Assessing the benefits of residential demand response in a real time distribution energy market

Applied Energy

Volumn 161, Issue , 2016, Pages 533-551

  Siano, Pierluigi ^a   Sarno, Debora ^a  

^a UNIVERSITY OF SALERNO   (Italy)

Author keywords

Demand response; Distribution locational marginal price; Distribution systems; Monte Carlo simulation; Smart grids; Transactive controller

Indexed keywords

AIR CONDITIONING; COMMERCE; COSTS; ELECTRIC POWER DISTRIBUTION; ELECTRIC POWER TRANSMISSION NETWORKS; ELECTRIC UTILITIES; HEATING; HOUSING; INTELLIGENT SYSTEMS; LOCAL AREA NETWORKS; LOCATION; POWER MARKETS; SMART POWER GRIDS; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS;

DEMAND RESPONSE; DISTRIBUTION SYSTEMS; ELECTRICITY DISTRIBUTION NETWORKS; ENVIRONMENTAL CONDITIONS; HEATING VENTILATION AND AIR CONDITIONING; LOCATIONAL MARGINAL PRICES; PROBABILISTIC METHODOLOGY; SMART GRID;

MONTE CARLO METHODS;

AIR CONDITIONING; ASSESSMENT METHOD; DEMAND ANALYSIS; DEMAND-SIDE MANAGEMENT; DISTRIBUTION SYSTEM; ELECTRICITY GENERATION; ENERGY MARKET; ENVIRONMENTAL CONDITIONS; HEATING; MONTE CARLO ANALYSIS; NUMERICAL MODEL; STOCHASTICITY; TEMPERATURE EFFECT;

EID: 84946197201     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.10.017     Document Type: Article

References (73)

