Model for evaluating impact of battery storage on microgeneration systems in dwellings

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Energy Conversion and Management 49 (2008) 2413–2424 www.elsevier.com/locate/enconman

Model for evaluating impact of battery storage on microgeneration systems in dwellings D.P. Jenkins *, J. Fletcher, D. Kane Energy Academy, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK Received 23 May 2007; accepted 6 January 2008 Available online 4 March 2008

Abstract Lead-acid batteries are a suitable technology for on site storage of microgenerated electricity. In this paper, an algorithm is developed that will assess, with an appropriate temporal resolution of data, the ability of lead-acid battery storage in capturing AC and DC power generated from photovoltaic cells, combined heat and power and wind turbines. The assessment includes the impact that the storage element has on the import and export of energy from the electrical grid and is a valuable tool in determining an optimum storage capacity. Used effectively, storage can increase the versatility of a microgeneration system by satisfying the highly variable electrical load of an individual dwelling, thus changing usage patterns on the national grid. Empirical electrical load demands are considered with a 1 min temporal resolution and compared with microgeneration (and battery) supply profiles with a similar temporal accuracy. The results show that, when producing on site electricity through microgeneration, suitably sized storage can reduce export substantially (by over 90% in some cases) and store this energy at a typical round trip efficiency of 70–72%. The developed model accounts for typical losses in a battery storage system, including that associated with inverters, power electronics and the efficiency of charge/discharge cycles. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Storage; Battery; Microgeneration; Energy; Demand

1. Introduction The issues relating to how to optimise the use of microgeneration for dwellings in the UK are many. Major problems, particularly with solar and wind generation, include the intermittent nature of the power produced and the mismatch between the instantaneous generation and demand at any time. The result of this is that, even when many different technologies are used in combination (see Section 3), there will generally be periods of excess generation (generation greater than demand) and shortage (demand exceeds generation). Studies [1–4] use a daily/annual kW h approach to look at the problem; if 1000 kW h is produced by a wind turbine, then all of this will be used to displace grid electricity. This approach implies either that the generated power is *

Corresponding author. Tel.: +44 (0) 131 451 4329. E-mail address: [email protected] (D.P. Jenkins).

0196-8904/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.enconman.2008.01.011

used by a neighbouring building (and any neighbouring building will do likewise) or that there is a storage solution present with 100% efficiency. It also ignores the problem of matching supply and demand on an appropriate temporal resolution. Other studies deal with this problem by explicitly stating the export conditions [5,6], so that export meters are assumed to be present that allow on site electricity to be sent to the grid (with associated cost and carbon credits). Subsequent carbon calculations will, therefore, account for this export in a suitable way (for example, carbon credit per kW h export). However, this approach does reveal a dependency, should such generation become widespread, on the utility companies dealing with the export in a way that actually reduces the output of power stations [7,8]. It also poses the paradox of a utility assisting the occupant of a building in purchasing less electricity. Another approach is to assess accurately the amount of energy that can be stored on site, so that export becomes

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Nomenclature Dt PD PAC PDC PBUS P+ P PRES Pgrid Pbatt Pexp eA eB1 eB2 eC VB

time resolution of demand data (h) power demand of building (W) AC power generated at source (W) DC power generated at source (W) total power at AC bus (W) power surplus (W) power shortfall (W) renewable energy system power used towards demand (W) demand satisfied by grid (W) power reaching demand from battery after losses (W) power exported to grid (W) efficiency of inverter A (0 < eA < 1) efficiency of inverter B (AC bus to battery) (0 < eB1< 1) efficiency of inverter B (battery to load) (0 < eB2 < 1) efficiency of charge controller (0 < eC < 1) voltage across battery (V)

