# Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Structure of User-Side Multi-Transformer-Integrated Microgrid

## 3. Optimal Configuration of User-Side Decentralized ESS under Multi-Transformer

#### 3.1. ESS Constraints under Multi-Transformer

_{i}is a binary variable, if n

_{i}= 1, it means that there is an ESS installed under the ith transformer, and if n

_{i}= 0, it indicates that there is not an ESS installed under the ith transformer:

#### 3.2. Economic Benefit Analysis of Industrial Park with ESS

## 4. Planning Parameter Description of ESS

#### 4.1. PV Outputs in Different Months

^{2}, as shown in Figure 3, which shows the monthly statistics of light and power generation in the area, based on NASA satellite monitoring data.

#### 4.2. Typical Daily Load Characteristics

#### 4.3. Description of Other Parameters

## 5. Numerical Analysis

#### 5.1. Economic Analysis of Decentralized ESS

#### 5.2. Optimal Configuration Analysis of Decentralized ESS under Different Return on Investments

## 6. Conclusions

- (1)
- ESS can effectively lower electricity and demand costs of industrial parks, which fall by 11.90% and 19.35%, respectively, in contrast to those without the installation of ESS.
- (2)
- It is important to take into account the location optimization of ESS in multiple-transformer-integrated industrial park microgrids. The ROI and annualized investment are in conflict with each other, which should be considered simultaneously in ESS planning.
- (3)
- The installation of ESS can help industrial parks accommodate PV power. PV becomes less accommodating as the annual net return rises. This is due to the fact that a low ESS configuration capacity is correlated with a high yearly net return, which lowers the amount of accommodation, and the PV accommodation level increases from 84.68% to 88%.
- (4)
- As for an industrial park, ESS configuration is not the more the better. This also verifies the necessity to optimize ESS in industrial parks with PV.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Abdalla, A.N.; Nazir, M.S.; Tao, H.; Cao, S.; Ji, R.; Jiang, M.; Yao, L. Integration of Energy Storage System and Renewable Energy Sources Based on Artificial Intelligence: An Overview. J. Energy Storage
**2021**, 40, 102811. [Google Scholar] [CrossRef] - Wei, Y.M.; Chen, K.; Kang, J.N.; Chen, W.; Wang, X.Y.; Zhang, X. Policy and Management of Carbon Peaking and Carbon Neutrality: A Literature Review. Engineering
**2022**, 14, 52–63. [Google Scholar] [CrossRef] - Okubo, T.; Shimizu, T.; Hasegawa, K.; Kikuchi, Y.; Manzhos, S.; Ihara, M. Factors Affecting the Techno-Economic and Environmental Performance of on-Grid Distributed Hydrogen Energy Storage Systems with Solar Panels. Energy
**2023**, 269, 126736. [Google Scholar] [CrossRef] - Anastasovski, A. What Is Needed for Transformation of Industrial Parks into Potential Positive Energy Industrial Parks? A Review. Energy Policy
**2023**, 173, 113400. [Google Scholar] [CrossRef] - Wei, Y.; Han, T.; Wang, S.; Qin, Y.; Lu, L.; Han, X.; Ouyang, M. An Efficient Data-Driven Optimal Sizing Framework for Photovoltaics-Battery-Based Electric Vehicle Charging Microgrid. J. Energy Storage
**2022**, 55, 105670. [Google Scholar] [CrossRef] - Tan, Q.; Ding, Y.; Zheng, J.; Dai, M.; Zhang, Y. The Effects of Carbon Emissions Trading and Renewable Portfolio Standards on the Integrated Wind–Photovoltaic–Thermal Power-Dispatching System: Real Case Studies in China. Energy
**2021**, 222, 119927. [Google Scholar] [CrossRef] - Li, B.; Li, M.; Yan, S.; Zhang, Y.; Shi, B.; Ye, J. An optimal energy storage system sizing determination for improving the utilization and forecasting accuracy of photovoltaic (PV) power stations. Front. Energy Res.
**2023**, 10, 1–12. [Google Scholar] [CrossRef] - Su, R.; He, G.; Su, S.; Duan, Y.; Cheng, J.; Chen, H.; Wang, K.; Zhang, C. Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network. Front. Energy Res.
**2023**, 10, 1875. [Google Scholar] [CrossRef] - Xie, R.; Wei, W.; Shahidehpour, M.; Wu, Q.; Mei, S. Sizing renewable generation and energy storage in stand-alone microgrids considering distributionally robust shortfall risk. IEEE Trans. Power Syst.
