Electric Vehicles Support for Intentional Islanding: a Prediction for 2030 I. Grau, S. Skarvelis-Kazakos, P.Papadopoulos, L. M. Cipcigan and N. Jenkins School of Engineering Cardiff University Cardiff, Wales, UK
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Abstract—A future perspective micro-grid with a medium-term prediction for 2030 is described. The present state of intentional islanding is briefly reviewed. Distributed Generators (DGs) and Electric Vehicles (EVs) to sustain the local distribution system in the event of interruption of supply are considered. Projections regarding their availability are presented and different penetration scenarios are addressed. Environmental benefits of a future micro-grid are described through a comparison of the maximum and minimum EV and micro-generation penetration predictions.
I.
INTRODUCTION
Major part of CO2 emissions in UK arise from the electricity and transportation sector [1]. Power system modifications and updates may assist to CO2 emissions reduction targets of 60% by 2050 [1]. The roadmap for those targets has already been planned, beginning from the ´20/20/20´ targets mentioned in [2]. Power generation trends aim to increase the installed capacity of renewable energy. In the UK, 30% of electricity is expected to be generated from renewable sources by 2030 [1]. According to a study headed by Element Energy, about 1 million micro-generation units are expected to be installed by 2020 and 3.3 million units by 2030, without any additional policy intervention. This corresponds to installation in up to 10% of UK homes by 2030. Under the more optimistic scenarios presented in this report, micro-generation technologies will contribute up to 30 Mt CO2 reduction by 2030 [3]. With respect to the transportation sector, policies are starting to develop, aiming to promote the growth of Electric Vehicles (EVs), for contribution to the targeted decarbonisation [4]. Switching though from fossil fuel driven cars to electric driven ones, would imply a high increase in the electric demand. In order to cope with the new demand, new infrastructures or the upgrading of the existing ones may be required. The projected annual UK demands that [4]’s authors provide for 2030, is 390 TWh while generating
capacity of 120 GW will be available. [4]’s authors have categorized their projections into four different scenarios, based on the incentives provided for encouraging EV penetration. Those scenarios are discussed in Section IV of the paper. The massive integration of EVs’ batteries into the grid may be seen as an opportunity for high energy storage capacity availability. Although the mobility of EVs may be seen as a constraint, it is found that the cars are parked on average, 90% of their lifetime [5]. The biggest fraction of this time is overnight, when wind power generation is increased. The utilization of the EVs’ batteries at such times could maximize wind power generation’s benefits. Further, the development of Vehicle to Grid (V2G) technology could enable ancillary services provision [5] or normalization of daily peak loads. A proposition made in this research, is the use of DG and EVs to support intentional islanding. Opportunities for improvement of security of supply to customers during major events, such as floods or storms, may hence arise. Higher reliability and safety of the future network can be achieved with reasonable cost, through utilization of the already available assets. The paper is organized as follows: Section II presents a description of the UK Urban Radial Network used for the hypothesis made in this paper. Section III discusses issues concerning intentional islanding for the medium term future, compared with the actual strategies followed up to date. In order to support the concept presented, sufficient microgeneration and EV penetration should exist. Section IV presents the projections made for EV per household penetration for 2030 compared to 2010 ones. Section V provides the micro-generation penetration levels and CO2 emissions savings by the 2030 proposed system. Section VI concludes the paper aggregating the major findings of this work.
II.
UK GENERIC DISTRIBUTION NETWORK DESCRIPTION
In order to obtain a prediction for the 2030 usage of DGs and EVs for intentional islanding, a UK LV generic network was considered. The considered network topology and data has been approved by a number of UK DNOs and is deemed to be representative of typical urban UK distribution networks [6, 7]. This topology consists of a HV/MV primary substation feeding the MV network. The MV network comprises of two 33/11.5kV 15MVA YY0 transformers in order to ensure the security of supply, and eight MV/LV distribution substations feeding the LV network. A number of 18,432 customers are supplied in total. From each MV/LV substation, there are four outgoing radial feeders, serving in total 384 single phase customers. One feeder is modelled in detail while the rest are simplified as lumped load. Fig. 1 presents a schematic representation of this case. In the UK generic model load demand figures produced by the Electricity Association were used, with 0.16kVA for minimum and 1.3kVA for maximum demand respectively [6]. 33/11.5kV
Source
~ 500MVA
11kV/0.433kV Smart microgrid for network emergencies
Figure 1. UK Generic LV Distribution Network
The LV side of the UK generic network model with 384 customers is used as base to illustrate the 2030 proposed intentional islanding micro-grid. III.
