# Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Scenario Generation and Reduction Method

#### 2.1. K-Means Clustering Algorithm Based on the Elbow Method

- Sept 1: Set the number of clusters as k and take any one of all of the initial scenes as the cluster center K
_{1}. - Sept 2: Compare the distance between other scenes and the clustering center K
_{1}, and take the scene with the largest distance from K_{1}as the new clustering center K_{2}; then, compare the distance between other scenes outside the two clustering centers and the two clustering centers, and take the scene with the largest distance as the new clustering center K_{3}, and repeat the above steps to finally obtain k initial clustering centers. - Sept 3: Redetermine the cluster centers of all clusters by comparing the distances between the scenes outside the cluster centers and all the cluster centers, and classify these scenes to the cluster center with the smallest distance from each other.
- Sept 4: Use the sum of the squares of the distances between all the scenes and the clustering center, according to which the scene is classified as the clustering error H. When there is a situation where the value of the clustering error H subtracted from the clustering center of two iterations is smaller than a certain convergence accuracy, the iteration ends and the clustering center of the last iteration is obtained—otherwise, repeat step 3.

#### 2.2. Typical New Energy Scenario Generation and Reduction Method

_{T}(s) denotes the superimposed power value of wind and PV in the t-th time period under scenario $s$; ${P}_{t}^{wt}\left(s\right)$ and ${P}_{t}^{pv}\left(s\right)$ are the wind and PV power in the t-th time period under scenario $s$, respectively; and $\mathsf{\Delta}T$ is the value of the flexibility time scale.

## 3. Robust Scheduling Rules

## 4. Robust Optimal Scheduling Model for a Combined Power Plant System of New Energy and Energy Storage

#### 4.1. Optimization Objectives

- (1)
- Coal consumption cost

- (2)
- Pollutant emission cost

#### 4.2. Constraints

- (1)
- Thermal power unit constraints

- (2)
- Energy storage constraints

_{T}is the scheduling period, which takes the value of 24 h.

