A Comprehensive Literature Review for Optimal Planning of Distributed Energy Resources in Distribution Grids

Leng, Ruoxuan; Li, Zhengmao; Xu, Yan

doi:10.1109/isgtasia54193.2022.10003621

Cited by 2 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, it can be noted from Figure 7 that the seasonality of the operation pattern of the long-term storage device FIGURE 6 The comparison of the RMS error of all aggregated time series via the k-means method without decomposition and the proposed Time Series Decomposition-Aggregation method. The aggregated time series include electricity load, heating load, cooling load, solar power, and wind power profiles, and the number of typical scenarios ranges from 1 to 30 days.…”

Section: Figurementioning

confidence: 99%

“…In the complex MES, both the multi-energy demands and renewable energy outputs show significant variations, including daily and seasonal variations. [3] In this context, the collaborative planning of short-term and long-term storage has attracted more attention for compensating for these two variations respectively [4][5][6]. However, the optimal planning of MES with these two kinds of storage is a large-scale program problem because a massive number of decision variables should be introduced to model the operation of short-term and long-term storage for accurate operational cost estimation [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient local multi‐energy systems planning method with long‐term storage

Jian

Zhang

Wen

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large‐scale program because numerous decision variables, including binary variables, should be used to model long‐term energy dependencies for accurate operational cost estimation. How to largely reduce decision variables as well as guarantee the planning model accuracy becomes one main concern. To this end, this paper proposes a novel efficient aggregation and modeling method for local MES planning. The aggregation method first decomposes input time series data (renewable energy output and energy demand) into hourly and daily components, based on which more accurate aggregation results with a few typical scenarios can be derived. By incorporating similar decomposition into the operation model of energy devices, the planning model can describe the long‐term energy cycle and the hourly operation characteristic at the same time and yield accurate optimization results with limited complexity. Experimental results show that the proposed method can considerably decrease the complexity of the problem while maintaining agreement with the results based on the optimization of the full‐time series.

show abstract

Section: Figurementioning

confidence: 99%