Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system

Kai GONG , Jianlin YANG , Xu WANG , Chuanwen JIANG , Zhan XIONG , Ming ZHANG , Mingxing GUO , Ran LV , Su WANG , Shenxi ZHANG

Front. Energy ›› 2022, Vol. 16 ›› Issue (1) : 74 -94.

PDF (2590KB)
Front. Energy ›› 2022, Vol. 16 ›› Issue (1) : 74 -94. DOI: 10.1007/s11708-021-0792-6
REVIEW ARTICLE
REVIEW ARTICLE

Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system

Author information +
History +
PDF (2590KB)

Abstract

Smart buildings have been proven to be a kind of flexible demand response resources in the power system. To maximize the utilization of the demand response resources, such as the heating, ventilating and air-conditioning (HVAC), the energy storage systems (ESSs), the plug-in electric vehicles (PEVs), and the photovoltaic systems (PVs), their controlling, operation and information communication technologies have been widely studied. Involving human behaviors and cyber space, a traditional power system evolves into a cyber-physical-social system (CPSS). Lots of new operation frameworks, controlling methods and potential resources integration techniques will be introduced. Conversely, these new techniques urge the reforming requirement of the techniques on the modeling, structure, and integration techniques of smart buildings. In this paper, a brief comprehensive survey of the modeling, controlling, and operation of smart buildings is provided. Besides, a novel CPSS-based smart building operation structure is proposed, and the integration techniques for the group of smart buildings are discussed. Moreover, available business models for aggregating the smart buildings are discussed. Furthermore, the required advanced technologies for well-developed smart buildings are outlined.

Graphical abstract

Keywords

smart buildings / cyber-physical-social-system / optimization / modeling / demand response / virtual power plant

Cite this article

Download citation ▾
Kai GONG, Jianlin YANG, Xu WANG, Chuanwen JIANG, Zhan XIONG, Ming ZHANG, Mingxing GUO, Ran LV, Su WANG, Shenxi ZHANG. Comprehensive review of modeling, structure, and integration techniques of smart buildings in the cyber-physical-social system. Front. Energy, 2022, 16(1): 74-94 DOI:10.1007/s11708-021-0792-6

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Swilling M, Robinson B, Marvin S, City-level decoupling: urban resource flows and the governance of infrastructure transitions. A Report of the Working Group on Cities of the International Resource Panel. United Nations Environment Programme, Nairobi, Kenya, 2013

[2]

Wilson M. By 2050, 70% of world’s population will be urban. Is that a good thing? 2012-3-12, available at

[3]

Taha A F, Gatsis N, Dong B, Buildings-to-grid integration framework. IEEE Transactions on Smart Grid, 2019, 10(2): 1237–1249

[4]

Efkarpidis N A, Christoforidis G C, Papagiannis G K. Modeling of heating and cooling energy needs in different types of smart buildings. IEEE Access: Practical Innovations, Open Solutions, 2020, 8: 29711–29728

[5]

Zhang X, Pipattanasomporn M, Chen T, An IoT-based thermal model learning framework for smart buildings. IEEE Internet of Things Journal, 2020, 7(1): 518–527

[6]

Zhang W, Hu W, Wen Y. Thermal comfort modeling for smart buildings: a fine-grained deep learning approach. IEEE Internet of Things Journal, 2019, 6(2): 2540–2549

[7]

Hu W, Wen Y, Guan K, iTCM: toward learning-based thermal comfort modeling via pervasive sensing for smart buildings. IEEE Internet of Things Journal, 2018, 5(5): 4164–4177

[8]

Yu L, Xie D, Jiang T, Distributed real-time HVAC control for cost-efficient commercial buildings under smart grid environment. IEEE Internet of Things Journal, 2018, 5(1): 44–55

[9]

Ostadijafari M, Dubey A, Yu N. Linearized price-responsive HVAC controller for optimal scheduling of smart building loads. IEEE Transactions on Smart Grid, 2020, 11(4): 3131–3145

[10]

Yu L, Sun Y, Xu Z, Multi-agent deep reinforcement learning for HVAC control in commercial buildings. IEEE Transactions on Smart Grid, 2021, 12(1): 407–419

[11]

Sadid W H, Abobakr S A, Zhu G. Discrete-event systems-based power admission control of thermal appliances in smart buildings. IEEE Transactions on Smart Grid, 2017, 8(6): 2665–2674

[12]

Li W. Application of economical building management system for Singapore commercial building. IEEE Transactions on Industrial Electronics, 2020, 67(5): 4235–4243