**2022**, 37, 4054–4066. [Google Scholar] [CrossRef] - Matin, S.A.A.; Mansouri, S.A.; Bayat, M.; Jordehi, A.R.; Radmehr, P. A multi-objective bi-level optimization framework for dynamic maintenance planning of active distribution networks in the presence of energy storage systems. J. Energy Storage
**2022**, 52, 104762. [Google Scholar] [CrossRef] - Mishra, D.K.; Ghadi, M.J.; Li, L.; Zhang, J.; Hossain, M.J. Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage. Appl. Energy
**2022**, 311, 118665. [Google Scholar] [CrossRef] - Moshe, S.; Oz, B. Charging More for Priority via Two-Part Tariff for Accumulating Priorities. Eur. J. Oper. Res.
**2023**, 304, 652–660. [Google Scholar] [CrossRef] - Hosseini Imani, M.; Niknejad, P.; Barzegaran, M.R. Implementing Time-of-Use Demand Response Program in Microgrid Considering Energy Storage Unit Participation and Different Capacities of Installed Wind Power. Electr. Power Syst. Res.
**2019**, 175, 105916. [Google Scholar] [CrossRef] - Wei, X.; Qiu, R.; Liang, Y.; Liao, Q.; Klemeš, J.J.; Xue, J.; Zhang, H. Roadmap to Carbon Emissions Neutral Industrial Parks: Energy, Economic and Environmental Analysis. Energy
**2022**, 238, 121732. [Google Scholar] [CrossRef] - Bahramirad, S.; Reder, W.; Khodaei, A. Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid. IEEE Trans. Smart Grid
**2012**, 3, 2056–2062. [Google Scholar] [CrossRef] - Fallahifar, R.; Kalantar, M. Optimal Planning of Lithium Ion Battery Energy Storage for Microgrid Applications: Considering Capacity Degradation. J. Energy Storage
**2023**, 57, 106103. [Google Scholar] [CrossRef] - Du, X.; Li, X.; Hao, Y.; Chen, L. Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach. Front. Energy Res.
**2022**, 10, 954833. [Google Scholar] [CrossRef] - Ma, M.; Huang, H.; Song, X.; Peña-Mora, F.; Zhang, Z.; Chen, J. Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach. Appl. Energy
**2022**, 307, 118170. [Google Scholar] [CrossRef] - Hong, Z.; Wei, Z.; Li, J.; Han, X. A Novel Capacity Demand Analysis Method of Energy Storage System for Peak Shaving Based on Data-Driven. J. Energy Storage
**2021**, 39, 102617. [Google Scholar] [CrossRef] - Schaefer, E.W.; Hoogsteen, G.; Hurink, J.L.; van Leeuwen, R.P. Sizing of Hybrid Energy Storage through Analysis of Load Profile Characteristics: A Household Case Study. J. Energy Storage
**2022**, 52, 104768. [Google Scholar] [CrossRef] - Samanta, A.; Chowdhuri, S. Active Cell Balancing of Lithium-Ion Battery Pack Using Dual DC-DC Converter and Auxiliary Lead-Acid Battery. J. Energy Storage
**2021**, 33, 102109. [Google Scholar] [CrossRef] - Zhang, Y.; Augenbroe, G. Optimal demand charge reduction for commercial buildings through a combination of efficiency and flexibility measures. Appl. Energy
**2018**, 221, 180–194. [Google Scholar] [CrossRef] - Wei, J.; Zhang, Y.; Wang, J.; Wu, L. Distribution LMP-based demand management in industrial park via a bi-level programming approach. IEEE Trans. Sustain. Energy
**2021**, 12, 1695–1706. [Google Scholar] [CrossRef] - Wikstrom, P.; Terens, L.A.; Kobi, H. Reliability, availability, and maintainability of high-power variable-speed drive systems. IEEE Trans. Ind. Appl.
**2000**, 36, 231–241. [Google Scholar] [CrossRef] - Chen, J.J.; Qi, B.X.; Rong, Z.K.; Peng, K.; Zhao, Y.L.; Zhang, X.H. Multi-Energy Coordinated Microgrid Scheduling with Integrated Demand Response for Flexibility Improvement. Energy
**2021**, 217, 119387. [Google Scholar] [CrossRef] - Qu, Z.L.; Chen, J.J.; Peng, K.; Zhao, Y.L.; Rong, Z.K.; Zhang, M.Y. Enhancing stochastic multi-microgrid operational flexibility with mobile energy storage system and power transaction. Sustain. Cities Soc.
**2021**, 71, 102962. [Google Scholar] [CrossRef] - Ma, G.; Li, J.; Zhang, X.P. Energy Storage Capacity Optimization for Improving the Autonomy of Grid-connected Microgrid. IEEE Trans. Smart Grid
**2023**. [Google Scholar] [CrossRef] - Ullah, A.; Imran, H.; Maqsood, Z.; Butt, N.Z. Investigation of optimal tilt angles and effects of soiling on PV energy production in Pakistan. Renew. Energy
**2019**, 139, 830–843. [Google Scholar] [CrossRef] - Bakker, K.; Whan, K.; Knap, W.; Schmeits, M. Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation. Sol. Energy
**2019**, 191, 138–150. [Google Scholar] [CrossRef] - Chen, C.; Duan, S.; Cai, T.; Liu, B.; Hu, G. Smart energy management system for optimal microgrid economic operation. IET Renew. Power Gener.
**2010**, 5, 258–267. [Google Scholar] [CrossRef]

