SCIENCE CHINA Information Sciences, Volume 64 , Issue 1 : 112202(2021) https://doi.org/10.1007/s11432-019-2638-2

## Distributed fixed step-size algorithm for dynamic economic dispatch with power flow limits

• AcceptedAug 5, 2019
• PublishedOct 28, 2020
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### References

[1] Yu W W, Li C J, Yu X H. Economic power dispatch in smart grids: a framework for distributed optimization and consensus dynamics. Sci China Inf Sci, 2018, 61: 012204 CrossRef Google Scholar

[2] Qian Y W, Zhang C, Xu Z W. A reliable opportunistic routing for smart grid with in-home power line communication networks. Sci China Inf Sci, 2016, 59: 122305 CrossRef Google Scholar

[3] Tang C B, Li X, Wang Z. Cooperation and distributed optimization for the unreliable wireless game with indirect reciprocity. Sci China Inf Sci, 2017, 60: 110205 CrossRef Google Scholar

[4] Wang Y H, Lin P, Hong Y G. Distributed regression estimation with incomplete data in multi-agent networks. Sci China Inf Sci, 2018, 61: 092202 CrossRef Google Scholar

[5] Li C J, Yu X H, Huang T W. Distributed Optimal Consensus Over Resource Allocation Network and Its Application to Dynamical Economic Dispatch.. IEEE Trans Neural Netw Learning Syst, 2018, 29: 2407-2418 CrossRef PubMed Google Scholar

[6] Bai L, Ye M J, Sun C. Distributed Economic Dispatch Control via Saddle Point Dynamics and Consensus Algorithms. IEEE Trans Contr Syst Technol, 2019, 27: 898-905 CrossRef Google Scholar

[7] He X, Ho D W C, Huang T W. Second-Order Continuous-Time Algorithms for Economic Power Dispatch in Smart Grids. IEEE Trans Syst Man Cybern Syst, 2018, 48: 1482-1492 CrossRef Google Scholar

[8] He X, Yu J Z, Huang T W. Distributed Power Management for Dynamic Economic Dispatch in the Multimicrogrids Environment. IEEE Trans Contr Syst Technol, 2019, 27: 1651-1658 CrossRef Google Scholar

[9] Zou D X, Li S, Kong X Y. Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling. Energy, 2018, 147: 59-80 CrossRef Google Scholar

[10] Jin X L, Mu Y F, Jia H J. Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system. Appl Energy, 2017, 194: 386-398 CrossRef Google Scholar

[11] Qu B Y, Zhu Y S, Jiao Y C. A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evolary Computation, 2018, 38: 1-11 CrossRef Google Scholar

[12] Xu Y L, Zhang W, Liu W X. Distributed Dynamic Programming-Based Approach for Economic Dispatch in Smart Grids. IEEE Trans Ind Inf, 2015, 11: 166-175 CrossRef Google Scholar

[13] Shuai H, Fang J K, Ai X M. Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming. IEEE Trans Smart Grid, 2019, 10: 2440-2452 CrossRef Google Scholar

[14] Papavasiliou A, Mou Y, Cambier L. Application of Stochastic Dual Dynamic Programming to the Real-Time Dispatch of Storage Under Renewable Supply Uncertainty. IEEE Trans Sustain Energy, 2018, 9: 547-558 CrossRef ADS Google Scholar

[15] Li C J, Yu X H, Yu W W. Distributed Event-Triggered Scheme for Economic Dispatch in Smart Grids. IEEE Trans Ind Inf, 2016, 12: 1775-1785 CrossRef Google Scholar

[16] Liu Z W, Yu X, Guan Z H. Pulse-Modulated Intermittent Control in Consensus of Multiagent Systems. IEEE Trans Syst Man Cybern Syst, 2017, 47: 783-793 CrossRef Google Scholar

[17] Rockafellar R T, Wets R J B. Variational Analysis. New York: Springer Science & Business Media, 2009. Google Scholar

[18] Singh H, Hao S, Papalexopoulos A. Transmission congestion management in competitive electricity markets. IEEE Trans Power Syst, 1998, 13: 672-680 CrossRef ADS Google Scholar

[19] Zhao C C, He J P, Cheng P. Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication. IEEE Trans Smart Grid, 2017, 8: 2049-2061 CrossRef Google Scholar

[20] Yi P, Pavel L. A distributed primal-dual algorithm for computation of generalized Nash equilibria with shared affine coupling constraints via operator splitting methods. 2017,. arXiv Google Scholar

• Figure 1

(Color online) Optimal dispatch of each generator.

• Figure 2

(Color online) (a) Structure chart of IEEE 14-bus; (b) topology of IEEE 14-bus.

• Figure 3

(Color online) (a) Power output of the distributed generators; (b) load demand of the users.

• Figure 4

(Color online) (a) Mismatch of entire grid and (b) the incremental cost of each generation in five time slots.

• Figure 5

(Color online) Values of (a) $\mu_{i,h}$ and (b) $\nu_{i,h}$ in five time slots.

• Figure 6

(Color online) Consensus values of the Lagrangian multipliers $\theta_i$ (a) and $\gamma_i$ (b) of the power flow constraints.

• Figure 7

(Color online) Active power flow of the transmission lines.

• Table 1

Table 1Parameters of the distributed generators

 Parameter Generator number 1 3 4 13 14 $a_i$ 0.08 0.062 0.075 0.072 0.066 $b_i$ 2.25 4.2 3.25 6.25 3.2 $c_i$ 23 12 23 14 19 $P_i^m$ 20 10 20 9 5 $P_i^M$ 80 80 95 70 80 $P_i^R$ 10 10 10 8 7 $\beta_i$ 0.001 0.001 0.001 0.001 0.001
• Table 2

Table 2Output of each renewable generator

 User number Time 1 Time 2 Time 3 Time 4 Time 5 7 10 15 6 13 6 8 5 12 5 15 10 10 4 17 3 12 18 11 15 32 10 20 13
• Table 3

Table 3Parameters of the users' load demand

 User number $\omega_i$ $\upsilon_i$ $d_i^m$ $d_i^M$ 2 0.06 15.12 30 60 5 0.082 11.984 15 39 6 0.065 12.992 15 35 7 0.062 13.216 15 37 8 0.066 12.04 15 35 9 0.071 15.904 20 58 10 0.062 12.544 10 25 11 0.075 13.3 15 30 12 0.076 12.572 18 32
• Table 4

Table 4Corresponding parameters of the transmission lines

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 From 1 1 2 2 2 3 4 4 4 5 6 6 6 7 7 9 9 10 12 13 To 2 5 3 4 5 4 5 7 9 6 11 12 13 8 9 10 14 11 13 14 $T_l$ 54 66 36 42 69 96 28 48 36 36 46.8 36 36 36 54 42 54 25.2 46.8 72

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