negligible or even non-existent [9–14]. As well as reducing the need for export meters, this raises the possibility of dwellings becoming semi-autonomous (though for full autonomy a fully renewable solution would be difficult, an on site fossil fuel/biomass generator is likely to be needed for back up). To perform an adequate assessment and optimisation of energy storage, the variability in demand and supply, on a suitable timescale, must be considered. Using, for example, an hourly-averaged demand profile ignores the peak electrical demands throughout a given day, but these peak demands must be satisfied instantaneously. For example, during a time of low on site power generation, if there is a power requirement of 4 kW at 12:15, then we cannot assume this will be met by, say, 4 kW of power generation that occurred at 12:00. In fact, this 4 kW demand would only be met by the on site generation if there is an adequate storage facility. For short time scales, capacitors or supercapacitors may be appropriate solutions [15]. However, if longer duration storage options are considered, lead-acid batteries are a suitable option that uses a mature, low cost storage technology. This study develops an algorithm, for minutely electrical demands, to assess the efficiency of storing renewably generated electricity that would otherwise be exported. It also provides a tool to optimise energy storage for different generation and demand profiles (for example, PV generation profiles are different from wind derived energy, and this has implications for supply–demand matching and import–export profiles).

VC ID0 ID IDmax IC0 IC ICmax a n N CD C0 Ct DC

voltage across cell (V) require discharge current to meet load (A) actual discharge current from battery (A) maximum discharge current allowed by charge controller (A) available current for charging battery (A) actual charge current (A) maximum charge current allowed by charge controller (A) discharge efficiency (0 < a < 1) number of cells in series (within individual battery) number of batteries in parallel (in battery pack) charge removed from battery during discharge (A h) maximum capacity of battery (A h) capacity of battery at time t (A h) change in charge of battery over time Dt (A h)

2. Battery model A simple model was developed to account for electrical storage on a suitable (minutely) time basis. The model simulates varying sources of generation (AC and DC) and power losses in associated inverters and power electronic equipment. The following is a description of the algorithm used to assess the effect of storing rather than exporting surplus generated energy. Model parameters are required (see Table 1), relating to the power generated on site, the power demand of the building and the battery configuration and size. While the described algorithm is designed with lead-acid batteries in mind, the information obtained from other studies [16,17] can be used to align the model with other battery types. 2.1. Temporal input To define the generated power and the required power, a certain temporal precision is necessary. 2.1.1. Electrical demand data The first step is to define the energy demand of the building. The temporal resolution of the electrical demand of the building is vital for an accurate assessment of energy use. Studies [18,19] have shown that using, for example, hourly data rather than minutely data to define a domestic electrical profile throughout the year can dramatically affect the process of supply and demand matching. For this reason, the demand data, by default, has a minutely temporal precision.

D.P. Jenkins et al. / Energy Conversion and Management 49 (2008) 2413–2424 Table 1 Battery model parameters with default values Required input

Suggested default

Building electrical demand (kW)

Minutely profile throughout year Minutely profile throughout year Minutely profile throughout year 2.1 C/5 C/10 User defined User defined 50 A h 20% 100% Based on max. discharge power Based on max. DC generation 98%

DC onsite generation (kW) AC onsite generation (kW) Voltage across cell (V) Maximum discharge rate Maximum charge rate No. of cells in series No. of batteries in parallel Maximum cell capacity Lower capacity limit of battery Maximum capacity limit of battery Battery-to-load inverter power rating (kW) DC-to-load inverter power rating (kW) Efficiency of charge controller

Improved resolution, for example demand per second, can be important for a domestic profile, where a kettle or other short duration heating element can cause clear spikes in demand over short time scales. However, for this study, it is assumed that variations on such time scales could be met by, for example, capacitors or super-capacitors that can rapidly charge and discharge energy over the very short sub-minutely durations involved. It is assumed that the load demanded by the building is AC. 2.1.2. Microgeneration The main generation technologies used with the storage model are photovoltaic (PV) panels, combined heat and power (CHP) units (both fuel cell and Stirling engine) and wind turbines. The output from a wind turbine or Stirling engine CHP is assumed to be AC (regulated for a domestic load), and the output from PV and fuel cell CHP is assumed to be DC (prior to being passed through an inverter). While, as with the demand data, minutely generation data is preferable, this might not always be possible. For this study, minutely CHP electrical output is used, as discussed elsewhere [20]. However, the output of PV and wind turbines, if no empirical generation data exists, is dependent on the climate data used. In the case of the wind turbine, 10 min wind speed data was collected at Heriot-Watt University (suburban environment) for an entire year. This provided enough detail to account for the natural variability of the wind (it is suggested that half hourly data and, certainly, hourly data would be unsuitable in this respect). The power curves used for wind turbine outputs are documented elsewhere [21]. For the PV output, a simple PV model was used [22] with hourly radiation data from CIBSE Test Reference Year data. The less detailed resolution of the solar data

