Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems
1.1. Literature Review
1.2. Motivation and Major Contributions of the Paper
- Since most of the BESS-controlling strategies are based on local information, they did not provide a system-level optimality guarantee. That is, the optimum amount of active power to be stored by each installed battery in the network is absent. This study tackles this point by providing a network-level optimization for BESS operation in grid-connected solar PV.
- Most of the studies use analytical approaches and local information to control the operation of the batteries. This research work, on the other hand, provides a charging/discharging strategy, taking into consideration all batteries in the distribution system to improve the voltage profile.
- In addition, different daily charging/discharging strategies have been proposed in the literature. Some of them proposed a load-following control method, while others utilized a droop-based method. Therefore, the proposed method examines a new controlling approach that shows its effectiveness.
- In addition, most papers have assumed predetermined sizes, numbers, and BESS locations in the distribution network. Their assumption may not be ideal since considering different sizes, numbers, or locations could result in better operation scenarios. Results indicate that the proposed voltage strategy is useful in the optimal planning of the BESS in the network.
2. Materials and Methods
2.1. Problem Description
2.2. Study Framework
- Step 1:
- The algorithm receives as input data the PV power output, load data of each house, and prespecified available BESS.
- Step 2:
- The NNA produces the initial population for the optimization problem. The explanation of NNA is presented in Section 3. NNA pattern solution populations are represented as a vector containing battery sizes in and and location of each BESS, which are the variables to be optimized in the upper-level optimization problem. In other words, the vectors’ dimension is three times the number of BESS that are deployed in the network and are real variables, while the location () is an integer variable as it represents the house number. If we have, for instance, three available batteries to be installed in the network, nine decision variables are optimized by NNA.
- Step 3:
- Using the LP, the BESS schedule for the following day is determined and solved in MATLAB (see Section 2.2). After that, the Open Distribution System Simulator (OpenDSS) software receives the ideal daily charge/discharge of BESS  and measures its effectiveness on the distribution grid. The role of OpenDSS is to model and simulate the distribution system. The power flow constraints are satisfied since the power flow is conducted in real-time simulation through the OpenDSS.
- Step 4:
- The evaluation is accomplished using the fitness function, Equation (2), which minimizes voltage deviation. Three-phase load flow in OpenDSS is used to obtain the voltage magnitudes. This technique is continued until the desired number of iterations has been reached.
2.3. Battery Modeling
- The PV system extracts the available power from the sun and produces solar energy.
- See if the produced energy can be utilized to supply the demand.
- Any excess energy is considered as the available energy to charge the BESS.
- If the BESS is completely charged, the surplus energy is exported to the main grid.
- The PV system generates little amounts of no solar energy.
- The BESS initially supplies the demand.
- If the load is completely supplied by BESS, the remaining energy in BESS is exported to the main grid.
- If the BESS is fully discharged, the load is supplied from the main grid.
3. Metaheuristic Optimization Algorithms
Neural Network Optimization Algorithm
- Step 1:
- Select the pattern solutions population and set the lower and upper bounds and the maximum number of iterations of the algorithm . In this study, the is set to 50. Since we have three variables to optimize, the and bounds are set to adhere to BESS rated capacity, rated power, and the number of buses in the examined test feeder. For , 250 is used in this study.
- Step 2:
- Initiate the population of pattern solutions with the predetermined bounds using the following formula:
- Step 3:
- Evaluate the fitness values of the initial population using Equation (2).
- Step 4:
- Randomly generate the weigh matrix, values of which are selected based on a uniform distribution between zero and one and the summation of these values should be below or equal to one. See Equations (14) and (15).
- Step 5:
- Determine the target solution and the associated target weight . Since NNA has only one target response, the target solution and weight are selected from the population weight (weight matrix).
- Step 6:
- Create a new pattern solution and update the pattern solutions using Equations (16) and (17).
- Step 7:
- Update the weight matrix using Equation (18), taking into consideration the constraints in Equations (14) and (15).
- Step 8:
- Evaluate the bias condition. If conducts that bias operator, see Equation (19) for both the new position of pattern solutions and the updated weight matrix.
- Step 9:
- Employ the transfer function operator to update the new position of pattern solutions if , using the following equation:
- Step 10:
- Find the fitness values of the updated pattern solutions using the objective function, Equation (2).
- Step 11:
- Update the target solution and its associated target weight.
- Step 12:
- Update the value of using the reduction formulation, Equation (19).
- Step 13:
- Check that the stop criteria are met; otherwise, the algorithm should go back to Step 6.
4. Results and Discussion
4.1. Test System
4.2. Study Dataset
4.3. Case Studies and Simulation Results
4.4. Voltage before and after PV Systems
4.5. Analysis of Optimal Sizing and Allocation
4.6. BESS Operation
4.7. Transformer Loading and System Losses
4.8. Network Voltage with BESS
5. Conclusions and Future Work
- The simulation results show that the overvoltage problems appear in the distribution network with high PV integration represented by rooftop PV units.
- Optimal sizing, placement, and operating through utilizing the proposed voltage mitigation method helped overcome the voltage problems.
