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Power management scheme development for large-scale solar grid integration


As solar power is an intermittent source of energy, it causes uncertainties when connected to the electrical grid. This work presents an optimised power management system for the integration of large-scale solar with improved voltage profile and power quality in compliance with the Malaysian Grid Code Requirements of 0.95–1.05 p.u. at the Point of Common Coupling. An integration of volt-var regulation using a smart inverter for solar photovoltaic system and optimised capacitor placements to attain a voltage profile close this Grid Code Requirements on all buses in the IEEE-9 bus will be carried out. The 116 MWp (peak) solar farm will be modelled to obtain the load profile of electricity injected into the grid on an annual hourly basis. This will be used to model the solar bus in the power system to test the changes in voltage profile, voltage deviations and reactive power flow. In the final results, the voltage profile improved to a range of 0.94–1.01 p.u. in the IEEE-9 bus even for the worse case scenarios of solar power penetration which happens from 12 to 1 pm.


Malaysia is a country that receives abundant amounts of sunlight throughout the year due to its equatorial location and it has great potential for the development of solar PV technologies to reach its composition of renewable energy installed capacity of 7280 MW in Malaysia for 2035 under the MyRER 2035 New Capacity Target (NCT) Scenario [1]. Based on research by [2] with secondary data from the Malaysian Meteorological Department, the annual average daily PV energy was 18.93 MJ/m2 with peak hourly average solar irradiance of 1139 W/m. This study indicated that Terengganu has great potential for PV power development in which the energy yield calculations for the Coara Marang Solar Farm, Terengganu will be developed. As this renewable energy technology is emerging to be more advanced and is growing in Malaysia, there are challenges faced when it is integrated into the grid which could cause many issues such as voltage stability, harmonics and flickers. For this problem to be resolved, reactive power compensation has to be carried out for power factor correction so that the voltage levels of buses will increase or decrease according to the grid code requirements. This will improve the real power transferred to the load as reactive power will be compensated by devices such as capacitor banks, static VAr compensators (SVC) and STATCOMs in combination with new optimisation algorithms that are being developed in this research field.


With the literature reviewed, journals [4, 5] were presented for complementation of research methodologies used for reactive power compensation, while journals [3, 8] were eliminated. Journals [6, 7, 9] were used for verification of work in this paper as it presents methods of optimal allocation of reactive power compensation devices. Table 1 presents the key findings, final results and parameters used from the papers reviewed together with this paper for comparative analysis and validation of the final results obtained.

Table 1 Review of papers on power management systems for renewable energy

Therefore, to complement the research in [4], further analyses were carried out based on the fluctuation of voltage profile on a time series basis which involves the load profile of solar bus modelling to incorporate varying solar power penetration throughout the day. The optimal placement of capacitors with compensation optimisation iteration algorithm was used to improve the voltage profile of all buses in addition to just the worst bus. Based on research carried out by [5], the analysis done was adapted accordingly to model solar in-feeder and IEEE bus by using a PV bus with a smart inverter containing the volt-var regulation Q(U) function. This was adapted to be integrated with the optimal capacitor placement to improve the voltage profile and reduce voltage deviation. This analysis was used to analyse voltage and reactive power changes throughout the day with a time series simulation to find the worst-case scenario of a day in 24 h and to mitigate these issues via the methods presented.


This research project gave detailed emphasis on the analysis of a power management system for the integration of grid-connected LSS in the current development of an LSS power plant. Firstly, the LSS farm was modelled based on its rated power to approximately match the annual demand as proposed by the company which is Coara Solar Sdn. Bhd. The methodological stages involved a selection of PV module, inverter, and PV tracking based on the desired specifications to obtain the annual energy yield. The annual hourly averages of electricity injected into the grid were required for time series analysis. This PV bus was injected into the IEEE-9 bus to study the voltage profile changes daily. With these changes, the optimal placement of capacitors and volt-var regulation was used to improve the voltage profile and reduce variations in voltage, respectively. The design of the solar bus was done in PVsyst [12], while the power management was executed using Siemens PSS SINCAL [13].