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is deemed less of a problem than that of the wind, as solar radiation, throughout the year, is more predictable for a given location and sky clearness. Both the PV and wind output were converted into minutely values (by over sampling the 10 min data at the minute rate) for easy comparison with the minutely electrical demand. 2.2. Battery size and configuration The type and size of the battery used can be varied, depending on the needs of the user and the level of on site generation for the building. 2.2.1. Battery size The total size of the battery, in terms of voltage output VB, relates to the number, n, of 2.1 V cells that are placed in series. The actual storage capacity of the entire battery system (in A h), C0, is the sum of the number of batteries, N, in parallel. The storage of each individual cell, Ccell, also needs to be defined by the user, though 50 A h is a suitable default value. Finally, the upper and lower state of charge (SOC) of the battery should be defined. The suggested values (used for this study) are 100% and 20%, respectively. Discharging the cells to 20% of rated capacity is at the margins of acceptability for lifetime [23]. The depth of discharge can easily be changed in the developed model. 2.2.2. Discharge and charge currents The value, at any time, of the battery charge and discharge currents will vary according to the excess or shortage of local power available. However, it is sensible, through the use of a charge controller, to limit the discharge and charge rates to maximum values (IDmax and ICmax, respectively) to protect the battery and ensure an efficient operation. The values chosen for this study correspond to rates of C/5 for discharging the battery and C/10 for charging the battery [24]. When discharging, charge is removed most efficiently at lower currents (see Fig. 1); a suitable discharge rate limit will reduce inefficient operation in this respect. The relationship in Fig. 1 is included to model the impact of discharge current on charge (hence, energy) recovery from the battery. A similar relationship occurs when charging the battery, though to a lesser extent; this relationship is ignored for the battery model operation as the charge rate limit (of C/10) is low enough to justify a charge efficiency of 100%. 2.2.3. Inverter/power electronics As charge is stored, energy losses occur, particularly during the inversion of DC to AC (for use by the building) and the conversion of AC to DC (during battery storage). Fig. 2 shows the conceptual set up for microgeneration being used with battery storage (the charge controller will be part of inverter B; it is shown separately here to high-

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0.8

Inverter efficiency

Ratio of IΔt/Ah removed (%)

1.0 90

80

70

60

0.6

0.4

0.2 50 0

2

4

6

8

10

C/discharge current Fig. 1. Empirical data [24] of charge removed for different discharge rates.

0.0 0

20

40

60

80

100

Inverter power to inverter rating ratio (%) Fig. 3. Measured inverter efficiencies as function of power output (curve fitted to SunnyBoy SB2500).

[25]. Therefore, the combination of charge controller and battery inverter displays a falling efficiency at part loads. 2.3. Calculation algorithm With the building demand and generated electricity available on a minute basis and with the battery sized and specified, the model can calculate the expected storage performance throughout the year.

Fig. 2. Proposed configuration for microgeneration with storage.

light a component that produces a drop in overall system efficiency and is modelled separately of the actual inverter). The configuration is a deemed a suitable compromise between a practical system (suitable for installation in a domestic setting) and also producing minimum losses through power electronics. The efficiencies of the PV and wind turbine inverters are modelled on empirical data from the Sunny Boy SB2500 (see Fig. 3). The efficiency of a given inverter will vary depending on the ratio of the instantaneous power output of the inverter to the power rating and will be a function of fixed and variable inverter losses. The power flow through each inverter is determined by the model at a given time, and the only values required are the power ratings of inverters A (sized based on the maximum DC power generated throughout the year) and B (sized based on the maximum discharge rate from the battery). The inverter between the battery and the AC bus is bidirectional to allow seamless transitions from charging to discharging modes. The efficiency of the charge controller between the AC bus and the battery is a constant value, by default 98%

2.3.1. Power at AC bus The AC bus circuit specified (Fig. 2) is relatively straightforward, with all power being combined at the AC bus and so treated in the same way. Other configurations, for example, allowing DC power to pass directly to the battery without going through the inverter stages, may provide improved efficiencies but involves more complex design and, perhaps, greater expense. Therefore, for the chosen design, the total power at the AC bus, PBUS, is given by P BUS ¼ P AC þ eA P DC

