# Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation

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## Abstract

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## 1. Introduction

## 2. Approach

#### 2.1. Autonomously Optimized Storages

#### 2.1.1. Battery Model and Optimization

#### 2.1.2. Incentives

#### 2.2. Simulation Setup

#### 2.2.1. Grid Topology

#### 2.2.2. Load and Photovoltaics Data

^{2}and a nominal output power of 62.1 kWp [50]. For the simulations, the PV systems at the load nodes are scaled to typically residential dimensions of 3, 5, and 6 kWp [51]. The location in the grid is chosen randomly, attaching a PV system of 3 kWp at node 37, of 5 kWp at node 21, and of 6 kWp at node 24. The total photovoltaic peak power accounts for 14 kWp. This corresponds to approximately one quarter of the maximum load noted at the slack node over the course of the simulation period, and in the absence of photovoltaics, which is a feasible penetration rate for low-voltage distribution grids [5,52].

#### 2.2.3. Battery Parameters

#### 2.2.4. Evaluation Criteria

## 3. Results

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Nomenclature

$C$ | Set of neighboring nodes (–) |

$c$ | Incentive (–) |

$DOD$ | Depth of discharge (%) |

${E}_{\mathrm{el}}$ | Electrical energy content (J) |

${E}_{\mathrm{losses}}$ | Cumulative distribution losses (Wh) |

$I$ | Alternating current (A) |

${I}_{\mathrm{slack}}$ | Alternating current at the slack node (A) |

$N$ | Total set of nodes (–) |

$n$ | Total number of data points (–) |

$\mathrm{PAPR}$ | Peak-to-average power ratio (–) |

${P}_{\mathrm{AC}}$ | Alternating power (W) |

${P}_{\mathrm{DC}}$ | Direct power (W) |

${P}_{\mathrm{HH}}$ | Household power (W) |

${P}_{\mathrm{loss}}$ | Linearized battery losses (W) |

${S}_{\mathrm{slack}}$ | Power at the slack node (VA) |

${P}_{\mathrm{PV}}$ | Photovoltaic power (W) |

$SOC$ | State of charge (%) |

$t$ | Time (s) |

$U$ | Alternating voltage (V) |

${U}_{\mathrm{d}/\mathrm{r}}$ | Alternating voltage drop/rise (V) |

${U}_{\mathrm{node}}$ | Alternating voltage at the individual grid nodes (V) |

${U}_{\mathrm{slack}}$ | Alternating voltage at the slack node (V) |

${u}_{\mathrm{DC}}$ | Decision variable on DC power side (–) |

$\mathcal{Z}$ | Impedance matrix (Ω) |

${\eta}_{\mathrm{bat}}$ | Battery efficiency (–) |

${n}_{\mathrm{load}}$ | Amount of loads (–) |

## Appendix A

**Table A1.**PAPR, power, and loss results achieved for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives. The superscript * refers to normed quantities with respect to the reference case, i.e., ${E}_{\mathrm{losses}}^{*}=\frac{{E}_{\mathrm{losses}}}{{E}_{\mathrm{losses},\mathrm{REF}}}$ and analogously for PAPR.

Abbreviation | Configuration | ${\mathit{S}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ (kVA) | ${\mathit{S}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ (kVA) | ${\mathit{S}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ (kVA) | $\mathbf{P}\mathbf{A}\mathbf{P}\mathbf{R}$ (–) | $\mathbf{P}\mathbf{A}\mathbf{P}{\mathbf{R}}^{*}$ (–) | ${\mathit{E}}_{\mathbf{l}\mathbf{o}\mathbf{s}\mathbf{s}\mathbf{e}\mathbf{s}}$ (kWh) | ${\mathit{E}}_{\mathbf{l}\mathbf{o}\mathbf{s}\mathbf{s}\mathbf{e}\mathbf{s}}^{*}$ (–) |
---|---|---|---|---|---|---|---|---|