intentional islanding strategies can be followed in future networks, including the participation of EVs and augmented micro-generation installed capacity. Since EVs are likely to be present, their availability as sinks or sources of power could be combined with the increased output from micro-generation, in order to minimize the amount of backup generation needed. In addition, microgrids conventional central energy storage capacity may be reduced and replaced by mobile EVs batteries. The prerequisite for this coordination is that EVs should be V2G capable. Furthermore, portable backup generators could be considered as a feasible solution, in order to cover generation needs in the case of a major event. The smart micro-grids for network emergencies is an integrated system which can operate connected to the grid or during intentional islanding, in a coordinated, controlled way. In order for a micro-grid to be able to operate autonomously it has to be controlled such that it can respond to load variations. This must be achieved rapidly enough to ensure that voltage and frequency regulation requirements can be met. In order to meet these requirements the micro-grid has a number of units which can be controlled: local micro-sources, electric vehicles, backup generator, loads and large energy storage unit. The 2030 micro-grid concept would need a micro-grid central control unit which would be responsible for the coordination and management of all the distributed unit controllers as presented in Fig. 2. The 2030 central controller would need to have the information of the additional grid assets, namely EVs and additional micro-sources. This would enhance the plug and play capability of each device, simplifying its functionality and improving the operation of the micro-grid.
INTENTIONAL ISLANDING
Short-term intentional islanding strategies rely on energy storage devices and backup generators; the function of these appliances is to ensure a balanced system in the case of the loss of the main grid. The mentioned equipment is expected to cope with the system voltage and frequency regulations through different control strategies [8]. While the backup generator is the main responsible for power injection, the role of the energy storage is to absorb the excess of energy supplied by the micro-grid and to support the generator during abrupt balance changes. Inverter based control for micro-sources and energy storage has been identified in [9, 10] and artificial intelligent multi-agent control has been implemented in [11]. Present technical recommendations, such as G59/1 [12], G83/1 [13] and IEEE 1547 [14], specify that the microsources allocated in the micro-grid must be disconnected in the case of loss of grid. The ideal future scenario is to achieve a smooth transition between grid-connected and islanded mode without micro-sources disconnection [15]. New
Figure 2. 2030 Smart micro-grid for network emergencies
A way to do a 2030 prediction is by looking at trend. Some of the identified trends [3] present that the amount of micro-generation will be significant in 2030. Predictions regarding EV availability are discussed in Section IV as follows. IV.
ELECTRIC VEHICLE PENETRATION PREDICTIONS
Thousands
The availability of EVs for the years under study is examined. Estimations for 2010 and 2030, to reflect short and medium term case scenarios, are presented. Since considerable penetration of EVs is possible to commence by 2030 [4, 16], two different scenarios based on [4] are reproduced. These scenarios assume minimum EV penetration, of 3 million EVs and maximum EV penetration of 20.6 million for UK as a whole in 2030. For 2010, the electric vehicles considered to be in the UK roads are between 4,000 and 5,000. In the local level of the residential LV urban distribution network, the number of households that each 400V substation serves is 384 [17]. The level of aggregation considers four LV segments per feeder with 24 customers each. Thus, in order to reflect the EV penetration scenarios in the network under study, the number of households and the total number of cars for 2010 and 2030 is estimated. Latest published data from [18] are used for this scope. The authors of [19] support that although the European population will remain stable until 2030, the number of households will increase by 18% compared to 2006. Fig. 3 presents the UK population and number of households from 1970 to 2020 according to [18].
