1. Introduction
Over the last decade, residential rooftop photovoltaic (PV) systems have gained popularity in many countries [
1]. This is mainly driven by its low upfront investment, coupled with strong government-led support schemes, such as feed-in tariffs and net metering [
2,
3] to achieve a low carbon future in the respective countries. The proliferation of residential solar PV is expected to continue in the years to come. However, the increasing penetration of residential PV systems has started to witness technical challenges in the low voltage distribution network, particularly overvoltages and frequent voltage fluctuations [
4,
5,
6,
7]. The residential PV system is also unable to reduce peak demand since the maximum solar output at midday does not coincide with the evening peak normally experienced by the residential household [
8].
One of the promising ways to address the these issues is by utilizing an on-site battery energy storage system (BESS) in which surplus PV power can be stored locally [
9]. This avoids reverse power flow occurrence when PV generation exceeds local demand. In addition, residential BESS can be controlled to achieve different aims, such as reduce peak load [
10,
11], mitigate the volatility of renewable energy sources [
12,
13], and minimize the electricity cost based on economic incentives [
14]. The high costs for the integrated PV–BESS system still remain as the main barrier for adoption amongst the residential prosumer [
15,
16]. Nevertheless, several studies have suggested that with the advancement in the battery technology, BESS could become profitable in the future [
14,
17]. This stimulates interest in developing methodologies that can support the quantification of BESS benefits, and its associated technical impacts to the system under various operating strategies.
The benefits of integrated PV–BESS have been well reported in the literature, given its superior ramping speed and flexibility in managing the solar output intermittency [
18]. Recent studies [
19,
20] examined the technical benefits of BESS without considering any network topology. Instead, simplified network models have been utilized as the test case in [
14], where these idealized network models have the limitation on representing the consumers’ diversity and network characteristics adequately. In addition, a limited number of BESS studies have considered multiperiod load and PV generation profiles at the final user level. The authors in [
21] proposed a control strategy to increase the penetration of solar power with proper control of BESS. The Monte Carlo simulation performed in [
22] only considers a fixed time horizon of 24 h without the influence of demand diversity. An enhanced probabilistic load forecasting technique was proposed for optimal energy storage system operation; however, the authors did not consider simultaneously the presence of PV generation in their optimization model [
23]. Analysis in [
24] that apply annual solar irradiation data suggests that the output from the solar PV system is highly uncertain and able to be broadly categorized into five variability days. Averaging the multiday PV profiles into a typical PV profile for a case study may underestimate the BESS capacity requirement. Thus, considering the wide spectrum of PV variability is of paramount importance in designing the BESS system. Furthermore, it is also worth noting that none of these papers consider the concept of battery-autonomy days in determining the optimal BESS capacity. This is important as the solar PV generation varies in consecutive days. This phenomenon is particularly acute for tropical countries that experience frequent cloud passing. This will result the state of charge (SoC) of the BESS system, which will vary significantly over the days.
In light of this, the BESS design approach developed in this paper is based on the real residential low voltage (LV) distribution network in Malaysia [
8]. Moreover, the residential PV system’s rating is based on the actual PV installation statistics as provided by the Sustainable Energy Development Authority (SEDA) Malaysia [
25]. More importantly, each house is assigned with an individual load profile with actual annual solar generation profiles. This allows the PV–BESS studies to be carried out more realistically, as compared to the abovementioned papers, which only consider a simplistic network model with daily or weekly profiles.
An optimal operation of centralized control of battery energy storage in low voltage networks with a few hundred nodes is computationally infeasible [
19]. This is because the optimization problem becomes too complex to be solved efficiently. Furthermore, these centralized control strategies would normally require advanced communication infrastructure at the LV distribution substation or end user level, which is scarcely available nowadays. Hence, this paper proposes the design of a BESS system with two practical operation strategies for a typical residential LV network in Malaysia. The first strategy aims to effectively avoid the occurrence of reverse power flow incident at an LV distribution substation when the solar output is higher than the aggregated demand, thus eliminating the adverse impacts of reverse power flow on the operation of the distribution network. The second strategy utilizes all of the generated solar energy to minimize the maximum demand at the substation level. Subsequently, both strategies were evaluated and compared in terms of the required BESS power and energy ratings, maximum demand reduction, and the smoothness of the load profiles. It is also worth noting that annual demand and generation profiles were considered in this paper.
The paper is organized as follows.
Section 2 presents the modeling approach for a low voltage distribution network, as well as the associated demand and solar profiles. The formulation of battery control strategies is presented in
Section 3.
Section 4 provides the results with detailed discussion, followed by concluding remarks in
Section 5.
4. Results and Discussion of BESS Allocation
These strategies proposed for BESS integration are demonstrated through a residential LV distribution network in Malaysia. The proposed BESS is centrally installed at the distribution substation level. The case study aims to compare the BESS power rating and capacity, in the presence of residential PV systems. The time resolution of the sample data and duration of the study are the two important factors that significantly influence the design of BESS, which are demonstrated in the following analysis.
