Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management

Cognitive Computation

Volumn 5, Issue 2, 2013, Pages 264-277

  Boaro, Matteo ^a   Fuselli, Danilo ^a   Angelis, Francesco De ^a   Liu, Derong ^b   Wei, Qinglai ^b   Piazza, Francesco ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Adaptive dynamic programming; Approximate dynamic programming; Battery management; Energy scheduling; Neural networks

Indexed keywords

ADAPTIVE SYSTEMS; BATTERY MANAGEMENT SYSTEMS; ELECTRIC BATTERIES; ELECTRIC POWER TRANSMISSION NETWORKS; ENERGY EFFICIENCY; ENERGY MANAGEMENT SYSTEMS; HOUSING; NEURAL NETWORKS; SCHEDULING; SMART POWER GRIDS;

ADAPTIVE DYNAMIC PROGRAMMING; APPROXIMATE DYNAMIC PROGRAMMING; BATTERY MANAGEMENT; INTELLIGENT ENERGY MANAGEMENT SYSTEMS; INTELLIGENT MANAGEMENT; OPTIMIZATION POLICY; RENEWABLE ENERGIES; RENEWABLE RESOURCE;

DYNAMIC PROGRAMMING;

EID: 84877069560     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-012-9191-y     Document Type: Article

References (48)