1. Smart Grids. European technology platform for the electricity networks of the future. <>. http://www.smartgrids.eu/.
2. Logenthiran T., Srinivasan D., Khambadkone A., Aung H.N. Multiagent system for real-time operation of a microgrid in real-time digital simulator. IEEE Trans Smart Grid 2012, 3(2):925-933.
3. Venkatesan N, Solanki J, Solanki SK. Market optimization for microgrid with Demand Response model. In: North American power symposium (NAPS), 2011; 2011. p. 1-6.
4. Palensky P., Dietrich D. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Industr Inf 2011, 7(3):381-388.
5. Siano P. Demand response and smart grids - a survey. Renew Sustain Energy Rev 2014, 30:461-478.
6. Cecati C., Citro C., Siano P. Combined operations of renewable energy systems and responsive demand in a smart grid. IEEE Trans Sustain Energy 2011, 2(4):468-476.
7. Atamtur N., Zafar M. Transactive energy: a surreal vision or a necessary and feasible solution to grid problems? 2014, California Public Utilities Commission, Policy & Planning Division.
8. Moradzadeh B., Tomsovic K. Two-stage residential energy management considering network operational constraints. IEEE Trans Smart Grid 2013, 4(4):2339-2346.
9. Samadi P., Mohsenian-Rad H., Wong V.W.S., Schober R. Real-time pricing for demand response based on stochastic approximation. IEEE Trans Smart Grid 2014, 5(2):789-798.
10. Subbarao K., Fuller J., Kalsi K., Pratt R., Widergren S., Chassin D. Transactive control and coordination of distributed assets for ancillary services 2013, Pacific Northwest National Lab, Richland, WA.
11. Conejo A.J., Morales J.M., Baringo L. Real-time demand response model. IEEE Trans Smart Grid 2010, 1(3):236-242.
12. Meyer S, Rakotonirainy A. A survey of research on context-aware homes. In: Proceedings of the Australasian information security workshop conference on ACSW frontiers 2003, vol. 21. Darlinghurst, Australia, Australia; 2003. p. 159-68.
13. Missaoui R., Joumaa H., Ploix S., Bacha S. Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build 2014, 71:155-167.
14. Fuller JC, Schneider KP, Chassin D. Analysis of residential demand response and double-auction markets. In: 2011 IEEE power and energy society general meeting; 2011. p. 1-7.
15. Li R., Wu Q., Oren S.S. Distribution locational marginal pricing for optimal electric vehicle charging management. IEEE Trans Power Syst 2014, 29(1):203-211.
16. Faria P, Morais H, Vale Z, Ferreira J. LMP triggered real time demand response events. In: Energy Market (EEM), 2011 8th international conference on the European; 2011. p. 45-50.
17. Borenstein S, Holland SP. On the efficiency of competitive electricity markets with time-invariant retail prices. National Bureau of Economic Research, Working Paper 9922, ago; 2003.
18. Palma-Behnke R., Vargas J.L.C.A.L.S., Jofre A. A distribution company energy acquisition market model with integration of distributed generation and load curtailment options. IEEE Trans Power Syst 2005, 20(4):1718-1727.
19. Mokryani G., Siano P. Evaluating the integration of wind power into distribution networks by using Monte Carlo simulation. Int J Elec Power 2013, 53:244-255.
20. Charwand M., Ahmadi A., Siano P., Dargahi V., Sarno D. Exploring the trade-off between competing objectives for electricity energy retailers through a novel multi-objective framework. Energy Convers Manage 2015, 91:12-18.
21. Safdarian A., Fotuhi-Firuzabad M., Lehtonen M. A stochastic framework for short-term operation of a distribution company. IEEE Trans Power Syst 2013, 28(4):4712-4721. Nov.
22. Siano P., Mokryani G. Probabilistic assessment of the impact of wind energy integration into distribution networks. IEEE Trans Power Syst 2013, 28(4):4209-4217. Nov.
23. Siano P., Mokryani G. Assessing wind turbines placement in a distribution market environment by using particle swarm optimization. IEEE Trans Power Syst 2013, 28(4):3852-3864. Nov.
24. Sahriatzadeh F, Nirbhavane P, Srivastava A. Locational marginal price for distribution system considering demand response. In: North American power symposium (NAPS), 2012; 2012. p. 1-5.
25. Sotkiewicz P.M., Vignolo J.M. Nodal pricing for distribution networks: efficient pricing for efficiency enhancing DG. IEEE Trans Power Syst 2006, 21(2):1013-1014. mag.
26. Siano P., Cecati C., Yu H., Kolbusz J. Real time operation of smart grids via FCN networks and optimal power flow. IEEE Trans Industr Inf 2012, 8(4):944-952.
27. Lamaina P., Sarno D., Siano P., Zakariazadeh A., Romano R. A model for wind turbines placement within a distribution network acquisition market. IEEE Trans Industr Inf 2015, 11(1):210-219. Feb.
28. National assessment of demand response. Federal Energy Regulatory Commission (FERC), USA, Tech. Rep.; 2009. <>. http://www.ferc.gov.
29. Assessment of demand response and advanced metering: staff report. Federal Energy Regulatory Commission (FERC), USA, Tech. Rep.; 2012. <>. http://www.ferc.gov.
30. Cappers P., Goldman C., Kathan D. Demand response in US electricity markets: empirical evidence. Energy 2010, 35(4):1526-1535.
31. Shen B., Ghatikar G., Lei Z., Li J., Wikler G., Martin P. The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges. Appl Energy 2014, 130:814-823.
32. Mokryani G., Siano P. Combined Monte Carlo simulation and OPF for wind turbines integration into distribution networks. Electr Pow Syst Res 2013, 103:37-48.
33. Jessica Stromback Demand response: the value of non-use smart energy demand coalition, smart energy demand coalition 2014, Berlin Energy Forum, Berlin, 10-11 February.
34. European Commission Staff Working Document. Incorporating demand side flexibility, in particular demand response, in electricity markets; 2013.
35. Motegi N, Piette MA, Watson DS, Kiliccote S, Xu P. Introduction of commercial building control strategies and techniques for demand response. Ernest Orlando Lawrence Berkeley National Laboratory, Report no. LBNL-59975; 2007. <>. http://drrc.lbl.gov/pubs/59975.pdf.
36. Ipakchi A., Albuyeh F. Grid of the future. IEEE Power Energ Mag 2009, 8(4):52-62.
37. Gkatzikis L., Koutsopoulos I., Salonidis T. The role of aggregators in smart grid demand response markets. IEEE J Select Areas Commun 2013, 31:1247-1257.