ð2:1Þ

where PAC and PDC are the AC and DC power generated at the source and eA is the efficiency of ‘‘inverter A” (i.e. DC to bus). This efficiency is based on the empirical relationship shown in Fig. 3 and is a function of the ratio of inverter power output at any time to the inverter power rating. The surplus P+ is the difference between the generated power and the load required by the building at that time: P þ ¼ P BUS  P D ;

P BUS > P D

ð2:2Þ

where PD is the demand of the dwelling. Alternatively, for times where the generated power is less than the demand, then a shortfall P exists where P  ¼ P D  P BUS ;

P D > P BUS

ð2:3Þ

D.P. Jenkins et al. / Energy Conversion and Management 49 (2008) 2413–2424

In the event of a surplus, the surplus power is used to satisfy the demand, with the generated power used towards the demand, PRES, given by P RES ¼ P BUS  P þ ;

Pþ > 0

ð2:4Þ

If there is no demand, then the surplus will be equal to the power at the bus (i.e. PRES = 0). 2.3.2. Battery charging During periods of surplus, the battery, if not already fully charged, will receive a charge current, limited by the maximum allowed charge current ICmax. The value of the available charge current IC0 (i.e. ignoring the state of the battery) at any time is given by I C0 ¼

P þ  eC  eB1 VB

ð2:5Þ

where eC is the efficiency of the charge controller (98%), eB1 is the efficiency of the AC to DC converter and VB is the voltage across the battery (the product of the number of cells in series, n, and cell voltage VC (usually 2.1 V)). The actual charge current, IC, that accounts for the state of the battery, is limited by IC 6 ICmax and also SOC < SOCmax, where SOCmax is the maximum allowed SOC for the battery (typically 100%). 2.3.3. Battery discharging At the start of the simulation, it is assumed that the battery is fully charged. In the event of a shortfall, the battery will meet this shortfall by discharging (up to the specified maximum discharge current IDmax). The current from the battery that would be required to meet the shortfall in power, ID0, is given by I D0 ¼

P V B  eC  eB2

ð2:6Þ

where eB2 is the efficiency of the DC to AC inversion (as empirically modelled by Fig. 3). The actual discharge current, ID, accounting for the state of the battery, is limited by ID 6 IDmax and also SOC P SOCmin, where SOCmin is the minimum allowed battery SOC (typically 20%). 2.3.4. Battery SOC Eqs. (2.5) and (2.6) calculate the current entering and leaving the battery at any time, thus the SOC of the battery can be determined. The total capacity of the battery pack at 100% SOC, C0, is a product of the number of batteries used in parallel, N, and the capacity of each individual battery (when referring to the battery in subsequent calculations, it will be this ‘‘battery pack” that is being considered). The discharged capacity CD (in A h) during a time period Dt will, at first approximation, just be the product ID  Dt. However, at high discharge currents, the efficiency of removing this charge is significantly less than 100%. In actuality, the following relationship is more accurate: CD ¼

I D  Dt ; a

06a61

ð2:7Þ

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where a is the discharge efficiency given by an empirical relationship based on Fig. 1, of a¼

13:3  lnðC 0 =I D Þ þ 59:8 100

ð2:8Þ

It should be noted that Eq. (2.8) would have to be limited so that it does not exceed 1. This will ensure that the maximum charge removed in a given time period will always be ID  Dt. A similar relationship exists when charging the battery (i.e. a ‘‘charge efficiency”). However, as already mentioned, this is less pronounced than the discharge equivalent, particularly with the charge current limitations suggested by Section 2.2.2 (so that the charge current should not be high enough to cause large inefficiencies during charging the battery). As a result, the change in battery capacity, Dt, during time Dt is simply DC ¼ I C  Dt  C D

ð2:9Þ

Negative values of DC result during discharge and positive values during charging. The battery charge, Ct, at any time t is, therefore C t ¼ C t1 þ DC

ð2:10Þ

where Ct1 is the charge of the battery during the previous time period. This will be limited by the expression SOCmin 6 Ct 6 SOCmax. 2.3.5. Power satisfying electrical demand The on site power generation will be distributed between the building demand, the battery charge demand and, if surplus is available, export (in that order of preference). The grid is presumed available to ensure that the demand is always met. The power being met by the grid Pgrid is given by P grid ¼ P D  P BUS  P batt ;

