Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost

Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost

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Short Communication

Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost Sleiman Farah*, David Whaley, Wasim Saman University of South Australia, Barbara Hardy Institute, Adelaide, Australia

article info

abstract

Article history:

Electricity grid capacity is often oversized to ensure it accommodates maximum antici-

Received 15 March 2016

pated peak demand. In South Australia, 25% of the grid capacity is required for less than 1%

Received in revised form

of the time. To reflect the cost of peak demand in electricity tariffs, demand tariffs consider

17 June 2016

not only electrical energy consumption (kWh), but also electrical power demand (kW)

Accepted 18 June 2016

during a peak period which is from 16:00e21:00 in South Australia. Demand tariffs increase

Available online xxx

electricity costs for users needing intermittent electrical energy supply with large electrical peak power demand. To reduce the peak demand and the subsequent electricity cost,

Keywords:

batteries are being included in the energy system. In this paper, four control strategies are

Peak demand

developed for charging and discharging a battery, and to export and import electricity from

Tariff

the grid. The strategies are simulated with and without a photovoltaic (PV) system using

Photovoltaic

real-time monitored electricity consumption and gross PV generated electricity of a

Battery

monitored energy-efficient house. The results show that using PV with electrical storage

Electrical storage

and proper control strategies can reduce both the electricity peak demand and life cycle

Control strategy

cost. These results are timely given the recent emergence of small-scale storage technologies and the prediction that these technologies may become commonplace in the near future. © 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction The residential sector accounts for a significant proportion (25%, 1041 PJ) of Australia's total energy consumption [1], with most of this energy being produced from polluting fossil fuels. The use of renewable energy technologies, such as solar energy, reduces the reliance on polluting fuels. The use of solar energy technologies is being further encouraged in the residential sector by the dropping cost of photovoltaic (PV) modules and the escalating cost of utility purchased electricity.

South Australian photovoltaic feed-in tariffs In South Australia, rooftop PV systems were initially encouraged by generous feed-in tariffs that paid customers for energy exported at twice the rate of that imported from the grid. These feed-in tariffs have been reduced to considerably less than the price of imported electricity. Currently, householders supplying electricity to the grid may be eligible under specific conditions for a minimum retailer payment, which can be as low as 5.3 cents per kWh exported to the grid [2]. Under the current feed-in scheme, exporting surplus electricity to the

* Corresponding author. E-mail address: [email protected] (S. Farah). http://dx.doi.org/10.1016/j.ijhydene.2016.06.164 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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grid provides minor economic benefits, as the average price of 1 kWh imported from the grid is more than 30 cents [3,4]. Storing excess PV generated electricity on site for later use, rather that exporting electricity to the grid, is becoming a more attractive alternative, especially with the move towards demand tariffs which will become mandatory in 2017, and the continual decrease of battery costs. Lithiumeion (Lieion) batteries are commercially available for AUD$1100/kWh [5].

Shift to time-based demand tariffs Conventional electricity tariffs are based on the consumption of electricity (kWh) regardless of the time of consumption. However, electricity tariffs are changing to better reflect the costs of peak demand. The demand frequency distribution for South Australia reveals that the peak demand is approximately twice the mean demand as shown in Fig. 1 [6]; 25% of the electricity capacity is used less than 1% of the time. The oversized capacity needed to supply peak demand for short periods adds to electricity distribution costs, which are passed on to the end-users. New electricity demand tariffs will be based not only on the total electricity consumption (kWh) but also on the monthly peak demand (kW) of electricity measured every 30 min from 16:00e21:00, with a baseline peak demand rate set at 1.5 kW.

Previously examined control strategies Researchers have investigated the impact of electricity storage and control strategies on reducing the electricity cost under different non-demand tariffs in Refs. [7,8], whilst three control strategies for reducing peak demand from the grid were simulated in Ref. [9]. The strategies included charging the battery from the grid and PV, and discharging the battery to maintain the demand to a desired magnitude. The results showed that electricity storage could be economic for different non-demand tariffs. A separate study considered optimizing both the storage capacity and the control strategy and evaluating the profitability of installing a storage system [10]. The strategy consisted of charging the battery from the grid only when the cost of electricity is low and discharging the battery only when the cost of electricity is high, which

made this strategy useful for time-of-use and real-time pricing tariffs. The results showed that a reduction of the battery cost by more than 50% was required to make the installation of an electricity storage system profitable [10]. The review reveals that research on using electrical storage and control strategies as a means to reduce the peak demand and life cycle cost (LCC) of electricity have been limited. This paper evaluates the impact of the size of both PV and storage systems and investigates four control strategies for managing the stored energy to reduce the monthly peak demand and reduce the 20-year LCC of electricity under a monthly demand tariff. Although these strategies exhibit some similarities with those presented in Ref. [9], the strategies presented in this paper use a novel charging approach which maximizes battery charging from PV generated electricity.