38. Sortomme E., El-Sharkawi M.A. Optimal scheduling of vehicle-to-grid energy and ancillary services. IEEE Trans Smart Grid 2012, 3:351-359.
39. Strauss P., Braun M. A review on aggregation approaches of controllable distributed energy units in electrical power systems. Int J Distrib Energy Resour 2008, 4:297-319.
40. Quinn C., Zimmerle D., Bradley T.H. The effect of communication architecture on the availability, reliability, and economics of plugin hybrid electric vehicle-to-grid ancillary services. J Power Sources 2010, 195:1500-1509.
41. Guille C., Gross G. A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy 2009, 37:4379-4390.
42. Li R., Wu Q., Oren S. Distribution locational marginal pricing for optimal electric vehicle charging management. IEEE Trans Power Syst 2013, 29:203-211.
43. Safdarian A., Fotuhi-Firuzabad M., Lehtonen M. A distributed algorithm for managing residential demand response in smart grids. IEEE Trans Industr Inf 2014, 10(4):2385-2393. Nov.
44. Christie R.D., Wollenberg B.F., Wangensteen I. Transmission management in the deregulated environment. Proc IEEE 2000, 88(2):170-195. Feb.
45. Verzijlbergh R.A., De Vries L.J., Lukszo Z. Renewable energy sources and responsive demand. Do we need congestion management in the distribution grid?. IEEE Trans Power Syst 2014, vol. X, n. Y, pp. XX-YY.
46. http://www.smartgridnews.com/artman/publish/Delivery_Command_Control/Smart-Grid-Command-Control-The-Death-of-the-Giant-Brain-in-the-Sky-1959.html.
47. http://www.globalenvironmentfund.com/data/uploads/The%20Electricity%20Economy.pdf.
48. Setlhaolo D., Xia X., Zhang J. Optimal scheduling of household appliances for demand response. Electr Power Syst Res 2014, 116(11):24-28.
49. Yoon J.H., Baldick R., Novoselac A. Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Trans Smart Grid 2014, 5(1):121-129.
50. Hammerstrom DJ, et al. Pacific northwest gridwise testbed demonstration projects: Part I. Olympic Peninsula Project. Technical Report PNNL-17167. Richland, WA: Pacific Northwest National Laboratories; 2007.
51. Broeer T., Fuller J., Tuffner F., Chassin D., Djilali N. Modeling framework and validation of a smart grid and demand response system for wind power integration. Appl Energy 2014.
52. Shi W., Li N., Xie X., Chu C.-C., Gadh R. Optimal residential demand response in distribution networks. IEEE J Sel Areas Commun 2014, 32(7):1441-1450. lug.
53. Matsumoto J, Zhong W. New demand response architecture for stabilization of power quality in smart grid. In: Communications and signal processing (ICICS) 2013 9th international conference on information; 2013. p. 1-5.
54. Maenhoudt M., Deconinck G. Strategic offering to maximize day-ahead profit by hedging against an infeasible market clearing result. Power Syst, IEEE Trans 2014, 29(2):854-862. March.
55. Soroudi A. Robust optimization based self scheduling of hydro-thermal genco in smart grids. Energy 2013, 61:262-271.
56. Evolvdso. D1.2 evaluation of current market architectures and regulatory frameworks and the role of dsos; 2014. <>. http://www.evolvdso.eu/.
57. Chen Z., Wu L., Fu Y. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. Smart Grid, IEEE Trans 2012, 3(4):1822-1831. Dec.
58. An Y., Zeng B. Exploring the modeling capacity of two-stage robust optimization: variants of robust unit commitment model. Power Systems, IEEE Trans 2015, 30(1):109-122.
59. Rahimiyan M., Baringo L., Conejo A. Energy management of a cluster of interconnected price-responsive demands. Power Systems, IEEE Trans 2014, 29(2):645-655. March.
60. Conejo J., Contreras J., Espnola R., Plazas M.A. Forecasting electricity prices for a dayahead pool-based electric energy market. Int J Forecast 2005, 21(3):435-462.
61. Zimmerman R.D., Murillo-Sánchez C.E., Thomas R.J. MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 2011, 26(1):12-19. Feb.
62. Billinton R., Chen H., Ghajar R. A sequential simulation technique for adequacy evaluation of generating systems including wind energy. IEEE Trans Energy Convers 1996, 11:728-734.
63. Piccolo A., Siano P. Evaluating the impact of network investment deferral on distributed generation expansion. IEEE Trans Power Syst 2009, 24(3):1559-1567.
64. European Technical Standard EN 15217 Energy performance of buildings - methods for expressing energy performance and for the energy certification of buildings 2007, CEN, Brussels. 1st ed.
65. Ippolito M.G., Riva Sanseverino E., Zizzo G. Impact of building automation control systems and technical building management systems on the energy performance class of residential buildings: an Italian case study. Energy Build 2014, 69(2):33-40.
66. Shena B., Ghatikara G., Leib Z., Lia J., Wiklerc G., Martin P. The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges. Appl Energy 2014, 130(1):814-823. October.
67. Strbac G. Demand side management: benefits and challenges. Energy Policy 2008, 36(12):4419-4426.
68. Xue Xue, Wang Shengwei, Yan Chengchu, Cui Borui A fast chiller power demand response control strategy for buildings connected to smart grid. Appl Energy 2015, 137:77-87.
69. Vardakas J.S., Zorba N., Verikoukis C.V. A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun Surv Tutor 2015, 17(1):152-178.
70. Wang Xiaonan, Palazoglu Ahmet, El-Farra Nael H. Operational optimization and demand response of hybrid renewable energy systems. Appl Energy 2015, 143:324-335.
71. Erdinc Ozan, Paterakis Nikolaos G., Pappi Iliana N., Bakirtzis Anastasios G., Catalão João P.S. A new perspective for sizing of distributed generation and energy storage for smart households under demand response. Appl Energy 2015, 143:26-37.
72. Graditi G., Ippolito M.G., Lamedica R., Piccolo A., Ruvio A., Santini E., et al. Innovative control logics for a rational utilization of electric loads and air-conditioning systems in a residential building. Energy Build 2015, 102:1-17.
73. Ahmadia A., Kaymanesh A., Siano P., Janghorbanid M., Nezhade A.E., Sarno D. Evaluating the effectiveness of normal boundary intersection method for short-term environmental/economic hydrothermal self-scheduling. Electr Pow Syst Res 2015, 123:192-204.