Modelling of the solar farm

The chosen site of the study was a recent development of an LSS project in Malaysia known as the 116MWp (peak) Coara Marang Solar Farm in Terengganu, Malaysia (4.978°, 103.3198°) by Coara Solar Sdn. Bhd. and its German partner, Ibvogt. A 21-year Power Purchase Agreement (PPA) was signed by the director of Coara Marang with Tenaga Nasional Berhad (TnB) for this project. As written by [10], bifacial technology of PV panels will be used together with single-axis trackers for maximising energy capture and efficiency. As forecasted to be operated in 2022, this LSS farm is targeted to deliver an energy output of 230 GWh annually to avoid approximately 170,000 tonnes of carbon dioxide emissions per year as well. These details were considered when inputting parameters into PVsyst to better represent the proposed LSS farm. The design boundary in this paper is that the meteorological data for Terengganu were not present in the database of PVsyst. Therefore, the available data for the closest approximate location to Terengganu were used which were the Butterworth data which resulted in a Global horizontal irradiation of 1808 kWh/m2. The measurements for the tilt and azimuth angle were modelled to follow an optimised value based on research in [11]. According to [11], the optimum tilt angle for Kuala Terengganu was found to be 0° to 23° based on the Liu and Jordan method executed in MATLAB. A tilt angle of the midpoint of these values which was 12° was chosen for this project. An azimuth angle of 15° was chosen as well for a more realistic scenario of the solar panel orientation with respect to the sun. The results of global horizon irradiance, ambient temperature and wind velocity were obtained from the simulation based on the chosen angles. The main parameters applied in the calculation of annual energy yield and hourly averages of energy injected into the electricity grids were reviewed. The inverters with featured maximum power point tracking (MPPT) technique were interfaced with the PV modules to get the maximum power generation. The modelling of these inverters was tested in accordance with the size of the solar model to obtain an acceptable sizing without error of under sizing or oversizing. Manual adjustments of the PV system specifications were carried out with simulations to obtain a close match to the targeted demand of 230 GWh per annum of energy yield. Specifications such as the bifacial technology of the solar panel model incorporated with single axis tracking mechanism which was modelled to be the tracking plane, tilted axis were incorporated by the desired specifications as written by [10].

Modelling and optimisation of IEEE-9 bus topology

The IEEE-9 bus topology is a standardised network based on the Western System Coordinating Council (WSCC), and it was used for this research to model an electrical network with different voltage levels to be integrated with LSS. This bus test system consists of three loads, three generators and nine buses, and there are four base voltage levels of the bus which are the 13.8 kV, 16.5 kV, 18 kV and 230 kV lines. The three load buses in this topology are bus 5 (125 MW, 50 MVAr), bus 6 (90 MW, 30 MVAr) and bus 8 (100 MW, 35 MVAr). This bus system has the following standardised generator, busbar, load and line parameters in terms of voltage levels, resistance, reactance and admittance which were modelled to carry out load flow analysis [14]. The IEEE-9 bus with its input data is shown in Fig. 1 as follows.

Fig. 1
figure 1

The IEEE-9 bus topology with its input data

For the IEEE-9 bus, the bus of PV generation was interfaced with each bus excluding the generation buses from synchronous generators. This was bus 4 to bus 9 done case-by-case in the IEEE-9 bus to determine the worst-case scenarios of the voltage profile based on peak periods of solar power penetration. Besides modelling the PV system to have a rating of 116 MW peak, additional data of the hourly averages of energy injected into the grid (MW) were imported from the PVSyst calculation procedures to model the load profile of the solar system to model intermittent characteristics of solar power generation with its peak loads and base loads so that a time series evaluation of voltage profile at each bus can be carried out. Optimised placement of capacitors method was used to regulate the voltage levels on buses using automatically rated capacitors based on the reactive power demand of the weakest buses.

The algorithm inserted capacitors at the predefined nodes which resulted in the least losses in the network. This was done by the power flow analysis that was calculated and all the nodes that needed capacitive reactive power were listed down in terms of their reactive power demand in negative MVAr values. The approximation of close values of this reactive power demand was executed at the weakest nodes to place the capacitors to supply the same reactive power to match this demand. Once this was done, the power flow analysis was run again and a greater voltage profile was observed in all the buses as the reactive power demand had been matched in the best possible configuration. The difference in loss with reference to the initial network was also calculated. This procedure of capacitor placement consisted of the following advantages for the network that was integrated with solar power. It reduced the apparent power transferred and losses within the network. In addition, it reduced the utilisation of equipment and improved the voltage profile in the network. This aided in avoiding violations of the voltage limits as per the Grid Code Requirements. The configuration of optimal capacitor placements with its reactive power rating values after solar was injected into the worst buses of the IEEE-9 bus system was executed and is presented in Fig. 2.