Methodology The impact of domestic electrical energy storage on the monthly peak demand and the LCC of electricity is examined for an energy-efficient house which has an annual electricity consumption (6265 kWh) similar to the average annual electricity consumption of Australian residential sector (5915 kWh) [11]. The house is located in Australia's most comprehensively monitored and sustainable housing estate, Lochiel Park. This Green Village is an exemplary energyefficient housing estate that contains approximately 70 energy-efficient houses that each utilize a grid-connected PV system, gas-boosted solar water heater, an in-home display and energy monitoring system with an array of intelligent meters and sensors [12,13]. The analysis presented in this study uses real-time, monitored gross PV generation, total household electricity consumption, and imported and exported electricity data for a period of 12 months together with a simulated battery system using one of four battery charging/discharging control strategies. The data from the selected household has high-resolution of 1-min which provides detailed information about the electricity consumption and production. The data have been processed to generate 5-min resolution to reduce the computation time, whilst maintaining sufficient details of electricity imported and exported, i.e. within 30-min intervals [14].

Energy storage properties The impact of introducing the energy storage system on monthly peak demand and LCC depends on battery properties which are summarized in Table 1 [15]. In addition, the impact is affected by the system cost, the demand tariff and the

Table 1 e Assumed battery properties.

Fig. 1 e Electrical load duration in South Australia [6].

Type Charging efficiency Discharging efficiency Minimum state of charge (SOC) Charging time Capacity Battery life

Lieion 96% 96% 20% 180 min Varies 10 years

Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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control strategy employed to charge and discharge the battery.

Demand tariff and component costs Each control strategy examined has a different impact on the monthly peak demand and the subsequent cost of electricity. The LCC is based on the best current cost of electricity estimates, PV and energy storage system components, which are summarized in Table 2. All costs shown are in Australian dollars (AUD), and no allowances have been made for discount rates and the likely change of costs over the 20-year calculation period. The calculated electricity cost considers both inflation and nominal interest rates. The adopted inflation rate is 2.5%; the mid-value of inflation range target set by the Australian Federal Reserve Bank [16], and the adopted nominal interest rate is 5%, the average interest rate over the last twenty five years [17]. Based on these rates, the real discount rate for calculating the net present cost is 2.5%.

Charging/discharging control strategies The four charging and discharging control strategies applied in this analysis are described in the four following sections.

Control strategy 1 (CS1) e charge from PV only This control strategy charges the battery with any surplus PV generated electricity and only exports energy to the grid when the battery is fully charged. When electricity is required, the battery is first discharged and electricity from the grid is only imported when the battery reaches the minimum state of charge (SOC). By discharging the battery whenever the PV electricity is insufficient, CS1 maximizes the ability of the battery to store surplus PV electricity, which effectively reduces the electricity imported from the grid. This strategy is effective in reducing the total electricity consumption from the grid by maximizing the battery capacity usage and therefore reduces the electricity cost under conventional electricity consumption tariffs. However, CS1 could be unsuitable for electricity demand tariffs as discharging the battery whenever electricity is required may not have significant impact on reducing monthly peak demand.

Table 2 e Cost of electricity, PV and battery. Descriptions Peak consumption Supply charge Summer demand peak Winter demand peak Demand off-peak Meter reading fee Minimum retailer payment PV cost

Battery cost

Units

References

0.26345 0.40392 0.54197 0.26950 0.00000 0.18030 0.0530

Values

$/kWh $/day $/kW/day $/kW/day $/kW/day $/day $/kWh

[18]

¼1148  PV capacity (kWp) þ 1310 1100

$

[19]

$/kWh

[5]

3

Control strategy 2 (CS2) e partial charging from grid This control strategy charges the battery with the surplus of PV generated electricity and exports electricity to the grid only when the battery is fully charged. Unlike CS1, CS2 considers both the time and amount of discharge from the battery. When electricity is required during an off-peak period, the stored energy in the battery is kept charged and electricity is imported from the grid. When electricity is required during a peak period, the battery is discharged to limit the electricity demanded from the grid to 1.5 kW; this limit is equal to the baseline peak demand charged by the retailer under the new tariff, regardless of whether the actual peak demand is lower than 1.5 kW. This strategy also allows the battery to be charged from the grid during the hour leading up to the start of the peak period, if the battery SOC is less than 75%. This time-dependent charging limits the amount of electricity imported from the grid to charge the battery and ensures that the battery is not at its minimum SOC at the start of peak period. The SOC of the battery can exceed 75% before the start of peak period when surplus of electricity is available from PV.