- In terms of the locations of the BESS units, the end of the feeder homes are the ideal sites to install batteries as they encounter the worst voltage scenarios.
- In addition, results showed that the optimal siting and sizing of the available BESS are highly impacted by network topology, load profiles, and the amount of PV power that flows into the grid.
- The developed BESS management strategy successfully minimizes the transformer burden; thus, it never exceeds the distribution system’s capacity limit. Therefore, the optimal installation of batteries in distribution grids can assist in delaying any scheduled transformer upgrades.
- Embracing and optimal operation of BESS in the smart grids will permit ideal resource usage and increase the PV hosting capacity in the LV grids.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
- REN21. Renewables 2020 Global Status Report; REN21 Secretariat: Paris, French, 2020. [Google Scholar]
- IEA. Global Energy Review 2020. 2020. Available online: https://www.iea.org/reports/global-energy-review-2020 (accessed on 15 January 2022).
- Novoa, L.; Flores, R.; Brouwer, J. Optimal Renewable Generation and Battery Storage Sizing and Siting Considering Local Transformer Limits. Appl. Energy 2019, 256, 113926. [Google Scholar] [CrossRef]
- Unigwe, O.; Okekunle, D.; Kiprakis, A. Smart Coordination Schemes for Multiple Battery Energy Storage Systems for Support in Distribution Networks with High Penetration of Photovoltaics. IET Smart Grid. 2019, 2, 347–354. [Google Scholar] [CrossRef]
- Torquato, R.; Salles, D.; Pereira, C.O.; Meira, P.C.M.; Freitas, W. A Comprehensive Assessment of PV Hosting Capacity on Low-Voltage Distribution Systems. IEEE Trans. Power Deliv. 2018, 33, 1002–1012. [Google Scholar] [CrossRef]
- Long, Y.; Kirschen, D.S. Bi-Level Volt/VAR Optimization in Distribution Networks with Smart PV Inverters. IEEE Trans. Power Syst. 2022, 37, 3604–3613. [Google Scholar] [CrossRef]
- Babacan, O.; Torre, W.; Kleissl, J. Siting and Sizing of Distributed Energy Storage to Mitigate Voltage Impact by Solar PV in Distribution Systems. Sol. Energy 2017, 146, 199–208. [Google Scholar] [CrossRef]
- Baran, M.E.; Hooshyar, H.; Shen, Z.; Huang, A. Accommodating High PV Penetration on Distribution Feeders. IEEE Trans. Smart Grid 2012, 3, 1039–1046. [Google Scholar] [CrossRef]
- Das, C.K.; Bass, O.; Kothapalli, G.; Mahmoud, T.S.; Habibi, D. Overview of Energy Storage Systems in Distribution Networks: Placement, Sizing, Operation, and Power Quality. Renew. Sustain. Energy Rev. 2018, 91, 1205–1230. [Google Scholar] [CrossRef]
- Conde, A.; Perez, G.; Gutierrez-Alcaraz, G.; Leonowicz, Z. Frequency Improvement in Microgrids through Battery Management System Control Supported by a Remedial Action Scheme. IEEE Access 2022, 10, 8081–8091. [Google Scholar] [CrossRef]
- Li, X.; Ma, R.; Gan, W.; Yan, S. Optimal Dispatch for Battery Energy Storage Station in Distribution Network Considering Voltage Distribution Improvement and Peak Load Shifting. J. Mod. Power Syst. Clean Energy 2022, 10, 131–139. [Google Scholar] [CrossRef]
- Sant’Ana, W.C.; Mollica, D.; Lambert-Torres, G.; Guimaraes, B.P.B.; Pinheiro, G.G.; Bonaldi, E.L.; Pereira, R.R.; Borges-Da-Silva, L.E.; Gonzatti, R.B.; Santana-Filho, J. 13.8 KV Operation of a Peak-Shaving Energy Storage Equipment with Voltage Harmonics Compensation Feature. IEEE Access 2020, 8, 182117–182132. [Google Scholar] [CrossRef]
- Spiliotis, K.; Claeys, S.; Gutierrez, A.R.; Driesen, J. Utilizing Local Energy Storage for Congestion Management and Investment Deferral in Distribution Networks. In Proceedings of the 2016 13th International Conference on the European Energy Market (EEM), Porto, Portugal, 6–9 June 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Yan, X.; Zhang, X.; Chen, H.; Xu, Y.; Tan, C. Techno-Economic and Social Analysis of Energy Storage for Commercial Buildings. Energy Convers. Manag. 2014, 78, 125–136. [Google Scholar] [CrossRef]
- IRENA. Future of Solar Photovoltaic: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects (A Global Energy Transformation: Paper). Int. Renew. Energy Agency Abu. Dhabi. 2019, 1–88. [Google Scholar]
- Jayasekara, N.; Masoum, M.A.S.; Wolfs, P.J. Optimal Operation of Distributed Energy Storage Systems to Improve Distribution Network Load and Generation Hosting Capability. IEEE Trans. Sustain. Energy 2016, 7, 250–261. [Google Scholar] [CrossRef]
- Mehrjerdi, H. Simultaneous Load Leveling and Voltage Profile Improvement in Distribution Networks by Optimal Battery Storage Planning. Energy 2019, 181, 916–926. [Google Scholar] [CrossRef]