Fig. 2
figure 2

Configuration of the optimised placement of capacitors for solar bus connected to bus 8 of the IEEE-9 bus

Volt-Var Regulation Q(U) Function was used to model the smart inverter PV for regulation of reactive power to improve reliability indices in the network model. This was implemented via the controller settings of the solar bus in the power system model. Table 2 and Fig. 3 present the controller settings for the volt-var function used. This function worked to regulate the absorption of reactive power if the voltage at the PCC of solar exceeds the upper limit. If this voltage goes below the lower level, then there will be an injection of reactive power into the bus for the normal voltage levels to be maintained. This function which is based on parameters of voltage and reactive power was modulated manually and tested in the power system by matching the bus voltage target for the Malaysian Grid Code Requirements [15] which are shown as follows:

  • 0.95–1.05 p.u. of voltage at the PCC.

  • 49.5–50.5 Hz of electrical grid frequency

  • Power factor range of 0.95 lagging to 0.95 leading at the PCC.

Table 2 Controller settings of smart inverter with volt-var function
Fig. 3
figure 3

Volt-Var Q(U) Function

Results and discussion

Energy yield of modelled solar farm

After the modelling of the LSS farm, the results of energy injected into the grid as shown in Fig. 4 were extracted and imported to model the load profile of the solar bus. Based on the analysis on a daily, monthly and annual period, the annual hourly averages of this parameter were taken into account. This energy was modelled in the solar bus for injection of power into the IEEE-9 bus.

Fig. 4
figure 4

Annual hourly average of energy injected into the grid in a day

Voltage profile analysis of the IEEE-9 bus

The power flow analysis was carried out for different cases of the interconnection between the solar bus and the IEEE-9 bus. The reference case was selected to be the IEEE-9 bus without the connection of the solar bus. From these results, it was observed that there were major deviations of voltage in buses 3, 4, 5, and 6 when the solar bus was not connected to the 9 bus. With these results and the ideal case serving as a benchmark of desired voltage profile levels, the solar bus was integrated into bus 4 to bus 9 (buses without synchronous generators) on a case-by-case basis to find out the worst-case scenario of voltage drop based on the time of day at which the voltage deviation was maximum. This could be a drop or rise in voltage depending on the time series results as the variation in the solar bus profile will cause fluctuations in voltage throughout the day. After the solar bus was integrated into each bus, analysis was carried out on the voltage profiles in each bus in an excel sheet and the mean value of the voltage profile was calculated to determine the worst-case scenario for compensation. The levels of these voltages were picked based on a particular time of day in which voltage levels were out of the limits in its worst case. From these tests carried out, every case was able to be compensated to the optimised configuration of capacitor placement except cases of solar bus connected to bus 5 or bus 9 as overload occurs at the base power flow. This was due to the occurrence of islanding on generator 2 (bus 2). In this case, the worst-case scenario of the lowest average voltage profile (except for bus 5 and bus 9) was chosen for further analysis of compensation procedures and this was bus 8 which has a value of 88.3841%. The best-case scenario for this configuration of solar integration to the IEEE-9 bus was bus 6 with an average voltage profile of 91.3282%.

Voltage profile improvement by optimal capacitor placements

When the solar bus was connected to bus 8, load flow analysis was carried out again to study how the fluctuations of voltage vary throughout the day. This was done to obtain the point of time in the day in which the voltage profile was at its worst. Figure 5 illustrates the constant voltage profile of the IEEE-9 bus without solar penetration followed by the changes that happen when the solar bus is connected to bus 8. After the connection of the solar bus to bus 8, there was a drop and fluctuation of the voltage profile in every bus except for bus 2. This fluctuation pattern followed the same uniform pattern for every bus in which it rises from 7:15 to 8 am and then dropped to its minimum value at 12 pm. Then, it rose again until 6:30 pm and dropped back to its original value of the voltage profiles in the IEEE-9 bus at 7 pm as there was no more sunshine based on the PVsyst load profile data imported to the solar bus. This can be observed in Fig. 6.