REF | - | 26.21 | 4.06 | 57.88 | 2.21 | 1.00 | 38.76 | 1.00 |

RTP | c | 26.24 | −3.84 | 66.41 | 2.53 | 1.15 | 38.97 | 1.01 |

d | 26.31 | −3.62 | 66.85 | 2.54 | 1.15 | 50.38 | 1.30 | |

GRID | c | 26.14 | 12.56 | 49.35 | 1.89 | 0.85 | 38.50 | 0.99 |

d | 26.16 | 12.51 | 49.12 | 1.88 | 0.85 | 41.29 | 1.07 | |

PV | c | 26.38 | 2.80 | 51.32 | 1.95 | 0.88 | 38.83 | 1.00 |

d | 26.42 | 2.88 | 51.68 | 1.96 | 0.89 | 45.03 | 1.16 | |

CONS | d | 26.42 | 0.37 | 54.26 | 2.05 | 0.93 | 44.72 | 1.15 |

SELF | d | 26.63 | 3.10 | 51.68 | 1.94 | 0.88 | 43.35 | 1.12 |

**Table A2.**Voltage results achieved for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives. The superscript * refers to normed quantities with respect to the reference case, i.e., ${U}_{\mathrm{d}/\mathrm{r}\text{}}^{*}=\frac{{U}_{\mathrm{d}/\mathrm{r}\text{}}^{}}{{U}_{\mathrm{d}/\mathrm{r},\text{}\mathrm{REF}\text{}}^{}}$.

Abbreviation | Configuration | ${\mathit{U}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ (V) | ${\mathit{U}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ (V) | ${\mathit{U}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ (V) | ${\mathit{U}}_{\mathbf{d}/\mathbf{r}}^{*}$ (–) |
---|---|---|---|---|---|

