Energy Internet and We-Energy pp 265-298 | Cite as

# Distributed Optimal Energy Management for Energy Internet

## Abstract

In this chapter, a novel energy management framework for Energy Internet with many energy bodies is presented, which features multi-coupling of different energy forms, diversified energy roles and peer-to-peer energy supply/demand, etc. The energy body as an integrated energy unit, which may have various functionalities and play multiple roles at the same time, is formulated for the system model development. Forecasting errors, confidence intervals and penalty factor are also taken into account to model renewable energy resources to provide trade-off between optimality and possibility. Furthermore, a novel distributed-consensus alternating direction method of multipliers (ADMM) algorithm, which contains a dynamic average consensus algorithm and distributed ADMM algorithm, is presented to solve the optimal energy management problem of energy internet. The proposed algorithm can effectively handle the problems of power-heat-gas-coupling, global constraint limits and non-linear objective function. With this effort, not only the optimal energy market clearing price but also the optimal energy outputs/demands can be obtained through only local communication and computation. Simulation results are presented to illustrate the effectiveness of the proposed distributed algorithm.

## References

- 1.A.Q. Huang, M.L. Crow, G.T. Heydt et al., The future renewable electric energy delivery and management (FREEDM) system: the energy internet. Proc. IEEE
**99**(1), 133–148 (2011)CrossRefGoogle Scholar - 2.Q. Sun, Y. Zhang, H. He et al., A novel energy function-based stability evaluation and nonlinear control approach for energy internet. IEEE Trans. Smart Grid
**8**(3), 1195–1210 (2017)CrossRefGoogle Scholar - 3.Q. Sun, R. Han, H. Zhang et al., A multi-agent-based consensus algorithm for distributed coordinated control of distributed generators in the energy internet. IEEE Trans. Smart Grid
**6**(6), 3006–3019 (2015)CrossRefGoogle Scholar - 4.T. Niknam, R. Azizipanah-Abarghooee, A. Roosta et al., A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch. Energy
**41**(1), 530–545 (2012)CrossRefGoogle Scholar - 5.E. Abdollahi, H. Wang, R. Lahdelma, An optimization method for multi-area combined heat and power production with power transmission network. Appl. Energy
**168**, 248–256 (2016)CrossRefGoogle Scholar - 6.M.R. Benam, S.S. Madani, S.M. Alavi et al., Optimal configuration of the CHP system using stochastic programming. IEEE Trans. Power Deliv.
**30**(3), 1048–1056 (2015)CrossRefGoogle Scholar - 7.T. Fang, R. Lahdelma, Optimization of combined heat and power production with heat storage based on sliding time window method. Appl. Energy
**162**, 723–732 (2016)CrossRefGoogle Scholar - 8.D. Papadaskalopoulos, G. Strbac, Nonlinear and randomized pricing for distributed management of flexible loads. IEEE Trans. Smart Grid
**7**(2), 1137–1146 (2016)CrossRefGoogle Scholar - 9.N. Rahbari-Asr, M.Y. Chow, J. Chen et al., Distributed real-time pricing control for large scale unidirectional V2G with multiple energy suppliers. IEEE Trans. Ind. Inf.
**12**(5), 1953–1962 (2016)CrossRefGoogle Scholar - 10.W. Zeng, M.Y. Chow, A reputation-based secure distributed control methodology in D-NCS. IEEE Trans. Ind. Electron.
**61**(11), 6294–6303 (2014)CrossRefGoogle Scholar - 11.S. Yang, S. Tan, J. Xu, Consensus based approach for economic dispatch problem in a smart grid. IEEE Trans. Powers Syst.
**28**(4), 4416–4426 (2013)CrossRefGoogle Scholar - 12.G. Chen, F.L. Lewis, E.N. Feng et al., Distributed optimal active power control of multiple generation systems. IEEE Trans. Ind. Electron.
**62**(11), 7079–7090 (2015)CrossRefGoogle Scholar - 13.H. Xing, Y. Mou, M. Fu et al., Distributed bisection method for economic power dispatch in smart grid. IEEE Trans. Powers Syst.
**30**(6), 3024–3035 (2015)CrossRefGoogle Scholar - 14.F. Guo, C. Wen, J. Mao et al., Distributed economic dispatch for smart grids with random wind power. IEEE Trans. Smart Grid
**7**(3), 1572–1583 (2016)CrossRefGoogle Scholar - 15.Y. Xu, W. Zhang, W. Liu, Distributed dynamic programming-based approach for economic dispatch in smart grids. IEEE Trans. Ind. Inf.
**11**(1), 166–175 (2015)CrossRefGoogle Scholar - 16.G. Binetti, A. Davoudi, F.L. Lewis et al., Distributed consensus-based economic dispatch with transmission losses. IEEE Trans. Powers Syst.
**29**(4), 1711–1720 (2014)CrossRefGoogle Scholar - 17.G. Chen, J. Ren, E.N. Feng, Distributed finite-time economic dispatch of a network of energy resources. IEEE Trans. Smart Grid (2017). https://doi.org/10.1109/TSG.2016.2516017
- 18.G. Binetti, A. Davoudi, D. Naso et al., A distributed auction-based algorithm for the nonconvex economic dispatch problem. IEEE Trans. Ind. Inf.
