Distributionally robust optimization of home energy management system based on receding horizon optimization

Abstract

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.

Key words： distributionally robust optimization (DRO); home energy management system (HEMS); receding horizon optimization (RHO); uncertainties

Acknowledgments

This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0901102).

References

1 Du P, Lu N. Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2011, 2(2): 411–419

2 Wang R, Sun Q, Ma D, Liu Z. The small-signal stability analysis of the droop-controlled converter in electromagnetic timescale. IEEE Transaction on Sustainable Energy, 2019, 10(3): 1459–1469

3 Huang Y, Wang L, Guo W, Kang Q, Wu Q. Chance constrained optimization in a home energy management system. IEEE Transactions on Smart Grid, 2018, 9(1): 252–260

4 Hu Q, Li F. Hardware design of smart home energy management system with dynamic price response. IEEE Transactions on Smart Grid, 2013, 4(4): 1878–1887

5 Zhao Z, Lee W C, Shin Y, Song K B. An optimal power scheduling method for demand response in home energy management system. IEEE Transactions on Smart Grid, 2013, 4(3): 1391–1400

6 Yoon J H, Baldick R, Novoselac A. Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Transactions on Smart Grid, 2014, 5(1): 121–129

7 Anvari-Moghaddam A, Monsef H, Rahimi-Kian A. Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Transactions on Smart Grid, 2015, 6(1): 324–332

8 Angelis F D, Boaro M, Fuselli D, Squartini S. Optimal home energy management under dynamic electrical and thermal constraints. IEEE Transaction on Industrial informatics, 2013, 9(3): 1518–1527

9 Wang C, Zhou Y, Wang J, Peng P. A novel traversal-and-pruning algorithm for household load scheduling. Applied Energy, 2013, 102: 1430–1438

10 Sheikhi A, Rayati M, Bahrami S, Mohammad Ranjbar A. Integrated demand side management game in smart energy hubs. IEEE Transactions on Smart Grid, 2015, 6(2): 675–683

11 Ma T, Wu J, Hao L, Lee W J, Yan H, Li D. The optimal structure planning and energy management strategies of smart multi energy systems. Energy, 2018, 160: 122–141

12 Wang C, Zhou Y, Wu J, Wang J. Robust-index method for household load scheduling considering uncertainties of customer behavior. IEEE Transactions on Smart Grid, 2015, 6(4): 1806–1818

13 Wu X, Hu X, Yin X, Moura S J. Stochastic optimal energy management of smart home with PEV energy storage. IEEE Transactions on Smart Grid, 2018, 9(3): 2065–2075

14 Wu Z, Zhou S, Li J, Zhang X P. Real-time scheduling of residential appliances via conditional risk-at-value. IEEE Transactions on Smart Grid, 2014, 5(3): 1282–1291

15 Alipour M, Mohammadi-Ivatloo B, Zare K. Stochastic scheduling of renewable and CHP-based microgrids. IEEE Transaction on Industrial informatics, 2015, 11(5): 1049–1058

16 Wang J, Shi Y, Fang K, Zhou Y, Li Y. A robust optimization strategy for domestic electric water heater load scheduling under uncertainties. Applied Sciences (Basel, Switzerland), 2017, 7(11): 1136

17 Fanzeres B, Street A, Barroso L A. Contracting strategies for renewable generators: a hybrid stochastic and robust optimization approach. IEEE Transactions on Power Systems, 2015, 30(4): 1825–1837

18 Wang J, Li P, Fang K, Zhou Y. Robust optimization for household load scheduling with uncertain parameters. Applied Sciences (Basel, Switzerland), 2018, 8(4): 575

19 Wang J, Li Y, Zhou Y. Interval number optimization for household load scheduling with uncertainty. Energy and Building, 2016, 130: 613–624

20 Chen Z, Wu L, Fu Y. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Transactions on Smart Grid, 2012, 3(4): 1822–1831

21 Xiong P, Jirutitijaroen P, Singh C. A distributionally robust optimization model for unit commitment considering uncertain wind power generation. IEEE Transactions on Power Systems, 2017, 32(1): 39–49

22 Shi Z, Liang H, Huang S, Dinavahi V. Distributionally robust chance-constrained energy management for islanded microgrids. IEEE Transactions on Smart Grid, 2019, 10(2): 2234–2244

23 Zhao P, Wu H, Gu C, Hernando-Gil I. Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach. IET Renewable Power Generation, 2019, 13(11): 1911–1919

24 Godina R, Rodrigues E M G, Pouresmaeil E, Matias J, Catalão J. Model predictive control home energy management and optimization strategy with demand response. Applied Sciences (Basel, Switzerland), 2018, 8(3): 408

25 Beaudin M, Zareipour H. Home energy management systems: a review of modelling and complexity. Renewable & Sustainable Energy Reviews, 2015, 45: 318–335

26 Li Y, Zhang H, Liang X, Huang B. Event-triggered-based distributed cooperative energy management for multienergy systems. IEEE Transaction on Industrial information, 2019, 15(4): 2008–2022

27 Althaher S, Mancarella P, Mutale J. Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Transactions on Smart Grid, 2015, 6(4): 1874–1883

28 Nguyen H T, Nguyen D T, Le L B. Energy management for households with solar assisted thermal load considering renewable energy and price uncertainty. IEEE Transactions on Smart Grid, 2015, 6(1): 301–314

29 Paterakis N G, Erdinc O, Bakirtzis A G, Catalão J P S. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Transaction on Industrial information, 2015, 11(6): 1509–1519

30 Du Y F, Jiang L, Duan C, Li Y Z, Smith J S. Energy consumption scheduling of HVAC considering weather forecast error through the distributionally robust approach. IEEE Transaction on Industrial information, 2018, 14(3): 846–857

31 Bozchalui M C, Hashmi S A, Hassen H, Canizares C A, Bhattacharya K. Optimal operation of residential energy hubs in smart grids. IEEE Transactions on Smart Grid, 2012, 3(4): 1755–1766

32 Rockafellar R T, Uryasev S. Optimization of conditional value-at-risk. Journal of Risk, 2000, 2(3): 21–41

33 Sharma I, Dong J, Malikopoulos A A, Street M, Ostrowski J, Kuruganti T, Jackson R. A modeling framework for optimal energy management of a residential building. Energy and Building, 2016, 130: 55–63

34 Maasoumy M, Sangiovanni-Vincentelli A. Optimal control of building HVAC systems in the presence of imperfect predictions. In: ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference (DSCC 2012-MOVIC 2012). Fort Lauderdale, USA, 2002, 2: 257–266

35 Hashmi S A. Evaluation and improvement of the residential energy hub management system. Dissertation for the Master Degree. Waterloo, Canada: University Waterloo, 2010