Control strategy 3 (CS3) e fully charged at peak period This control strategy is similar to CS2, however, in addition, this strategy imports electricity from the grid to ensure that the battery is fully charged at the start of the peak period. The SOC is set to increase linearly from the minimum SOC (20%) two and a half hours before the start of the peak period, to 100% at the start of the peak period. Grid electricity is not used if the SOC is above the set level prior the start of the peak period. This control strategy discharges the battery during the peak period to limit the electricity demanded from the grid to 1.5 kW. However, if the demand is high during the peak period, the energy stored in the battery may be insufficient to limit the electricity import from the grid to 1.5 kW for the entire peak period (16:00e21:00).

Control strategy 4 (CS4) e higher demand targets Considering high peak loads may result in insufficient reduction of monthly peak demand, CS4 discharges the battery to limit electricity demand from the grid for each month at magnitudes which, unlike CS3, can exceed 1.5 kW. This control strategy increases the stored energy available towards the end of the peak period. Consequently, CS4 is better suited than CS3 when high peak demand occurs during the peak period. In contrast, CS3 may be better suited than CS4 when high peak demand occurs towards the start of peak period.

Results and discussion Monthly peak demand and life cycle cost without PV To demonstrate the impact of electricity storage and the different control strategies, the analysis considers first the case where no PV system is used, i.e. the PV generation capacity is zero. The monitoring data shows that the electricity consumption during the peak period varies significantly as

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shown in Fig. 2. The aim is to use the battery to reduce the electricity demand to the minimum charged value of 1.5 kW. Control Strategy 1 is ineffective without a PV system as the battery can only be charged from excess PV generated electricity. Consequently, the monthly peak demand cannot be reduced without a PV system regardless of the battery capacity. The monthly peak demand are significantly higher than the baseline demand (1.5 kW), as shown in Fig. 3. Control Strategy 2 however, significantly reduces the monthly peak demand as the battery capacity increases. The reduction of monthly peak demand is summarized in Fig. 4, which shows that using a 6 kWh battery, the peak demand does not exceed 1.5 kW for eight months of the year. If the battery capacity is increased to 9 kWh, the peak demand does not exceed 1.5 kW for ten months of the year. The monthly peak demand corresponding to July and August, which remain higher than 1.5 kW, reveal several occurrences of peak demand exceeding 1.5 kW and that charging battery with electricity from the grid is insufficient to reduce the peak demand.

The ability to further reduce the monthly peak demand is demonstrated by using CS3, as shown in Fig. 5. Using a 7 kWh battery, the monthly peak demand decreased to 1.5 kW for every month except July. This peak demand can by limited to 2 kW by increasing the battery capacity to 10 kWh. Although these results may seem expected as CS3 allows more charging of the battery before the peak period, reduction of monthly peak demand may not be achieved when several occurrences of demand exceed 1.5 kW. The additional charging may increase the electricity consumption without reducing the peak demand. Although CS3 significantly reduces the monthly peak demand, the electricity cost increases as the battery capacity increases, as shown in Fig. 6. This rate of increase is reduced for a 2e3 kWh battery capacity using CS3, which reveals that a 2e3 kWh battery capacity can achieve the maximum savings from the reduction of the monthly peak demand. However, the battery capital cost makes the system more expensive than that without electrical storage.

Fig. 2 e Peak period electricity consumption (PV ¼ 0 kWp, no battery) (30-min resolution).

Fig. 3 e Monthly peak demand using CS1 (PV ¼ 0 kWp). Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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Fig. 4 e Monthly peak demand using CS2 (PV ¼ 0 kWp).

Fig. 5 e Monthly peak demand using CS3 (PV ¼ 0 kWp).

