Office building participation in demand response programs supported by intelligent lighting management
Tóm tắt
According to importance of demand response programs in smart grids and microgrids, many efforts have been made to change the consumption patterns of the users, and the use of renewable resources has also increased. Significant part of energy consumption belongs to buildings such as residential, commercial, and office buildings. Many buildings are equipping with components that can be used for the participation in demand response programs. The SCADA system plays a key role in this context, which enables the building operator to have control and monitor the consumption and generation. This paper presents a real implementation of an optimization based SCADA system, which employs several controlling and monitoring methods in order to manage the consumption and generation of the building for decision support and participating in demand response events. Since the air conditioning devices are suitable controllable appliances for direct load control demand response, and lighting system as flexible loads for reduction and curtailment, they can play a key role in the scope of demand response programs. In this system, several real controller components manage the consumption of lighting system and air conditioning of the building based on an optimization model. In the case study of the paper, the SCADA system is considered as a player of an aggregation model, which is considered as demand response managing entity, and its performance during demand response events will be surveyed. The obtained results show that with adequate small reduction in the lighting system and air conditioning devices, the electricity customers are able to actively participate in the electricity markets using demand response programs and also for internal efficient use of electricity.
Tài liệu tham khảo
Abrishambaf O, Faria P, Gomes L, Spínola J, Vale Z, Corchado J (2017) Implementation of a real-time microgrid simulation platform based on centralized and distributed management. Energies 10(6):806
Abrishambaf O, Ghazvini M, Gomes L, Faria P, Vale Z, Corchado J (2016) Application of a Home Energy Management System for Incentive-Based Demand Response Program Implementation. Paper presented at 27th International Workshop On Database And Expert Systems Applications (DEXA), Porto, Portugal, 5–8 Sept. 2016
Erdinc O, Tascikaraoglu A, Paterakis N, Catalao J (2017) An energy credit based incentive mechanism for the direct load control of residential HVAC systems incorporation in day-ahead planning. Paper presented at 2017 IEEE Manchester Powertech, Manchester, UK, 18–22 June 2017
Faria P, Pinto A, Vale Z, Khorram M (2017) Lighting Consumption Optimization using Fish School Search Algorithm. Paper presented at 2017 IEEE Symposium on Computational Intelligence Applications in Smart Grid (IEEE CIASG'17), Hawaii, USA, 27–1 Nov/Dec 2017
Faria P, Soares J, Vale Z, Morais H, Sousa T (2013) Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. IEEE Trans Smart Grid 4(1):606–616
Faria P, Spinola J, Vale Z (2016) Aggregation and remuneration of electricity consumers and producers for the definition of demand-response programs. IEEE Trans Ind Inf 12(3):952–961
Faria P, Vale Z (2011) Demand response in electrical energy supply: an optimal real time pricing approach. Energy 36(8):5374–5384
Fotouhi Ghazvini M, Soares J, Abrishambaf O, Castro R, Vale Z (2017) Demand response implementation in smart households. Energ Buildings 143:129–148
Hao H, Corbin C, Kalsi K, Pratt R (2017) Transactive control of commercial buildings for demand response. IEEE Trans Power Sys 32(1):774–783
Hasan M, Mouftah H (2016) Optimal trust system placement in smart grid SCADA networks. IEEE Access 4:2907–2919
Hu J, Cao J, Yong T, Guerrero J, Chen M, Li Y (2017) Demand response load following of source and load systems. IEEE Trans Control Syst Technol 25(5):1586–1598
Jia Q, Shen J, Xu Z, Guan X (2012) Simulation-based policy improvement for energy Management in Commercial Office Buildings. IEEE Trans Smart Grid 3(4):2211–2223
Kjaergaard, M., Arendt, K., Clausen, A., Johansen, A., Jradi, M., Jorgensen, B. et al. (2016). Demand response in commercial buildings with an Assessable impact on occupant comfort. Paper presented at IEEE International Conference On Smart Grid Communications (Smartgridcomm), Sydney, NSW, Australia, 6–9 Nov. 2016
Kwon S, Ntaimo L, Gautam N (2017) Optimal day-ahead power procurement with renewable energy and demand response. IEEE Trans Power Syst 32(5):3924–3933
Lujano-Rojas J, Dufo-Lopez R, Bernal-Agustin J, Catalao J (2017) Optimizing daily operation of battery energy storage systems under real-time pricing schemes. IEEE Trans Smart Grid 8(1):316–330
Mega, T., Kitagami, S., Kawawaki, S., Kushiro, N. (2017). Experimental Evaluation of a Fast Demand Response System for Small/Medium-Scale Office Buildings. Paper presented at 31St International Conference On Advanced Information Networking And Applications Workshops (WAINA), Taipei, Taiwan, 27–29 Mar. 2017
Pan X, Lee B (2016) An Approach of Reinforcement Learning Based Lighting Control for Demand Response. Paper presented at PCIM Europe 2016; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renew Energy and Energy Management, Nuremberg, Germany, 10–12 May 2016
Parvania M, Fotuhi-Firuzabad M, Shahidehpour M (2013) Optimal demand response aggregation in wholesale electricity markets. IEEE Trans Smart Grid 4(4):1957–1965
Paterakis N, Erdinç O, Catalão J (2017) An overview of demand response: key-elements and international experience. Renew Sust Energ Rev 69:871–891
Pellegrino A, Lo Verso V, Blaso L, Acquaviva A, Patti E, Osello A (2016) Lighting control and monitoring for energy efficiency: a case study focused on the interoperability of building management systems. IEEE Trans Ind Appl 52(3):2627–2637
Ruelens F, Claessens B, Vandael S, De Schutter B, Babuska R, Belmans R (2017) Residential demand response of thermostatically controlled loads using batch reinforcement learning. IEEE Trans Smart Grid 8(5):2149–2159
Silva M, Fernandes F, Morais H, Ramos S, Vale Z (2015) Hour-ahead energy resource management in university campus microgrid. Paper presented at IEEE Eindhoven Powertech, Eindhoven, Netherlands, 29 June-2 July 2015
Wang F, Xu H, Xu T, Li K, Shafie-khah M, Catalão J (2017) The values of market-based demand response on improving power system reliability under extreme circumstances. Appl Energy 193:220–231
Wu D, Zeng H, Lu C, Boulet B (2017) Two-stage energy Management for Office Buildings with Workplace EV charging and renewable energy. IEEE Trans Transp Electrification 3(1):225–237