[13]

Razmara M, Bharati G R, Shahbakhti M, Bilevel optimization framework for smart building-to-grid systems. IEEE Transactions on Smart Grid, 2018, 9(2): 582–593

[14]

Mocanu E, Mocanu D C, Nguyen P H, On-line building energy optimization using deep reinforcement learning. IEEE Transactions on Smart Grid, 2019, 10(4): 3698–3708

[15]

Pinzon J A, Vergara P P, da Silva L C P, Optimal management of energy consumption and comfort for smart buildings operating in a microgrid. IEEE Transactions on Smart Grid, 2019, 10(3): 3236–3247

[16]

Chouikhi S, Merghem-Boulahia L, Esseghir M, A game-theoretic multi-level energy demand management for smart buildings. IEEE Transactions on Smart Grid, 2019, 10(6): 6768–6781

[17]

Cui S, Wang Y, Shi Y, A new and fair peer-to-peer energy sharing framework for energy buildings. IEEE Transactions on Smart Grid, 2020, 11(5): 3817–3826

[18]

Zhou Z, Zhao F, Wang J. Agent-based electricity market simulation with demand response from commercial buildings. IEEE Transactions on Smart Grid, 2011, 2(4): 580–588

[19]

Cui S, Wang Y, Xiao J. Peer-to-peer energy sharing among smart energy buildings by distributed transaction. IEEE Transactions on Smart Grid, 2019, 10(6): 6491–6501

[20]

Nguyen D T, Le L. Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics. IEEE Transactions on Smart Grid, 2014, 5(4): 1608–1620

[21]

Yu X, Xue Y. Smart grids: a cyber–physical systems perspective. Proceedings of the IEEE, 2016, 104(5): 1058–1070

[22]

Burg A, Chattopadhyay A, Lam K Y. Wireless communication and security issues for cyber–physical systems and the Internet-of-Things. Proceedings of the IEEE, 2018, 106(1): 38–60

[23]

Kalluri B, Chronopoulos C, Kozine I. The concept of smartness in cyber-physical systems and connection to urban environment. Annual Reviews in Control, 2021, 51: 1–22

[24]

Jin M, Jia R, Das H P, BISCUIT: building intelligent system customer investment tools. Energy Procedia, 2019, 158: 6152–6157

[25]

Zhou Y, Yu F R, Chen J, Cyber-physical-social systems: a state-of-the-art survey, challenges and opportunities. IEEE Communications Surveys and Tutorials, 2020, 22(1): 389–425

[26]

Samad T, Koch E, Stluka P. Automated demand response for smart buildings and microgrids: the state of the practice and research challenges. Proceedings of the IEEE, 2016, 104(4): 726–744

[27]

Minoli D, Sohraby K, Occhiogrosso B. IoT considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 2017, 4(1): 269–283

[28]

Rashidizadeh-Kermani H, Vahedipour-Dahraie M, Shafie-Khah M, A stochastic short-term scheduling of virtual power plants with electric vehicles under competitive markets. International Journal of Electrical Power & Energy Systems, 2021, 124: 106343

[29]

Royapoor M, Pazhoohesh M, Davison P J, Building as a virtual power plant, magnitude and persistence of deferrable loads and human comfort implications. Energy and Building, 2020, 213: 109794

[30]

Kim Y J. Optimal price based demand response of HVAC systems in multizone office buildings considering thermal preferences of individual occupants buildings. IEEE Transactions on Industrial Informatics, 2018, 14(11): 5060–5073

[31]

Delcroix B. Modeling of thermal mass energy storage in buildings with phase change materials. Dissertation for the Doctoral Degree. Montréal: Université de Montréal, 2015

[32]

Koo C, Park S, Hong T, An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method. Applied Energy, 2014, 115: 205–215

[33]

Dong X, Griffo A, Wang J. Multiparameter model order reduction for thermal modeling of power electronics. IEEE Transactions on Power Electronics, 2020, 35(8): 8550–8558

[34]

Beneventi F, Bartolini A, Tilli A, An effective gray-box identification procedure for multicore thermal modeling. IEEE Transactions on Computers, 2014, 63(5): 1097–1110

[35]

Sechilariu M, Wang B, Locment F. Building integrated photovoltaic system with energy storage and smart grid communication. IEEE Transactions on Industrial Electronics, 2013, 60(4): 1607–1618

[36]

Du L, Zhang L, Tian X. Deep power forecasting model for building attached photovoltaic system. IEEE Access: Practical Innovations, Open Solutions, 2018, 6: 52639–52651