ðfor P D > P BUS Þ

ð2:11Þ

therefore P grid ¼ P   P batt

ð2:12Þ

or P grid ¼ P   I D  V B  eB2  eC

ð2:13Þ

where Pbatt is the power output of the battery at any time. Clearly, when no power is being produced by the battery, the power being supplied from the grid will be the shortfall between on site generated power and demand. Also, when the power from the battery is equal to the shortfall, there will be no need for grid import. 2.3.6. Exported power If P+ is positive, it will be possible to export power if either: (a) the battery is already fully charged or (b) the available charge current is greater than the maximum allowed by the battery charge controller (i.e. ICO > ICmax), so that there is surplus power. The power exported as a result of these conditions, Pexp, is given by

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 P exp ¼

 I C0  IC  V B eB1  eC

is situated in Edinburgh (Table 2 is used as a summary only, the full minutely demand profiles are used for all calculations).

ð2:14Þ

for I C0 > I C

This equation is simply the difference in available and allowed charge currents (accounting for efficiencies of the power electronics prior to the battery), multiplied by the battery voltage. VB is assumed relatively constant with battery temperature [26].

3.1. Photovoltaic panels The first scenario chosen is for PV panels with a total peak output of 2.5 kW (close to the upper limit for a domestic installation [28]). This is then passed through appropriate power electronics (as described in Section 2). Through use of the model, the performance of the battery in storing surplus can be evaluated. This is simulated with a 500 A h battery (10  50 A h batteries, in parallel, each battery consisting of 10 cells each in series, with nominal voltage of 2.1 V per cell). Figs. 4 and 5 show the effect of storage compared to a non-storage (export only) solution. Because of the relatively large PV installation, there are significant levels of export for the case without storage. When storage is used, this export is reduced by 87% though, during the summer months, there is still surplus; this could be reduced further by a larger battery size (for example, when the calculation is repeated for a 1000 A h battery size, the export is reduced by nearly 100%). The surplus power (that would otherwise be exported) is stored with a round trip efficiency of 72% (i.e. 72% of the displaced export is recovered by storage).

3. Case studies The algorithm described in Section 2 will now be put into practice to demonstrate the performance of a lead-acid battery in storing on site renewable energy for dwellings. The electrical demand used will be from actual recorded data on a minutely basis to account suitably for supply– demand matching issues [27]. The electrical requirements of the dwelling are summarised in Table 2, and the building Table 2 Electrical requirement of exemplar dwelling Month

Electrical demand (kW h)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

607.3 471.0 398.7 450.5 409.7 371.6 465.1 473.2 489.7 486.0 489.6 531.2

Total

5643.5

3.2. Photovoltaic panels with wind turbine Export increases as the installed peak power level of microgeneration increases. Likewise, the requirement of storage might be similarly increased to account for this. The next scenario looks at a 1.5 kW rated wind turbine being used with 2.5 kW PV. The introduction of wind

Electrical energy provision (kWh)

600

450

300

150

0 Jan

Feb

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Apr

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Jun

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Aug

Sep

Oct

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-150 Export Grid import RES directly

-300

-450

Month Fig. 4. 2.5 kW PV in Edinburgh used to satisfy energy demand of dwelling without storage.

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Month Fig. 5. 2.5 kW PV in Edinburgh used to satisfy energy demand of dwelling with 500 A h battery storage.

makes the distribution of generated energy less predictable throughout the year. Figs. 6–9 show the effect of storage using weather sites with average annual wind speed of 2.0 and 4.9 m/s, respectively (in Edinburgh). For this circumstance, a large battery unit is chosen (1000 A h). The justification for this is that, if an individual were to install a large level of microgeneration, then they may choose to use a large storage device to optimise the return from these units. However, smaller dwellings would clearly have an issue with the size of such a storage device.

The average wind speed of 4.9 m/s results in a reasonable performance of the wind turbine, a calculated annual yield of 2541 kW h based on wind speed data of a 10 min resolution (c.f. 2592 kW h for PV in the same year). Therefore, every month is producing a significant amount of on site generation (with a PV dominated summer and a wind dominated winter). According to the model, the battery stores the surplus at a round trip efficiency of 71%. Because of several windy months, particularly February and March, the battery is not able to store all the export.

