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

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  • ReceivedJun 4, 2018
  • AcceptedOct 19, 2018
  • PublishedNov 22, 2018


There is no abstract available for this article.


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).


[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 $Q_{\rm~s}^*(t)$.



    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).