks to the correct behaviour of the system operator, while in case of a lack of energy, additional energy is requested to traditional plants. Both these solutions act in the opposite way of a smart management of the electric grid. Moreover, when a production plant is not able to provide the energy production set during the day ahead market, it will be responsible for unbalancing the system, being subject to an unbalance forfeiture that the owner has to pay to the transmission system operator.

### *Some Important Considerations*

As previously described, local CO2 emissions for electric vehicles are negligible, instead they contribute to global CO2 emissions. The latter depend on the manufacturing process of the batteries and the vehicle, but the most significant component depends heavily on the way electricity is generated. Indeed, for countries that use more renewable sources, CO2 emissions are significantly lower than

the equivalent for an ICE vehicle. On the other hand, for those countries mainly based on fossil fuels, the emissions are comparable to those produced by a very efficient ICE vehicle, as shown in Figure 1.

**Figure 1.** Life-cycle emissions (over 150,000 km) of electric and conventional vehicles in Europe in 2015 [83].

Using electric vehicles batteries for helping the grid, in managing power and energy unbalancing, could be an interesting solution since it occurs without additional costs related to dedicated storage systems.

Thanks to a full communication system between vehicles and their charging poles, it will be possible to elaborate charging strategies in order to use the parked vehicles as a supporting storage system for the grid. However, in order to have a high amount of energy available from the vehicles an aggregator will be needed.

The aggregator will be a market participant that will collect many distributed generation plants and storage systems in a certain area, in order to gather in a single figure a high amount of power increasing its impact on the market.

As a final consideration it is important to consider that if the future vehicles will be provided with batteries able to store a higher amount energy, each single vehicle will be able to exchange more energy during a discharge with a depth of 50%. This means that in that case the number of vehicles required will be even lower with respect to these simulations and the vehicle to grid function will be even more performant.