600

Electrical energy provision (kWh)

450

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150

0 Jan

Feb

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Apr

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Aug

Sep

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-150

Export Grid import RES directly

-300

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Month Fig. 6. 2.5 kW PV in Edinburgh with a 1.5 kW (high wind site) turbine without storage.

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-150 Export Grid import Battery RES directly

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Month Fig. 7. 2.5 kW PV in Edinburgh with a 1.5 kW (high wind site) turbine with 1000 A h battery storage.

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Export Grid import RES directly

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Month Fig. 8. 2.5 kW PV in Edinburgh with a 1.5 kW (low wind site) turbine without storage.

Throughout the year, 88.9% of the export is displaced and used by the battery. However, when the site is a lower wind speed (with an average of 2.0 m/s, arguably more indicative of a rooftop in the urban environment), a different distribution results, as illustrated in Figs. 8 and 9. At this more modest wind speed, the annual turbine yield is just 277 kW h (this is largely due to the wind speed being below the cut in speed of the turbine for large periods of the year; manufacturers

might not actually recommend installing a turbine in such conditions). The on site generation profile becomes PV dominated, and so, the large storage device displaces almost all (98.9%) of the export (stored with a round trip efficiency of 72%). It is suggested, therefore, that due to the more erratic nature of wind turbine generated energy, a larger sized storage system might be needed to use all of the turbine energy yield (though turbine size and location will affect the degree to which this is true).

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-150 Export Grid import Battery RES directly

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Month Fig. 9. 2.5 kW PV in Edinburgh with a 1.5 kW (low wind site) turbine with 1000 A h battery storage.

3.3. Photovoltaic panels, wind turbine and CHP As a demonstration of storage for a large scale on site generation system, a CHP–PV–wind system is used with a 1000 A h battery. The nature of a heat led CHP unit is that surplus electrical generation is to be expected [29] (for example, with heating load preceding morning rise times while occupants are sleeping). Electrical storage can, therefore, be of particular interest with CHP technol-

ogy. The chosen CHP unit is a 1 kW Stirling engine with 15% electrical efficiency. Figs. 10 and 11 again show the effects of storage. The large battery size displaces the export (using 97% of the export), with only March and April showing existing export available after storage. The round trip efficiency, at 71%, is similar to that of other scenarios. The CHP unit, with heat led control that limits thermal surplus [20,29], has an annual electrical yield of 2340 kW h. With a higher out-

600

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Export Grid import RES directly

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Month Fig. 10. 2.5 kW PV in Edinburgh with a 1.5 kW (low wind site) turbine and 1 kW CHP without storage.

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Electrical energy provision (kWh)

450

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-150 Export Grid import Battery RES directly

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Month Fig. 11. 2.5 kW PV in Edinburgh with a 1.5 kW (low wind site) turbine and 1 kW CHP with 1000 A h battery storage.

put during colder months, this offsets the low PV outputs during the winter (with wind making less of a contribution for the urban wind regime chosen). Unsurprisingly, with the levels of generation specified, a large percentage of the required demand is met, without storage, 56.5% of the demand is met by import from the grid, but with storage only 23.1% is met by import. A higher wind site (with a less sheltered roof top) would show an even smaller import percentage. However, even with this vast microgeneration and storage system, there is still a reliance on the grid to satisfy the total demand. This implies that a grid autonomous dwelling is a difficult target to reach through renewable microgeneration alone. Of further significance to the national grid is that, by using microgeneration with or without storage, the required demand profile from the grid, for an individual dwelling, or group of dwellings, is altered (both in terms of the daily profile and the change from month to month). This is also clearly dependent on the type of microgeneration system that is being considered. For example, a building using PV will have a substantially different grid import profile during the winter than during the summer (with far more energy being generated on site during the summer months). The existence of storage increases this difference further (comparing Fig. 5 with Fig. 4) so that, if PV microgeneration with storage were to be used for a large number of dwellings throughout the UK, there would be clear implications for the supply patterns to be satisfied by electricity generators. More generally, for other microgeneration systems, the model predicts that storage would increase the monthly variation in grid import, though this can be made less of an issue by using complimentary technologies (for example, CHP and PV would provide a rea-