- (3)
- System operation constraints

#### 4.3. Improved System Optimization Model Based on Robust Scheduling Rules

## 5. Solution Algorithm Based on C&CG

## 6. Results & Discussions

#### 6.1. Example Description

#### 6.2. Robust Scheduling Scheme

#### 6.3. System Value Assessment

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Central People’s Government of the People’s Republic of China. Guiding Opinions on Promoting Energy Storage Technology and Industrial Development. [EB/OL]. [2017-09-22]. Available online: http://www.gov.cn/xinwen/2017-10/12/content_5231304.htm (accessed on 1 September 2023).
- Yang, T.; Han, Z.; Shi, Z.; Man, L.; Ji, X.; Lou, S. Research on Coordinated Optimal Allocation Method of Composite Energy Storage in Energy Internet System. Electr. Meas. Instrum.
**2021**, 58, 8–13. [Google Scholar] - Hou, L.; Ma, T.; Cai, Y.; Li, N.; Jia, Y.; Jin, T.; Pan, C. Research on Planning and Operation Optimization Model of Isolated Multi-energy Complementary System. Electr. Meas. Instrum.
**2022**, 59, 108–116. [Google Scholar] - Javed, M.S.; Song, A.; Ma, T. Techno-economic Assessment of a Stand-alone Hybrid Solar-wind-battery System for a Remote Island Using Genetic Algorithm. Energy
**2019**, 176, 704–717. [Google Scholar] [CrossRef] - Jing, Z.; Hu, R.; Yuan, J.; Zhu, J.; Wu, Q. Optimal Configuration of Island Microgrid with Wind/Solar/Pumped Storage and Load Response. Power Syst. Autom.
**2017**, 41, 65–72. [Google Scholar] - Liu, Z.; Chen, X.; Zou, S.; Pan, Y.; Li, J. Capacity Optimization Allocation Method of Wind-Solar-Pumped Storage System Considering Carbon Emissions. Power Syst. Autom.
**2021**, 45, 9–18. [Google Scholar] - Li, J.; Guo, B.; Niu, M.; Xiu, X.; Tian, L. Optimal Allocation Strategy of Energy Storage Capacity of Wind-Solar Storage System. Trans. China Electrotech. Soc.
**2018**, 33, 1189–1196. [Google Scholar] - Ma, T.; Yang, H.; Lu, L.; Peng, J. Optimal design of an autonomous solar-wind-pumped storage power supply system. Appl. Energy
**2015**, 160, 728–736. [Google Scholar] [CrossRef] - Nguyen, D.T.; Le, L.B. Risk-constrained profit maximization for microgrid aggregators with demand response. IEEE Trans. Smart Grid
**2015**, 6, 135–146. [Google Scholar] [CrossRef] - Huang, J.; Wang, T.; Lin, X.; Li, Z.; Xiong, H.; Zhang, C. Robust Optimal Dispatch of Regional Integrated Energy System for Wind Power Consumption. Electr. Meas. Instrum.
**2021**, 58, 110–117. [Google Scholar] - Zhu, G.; Lin, J.; Luo, Z.; Dai, S.; Qin, L.; Liu, C. Review of the Application of Robust Optimization in Power System Power Generation Planning. Proc. CSEE
**2017**, 37, 5881–5892. [Google Scholar] - Wang, R.; Wang, P.; Xiao, G. A robust optimization approach for energy generation scheduling in microgrids. Energy Convers. Manag.
**2015**, 106, 597–607. [Google Scholar] [CrossRef] - Cho, Y.; Ishizaki, T.; Ramdani, N.; Imura, J.I. Box-based temporal decomposition of multi-period economic dispatch for two-stage robust unit commitment. IEEE Trans. Power Syst.
**2019**, 34, 3109–3118. [Google Scholar] [CrossRef] - Wang, X.; Liu, J.; Liu, Y.; Xu, L.; Ma, T.; Xu, W. Typical Load Curve Morphological Clustering Algorithm Using Adaptive Segmentation Aggregation Approximation. Power Syst. Autom.
**2019**, 43, 110–121. [Google Scholar] - Chen, H.; Wang, Y.; Xuan, P.; Liang, Z.; Hua, D. Robust Economic Dispatch Method of Microgrid System with High Permeability Wind Power. Control. Theory Appl.
**2017**, 34, 1104–1111. [Google Scholar] - Xie, D.; Hang, Z.; Zheng, F.; Pang, R.; Wan, Y.; Yuan, S.; Zhang, Z. Comparative Analysis of Reserve Configuration of Power System with Storage Considering the Uncertainty of New Energy. In Proceedings of the 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China, 22–24 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 2490–2495. [Google Scholar]
- Liu, H.; Jiang, J.; Zhan, S.; Yang, X.; Yu, D.; Yan, G. Study on energy management model of integrated new energy-storage-charging system considering the influence of uncertainties. Fuel
**2023**, 331, 125784. [Google Scholar] [CrossRef] - Huang, S.; Lu, H.; Chen, M.; Zhao, W. Integrated energy system scheduling considering the correlation of uncertainties. Energy
**2023**, 283, 129011. [Google Scholar] [CrossRef] - Xu, H.; Chang, Y.; Zhao, Y.; Wang, F. A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response. Int. J. Electr. Power Energy Syst.
**2023**, 147, 108832. [Google Scholar] [CrossRef] - Lu, Y.; Xiang, Y.; Huang, Y.; Yu, B.; Weng, L.; Liu, J. Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load. Energy
**2023**, 271, 127087. [Google Scholar] [CrossRef] - Emrani-Rahaghi, P.; Hashemi-Dezaki, H. Optimal scenario-based operation and scheduling of residential energy hubs including plug-in hybrid electric vehicle and heat storage system considering the uncertainties of electricity price and renewable distributed generations. J. Energy Storage
**2021**, 33, 102038. [Google Scholar] [CrossRef] - ALAhmad, A.K. Voltage regulation and power loss mitigation by optimal allocation of energy storage systems in distribution systems considering wind power uncertainty. J. Energy Storage
**2023**, 59, 106467. [Google Scholar] [CrossRef] - Lei, K.; Chang, J.; Wang, X.; Guo, A.; Wang, Y.; Ren, C. Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power. Renew. Energy
**2023**, 215, 118903. [Google Scholar] [CrossRef] - Han, R.; Hu, Q.; Cui, H.; Chen, T.; Quan, X.; Wu, Z. An optimal bidding and scheduling method for load service entities considering demand response uncertainty. Appl. Energy
**2022**, 328, 120167. [Google Scholar] [CrossRef] - Li, Y.; Sun, Y.; Liu, J.; Liu, C.; Zhang, F. A data driven robust optimization model for scheduling near-zero carbon emission power plant considering the wind power output uncertainties and electricity-carbon market. Energy
**2023**, 279, 128053. [Google Scholar] [CrossRef] - Liang, Z.; Chen, H.; Lei, J.; Zhang, C.; Zhao, W. Wind-Fire-Water-Gas-Nuclear-Pumped Storage Multi-Source Collaborative Rotation Backup Optimization Considering Wind Power Uncertainty. Power Grid Technol.
**2018**, 42, 2111–2119. [Google Scholar] - Liang, Z.; Chen, H.; Wang, Y.; Zhang, C.; Zheng, X.; Wan, C. Robust Economic Dispatch of Microgrid including Electric Vehicle. Power Grid Technol.
**2017**, 41, 2647–2658. [Google Scholar] - Wang, C.; Jiao, B.; Guo, L.; Tian, Z.; Niu, J.; Li, S. Robust scheduling of building energy system under uncertainty. Appl. Energy
**2016**, 167, 366–376. [Google Scholar] [CrossRef] - Guo, L.; Liu, W.; Li, X.; Liu, Y.; Jiao, B.; Wang, W.; Wang, C.; Li, F. Energy management system for stand-alone wind-powered-desalination microgrid. IEEE Trans. Smart Grid
**2016**, 7, 1079–1087. [Google Scholar] [CrossRef] - Luna, A.C.; Diaz, N.L.; Graells, M.; Vasquez, J.C.; Guerrero, J.M. Mixed-integer-linear-programming based energy management system for hybrid PV-wind-battery microgrids: Modeling, design and experimental verification. IEEE Trans. Power Electron.
**2017**, 32, 2769–2783. [Google Scholar] [CrossRef]

**Figure 1.**Flow chart of the C&CG method to solve the two-stage robust optimization model, the * represents the optimal solution in the current iteration process.

**Figure 9.**Curve of power plant revenue and energy storage costs under different distribution and storage capacities.

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**

Wang, S.; Sun, W.
Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules. *Sustainability* **2023**, *15*, 15327.
https://doi.org/10.3390/su152115327

**AMA Style**

Wang S, Sun W.
Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules. *Sustainability*. 2023; 15(21):15327.
https://doi.org/10.3390/su152115327

**Chicago/Turabian Style**

Wang, Sicheng, and Weiqing Sun.
2023. "Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules" *Sustainability* 15, no. 21: 15327.
https://doi.org/10.3390/su152115327