MINISTERIO DE EDUCACION

The microgrid studied during this Chapter is shown in Figure 5-2, with numerous network constraints that would be violated without smart control. It is likely that the microgrid will have been built over time to have such constraints, whereby it has been economical to utilise smart technologies rather than reinforce the network at each expansion. The progression of the microgrid from initial build to its constrained state is described below.

1. Initially only the large building is fed from the Transformers. The building peak load is within the thermal capacity of the cable supplying it from the Transformers. The Transformers are over rated with the expectation of further feeders being added with the re-development of the local area where the electrical network supplies.

2. A small solar system is installed with its exporting cable rated sufficient for the peak export.

81

3. An extension to the building load pushes its peak load above the capability of the supplying cable. It was economical to install a peak shaving ESS rather than reinforce the network.

4. An extension to the solar system is installed, which results in an overload of the supplying cable at peak export. A smart EV charging car park VESS is built next to the extended solar system to ensure that the power flow remains within the cable thermal limit.

5. An uncontrolled EV charging car park is built consisting of both rapid and standard charge points. This additional load means there is a risk of transformer overload and building under voltage in a transformer N-1 scenario. Due to the presence of the ESS and VESS, a smart solution was employed to protect the transformer and building under voltage in an N-1 condition.

Figure 5-2 Urban microgrid under analysis

The electrical network presented in Figure 5-2 was modelled in the IPSA2 software, and the impedance parameters are described in Section 5.2.1. IPSA2 is a commercial power systems analysis software package developed initially by the University of Manchester Institute of Science and Technology (UMIST) in 1975 and is now supported by TNEI Services Ltd [138]. The IPSA2 load flow algorithm is based on the Fast Decoupled Newton–Raphson algorithm [139]. In this work, IPSA2 has been scripted using Python to apply a load profile, run a load flow, and to extract the calculated voltages, power flow and electrical losses at all locations of the network. This allows the full AC load flow calculation to be utilised during control

82

algorithm decision making under uncertainty as described in Section 5.3, and for determining the network state once that decision has been made as described in Section 5.4.

The modelling of the large city centre office building load is described in Section 5.2.2. The model of the ESS is described in Section 5.2.3. The modelling of the solar PV generation is described in Section 5.2.4. The model of both uncontrolled and controlled EV load is described in Section 5.2.5. The value of the energy consumed within the urban microgrid is described in Section 5.2.6.

5.2.1 Electrical network parameters

The electrical network model consists of a source supplying two transformers which in turn supplies three cable feeders and the load of the microgrid. The source is described in Section 5.2.1.1, transformers in Section 5.2.1.2 and the cables in Section 5.2.1.3.

5.2.1.1 Grid connection

The grid connection is modelled as the slack bus with a fault level of 180 MVA, based on the Corporation Street 11 kV incomer to Science Central, Newcastle-Upon-Tyne, UK [140].

5.2.1.2 Transformers

Both transformers are of the same type, as described in Table 5-1. A replacement 11/0.4 kV 1.4 MW transformer was assumed to cost £40,000 based on discussions with contacts within industry.

Table 5-1 Transformer parameters in urban microgrid model

HV (kV) LV (kV) Rating (MVA) Impedance (pu) X/R Tap setting (%) 11.0 0.433 1.4 0.11 6.33 -2.5 5.2.1.3 Cables

The cables within the model are as described in Table 5-2. The impedance parameters are listed in Table 5-3 and based on the Universal Cable XLPE Cable Catalogue [141]. A replacement cable was assumed to cost £75/m based on [142, 143].

83

Table 5-2 Cable types in urban microgrid model

From To Cable type

(mm2₎ Number of cables Cable length (m) Transformer Building 120 5 250 Transformer Solar 95 1 100 Transformer EV charging 300 5 100

Table 5-3 Cable parameters

Cable type

(mm2₎ Voltage (kV) Ampacity (A)

Resistance (Ω/km) Reactance (Ω/km) 95 0.4 319 0.247 0.073 120 0.4 363 0.197 0.073 300 0.4 592 0.080 0.072

5.2.2 Large city centre office building

Load data was taken from the Newcastle University Business School for a single

representative day, 22 July 2013, and scaled to an average daily peak load of 750 kW. A summer day is chosen in order to be consistent across the network and is required in order to obtain a large uncertainty in solar generation. With regular repeat business operations based on time of day, it was assumed that this load profile could be forecasted with small variations, assumed to be within 5% of the load experienced on the representative day. The load for a particular settlement period is determined by sampling a normal distribution assuming 3 standard deviations between the nominal forecast value and the largest variation expected. Therefore 99.7% of all sampled values should fall within the modelled UI.

5.2.3 Energy storage

The ESS is assumed perfectly controllable with no uncertainty. It is appropriately sized such that its power and energy constraints are sufficiently large (1.0 MVA, 5.0 MWh) to not place additional constraints on the power set-points determined by the RO LP formulation

developed in Section 5.3. It is assumed the ESS is based on Vanadium Redox Flow technology, where the half cycle degradation cost was assumed to be £12/MW over a 30 minute settlement period with a round trip efficiency of 80% based on [144-146].

84

5.2.4 Solar PV generation

Assuming clear skies and a solar collector produces it’s rated power when the sun is directly overhead, and that solar collectors are on average placed facing normal to the earth’s surface, the maximum available power can be calculated using the cosine of the solar zenith angle, in per unit on the system’s rated power. The solar zenith angle is the angle between the ray normal to the earth’s surface at the point of interest and the ray that points directly at the sun [147]. The minimum generation of a solar collector is always zero. Based on this analysis, the nominal forecast value and largest variation expected around that forecast can be set equal to one another and to half the maximum generation calculated by the solar zenith angle based on time of day and time of the year. The load for a particular settlement period is determined by sampling a normal distribution assuming 3 standard deviations between the nominal forecast value and the largest variation expected. Therefore 99.7% of all sampled values should fall within the modelled UI.

5.2.5 Electric vehicle load

The smart charging EV car park is a VESS based on the analysis presented in Chapter 4 however the maximum and minimum power and energy bounds are considered uncertain and an appropriate UI is applied in this Chapter. Similar to the ESS described in Section 5.2.3, the power and energy bounds of the VESS are such that they do not place additional constraints on the power set-points determined by the RO LP formulation developed in Section 5.3. The VESS parameters were based on li-ion technology [144-146] since this is used for the majority of EVs [64-66, 75]. The degradation cost was applied only when discharging and thus the full cycle degradation cost of £30/MW over a 30 minute settlement period was used. The degradation cost associated with charging was assumed zero since the vehicles must charge anyway. A round trip efficiency of 95% was assumed.

The uncontrolled standard charging EVs are based on the analysis presented in Section 3.4.1. When considering the uncertainties associated with a 30-minute-ahead forecast, the developed forecasting model is employed. When considering the uncertainties associated with day-ahead forecasts, the longer term diurnal analysis uncertainty is applied.

The uncontrolled rapid charging EVs are based on the analysis presented in Section 3.4.2. Since the time-step of 30 minutes used in this Chapter is longer than the 24 minutes identified in Section 3.4.2 as that where the uncertainty can be reduced from the long term diurnal analysis, the long term diurnal uncertainty is applied regardless of whether considering the uncertainties associated with 30-minute-ahead or day-ahead forecasts.

85

5.2.6 Value of energy

The value of energy was assumed at £45/MWh based on UK wholesale spot prices observed during 2016-2017 [148].