sonably steady output of on site generation through winter and summer). Such issues lead to more complex arguments relating to exactly what would benefit the network operators. To understand this requires a more detailed investigation of the effect of storage on peak loads; this is also affected by the chosen control options of the storage technology (for example, is battery storage used to reduce the peak loads of a dwelling or to satisfy a baseload?). This is a non-trivial topic that is planned to comprise future work. 4. Discussion The calculations of Section 3 are designed to demonstrate the described battery model for specific microgeneration technologies. Furthermore, the case studies provide an insight in battery storage operation. Fig. 12 shows in detail one day of the year (for 21st October) and the operation of the battery during this period at a minutely temporal resolution. This demonstrates the usefulness of the model in looking at supply–demand variability on shorter time scales than an hour (such effects can be missed when just looking at the calculated kW h figures for the month). The on site renewable generation (RES) is summed and this, together with any available stored energy, attempts to meet the electrical demand. It is immediately evident that the battery is operating on a very irregular charge–discharge cycle. This is particularly problematic for the lifetime of the battery, where the number of cycles is directly related to the battery life time [30,31]. Both the on site generation and the required demand are highly variable over short time scales, and this is not ideal for a lead-acid battery. It is suggested that a long

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5.0 Grid Battery RES Export

4.5 4.0

Power (kW)

3.5 3.0 2.5 2.0 1.5 1.0 0.5

22:00

20:00

18:00

16:00

14:00

12:00

10:00

08:00

06:00

04:00

02:00

00:00

0.0

Time Fig. 12. Electrical demand–supply weekend day profile (21st October) for dwelling with 2.5 kW PV, 1.5 kW wind turbine and 1 kW CHP with 1000 A h battery storage.

term empirical study should be conducted to assess truly the likely lifetime of such a battery in such a varied charge/discharge regime. Such a study might provide recommendations with regards to optimising the discharge current so as to reduce the number of charge and discharge cycles. There is also a separate study planned by the authors to look at the effect of choosing different control methods. With individual domestic electrical profiles being so variable, it might be preferable to meet a certain percentage of the peak loads by the battery. This might allow a smaller battery size and a longer battery life, with the smaller demands (of, say, <1.5 kW) met by available renewable energy or the grid. Finally, the model emphasises the difficulty of relying on microgeneration for autonomy. The battery (or any other storage device) must be able to provide a large amount of energy but at relatively low powers (for example between 00:00 and 06:30 in Fig. 12) as well as small amounts of energy at a relatively high power (for example the peaks at 08:30). In the case of Fig. 12, the battery has discharged completely by 02:30 and is not able to re-charge until energy is produced at approximately 06:30. Peaks such as those at 08:30 can drain the battery of charge relatively quickly so that grid reliance, or back up from a fossil fuel generator, is inevitable. Future studies planned by the authors will look at how this grid reliance can be reduced further by applying different control solutions to the CHP unit. 5. Conclusions A major problem when using large scale microgeneration for an individual dwelling is the unreliability of the gener-

ated energy to meet specific power loads at specific times. Exporting the excess energy to the national grid and importing at times of low generation is one solution to this, though problems exist with regards to the economic payback (to the user) and accounting for actual carbon savings (for example, how much grid energy does that ‘‘clean” exported energy actually displace?). Another solution would be to aim for an autonomous dwelling that is removed, or more independent, from the grid. Storage is vital for this purpose. A microgeneration and storage model has been described for quantifying the performance of energy storage options with microgeneration. The calculations are performed with a minutely temporal precision so that all subsequent annual/monthly energy figures, and round trip efficiencies, account for the losses involved with storing electrical energy on, potentially, a short time basis. Losses due to inverters and power electronics are based on the size of the loads involved, thus total system losses show subtle variations depending in the scenario defined. However, typical round trip efficiencies are 70–72%. The results indicate that lead-acid batteries are suitable for storing large amounts of on site generation, with slight concerns evident over the effect of variable charge and discharge cycles on the lifetime of the battery. It is also evident that the storage device should be sized based on the level and type of microgeneration chosen. While PV systems are relatively predictable throughout the year, and CHP system electrical outputs are predictable based on the control logic applied (e.g. heat led), the variability of wind means that, for very high wind sites, sizing the storage device is slightly more problematic (and so you are more likely to have ‘‘unstored” export for high wind sites).

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