1. Amin SM, Wollenberg BF. Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag 2005; 3(5): 34-41.
2. Garrity TF. Getting smart. IEEE Power Energy Mag 2008; 6(2): 38-45.
3. Meliopoulos APS, Cokkinides G, Huang RK, Farantatos E, Choi S, Lee Y, Yu X. Smart grid technologies for autonomous operation and control. IEEE Trans Smart Grid 2011; 2(1): 1-10.
4. Klein KM, Springer PL, Black WZ. Real-time ampacity and ground clearance software for integration into smart grid technology. IEEE Trans Power Deliv 2011; 25(3): 1768-1777.
5. Li F, Qiao W, Sun H, Wan H, Wang J, Xia Y, Xu Z, Zhang P. Smart transmission grid: vision and framework. IEEE Trans Smart Grid 2010; 1(2): 168-177.
6. Roncero JR. Integration is key to smart grid management. In: Proceeding of the IET-CIRED seminar smartgrids for distribution, 2008. p. 1-4.
7. Sheble GB. Smart grid millionaire. IEEE Power Energy Mag 2008; 6(1): 22-28.
8. Belvedere B, Bianchi M, Borghetti A, Paolone M. A microcontroller-based automatic scheduling system for residential microgrids. In: Proceeding of IEEE Bucharest power technology, 2009. pp. 1-6.
9. Chiang SJ, Chang KT, Yen CY. Residential photovoltaic energy storage system. IEEE Trans Industr Electron 1998; 45(3): 385-394.
10. Momoh JA, Wang Y, Eddy-Posey F. Optimal power dispatch of photovoltaic system with random load. In: Proceeding of IEEE Power Engineering Society, 2004. p. 1939-1945.
11. Maly DK, Kwan KS. Optimal battery energy storage system (BESS) charge scheduling with dynamic programming, In: Proceeding of IEEE science, measurement and technology, 1995. p. 453-458.
12. Bakirtzis AG, Dokopoulos PS. Short term generation scheduling in a small autonomous system with unconventional energy system. IEEE Trans Power Syst 1988; 3(3): 1230-1236.
13. Corrigan PM, Heydt GT. Optimized dispatch of a residential solar energy system. In: Proceeding of the North American power symposium, 2007. p. 183-188.
14. Rupanagunta P, Baughman ML, Jones JW. Scheduling of cool storage using non-linear programming techniques. IEEE Trans Power Syst 1995; 10(3): 1279-1285.
15. Liang RH, Liao JH. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems. IEEE Trans Power Syst 2007; 22(4): 1665-1674.
16. Cau TDH, Kaye RJ, Jones JW. Multiple distributed energy storage scheduling using constructive evolutionary programming. In: Proceeding of the IEEE international conference on power industry computer applications, 2001. p. 402-407.
17. Fung CC, Ho SCY, Nayar CV. Optimization of a hybrid energy system using simulated annealing technique. In: Proceeding of IEEE international conference on computer communication control power engineering, 1993. p. 173-191.
18. Pedrasa MAA, Spooner TD, MacGilland IF. Scheduling of demand side resources using binary particle swarm optimization. IEEE Trans Power Syst 2009; 24(30): 1173-1181.
19. Lee TY. Operating schedule of battery energy storage system in a time-of-use rate industrial user with wind turbine generators: a multipass iteration particle swarm optimization approach. IEEE Trans Energy Convers 2007; 22(3): 774-782.
20. Zhang H, Quan Y. Modeling, identification and control of a class of nonlinear system. IEEE Trans Fuzzy Syst 2001; 9(2): 349-354.
21. Cui L, Zhang H, Zhang X, Luo Y. Data-based adaptive critic design for discrete-time zero-sum games using output feedback. In: IEEE symposium on adaptive dynamic programming and reinforcement learning, Paris, France, 2011. p. 190-195.
22. Zhang X, Zhang H, Wang X, Luo Y. A new iteration approach to solve a class of finite-horizon continuous-time nonaffine nonlinear zero-sum game. Int J Innov Comput Inf Control 2011; 7(2): 597-608.
23. Ikonen E, Najim K. Multiple model-based control using finite controlled markov chains. Cogn Comput 2009; 1(3): 234-243.
24. Bishop JM, Caines S. Creativity and autonomy in swarm intelligence systems. Cogn Comput. 2012; in press. doi: 10. 1007/s12559-012-9130-y.
25. Werbos PJ. A menu of designs for reinforcement learning over time. In: Miller WT, Sutton RS, Werbos PJ (eds). Neural Networks for Control. Cambridge, MA: MIT Press; 1991. p. 67-95.
26. Wang F, Zhang H, Liu D. Adaptive dynamic programming: an introduction. Comput Intell Mag 2009; 4(2): 39-47.
27. Wang F, Jin N, Liu D, Wei Q. Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound. IEEE Trans Neural Netw 2011; 22(1): 24-36.
28. Wei Q, Zhang H, Dai J. Model-free multiobjective approximate dynamic programming for discrete-time nonlinear systems with general performance index functions. Neurocomputing 2009; 72(7-9), 1839-1848.
29. Wei Q, Liu D. An iterative ε-optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state. Neural Networks, 2012; in press, doi: 10. 1016/j. neunet. 2012. 02. 027.
30. Prokhorov DV, Wunsch DC. Adaptive critic designs. IEEE Trans Neural Netw 1997; 8(5): 997-1007.
31. Barto AG. Reinforcement learning and adaptive critic methods. In: White DA, Sofge DA, editors. Handbook of intelligent control: neural, fuzzy and adaptive approaches. New York: van Nostrand Reinhold; 1992.
32. Bertsekas DP, Tsitsiklis JN. Neuro-dynamic programming. MA: Athena Scientific; 1996.
33. Liu D, Xiong X, Zhang Y. Action-dependent adaptive critic designs. In: Proceeding of the INNS-IEEE international joint conference on neural network, 2001. p. 990-995.
34. Murray JJ, Cox CJ, Lendari GG, Saeks R, Adaptive dynamic programming. IEEE Trans Syst Man Cybern C Appl Rev 2002; 32(2): 140-153.
35. Si J, Wang YT. On-line learning control by association and reinforcement. IEEE Trans Neural Netw 2001; 12(2): 264-276.
36. Balakrishnan SN, Biega V. Adaptive-critic-based neural networks for aircraft optimal control. In: Proceeding of the second international conference on computer modeling and simulation. 2010. p. 2288-2293.
37. Sutton RS, Barto AG. Reinforcement learning: an introduction. MA: MIT Press; 1988.
38. Werbos PJ. Advanced forecasting methods for global crisis warning and models of intelligence. In: Proceeding of the American control conference, 2008. p. 25-38.
39. Werbos PJ. Approximate dynamicprogramming for realtime control and neural modeling. Handbook of intelligent control: neural, fuzzy and adaptive approaches. New York: van Nostrand Reinhold; 1992.
40. Huang T, Liu D. Residential energy system control and management using adaptive dynamic programming. In: Proceeding of IEEE international joint conference on neural networks (IJCNN), 2011. p. 119-124.
41. Miceli R, La Cascia D, Di Stefano A, Fiscelli G, Giaconia C. Impact of novel energy management actions on household appliances for money savings and CO2 emissions reduction. In: Proceeding of the EVER09 conference, Principato di Monaco, Monaco, 2009. p. 1-5.
42. Markvart T. Solar electricity. 2nd ed. New York, NY: Wiley; 2000.
43. Chedid R, Akiki H, Rahman S. A decision support technique for the design of hybrid solar-wind power systems. IEEE Trans Energy Convers 1998; 13(1): 76-83.
44. Linden D, Reddy TB. Handbook of batteries. 3rd ed. New York, NY: McGraw-Hill; 2002.
45. Bellman RE. Dynamic programming. New Jersey: Princeton University Press; 1957.
46. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 1994; 5(6): 989-993.
47. National renewable energy laboratory (NREL) of U. S. Department of Energy: http://www. nrel. gov/rredc/, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
48. ComEd, USA. Accessed 16 May 2010. [Online] Available: http://www. thewattspot. com/.