Recipientes de bronce

Smart charging and V2G can help to mitigate the impact of bulk and uncontrolled EV charging, and consequently can help to accommodate a higher share of electric vehicles in the national vehicle fleet interacting with the electricity grid. More generally, smart and V2G charging from EVs could help to achieve an efficient utilization of the grid by addressing peak demands, integrating more intermittent renewable energy power and filling in the load curve in hours characterized by low power consumption. This can potentially lead to grid investment deferral. Following this idea, [50] evaluated the potential benefits for the DSO from investments in V2G services and compared them with the underlying grid investments. The authors inferred that there is a certain potential of peak electricity demand reduction resulting from a number of EVs providing peak shaving service. This in turn affects the duration curve of the network which depends on the electricity demand profiles. Ultimately, a balance is struck between the number of operational hours of storage, which determines battery degradation cost, and the avoided network investments. With 250 EVs, they showed that there was the potential of reducing the peak demand by 900 kW, by using 3.6 kW chargers. By considering a degradation cost of £/kWh 0.18 (resulting from a battery investment cost of £/kWh 267-623), they showed that below an annual energy throughput of 135 MWh/year, the avoided grid investments achieved by V2G were higher than the incurred battery degradation. However, they argued that with an average spot electricity price of euro/kWh 0.027-0.062 provided in North European countries, the economics of V2G did not make sense, as energy could have been bought from the wholesale market in order to satisfy the peak. However, as discussed in Section 2.3, the cost of lithium-ion batteries is currently in the range of £/kWh 150-300, and the associated battery degradation cost is £/kWh 0.075-0.3. Hence, peak power provision can become a profitable service in the near future. It should be

61 noted that [50] did not consider the travelling patterns of EVs, which would reduce the potential peak demand reduction with V2G, nor the cost of V2G chargers. Hence, an economic analysis of peak shaving, and the associated benefits that DSOs can reap, must be conducted. EVs can also be charged by imposing network constraints as was demonstrated in [51]. They tested the operation of a multi-agent system in a laboratory setup, where one EV was emulated by hardware in loop and 60 EVs were simulated. The emulated EV complied with network constraints.

[10], [52], [53], [54] and [55] further investigated the potential peak reduction capability of EV fleets equipped with V2G. In [10], the effect of smart charging on the electricity demand profile of a distribution network was analysed. The EVs were connected through a level 2 charging, either at home or in public areas, where renewable energy from PV and wind was available. 50,000 EVs performing smart charging enabled a peak demand reduction of 87 MW.

The location where information is stored, and hierarchy of computation can influence the potential achievable grid relief. In fact, measurements for an entire distribution network can be collected and utilised in a central server, or the decision-making privilege can be shared among multiple agents, distributed in the network. [10] and [55] evaluated the difference in these two strategies by exploiting intelligent EV charging to perform peak shaving and reduce the variability of the load profile in a local distribution grid. Local and global control strategies were performed and compared to a business-as-usual scenario with uncontrolled charging. Future scenarios with different PHEV penetration level were simulated and these are 15%, 45% and 75%. Given the nominal voltage level of 230±10%, uncontrolled charging led to more voltage deviation. Scenarios simulating a 10%, 30% and 60% of PHEV penetration rate were considered. The local control strategy let to improvements in peak demand in the order of 8-38% compared to the BAU case, while the global strategy achieved 8-42% of improvement. Both the local and global energy control strategies improved the flatness of the load profile, but the global energy control strategy resulted in the most optimal load profile. Although global control strategies provided the highest improvements in peak demand, it should be noted that the implementation cost of a centralised control strategy is

62 disproportionately higher than that of a decentralised system, due to the onerous communication infrastructure. The additional grid relief given by a centralised architecture must be compared against the incurring costs when choosing between the two strategies. It was further evidenced by [10] and [55] and confirmed by [54] and [56] that the penetration rate of EVs brings an additional dimension when evaluating grid benefits. The authors of [54] evaluated the provision of peak load support as well as voltage unbalance mitigation in a cluster of three feeders of a distribution network in Australia. They showed that above a rate of 40% EVs being available for those network services, there are beneficial effects in terms of voltage rise mitigation. In [56], for 25% and 50% EV penetration levels in a distribution network (corresponding to 31,250 and 62,500 EVs) it was shown that uncontrolled charging increased the peak demand by 36% and 74%, respectively. However, the benefits also scaled up proportionally as smart charging achieved peak levels that were 13% and 27% lower than those caused by uncontrolled charging.

As reported in [50], the category of the electricity demand profiles will have a substantial influence on the potential peak demand reduction achievable by EVs. For instance, if the load duration curve of a network exhibits a substantially high peak compared to its base demand, then EVs have to provide V2G support for a limited number of hours per year and targeted to critical moments. Conversely, if the load duration curve is flatter, than the EVs must be available for longer periods in order to achieve some peak demand reduction. This aspect was investigated by [53] where three case studies, namely high-rise residential buildings, office buildings and commercial buildings were analysed to quantify the benefits of peak shaving. 15 EVs achieved a peak demand reduction of 9.34-10.62%, 27.21% and 15.25%, respectively.

Few works evaluated the benefits of V2G for behind-the-meter services [57], [58]. In particular, in [57], the possibility of integrating EV charging with the energy generated by PV systems and a backup solution in case of emergency conditions were analysed. They applied their energy management strategy to a commercial building with 220 office-working stations, a 341.6 kWp PV installation, a 60kWh stationary storage and 48 EVs. The results showed that V2G can optimally integrate with PV by charging during periods of excessive generation

63 and supplying the evening demand. They also validated backup provision in emergency conditions. Similarly, in [58], a model for grid stabilisation with 250 EVs residential/commercial buildings in Brazil was developed. A three-level tariff was considered for the case of peak demand reduction, while the variability of the net power exchange was minimized to improve grid stability. However, they found that optimising grid stability does not lead to the maximum profit for users, which further emphasises the need for MOO strategies, as those implemented in this research.