REF | - | 228.67 | 222.35 | 233.87 | 1.00 |

RTP | c | 228.67 | 222.29 | 233.87 | 1.01 |

d | 228.66 | 218.44 | 236.39 | 1.51 | |

GRID | c | 228.67 | 222.40 | 233.82 | 0.99 |

d | 228.67 | 222.35 | 234.68 | 1.00 | |

PV | c | 228.67 | 222.40 | 233.82 | 0.99 |

d | 228.65 | 220.77 | 233.72 | 1.21 | |

LAOD | d | 228.65 | 221.99 | 235.87 | 1.05 |

SELF | d | 228.64 | 221.86 | 233.53 | 1.06 |

## References

- Biegel, B.; Hansen, L.H.; Stoustrup, J.; Andersen, P.; Harbo, S. Value of flexible consumption in the electricity markets. Energy
**2014**, 66, 354–362. [Google Scholar] [CrossRef] - Beaudin, M.; Zareipour, H.; Schellenberglabe, A.; Rosehart, W. Energy storage for mitigating the variability of renewable electricity sources: An updated review. Energy Sustain. Dev.
**2010**, 14, 302–314. [Google Scholar] [CrossRef] - Pina, A.; Silva, C.; Ferrão, P. The impact of demand side management strategies in the penetration of renewable electricity. Energy
**2012**, 41, 128–137. [Google Scholar] [CrossRef] - Lopes, J.A.P.; Hatziargyriou, N.; Mutale, J.; Djapic, P.; Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electr. Power Syst. Res.
**2007**, 77, 1189–1203. [Google Scholar] [CrossRef] - Bucher, C. Analysis and Simulation of Distribution Grids with Photovoltaics; ETH Zurich: Zurich, Switzerland, 2014. [Google Scholar]
- Paatero, J.V.; Lund, P.D. Effects of large-scale photovoltaic power integration on electricity distribution networks. Renew. Energy
**2007**, 32, 216–234. [Google Scholar] [CrossRef] - Ochoa, L.F.; Padilha-Feltrin, A.; Harrison, G.P. Evaluating distributed generation impacts with a multiobjective index. IEEE Trans. Power Deliv.
**2006**, 21, 1452–1458. [Google Scholar] [CrossRef] - Woyte, A.; Thong, V.V.; Belmans, R.; Nijs, J. Voltage fluctuations on distribution level introduced by photovoltaic systems. IEEE Trans. Energy Convers.
**2006**, 21, 202–209. [Google Scholar] [CrossRef] - Ackermann, T.; Knyazkin, V. Interaction between distributed generation and the distribution network: Operation aspects. In Proceedings of the IEEE/PES Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, Yokohama, Japan, 6–10 October 2002; Volume 2, pp. 1357–1362. [Google Scholar]
- Barker, P.P.; De Mello, R.W. Determining the impact of distributed generation on power systems. I. Radial distribution systems. In Proceedings of the IEEE Power Engineering Society Summer Meeting, Seattle, WA, USA, 16–20 July 2000; Volume 3, pp. 1645–1656. [Google Scholar]
- Stetz, T.; Marten, F.; Braun, M. Improved Low Voltage Grid-Integration of Photovoltaic Systems in Germany. IEEE Trans. Sustain. Energy
**2013**, 4, 534–542. [Google Scholar] [CrossRef] - Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev.
**2014**, 30, 461–478. [Google Scholar] [CrossRef] - Palensky, P.; Dietrich, D. Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Trans. Ind. Inform.
**2011**, 7, 381–388. [Google Scholar] [CrossRef] - Gelazanskas, L.; Gamage, K.A.A. Demand side management in smart grid: A review and proposals for future direction. Sustain. Cities Soc.
**2014**, 11, 22–30. [Google Scholar] [CrossRef] - Richardson, P.; Flynn, D.; Keane, A. Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems. IEEE Trans. Power Syst.
**2012**, 27, 268–279. [Google Scholar] [CrossRef] - Pang, C.; Dutta, P.; Kezunovic, M. BEVs/PHEVs as Dispersed Energy Storage for V2B Uses in the Smart Grid. IEEE Trans. Smart Grid
**2012**, 3, 473–482. [Google Scholar] [CrossRef] - Zhang, W.; Zhang, D.; Mu, B.; Wang, L.; Bao, Y.; Jiang, J.; Morais, H. Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids. Energies
**2017**, 10, 147. [Google Scholar] [CrossRef] - Roberts, B.P.; Sandberg, C. The Role of Energy Storage in Development of Smart Grids. Proc. IEEE
**2011**, 99, 1139–1144. [Google Scholar] [CrossRef] - Bragard, M.; Soltau, N.; Thomas, S.; De Doncker, R.W. The Balance of Renewable Sources and User Demands in Grids: Power Electronics for Modular Battery Energy Storage Systems. IEEE Trans. Power Electron.
**2010**, 25, 3049–3056. [Google Scholar] [CrossRef] - Qian, H.; Zhang, J.; Lai, J.-S.; Yu, W. A high-efficiency grid-tie battery energy storage system. IEEE Trans. Power Electron.
**2011**, 26, 886–896. [Google Scholar] [CrossRef] - Joerissen, L.; Garche, J.; Fabjan, C.; Tomazic, G. Possible use of vanadium redox-flow batteries for energy storage in small grids and stand-alone photovoltaic systems. J. Power Sources
**2004**, 127, 98–104. [Google Scholar] [CrossRef] - Toledo, O.M.; Oliveira Filho, D.; Diniz, A.S.A.C. Distributed photovoltaic generation and energy storage systems: A review. Renew. Sustain. Energy Rev.
**2010**, 14, 506–511. [Google Scholar] [CrossRef] - Bhatia, R.S.; Jain, S.P.; Jain, D.K.; Singh, B. Battery Energy Storage System for Power Conditioning of Renewable Energy Sources. In Proceedings of the IEEE International Conference on Power Electronics and Drives Systems, Kuala Lumpur, Malaysia, 28 November–1 December 2005; Volume 1, pp. 501–506. [Google Scholar]
- Borsche, T.; Ulbig, A.; Koller, M.; Andersson, G. Power and energy capacity requirements of storages providing frequency control reserves. In Proceedings of the 2013 IEEE Power and Energy Society General Meeting (PES), Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5. [Google Scholar]
- Lawder, M.T.; Suthar, B.; Northrop, P.W.C.; De, S.; Hoff, C.M.; Leitermann, O.; Crow, M.L.; Santhanagopalan, S.; Subramanian, V.R. Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications. Proc. IEEE