**Figure 7.**Charging and discharging curves of each ESS under different typical days. (

**a**) ESS characteristic below transformer1; (

**b**) ESS characteristic below transformer2; (

**c**) ESS characteristic below transformer3.

**Figure 8.**Power curves of gateway meter during typical days of different months. (

**a**) March; (

**b**) July; (

**c**) November.

**Figure 9.**Uplink and downlink power of different transformers throughout workdays, rainy days, Saturdays, and Sundays. (

**a**) Uplink and downlink power of transformer1; (

**b**) uplink and downlink power of transformer2; (

**c**) uplink and downlink power of transformer3.

Scenario | ${\mathit{\delta}}_{1}$ (%) | ${\mathit{\delta}}_{2}$ (%) | $\mathit{L}{\mathit{f}}_{\mathit{I}}$ (104 RMB) | $\mathit{f}$ (104 RMB) | ${\mathit{f}}_{1}-{\mathit{f}}_{2}$ (104 RMB) | ${\mathit{f}}_{\mathit{D}1}-{\mathit{f}}_{\mathit{D}2}$ (104 RMB) | ESS1 (kWh) | ESS2 (kWh) | ESS3 (kWh) |
---|---|---|---|---|---|---|---|---|---|

1 | 11.45 | 23.95 | 44.89 | 5.14 | 51.15 | 20.97 | 218 | 0 | 0 |

2 | 11.40 | 23.90 | 44.89 | 5.12 | 51.17 | 20.97 | 0 | 218 | 0 |

3 | 11.32 | 23.82 | 44.89 | 5.08 | 51.20 | 20.97 | 0 | 0 | 218 |

4 | 11.50 | 24.00 | 44.89 | 5.16 | 51.12 | 20.97 | 132 | 86 | 0 |

5 | 11.51 | 24.01 | 44.89 | 5.17 | 51.12 | 20.97 | 151 | 0 | 67 |

6 | 11.48 | 23.98 | 44.89 | 5.15 | 51.13 | 20.97 | 0 | 132 | 85 |

7 | 11.52 | 24.02 | 44.94 | 5.18 | 51.10 | 20.97 | 121 | 49 | 47 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. 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

**MDPI and ACS Style**

Chen, W.; Chen, J.; Xu, B.; Cong, X.; Yin, W.
Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid. *Energies* **2023**, *16*, 3115.
https://doi.org/10.3390/en16073115

**AMA Style**

Chen W, Chen J, Xu B, Cong X, Yin W.
Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid. *Energies*. 2023; 16(7):3115.
https://doi.org/10.3390/en16073115

**Chicago/Turabian Style**

Chen, Wengang, Jiajia Chen, Bingyin Xu, Xinpeng Cong, and Wenliang Yin.
2023. "Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid" *Energies* 16, no. 7: 3115.
https://doi.org/10.3390/en16073115