Figure 11 and
Figure 12 show the BESS design with the case study of PVGS and RFPS for seven consecutive days, 1–7 January 2016. The results show that the BESS capacity and power rating vary throughout the duration of the study. These can be seen in
Table 3. Additional simulation days resulted in a more accurate BESS design. Hence, this paper utilized one year’s data from 2016 to design the optimum BESS for the test network with different PV penetration levels.
Figure 13 shows the monthly BESS sizing for both RPFS and PVGS strategies. As expected, the BESS rating requirement for PVGS strategy is much higher than RPFS strategy. However, when the PV penetration increases, the BESS rating between these two strategies become less obvious. This is because the occurrence of reverse power flow will become more intense during high PV penetration levels.
Table 4 shows the strategies applied for charging (PVGS/RPFS) and discharging (PRS) of BESS design for 25% to 100% of PV penetrations. It shows that the BESS capacity and power ratings very much depend on the strategy and PV penetration levels that were considered. In addition, selecting higher resolution data sets is very important for designing the BESS ratings.
Figure 14a shows the comparison of the BESS size with RPFS charging for two different time resolutions, i.e., 10 and 60 min.
Figure 14b shows the PVGS charging and BESS size for different PV penetration level. It can be observed that the higher time resolution of input data resulted in a more accurate BESS sizing. Hence, this paper utilizes 10 min resolution data for the case studies.
In addition, this paper compares the outcome of various PV penetration scenarios based on the following factors: average maximum demand (MD) reduction, exported energy, imported energy, BESS size and capacity, reactive and active power losses, and MD reduction. The average MD reduction for BESS with under 25% PV integration is 9.5% for RPFS and 42% for PVGS, when compared with the base case. In addition, with 25% PV penetration, not much reverse power flow as observed. Hence, the size of the BESS in RPFS design is smaller than PVGS, but less MD reduction is obtained. Reduction of the net imported energy is 88.343 to 68.576 MWh with PVGS and 68.940 MWh with RPFS under 25% PV penetration scenario.
Figure A1,
Figure A2,
Figure A3 and
Figure A4 in
Appendix A show the results for all of the scenarios considered.
Figure 15 illustrates the MD reduction in kW with different PV penetration level. It can be observed that the month of March has the maximum MD reduction for months during the year, approximately from 220 to 58 kW with RPFS and to 46 kW with PVGS.
The other important factor for designing BESS is the consideration of autonomy days (AD) to cope with the weather uncertainty.
Figure 16 shows the BESS failures (blue line) due to inadequate BESS capacity. Discharging the BESS below its allowable limit is detrimental to its technical lifespan. Therefore, increasing the AD would address this problem satisfactorily. In this regard, this paper proposes optimum AD utilization to size the BESS.
Figure 17 shows a similar case study as the one in
Figure 16, but with increased AD from 20% to 40%. This helps the BESS to prevent sudden charging and stop discharging defects (spikes). In this case, by adjusting the AD to 40%, the capacity of the BESS increased from 3.45 to 4 MWh. The BESS power rating still remains the same at 500 kW. The results obtained show that the BESS successfully smoothes the load profile (LP) and lowers the peak demand.
This paper further proposes a smoothness index (SI) as an indicator to quantify the smoothness of the load profile. The SI is driven by the rate of changes of the load profile and can be written as Equation (13). Lower SI would indicate more smoothness load profile. The
SI value for the flat load profile is equal to zero, which means there is no fluctuation in the load profile. More fluctuations in the load profile result in a higher SI.
Figure 18 shows the SI comparison for the case study ranging from 25% to 100% of PV penetration for both PVGS and RPFS strategies. As can be seen from the figure, the SI for the base case remains constant with all of the PV penetration levels. It is interesting to observe that the SI index increased quite substantially after including the PV system. This mainly was caused by the intermittency of the PV system. After the BESS was integrated, the SI index reduced significantly due to the effects of BESS removing the PV intermittency. As expected, the results show that PVGS reduces the SI index more significantly than RPFS.
The main findings of the paper can be summarized as shown in
Table 5. The BESS design for different PV penetration levels for both PVGS and RPFS are determined. The energy to power ratio of the BESS (
Table 5) indicates that the battery is designed adequately based on the battery manufacturer requirements and range as reported in [
25]. In general, it can be observed from the results, in terms of MD reduction, that the PVGS with 1300 kWh BESS can achieve 36.8% of MD reduction as compared to RPFS, with a significantly higher BESS of 3450 kWh, can only achieve a comparable 39.9% of MD reduction. In other words, the results show that PVGS is more suitable for lower PV penetration, and that RPFS has a reasonable MD reduction for higher PV penetration with a smaller battery size.