**2014**, 102, 1014–1030. [Google Scholar] [CrossRef] - Chen, H.; Cong, T.N.; Yang, W.; Tan, C.; Li, Y.; Ding, Y. Progress in electrical energy storage system: A critical review. Prog. Nat. Sci.
**2009**, 19, 291–312. [Google Scholar] [CrossRef] - Ma, T.; Yang, H.; Lu, L. Feasibility study and economic analysis of pumped hydro storage and battery storage for a renewable energy powered island. Energy Convers. Manag.
**2014**, 79, 387–397. [Google Scholar] [CrossRef] - Barnhart, C.J.; Benson, S.M. On the importance of reducing the energetic and material demands of electrical energy storage. Energy Environ. Sci.
**2013**, 6, 1083–1092. [Google Scholar] [CrossRef] - Ramoni, M.O.; Zhang, H.-C. End-of-life (EOL) issues and options for electric vehicle batteries. Clean Technol. Environ. Policy
**2013**, 15, 881–891. [Google Scholar] [CrossRef] - Fäßler, B.; Kepplinger, P.; Kolhe, M.L.; Petrasch, J. Decentralized on-Site Optimization of a Battery Storage System Using One-Way Communication; Institution of Engineering and Technology: Stevenage, UK, 2015; pp. 1–7. [Google Scholar]
- Heymans, C.; Walker, S.B.; Young, S.B.; Fowler, M. Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling. Energy Policy
**2014**, 71, 22–30. [Google Scholar] [CrossRef] - Faessler, B.; Kepplinger, P.; Petrasch, J. Decentralized price-driven grid balancing via repurposed electric vehicle batteries. Energy
**2017**, 118, 446–455. [Google Scholar] [CrossRef] - Kepplinger, P.; Huber, G.; Petrasch, J. Demand Side Management via Autonomous Control-Optimization and Unidirectional Communication with Application to Resistive Hot Water Heaters; ENOVA: Eisenstadt, Austria, 2014; Volume 8. [Google Scholar]
- Finn, P.; Fitzpatrick, C. Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing. Appl. Energy
**2014**, 113, 11–21. [Google Scholar] [CrossRef] - Gottwalt, S.; Ketter, W.; Block, C.; Collins, J.; Weinhardt, C. Demand side management—A simulation of household behavior under variable prices. Energy Policy
**2011**, 39, 8163–8174. [Google Scholar] [CrossRef] - Logenthiran, T.; Srinivasan, D.; Khambadkone, A.M.; Aung, H.N. Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator. IEEE Trans. Smart Grid
**2012**, 3, 925–933. [Google Scholar] [CrossRef] - Roos, J.G.; Lane, I.E. Industrial power demand response analysis for one-part real-time pricing. IEEE Trans. Power Syst.
**1998**, 13, 159–164. [Google Scholar] [CrossRef] - Energy Exchange Austria (EXAA). Abwicklungsstelle für Energieprodukte AG EXAA Energy Exchange Austria. Available online: http://www.exaa.at/de (accessed on 18 December 2015).
- Xu, G.; Wu, S.; Tan, Y. Island Partition of Distribution System with Distributed Generators Considering Protection of Vulnerable Nodes. Appl. Sci.
**2017**, 7, 1057. [Google Scholar] [CrossRef] - Pötzinger, C.; Preißinger, M.; Brüggemann, D. Influence of Hydrogen-Based Storage Systems on Self-Consumption and Self-Sufficiency of Residential Photovoltaic Systems. Energies
**2015**, 8, 8887–8907. [Google Scholar] [CrossRef] - Ul-Haq, A.; Cecati, C.; Al-Ammar, E. Modeling of a Photovoltaic-Powered Electric Vehicle Charging Station with Vehicle-to-Grid Implementation. Energies
**2016**, 10, 4. [Google Scholar] [CrossRef] - Ghatak, U.; Mukherjee, V. An improved load flow technique based on load current injection for modern distribution system. Int. J. Electr. Power Energy Syst.
**2017**, 84, 168–181. [Google Scholar] [CrossRef] - Li, H.; Zhang, A.; Shen, X.; Xu, J. A load flow method for weakly meshed distribution networks using powers as flow variables. Int. J. Electr. Power Energy Syst.
**2014**, 58, 291–299. [Google Scholar] [CrossRef] - Ghatak, U.; Mukherjee, V. A fast and efficient load flow technique for unbalanced distribution system. Int. J. Electr. Power Energy Syst.
**2017**, 84, 99–110. [Google Scholar] [CrossRef] - Teng, J.-H. A direct approach for distribution system load flow solutions. IEEE Trans. Power Deliv.
**2003**, 18, 882–887. [Google Scholar] [CrossRef] - MATLAB. MATLAB 2017a; The MathWorks Inc.: Natick, MA, USA, 2017. [Google Scholar]
- Schuler, M. Simulation Elektrischer Netze zur Beurteilung von Verbraucherseitiger Laststeuerung; Vorarlberg University of Applied Sciences: Dornbirn, Austria, 2017; p. 107. [Google Scholar]
- Vorarlberger Energienetze GmbH. Available online: https://www.vorarlbergnetz.at (accessed on 1 June 2017).
- Vorarlberger Kraftwerke AG VKW (Vorarlberger Kraftwerke AG). Available online: https://www.vkw.at/ (accessed on 1 June 2017).
- Gawlik, W.; Groiß, C.; Litzlbauer, M.; Maier, C.; Schuster, A.; Zeilinger, F.; Kann, A.; Meirold-Mautner, I.; Günther, G.; Eugster, C.; et al. aDSM—Aktives Demand-Side-Management durch Einspeiseprognose; Vienna University of Technology—Institute of Energy Systems and Electrical Drives: Vienna, Austria, 2014; p. 208. [Google Scholar]
- Erge, T.; Hoffmann, V.U.; Kiefer, K. The German experience with grid-connected PV-systems. Sol. Energy
**2001**, 70, 479–487. [Google Scholar] [CrossRef] - Bucher, C. Bulletin Electrosuisse. March 2014, pp. 37–40. Available online: http://www.bulletin.ch/ (accessed on 25 October 2017).
- Vollmer, P. WirtschaftsWoche. 20 June 2016. Available online: http://www.wiwo.de/ (accessed on 31 May 2017).
- Sonnen GmbH. Sonnen GmbH—Energy Is Yours. Available online: https://sonnenbatterie.de (accessed on 31 May 2017).
- Sonnen GmbH. Technische Daten Sonnenbatterie Eco; Sonnen GmbH: Wildpoldsried, Germany, 2017. [Google Scholar]
- MATLAB. Optimization Toolbox; The MathWorks Inc.: Natick, MA, USA, 2017. [Google Scholar]
- EN Standard DIN EN 50160:2011-02. Merkmale der Spannung in öffentlichen Elektrizitätsversorgungsnetzen. 2011. Available online: https://www.beuth.de/ (accessed on 15 November 2017).