Considering a 1 kWh battery to reduce the capital cost, all the monthly peak demand remain higher than 2 kW (Fig. 5). Using CS4, the demand targets are set higher than 1.5 kW, which saves electricity in the battery that can be used to reduce peak demand occurring towards the end of the peak period. The results in Fig. 7 reveal that for a battery with a 1 kWh capacity, further reductions of the peak demand are achieved. The reduction of peak demand using CS4 and a 1 kWh battery capacity reduces the electricity LCC to that without a battery, as shown in Fig. 6. For the remaining control strategies, the electricity LCC increases as the battery capacity increases. These results indicate the use of electrical storage can be cost-competitive, and can improve energy security during the peak period.

Monthly peak demand and life cycle cost with PV The 30-min resolution of electricity consumption and production using a 2.5 kWp, shown in Fig. 8, reveals high variability of both imported and exported electricity. In July

(winter), the peak electricity import is significantly high, 3.20 kWh in 30 min (6.40 kW), compared to the average electricity import and export to the grid, 0.15 kWh in 30 min (0.30 kW). Although a PV system reduces the total electricity imported from the grid, the PV generated electricity is intermittent and does not significantly reduce the peak demand. The peak demand of electricity consumption without PV, shown in Fig. 2, are almost identical to the peak demand of electricity consumption with a PV system rated at 2.5 kWp, shown in Fig. 8. Slight differences of peak demand can be noticed in the summer months such as January and December, however, the peak demand in June, July and August remain unchanged. The unchanged peak demand in these months can possibly be explained by the peak demand due to space heating occurring after the sun is down. These results reveal that PV systems without electricity storage are unable to reduce household peak demand effectively. Nevertheless, using electricity storage together with a suitable control strategy can increase the usefulness of PV generated electricity to reduce the peak demand.

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Fig. 6 e Life cycle cost of electricity for different control strategies without PV.

Fig. 7 e Monthly peak demand using CS4 (PV ¼ 0 kWp, battery capacity ¼ 1 kWh).

Fig. 8 e Peak period electricity consumption and production (PV ¼ 2.5 kWp, no battery) (30-min resolution). Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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Fig. 9 e Monthly peak demand using CS1 (PV ¼ 2.5 kWp).

The use of CS1 seems unsuitable for a 2.5 kWp system as the increase of battery capacity does not effectively reduce the monthly peak demand shown in Fig. 9. Although the monthly peak demand of January, March and November decrease as the battery capacity increases, the monthly peak demand do not decrease for the winter months (June, July and August). The monthly peak demand of the majority of months remains significantly higher than 1.5 kW and does not further decrease for battery capacities higher than 7 kWh. The small impact of CS1 on the monthly peak demand is due to both the uncontrolled timing and the uncontrolled amount of battery discharge. Unlike CS1, the use of CS2 shows that the monthly peak demand decreases significantly as the battery capacity increases, as shown in Fig. 10. The peak demand is 1.5 kW for ten months using a battery with 5 kWh storage capacity. Due to both the limited charging from the grid and the limited PV generated electricity in July and August, the peak demand in these two months can be further reduced by using CS3. Using this strategy with a 10 kWh battery reduces the peak demand in July to less than 2 kW, whilst the peak demand in August can be reduced to 1.5 kW using a 7 kWh battery, as shown in

Fig. 11. However, the reduction of monthly peak demand for small battery capacities remains limited, while the use of a higher battery capacity is associated with a higher capital cost. Compared to the capital cost of a 5 kWh battery, the use of 7 kWh and 10 kWh batteries incurs 40% and 100% additional capital costs respectively. Using CS4 and considering a battery with a capacity of 1 kWh, the demand targets are set higher than 1.5 kW. For a 1 kWh battery, additional reductions of the monthly peak demand are achieved, as shown in Fig. 12. Similar to the case of having no PV, the reduction of peak demand using CS4 and a 1 kWh battery capacity reduces the electricity LCC by approximately 1% compared to the case without a battery, as shown in Fig. 13. For the remaining control strategies, the electricity LCC increases as the battery capacity increases. Nevertheless, a comparison between the minimum LCC achieved without PV (Fig. 6) and that with PV (Fig. 13) reveals that approximately 9% savings can be achieved. These savings are mainly achieved by the reduction of electricity consumption from the grid and the use of PV generated electricity. Similar to the previous analysis with a PV system, the control strategies are investigated for larger PV system

Fig. 10 e Monthly peak demand using CS2 (PV ¼ 2.5 kWp). Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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Fig. 11 e Monthly peak demand using CS3 (PV ¼ 2.5 kWp).

