SCIENCE CHINA Information Sciences, Volume 61, Issue 12: 129207(2018) https://doi.org/10.1007/s11432-018-9632-x

## Operation optimization for integrated energy system with energy storage

• AcceptedOct 19, 2018
• PublishedNov 22, 2018
Share
Rating

### Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61821004, 61733010, 61320106011, 61573224, 61573223), and the Young Scholars Program of Shandong University (Grant No. 2016WLJH29).

### References

[1] Fumo N, Mago P J, Chamra L M. Emission operational strategy for combined cooling, heating, and power systems. Appl Energy, 2009, 86: 2344-2350 CrossRef Google Scholar

[2] Sridhar S, Hahn A, Govindarasu M. Cyber-Physical System Security for the Electric Power Grid. Proc IEEE, 2012, 100: 210-224 CrossRef Google Scholar

[3] Li J H, Wen J Y, Cheng S J, et al. Minimum energy storage for power system with high wind power penetration using p-efficient point theory. Sci China Inf Sci, 2014, 57: 128202. Google Scholar

[4] Deng N, Cai R, Gao Y. A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin. Energy, 2017, 141: 1750-1763 CrossRef Google Scholar

[5] Bao Z, Zhou Q, Yang Z. A Multi Time-Scale and Multi Energy-Type Coordinated Microgrid Scheduling Solution-Part II: Optimization Algorithm and Case Studies. IEEE Trans Power Syst, 2015, 30: 2267-2277 CrossRef ADS Google Scholar

[6] Zheng C Y, Wu J Y, Zhai X Q. A novel thermal storage strategy for CCHP system based on energy demands and state of storage tank. Int J Electrical Power Energy Syst, 2017, 85: 117-129 CrossRef Google Scholar

[7] Liu Y, Yang J, Wang Y. Multi-objective optimal preliminary planning of multi-debris active removal mission in LEO. Sci China Inf Sci, 2017, 60: 072202 CrossRef Google Scholar

[8] Fang F, Wang Q H, Shi Y. A Novel Optimal Operational Strategy for the CCHP System Based on Two Operating Modes. IEEE Trans Power Syst, 2012, 27: 1032-1041 CrossRef ADS Google Scholar

• Figure 1

(Color online) Results of hybrid and traditional GA methods.

•

Algorithm 1 The hybrid optimization based on DP and GA

Require:Load forecasting data, device parameters, and GA parameters.

Output:Maximum $J(T)$, corresponding $u^*(t)$ and $Q_{\rm~s}^*(t)$.

$S(t)~=~0$;

$S(t-1)~=~0$;

Calculate $u^*(t)$ that maximize the object function $J(t)=J(t-1)+l(S(t),S(t-1),u(t),t)~$ from $S(t-1)$ to $S(t)$ by using the GA, and record $u^*(t)$ and maximum $J_{S(t)}^{S(t-1)}(t)$;

Increment $S(t-1)$ by $\Delta~Q_{\rm~s}$;

Repeat Steps 3 and 4, until $S(t-1)~=~S_{\rm~max}$;

Find maximum $J(t)$ from any $S(t-1)$ to $S(t)$, and record $J_{S(t)}^*(t)$ and corresponding $S^*(t-1)$ and $u^*(t)$;

Increment $S(t)$ by $\Delta~Q_{\rm~s}$;

Repeat Steps 2–7, until $S(t)~=~S_{\rm~max}$;

Increment $t$ by $1$;

Repeat Steps 1–9, until $t~=~T$;

Find maximum $J(T)$ and corresponding $u^*(t)$ and $S^*(t)$ $(1~\leq~t~\leq~T)$, and calculate $Q_{\rm~s}^*(t)$ using (10).

Citations

• #### 0

Altmetric

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有