**Figure 2.**Used and normalized incentives to drive the optimization of the BESS for a one-week period: EXAA day-ahead spot-market price for electricity (RTP); total grid load at the slack node (GRID); photovoltaic generation (PV); individual household loads (LOAD) for household at node 21, 24, and 37 comprising a distributed storage system; individual total household consumption including load and photovoltaic generation (SELF) for household at node 21, 24, and 37 comprising a distributed storage system.

**Figure 3.**Peak-to-average power ratio, voltages and cumulative distribution losses for all configurations for a single, central storage (c) and multiple, distributed storages (d). The superscript * refers to normed quantities with respect to the reference case, i.e., ${E}_{\mathrm{losses}}^{*}=\frac{{E}_{\mathrm{losses}}}{{E}_{\mathrm{losses},\text{}\mathrm{REF}}}$, analogously for ${U}_{\mathrm{d}/\mathrm{r}}$ and PAPR.

**Figure 4.**Power duration curve for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives.

**Figure 5.**Voltage duration curve for a single, central storage (c) and multiple, distributed storages (d) driven by different incentives.

**Table 1.**Incentives used to drive battery energy storage system (BESS) optimization. The considered configurations for BESS are abbreviated by c for a single, central storage and d for multiple, distributed storages.

Abbreviation | Description | Incentive | Configuration |
---|---|---|---|

REF | Reference case | - | - |

RTP | Real-time pricing | EXAA day-ahead market price | c/d |

GRID | Grid balancing | Total future grid load | c/d |

PV | Optimal PV utilization | Future PV generation | c/d |

LOAD | Load shifting | Future household consumption | d |

SELF | Self-consumption | Future household load (incl. PV) | d |

**Table 2.**Node position and corresponding specification (type, capacity, depth of discharge (DOD), battery efficiency, nominal power and converter efficiency) for the integrated BESSs in the simulation study.

Node | Model | Battery Characteristic | Converter Characteristic | |||
---|---|---|---|---|---|---|

Capacity (kWh) | DOD (%) | Efficiency ${\mathbf{\eta}}_{\mathbf{b}\mathbf{a}\mathbf{t}}$ (%) | Power ${\mathit{P}}_{\mathbf{A}\mathbf{C},\mathbf{m}\mathbf{a}\mathbf{x}}$ (kW) | Efficiency ${\mathbf{\eta}}_{\mathbf{c}\mathbf{o}\mathbf{n}}$ (%) | ||

37 | eco 8/4 | 4 | 100 | 98 | 2.5 | 96 |

21, 24 | eco 8/6 | 6 | 100 | 98 | 3.0 | 96 |

19 | - | 16 | 100 | 98 | 8.5 | 96 |

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## Share and Cite

**MDPI and ACS Style**

Faessler, B.; Schuler, M.; Preißinger, M.; Kepplinger, P.
Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation. *Energies* **2017**, *10*, 2161.
https://doi.org/10.3390/en10122161

**AMA Style**

Faessler B, Schuler M, Preißinger M, Kepplinger P.
Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation. *Energies*. 2017; 10(12):2161.
https://doi.org/10.3390/en10122161

**Chicago/Turabian Style**

Faessler, Bernhard, Michael Schuler, Markus Preißinger, and Peter Kepplinger.
2017. "Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation" *Energies* 10, no. 12: 2161.
https://doi.org/10.3390/en10122161