60,000 45,000 30,000 15,000 0
UK population UK Number of Households
1970 1980 1990 2000 2010 2020 Year Figure 3. UK Population and Number of Households
Table I presents the estimation of population and UK households for 2010 and 2030. The values of R-squared are also included for each prediction in the following table. The “R-squared quantity is a measure of the goodness of fit for a straight line” or as here for a polynomial equation [20]. “It is else called the Pearson correlation coefficient” [20]. The correlation coefficient can vary from 0 to 1, with 0 expressing no relationship between the dependent and independent variables and 1 a nearly perfect relationship between these variables. In this particular case, it expresses how well, the equations constructed to express the increase in population, households and cars, fit with the historical data trend.
TABLE I.
UK POPULATION AND NUMBER OF HOUSEHOLDS IN 2010 AND 2030
Number of UK Households (million) 2010 2030 26.5 30.5 0.9984 R-squared
UK Population (million people) 2010 2030 60.2 64.6 0.9948
In 2007 the number of cars in UK was 83.7% of the total vehicle fleet. Assuming that the trend in car increase shows the same trend as the total vehicle increase (since it has the biggest fraction) estimations for 2010 and 2030 for number of UK cars are presented in Table II. TABLE II.
UK NUMBER OF CARS FOR 2010 AND 2030 Number of UK Cars (million) 2010 2030 34.259 42.423 0.997 R-squared
The UK average number of cars per household is estimated to be 1.29 and 1.39 in 2010 and 2030 respectively. According to the EV market penetration levels proposed by [4], Table III presents the number of electrified vehicles per 24 customers, considering equal penetration per household. TABLE III.
NUMBER OF ELECTRIFIED VEHICLES FOR MINIMUM AND MAXIMUM PENETRATION PER 24 CUSTOMERS
Number of EVs per 24 Customers Minimum Penetration Maximum Penetration 2010 2030 2010 2030 -3 -3 2.36 16.2 3.63*10 4.53*10
This section, using primary projection data and EV penetration levels from the literature, attempted to predict the number of EVs that each household is likely to possess in 2010 and 2030. The possible emissions reduction that EV use is able to provide, compared to Internal Combustion Engine (ICE) vehicles, could be an additional incentive and boost the achievement of high EV penetration levels. Together with the prospective emissions reduction that micro-generation is likely to offer, a roadmap towards greener micro-grids might become a reality. V.
LIFE-CYCLE CARBON FOOTPRINT
In order to examine the life-cycle carbon emissions of the studied system, the Global Emission Model for Integrated Systems (GEMIS) was used. GEMIS is a life-cycle analysis program and database for energy, material, and transport systems. The analysis is comprised of three phases, the manufacturing, operational and decommissioning phase. The emissions from transporting the equipment prior to installation are also considered. In the manufacturing phase, the equipment is split into components, thus providing an inventory of materials used. The materials embodied carbon is then calculated. The operational phase includes the emissions associated directly
with the system usage. Finally, the decommissioning phase includes the emissions relative to the handling of the system end-of-life waste. A more detailed description of the analysis is provided in [21]. All emissions are expressed as CO2 equivalent. The micro-generation installed in the studied system is based on an estimate of the 2030 micro-generation mix, found in [22]. Minimum and maximum penetration predictions were taken from [3] and scaled down to the studied system level. Typical installed power per customer, according to [6] is 2.5kW for wind turbines, 1.5kW for photovoltaics and 1.1kW for micro-CHP (Combined Heat and Power). Micro-generation is aggregated, and 384 domestic customers are considered. Emissions for the current situation (2010), as well as for 2030 projections are calculated. The typical installed values are assumed to be the same for 2030. For the 2030 calculations, the number of electric vehicles is according to the predictions set in Section IV. Tables IV and V present the characteristics of the 2030 micro-grid. For 2010, only one photovoltaic installation is considered for the 384 customers, including balance of plant (inverter) [3]. TABLE IV.
2030 MICRO-GRID’S GENERATION CHARACTERISTICS [3, 22].
Wind Turbines
Minimum Power (kW) 10
Maximum Power (kW) 27.5
Photovoltaics
3
9
Component
Fuel Cell (Natural Gas)
9
27
Micro-turbine (Biogas)
6
12
Stirling Engine (Wood Pellets)
15.6
45.6
Total
TABLE V.