Fig. 5
figure 5

Time series voltage profile of IEEE-9 bus without the solar bus

Fig. 6
figure 6

Time series voltage profile of IEEE-9 bus with solar bus connected to bus 8

From here, it can be seen that the voltage profiles of every bus except bus 2 drop below 0.94 p.u. (94%) and this is slightly below the Grid Code Requirements of 0.95–1.05 p.u. (95–105%) of the voltage profile. Therefore, compensation procedures were carried out to improve these voltage profiles to be within the desired range mentioned during the peak hours. Based on this graph, the worst-case scenario of the voltage profile was seen to occur at 12 pm and since the pattern of the voltage profiles was uniform, then this value of the voltage profile at 12 pm was used as the benchmark for compensation results as the pattern of voltage fluctuation was the same for every bus. The respective capacitors had to supply the value of optimised reactive power demand which becomes their rated value of reactive power supply to the respective buses. After compensation was carried out, the voltage profile was improved for all buses as seen in Fig. 7. Based on the optimised scenario, the voltage profile for the minimum peak at 12 pm was improved for every bus to be above 94.5%. The worst buses which were buses 5 and 6 had a voltage profile of 83.974% and 83.414% was improved drastically to 94.94% and 95.481%, respectively. Figures 8 and 9 illustrate the comparative results of the voltage profile when the IEEE-9 bus is independent of solar power penetration, the IEEE-9 bus with solar at bus 8 and the optimal capacitor placement procedure for meeting the reactive power demand at weakest buses for improvement of voltage profile at 12 pm.

Fig. 7
figure 7

Time series voltage profile of the optimised scenario of solar bus connected to bus 8 of the IEEE-9 bus

Fig. 8
figure 8

Bus voltage profile of IEEE-9 Bus versus IEEE-9 bus with solar at 12 pm

Fig. 9
figure 9

Bus voltage profile improvement to its optimised scenario

Reactive power management by optimal capacitor placements

In the same case of connecting the solar bus to bus 8 of the IEEE-9 bus system, the power flow results were calculated as well. This was obtained during the peak minimum period of voltage profile for every bus which was 12 pm. The values of P and Q were the same for every bus before and after solar integration except for bus 2. In bus 2, it was observed that there was a decrease in real power from 164.514 to 102.761 MW and an increase in reactive power from 79.21 to 98.723 MVAr at the peak period of 12 pm. The increase in Q-flow after the integration of the solar bus is shown in Fig. 10. After the optimised placement of capacitors was carried out, the flow of reactive power throughout all the buses was reduced which resulted in lower apparent power. This was able to be executed due to the optimised supply of capacitive reactive power to match the inductive reactive power demand in the weak spots of reactive power demand based on the compensation optimisation algorithm to result in the best possible figures of voltage profile in all the buses. Figure 11 illustrates the flow of reactive power in the IEEE-9 bus with solar at bus 8 and the optimised scenario of capacitor placement.

Fig. 10
figure 10

Reactive power flow of IEEE-9 bus versus solar bus placed at bus 8

Fig. 11
figure 11

Reactive power flow of 9 bus with solar at bus 8 versus the optimised scenario

Voltage profile improvement by volt-var regulation

After implementing the volt-var controller to the solar bus, the power flow was calculated to observe how the voltage profile improves in the 9 bus system. After volt-var procedures were done, it was integrated with optimal capacitor placements for further improvement in not only voltage levels but voltage fluctuations as well. Figure 12 illustrates these voltage profile improvements. After integration of the smart inverter, a steadier voltage was obtained and the voltage profile was also improved but most of the buses were still below 0.95 p.u. To meet this requirement, optimal capacitor placements were carried out for greater voltage profiles and lesser fluctuations. As a result, instead of just improving the voltage profile with optimal capacitor placements, it had an approximate average voltage deviation of 2.17% for all buses and the integration of volt-var regulation with this resulted in similar levels of voltage profile but with a smaller deviation of approximately 0.81% as an average for all buses. Figures 13 and 14 illustrate the voltage profile improvement by volt-var regulation in the 9 bus at 12 pm.

Fig. 12
figure 12

Voltage profile improvement using volt-var methods and volt-var regulation with optimal capacitor placement in IEEE-9 bus

Fig. 13
figure 13

Voltage profile of IEEE-9 bus with solar bus (without volt-var) function

Fig. 14
figure 14

Voltage profile of IEEE-9 bus after volt-var regulation was implemented on the solar bus