Fig. 12 e Monthly peak demand using CS4 (PV ¼ 2.5 kWp).

Fig. 13 e Life cycle cost of electricity for different control strategies with PV ¼ 2.5 kWp.

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Fig. 14 e Life cycle cost of electricity for different control strategies with PV ¼ 5 kWp.

capacities (up to 10 kWp). The results indicate that for higher battery capacities CS1 provides significantly lower electricity cost than that of the other control strategies, as shown in Figs. 14e16. For a 10 kWp PV system and a 10 kWh battery, the electricity cost using CS1 is 15% less than that achieved using the other control strategies. This lower cost is due to maximizing the use of stored electricity in the battery which is charged from the PV system. However, electricity cost using large battery capacities remains higher than that without batteries, making the use of large battery capacities uneconomic. The results also indicate that CS2 and CS3 achieve similar electricity costs for the entire range of the considered battery capacities. The rate of change of electricity cost increases linearly at a higher rate as the battery capacity exceeds 4 kWh compared to that lower than 4 kWh. The higher rate is due to the additional battery cost which does not result in significant reduction of the monthly peak demand. Control strategies CS1e3 using batteries do not provide sound economic electricity cost outcomes compared to that without a battery. The increase of electricity cost using CS1 varies from

1% to more than 14% for a 1 kWh and 10 kWh battery capacity respectively, whilst the increase of electricity cost using CS2e3 varies from 2% to more than 34% for a 1 kWh and 10 kWh battery capacity respectively. Control strategy CS4 with a 1 kWh battery capacity provides a competitive electricity cost, however, the economic benefit is less than 1% compared to that without a battery. These results indicate that further reduction of battery cost is required to produce an economic solution of using batteries to reduce electricity costs under the demand tariff. Fig. 17 shows that the use of PV with and without a battery provides significant reduction of electricity cost. The minimum electricity cost corresponds to a PV system capacity of approximately 3.5 kWp with more than 9% savings compared to the case without a PV system. The electricity cost increases almost linearly as the PV capacity exceeds 5 kWp. The increase is due to an excess of PV generated electricity which is exported to the grid. However, the low value of retailer payment is insufficient in paying back the additional cost of a larger PV system. This result indicates that optimising the use

Fig. 15 e Life cycle cost of electricity for different control strategies with PV ¼ 7.5 kWp. Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164

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Fig. 16 e Life cycle cost of electricity for different control strategies with PV ¼ 10 kWp.

Fig. 17 e Life cycle cost of electricity for different control strategies with and without a battery. of PV generated electricity is more economic than exporting electricity to the grid. However, the use of batteries to maximize the use of PV generated electricity does not provide a significant economic benefit compared to that of using PV only, as shown in Fig. 17. This research shows that further reduction of battery cost is required for batteries to provide an economic solution of reducing peak demand. Consequently, the introduction of demand tariffs will likely encourage the use of PV rather than batteries to reduce electricity cost. Nevertheless, the use of more PV will increase the demand (kW) to consumption (kWh) ratio, which will defy the purpose of demand tariffs. Based on the adopted costs, the introduction of demand tariff will exacerbate the peak demand problem which is likely to force utilities to increase the cost of the demand components.

and life cycle cost of electricity under a new proposed monthly demand tariff in South Australia. The study used real household demand and PV data for a monitored energy-efficient house. The results showed that for the PV capacities considered, electrical storage using control strategy 1e3 could not reduce the life cycle cost of electricity, whilst control strategy 4 provided a slight reduction of the life cycle cost. The results also showed that using a correctly sized PV system provided more than 9% savings of the life cycle cost. Electricity storage can significantly reduce monthly peak demand while the use of PV is more effective in reducing electricity life cycle cost. The results suggest that the current cost of the demand component in the demand tariff may need to be increased and the cost of battery to further decrease before the widespread utilization of domestic electrical storage.

Summary and conclusions Acknowledgement This study examined the impact of various battery charging and discharging control strategies, with and without photovoltaic (PV) systems, to reduce both the monthly peak demand

The authors would like to acknowledge the financial support from the Cooperative Research Centre for Low Carbon Living.

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Please cite this article in press as: Farah S, et al., Control strategies of domestic electrical storage for reducing electricity peak demand and life cycle cost, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.06.164