**10**(2), 1124–1132 (2014)CrossRefGoogle Scholar - 19.Z. Wang, W. Wu, B. Zhang, A fully distributed power dispatch method for fast frequency recovery and minimal generation cost in autonomous microgrids. IEEE Trans. Smart Grid
**7**(1), 19–31 (2016)CrossRefGoogle Scholar - 20.N. Rahbari-Asr, U. Ojha, Z. Zhang et al., Incremental welfare consensus algorithm for cooperative distributed generation/demand response in smart grid. IEEE Trans. Smart Grid
**5**(6), 2836–2845 (2014)CrossRefGoogle Scholar - 21.Y. Xu, Z. Li, Distributed optimal resource management based on the consensus algorithm in a microgrid. IEEE Trans. Ind. Electron.
**62**(4), 2584–2592 (2015)CrossRefGoogle Scholar - 22.W. Zhang, Y. Xu, W. Liu et al., Distributed online optimal energy management for smart grids. IEEE Trans. Ind. Inf.
**11**(3), 717–727 (2015)CrossRefGoogle Scholar - 23.J. Wu, B. Zhang, K. Wang et al., Optimal economic dispatch model based on risk management for wind-integrated power system. IET Gener. Transm. Distrib.
**9**(15), 2152–2158 (2015)CrossRefGoogle Scholar - 24.C. Wan, Z. Xu, P. Pinson et al., Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans. Powers Syst.
**29**(29), 1033–1044 (2014)CrossRefGoogle Scholar - 25.N. Nikmehr, S.N. Ravadanegh, Optimal power dispatch of multi-microgrids at future smart distribution grids. IEEE Trans. Smart Grid
**6**(4), 1648–1657 (2015)CrossRefGoogle Scholar - 26.Z. Yang, R. Wu, J. Yang et al., Economical operation of microgrid with various devices via distributed optimization. IEEE Trans. Smart Grid
**7**(2), 857–867 (2016)Google Scholar - 27.Y. Zhang, N. Rahbari-Asr, J. Duan et al., Day-ahead smart grid cooperative distributed energy scheduling with renewable and storage integration. IEEE Trans. Sustain. Energy
**7**(4), 1739–1747 (2016)CrossRefGoogle Scholar - 28.J. Qiu, Z.Y. Dong, J.H. Zhao et al., Multi-stage flexible expansion co-planning under uncertainties in a combined electricity and gas market. IEEE Trans. Power Syst.
**30**(4), 2119–2129 (2015)CrossRefGoogle Scholar - 29.Z. Bao, Q. Zhou, Z. Yang et al., A multi time-scale and multi energy-type coordinated microgrid scheduling solution - part I: model and methodology. IEEE Trans. Powers Syst.
**30**(5), 2257–2266 (2015)CrossRefGoogle Scholar - 30.Z. Bao, Q. Zhou, Z. Yang et al., A multi time-scale and multi energy-type coordinated microgrid scheduling solution - part II: optimization algorithm and case studies. IEEE Trans. Powers Syst.
**30**(5), 2267–2277 (2015)CrossRefGoogle Scholar - 31.S. Boyd, L. Vandenberghe,
*Convex Optimization*(Cambridge University Press, Cambridge, 2004)Google Scholar - 32.N. Rahbari-Asr, Y. Zhang, M.Y. Chow, Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids. IET Gener. Transm. Distrib.
**10**(5), 1268–1277 (2016)CrossRefGoogle Scholar - 33.A. Teixeira, E. Ghadimi, I. Shames et al., The ADMM algorithm for distributed quadratic problems: parameter selection and constraint preconditioning. IEEE Trans. Signal Process.
**64**(2), 290–305 (2016)MathSciNetCrossRefGoogle Scholar - 34.T.H. Chang, M. Hong, X. Wang, Multi-agent distributed optimization via inexact consensus ADMM. IEEE Trans. Signal Process.
**63**(2), 482–497 (2015)MathSciNetCrossRefGoogle Scholar - 35.A. Shabanpour-Haghighi, A.R. Seifi, An integrated steady-state operation assessment of electrical, natural gas, and district heating networks. IEEE Trans. Power Syst. https://doi.org/10.1109/TPWRS.2015.2486819
- 36.N. Rahbari-Asr, M.Y. Chow, Cooperative distributed demand management for community charging of PHEV/PEVs based on KKT conditions and consensus networks. IEEE Trans. Ind. Inf.
**10**(3), 1907–1916 (2014)CrossRefGoogle Scholar