- Lakshmi, S.; Ganguly, S. Multi-Objective Planning for the Allocation of PV-BESS Integrated Open UPQC for Peak Load Shaving of Radial Distribution Networks. J. Energy Storage 2019, 22, 208–218. [Google Scholar] [CrossRef]
- Barzkar, A.; Hosseini, S.M.H. A Novel Peak Load Shaving Algorithm via Real-time Battery Scheduling for Residential Distributed Energy Storage Systems. Int. J. Energy Res. 2018, 42, 2400–2416. [Google Scholar] [CrossRef]
- Alam, M.J.E.; Muttaqi, K.M.; Sutanto, D. Distributed Energy Storage for Mitigation of Voltage-Rise Impact Caused by Rooftop Solar PV. In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22–26 July 2012; pp. 1–8. [Google Scholar] [CrossRef]
- Jayasekara, N.; Wolfs, P.; Masoum, M.A.S. An Optimal Management Strategy for Distributed Storages in Distribution Networks with High Penetrations of PV. Electr. Power Syst. Res. 2014, 116, 147–157. [Google Scholar] [CrossRef]
- Babacan, O.; Torre, W.; Kleissl, J. Optimal Allocation of Battery Energy Storage Systems in Distribution Networks Considering High PV Penetration. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting, Boston, MA, USA, 17–21 July 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Sardi, J.; Mithulananthan, N.; Gallagher, M.; Hung, D.Q. Multiple Community Energy Storage Planning in Distribution Networks Using a Cost-Benefit Analysis. Appl. Energy 2017, 190, 453–463. [Google Scholar] [CrossRef]
- Kabir, M.N.; Mishra, Y.; Ledwich, G.; Member, S.; Dong, Z.Y.; Wong, K.P. Coordinated Control of Grid-Connected Photovoltaic Reactive Power and Battery Energy Storage Systems to Improve the Voltage Profile of a Residential Distribution Feeder. IEEE Trans. Ind. Inform. 2014, 10, 967–977. [Google Scholar] [CrossRef]
- Chedid, R.; Sawwas, A. Optimal Placement and Sizing of Photovoltaics and Battery Storage in Distribution Networks. Energy Storage 2019, 1, e46. [Google Scholar] [CrossRef]
- Naupari, D.Z.; Molina, Y.P.; Coronel, A. Optimal Sizing of Photovoltaic Systems in Distribution Networks Using Particle Swarm Optimization. In Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition—Latin America, T and D-LA 2018, Lima, Peru, 18–21 September 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Lin, W.C.; Huang, W.T.; Chih, H.C.; Yao, K.C.; Li, Z.T.; Ma, C.C. Comparisons of Energy Loss Reduction by Phase Balancing in Unbalance Distribution Networks via Metaheuristic Algorithms. In Proceedings of the 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan, 3–5 December 2020; pp. 229–233. [Google Scholar] [CrossRef]
- Azam Muhammad, M.; Mokhlis, H.; Naidu, K.; Amin, A.; Fredy Franco, J.; Othman, M. Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm. J. Mod. Power Syst. Clean Energy 2020, 8, 86–93. [Google Scholar] [CrossRef]
- Helmi, A.M.; Carli, R.; Dotoli, M.; Ramadan, H.S. Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IEEE Trans. Autom. Sci. Eng. 2022, 19, 82–98. [Google Scholar] [CrossRef]
- Sadollah, A.; Sayyaadi, H.; Yadav, A. A Dynamic Metaheuristic Optimization Model Inspired by Biological Nervous Systems: Neural Network Algorithm. Appl. Soft Comput. 2018, 71, 747–782. [Google Scholar] [CrossRef]
- Electric Power Research Institute. OpenDSS, Distribution System Simulator. Available online: https://sourceforge.net/projects/electricdss/ (accessed on 21 October 2020).
- Guinness Global Investors. The Guinness Sustainable Energy Report; Guinness Global Investors: London, UK, 2022. [Google Scholar]
- Chicco, G.; Mazza, A. Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the “Rush to Heuristics”. Energies 2020, 13, 5097. [Google Scholar] [CrossRef]
- Cuevas, E.; Espejo, E.B.; Enríquez, A.C. Metaheuristics Algorithms in Power Systems; Springer International Publishing/Springer Nature: Cham, Switzerland, 2019. [Google Scholar] [CrossRef]
|AC voltage||120/240 V|
|Usable energy||13.5 kWh|
|Maximum charging/discharging power||5 kW|
|Minimum charging/discharging power||0 kW|
|Maximum state of charge||100%|
|Minimum state of charge||10%|
|Round trip efficiency||90%|
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alrashidi, M. Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems. Appl. Sci. 2022, 12, 9083. https://doi.org/10.3390/app12189083
Alrashidi M. Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems. Applied Sciences. 2022; 12(18):9083. https://doi.org/10.3390/app12189083Chicago/Turabian Style
Alrashidi, Musaed. 2022. "Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems" Applied Sciences 12, no. 18: 9083. https://doi.org/10.3390/app12189083