Ability of the VPP to deliver the EFR service using the developed control algorithm

In order for potential EFR providers to demonstrate their offering, National Grid has

published system frequency data at a one second resolution. One representative day has been simulated using this data, in conjunction with Figure 4-12 and the proposed control algorithm. The aggregate load of the VPPs flexible assets is shown in Figure 4-17 along with each assets VSoC. The same study was undertaken without managing the VSoC of the assets, shown in Figure 4-18. During the day of simulation there were 11 minutes when a service could not be realised, during which the service was requested for 5 minutes. The deadband of Figure 4-12 could have been used to ensure VSoC when the assets are operating individually and without coordination, however with the proposed algorithm this is not required meaning the deadband could be used to layer other commercial services, thus increasing revenues. Furthermore, it can be observed in Figure 4-17 that the VSoC for the ESS, Core and USB remain close to 50% with a relatively large headroom of storage capacity unutilised. This suggests that by using the proposed control algorithm either; the storage capacities could be minimised reducing initial capital expenditure, or power ratings increased maximising potential EFR revenues.

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Figure 4-18 VSoC of assets delivering the service without coordination

The proposed algorithm successfully maximizes power availability, however it does this without considering the economical or environmental costs associated with the use of each of the assets.

4.6 Chapter conclusions and contributions to knowledge

The stochastic nature of EV charging requirements has been considered and the aggregate flexibility calculated for the grid. When aggregated to form a VESS, a higher level controller can consider the vehicles as a more traditional ESS but with varying power and energy limits in time. An internal energy management control scheme has been developed to realise the grid requested demand within the advertised flexibility, prioritising at the highest level the EVs SoC to be at a minimum level at the departure time. MCS has been used to show the resulting aggregate power exchange delivered by the VESS to the grid for two fictitious grid requested demand profiles. Over the full day, the probability of realising profile A was 99.98% and the probability of realising profile B was 98.83%.

An example of how the VESS could be used within a real VPP at Newcastle Science Central has also been proposed, to deliver the new EFR service. It was shown that through

coordination, the various flexible assets within the VPP could appear in aggregate to have a larger energy capacity than if operating individually. This suggests that either the storage capacities could be minimised reducing initial capital expenditure, or power ratings increased maximising potential EFR revenues.

The Science Central site was considered as a VPP, rather than microgrid, because the flexible assets can operate anywhere within their controllable power range without causing power flow or voltage constraint issues within the network. The EFR service was delivered to the wider macrogrid, rather than the local electrical network. It was shown in Section 3.2 however

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that power flow constraint violations are expected in domestic distribution networks when EV penetrations reach 40%, and therefore the ability of the VESS to respond to the local needs of the network should also be considered. This is investigated in Chapter 5 where a formulation is developed to determine the optimal level of conservatism of determining ESS and VESS power set-points to protect network constraints against the load and generation uncertainty caused by uncontrolled EV charging and solar generation respectively.

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Chapter 5 Risk based approach to voltage control and power flow

management in urban microgrids

5.1 Introduction

It was determined in Chapter 3 that largescale uptake of uncontrolled EV charging could cause distribution networks to exceed their operational limits without mitigation measures implemented. One way of mitigating voltage and power flows limit excursions within networks is to utilise ESS, where a methodology was developed in Chapter 4 to enable a VESS from controlled EV charging. Determining the economically optimal power set-point of ESS and VESS to prevent voltage and power flow limits from being exceeded when the network is under load and generation uncertainty is the subject of this Chapter. The links of model and information flow from previous chapters is summarised in Figure 5-1.

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When a network is operating under load and generation uncertainty, the exact power required of an ESS or VESS to mitigate voltage and power flow constraint violations is unknown. A control system could over protect thus ensuring the limits are maintained while also costing more than necessary through battery degradation. On the other hand, a control system could under protect causing a limit violation with an associated cost. This Chapter takes a risk based approach, using a RO LP formulation and the BoU that controls conservatism, to balance the costs associated with over protecting against the costs associated with under protecting the network against power flow and voltage limitations while operating under load and generation uncertainty. In this Chapter, the term ‘optimal’ refers to the level of conservatism displayed when determining the power output of the ESS and VESS, despite the load and generation uncertainty, to appropriately balance the costs associated with failing to protect the network from power flow and voltage limit violations with the costs of procuring services from the ESS and VESS to achieve the lowest overall network operating cost.

The modelled urban microgrid utilised during this Chapter is introduced in Section 5.2 along with the uncertainty associated with the load and generation connected to the network. The RO LP formulation to determine the power set-points of the ESS and VESS is developed in Section 5.3, with the methodology used to determine the cost of operating the network following implementation of the ESS and VESS power set-point developed in Section 5.4. The economically optimal level of conservatism, and associated probability of ensuring voltages and power flows remain within their respective limit, is determined in Section 5.5.