43.6
121.1
are assumed to be powered by 50% electricity and 50% petrol [4]. Table VII shows the life-time carbon savings that would be achieved against conventional options for the considered micro-generation, the electric vehicles, as well as the whole micro-grid. Minimum and maximum penetration scenarios for the year 2030 are compared. The base for comparison is the UK grid carbon intensity (248 gCO2/kWh), as predicted in the low-carbon resilient scenario in [25] and a typical gas boiler (204 gCO2/kWh). Likewise, for the electric vehicle, results are compared with the 2020 vehicle emissions target of 95 gCO2/km set by the European Parliament in 2009 [26]. Finally, in 2010, the photovoltaic installation life-time emissions are estimated to be 5.4 tCO2. If the UK grid was to provide the same amount of energy, the predicted emissions would be 2.5 times higher.
TABLE VI. COMPARISON OF LIFE-TIME EMISSIONS AND EMISSION FACTOR OF ELECTRIC VEHICLES AND CONVENTIONAL VEHICLES [4] FOR 2030. Electric Vehicles
Conventional Vehicles
Vehicle Type
EV
PHEV
Petrol
Diesel
Life-time emissions (kgCO2)
9,882
15,421
21,639
19,606
Emission factor (gCO2/km)
54.90
85.67
120.22
108.92
TABLE VII.
LIFETIME CO2 SAVINGS FOR THE 2030 MINIMUM AND MAXIMUM PENETRATION SCENARIOS. Lifetime CO2 Savings (tCO2)
Scenario
Minimum
Maximum
Micro-generation (including heat)
1527.09 (72.78%)
4364.13 (73.07%)
EV / PHEV
98.28 (15.22%)
770.17 (17.37%)
Micro-grid
1625.37 (59.23%)
5134.30 (49.33%)
2030 MICRO-GRID’S ELECTRIFIED VEHICLES’ NUMBER. Vehicle Type
Minimum
Maximum
Electric Vehicles
6
60
Plug-in Hybrid Electric Vehicles
31
199
The electrical load that is not covered by the microgeneration, including the EVs, is covered by the grid. Similarly, the excess heat load is covered by a typical gas boiler. In addition, when the system is interconnected, a conventional power station would be part-loaded. The effect on emissions of part-loading a 500MW Combined Cycle Gas Turbine (CCGT) is also accounted for. Data for the materials of the electric vehicle were drawn from [23] and [24] and for the usage from [4]. In Table VI, a comparison between the predicted emissions from an electrified vehicle (full-electric or plug-in hybrid) and a conventional vehicle is given, for 2030. Plug-in hybrids
VI.
CONCLUSIONS
The importance of intentional islanding during major events is expected to raise in future smart grid scenarios. The opportunity of utilizing the already available EVs and microgeneration was examined in this paper. A study case for 2030 was constructed, due to the fact that EV and micro-generation penetration are likely to be considerable. In this paper, it was found that 10-67% of the customers in 2030 are likely to own an EV. Hence, the available mobile storage capacity in a 2030 micro-grid of 384 customers would vary between 391.2 kWh and 3,112.8 kWh (based on [4]).
For this result it is assumed that usable storage is 80% of the nominal battery rating, to increase Lithium batteries’ lifetime. It is predicted that for the 384 customer micro-grid the installed micro-generation would be between 43.6 kW and 121.1 kW. The carbon footprint, as well as the emissions savings, of the studied system was also investigated. The micro-generation considered has the potential of saving three quarters (73%) of the CO2 emissions that would otherwise be emitted by conventional generation to supply the same amount of energy (electricity and heat). Likewise, the electrified vehicles (EVs and PHEVs) would save 15.22%17.37% of the emissions compared to absolute utilisation of conventional vehicles. Smart micro-grids can be seen as a vehicle in which to package innovative technologies that will ensure the reliable supply of electricity for the consumers in the event of network emergencies. However, the applicability of the micro-grids for the 2030 networks cannot be accurately predicted. An attempt was made in this research, in identifying the opportunities that micro-generation sources and the use of new technologies like electric vehicles can bring in supporting intentional islanding. Based on this, it is proposed that network operators could make use of islanding as part of their strategy to improve the security of supply to customers in selected parts of the network.
[8]
[9] [10] [11] [12]
[13]
[14] [15]
[16] [17] [18] [19] [20]
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