An LSS farm based in Malaysia was modelled to meet a required annual energy yield of 200 GWh and the output results were analysed. With the intermittent load profile obtained, the data were used in the solar bus in the power system. For the IEEE-9 bus, the voltage deviations were studied to find out the worst-case scenario for compensation. It was found that bus 8 was the worst-case connection of solar power as it resulted in the lowest average voltage profile across buses at the peak period (12 pm) of solar power penetration. The average voltage profile for all buses showed a decrease of 4% as taken from 7 am to 12 pm. With the optimal capacitor placement method, the voltage profile for all buses was improved to be within the Grid Code Requirements in approximation which were 0.94 p.u. to 1.01 p.u. The average voltage profile for all buses of the 9 bus showed an increase of 13.3% as taken from 7 am to 12 pm. However, there were still slight fluctuations in the voltage profile within hours of solar power penetration. Therefore, the volt-var regulation was implemented in the solar bus to model a PV smart inverter for regulation of reactive power at the PCC, this resulted in not only voltage profile improvement, but also a voltage fluctuation reduction of 2.71% to 0.81% in the 9 bus. For future works, further research can be carried out using the real network of the Malaysian Grid for the implementation of the optimal capacitor allocation iteration algorithm to improve voltage profile depending on various levels of PV penetration.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


  1. SEDA (2021) ‘Malaysia renewable energy roadmap, pathway towards low carbon energy system’, [online], available at: Accessed 22 Dec 2022

  2. Muzathik A (2013) Potential of global solar radiation in Terengganu, Malaysia. Int J Energy Eng 3:130–136

    Google Scholar 

  3. Pompodakis EE, Drougakis IA, Lelis IS, Alexiadis MC (2016) Photovoltaic systems in low-voltage networks and overvoltage correction with reactive power control. IET Renew Power Gener 10(3):410–417.

    Article  Google Scholar 

  4. Mohanan M, Go YI (2020) Optimised power system management scheme for LSS PV grid integration in Malaysia using reactive power compensation technique. Glob Chall 4(4):1–12.

    Article  Google Scholar 

  5. Jurák V, Bukvišová Z, Orságová J, Topolánek D, Ptáček M (2020) Compensation of reactive power in LV network and its impact on reactive power flow through distribution grid. In: 2020 21st international scientific conference on electric power engineering (EPE). Prague, Czech Republic: CVUT—Czech Technical University in Prague, pp 1–5

  6. Peprah F, Gyamfi S, Effah-Donyina E, Amo-Boateng M (2022) Evaluation of reactive power support in solar PV prosumer grid. ePrime Adv Electr Eng Electron Energy 2:100057.

    Article  Google Scholar 

  7. Pereira LDL, Yahyaoui I, Fiorotti R, de Menezes LS, Fardin JF, Rocha HRO, Tadeo F (2022) Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations. Appl Energy 307:118097.

    Article  Google Scholar 

  8. Souri S, Shourkaei HM, Soleymani S, Mozafari B (2022) Flexible reactive power management using PV inverter overrating capabilities and fixed capacitor. Electr Power Syst Res 209:107927.

    Article  Google Scholar 

  9. Nusair K, Alasali F, Hayajneh A, Holderbaum W (2021) Optimal placement of FACTS devices and power-flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques. Int J Energy Res 45(13):18786–18809.

    Article  Google Scholar 

  10. Coara Solar (2021) Project description: Coara Marang solar plant. Accessed 29 Mar 2021

  11. Khatib T, Mohamed A, Sopian K (2012) Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: case study of Kuala Terengganu, Malaysia. Energy Build 47:321–331

    Article  Google Scholar 

  12. PVsyst Photovoltaic Software (2021) Home: a powerful software for your photovoltaic systems. Accessed 28 June 2021

  13. Siemens (2021) PSS SINCAL 17.0. Siemens AG, pp 21–28

  14. McGlashan K (2017) An economic analysis of injecting energy storage into power systems containing renewables. Master Thesis. Worcester Polytechnic Institute

  15. Tenaga Nasional Berhad (2021) Grid code requirements. Accessed 1 June 2021

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MIZ involved in data analysis, interpretation of data and manuscript writing, GYI involved in supervision, design of the work, manuscript revision. Both authors read and approved the final manuscript.

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Correspondence to Yun Ii Go.

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See Tables 3, 4, 5, 6, 7, 8, 9 and 10.

Table 3 PVsyst meteorological dataset for location in Malaysia
Table 4 Inverter design results of the grid-connected PV system
Table 5 Input data of grid-connected PV system design [10, 11]
Table 6 Main results 116 MWp LSS project modelling
Table 7 Energy injected into the grid (annual hourly averages)
Table 8 Balances and main results of LSS farm design
Table 9 IEEE-9 bus line parameters [14]
Table 10 Voltage profile of IEEE-9 bus at 12 pm

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Fernandez, M.I., Go, Y.I. Power management scheme development for large-scale solar grid integration. Journal of Electrical Systems and Inf Technol 10, 15 (2023).

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