[37]

Xu X, Xu Z, Zhang R, Data-driven-based dynamic pricing method for sharing rooftop photovoltaic energy in a single apartment building. IET Generation, Transmission & Distribution, 2020, 14(24): 5720–5727

[38]

Wi Y M, Lee J U, Joo S K. Electric vehicle charging method for smart homes/buildings with a photovoltaic system. IEEE Transactions on Consumer Electronics, 2013, 59(2): 323–328

[39]

Tang J, Cai D, Yuan C, Optimal configuration of battery energy storage systems using for rooftop residential photovoltaic to improve voltage profile of distributed network. Journal of Engineering (Stevenage, England), 2019, 2019(16): 728–732

[40]

Qolomany B, Al-Fuqaha A, Gupta A, Leveraging machine learning and big data for smart buildings: a comprehensive survey. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 90316–90356

[41]

Yang C, Yao J, Lou W, On demand response management performance optimization for microgrids under imperfect communication constraints. IEEE Internet of Things Journal, 2017, 4(4): 881–893

[42]

Zhou B, Zhang K, Chan K W, Optimal coordination of electric vehicles for virtual power plants with dynamic communication spectrum allocation. IEEE Transactions on Industrial Informatics, 2021, 17(1): 450–462

[43]

Ko K, Sung D K. The effect of cellular network-based communication delays in an EV aggregator’s domain on frequency regulation service. IEEE Transactions on Smart Grid, 2019, 10(1): 65–73

[44]

Pan J, Jain R, Paul S, An Internet of Things framework for smart energy in buildings: designs, prototype, and experiments. IEEE Internet of Things Journal, 2015, 2(6): 527–537

[45]

Abrol S, Mehmani A, Kerman M, Data-enabled building energy savings (D-E BES). Proceedings of the IEEE, 2018, 106(4): 661–679

[46]

Zhao T, Zhang C, Xu J, Data-driven correlation model between human behavior and energy consumption for college teaching buildings in cold regions of China. Journal of Building Engineering, 2021, 38: 102093

[47]

Johnson B J, Starke M R, Abdelaziz O A, A method for modeling household occupant behavior to simulate residential energy consumption. In: Innovative Smart Grid Technologies Conference, Washington, DC, USA, 2014

[48]

Basu K, Hawarah L, Arghira N, A prediction system for home appliance usage. Energy and Building, 2013, 67: 668–679

[49]

Aksanli B, Rosing T S. Human behavior aware energy management in residential cyber-physical systems. IEEE Transactions on Emerging Topics in Computing, 2020, 8(1): 45–57

[50]

Yu L, Xie D, Huang C, Energy optimization of HVAC systems in commercial buildings considering indoor air quality management. IEEE Transactions on Smart Grid, 2019, 10(5): 5103–5113

[51]

Gupta S K, Kar K, Mishra S, Incentive-based mechanism for truthful occupant comfort feedback in human-in-the-loop building thermal management. IEEE Systems Journal, 2018, 12(4): 3725–3736

[52]

Ajayi S O, Oyedele L O, Ilori O M. Changing significance of embodied energy: a comparative study of material specifications and building energy sources. Journal of Building Engineering, 2019, 23: 324–333

[53]

Jia Wen T, Chin Siong H, Noor Z Z. Assessment of embodied energy and global warming potential of building construction using life cycle analysis approach: case studies of residential buildings in Iskandar Malaysia. Energy and Building, 2015, 93: 295–302

[54]

Robati M, Daly D, Kokogiannakis G. A method of uncertainty analysis for whole-life embodied carbon emissions (CO2-e) of building materials of a net-zero energy building in Australia. Journal of Cleaner Production, 2019, 225: 541–553

[55]

Monahan J, Powell J C. An embodied carbon and energy analysis of modern methods of construction in housing: a case study using a lifecycle assessment framework. Energy and Building, 2011, 43(1): 179–188

[56]

Minunno R, O’Grady T, Morrison G M, Investigating the embodied energy and carbon of buildings: a systematic literature review and meta-analysis of life cycle assessments. Renewable & Sustainable Energy Reviews, 2021, 143: 110935

[57]

Alwan Z, Nawarathna A, Ayman R, Framework for parametric assessment of operational and embodied energy impacts utilising BIM. Journal of Building Engineering, 2021, 42: 102768

[58]

Abd Alla S, Bianco V, Tagliafico L A, Life-cycle approach to the estimation of energy efficiency measures in the buildings sector. Applied Energy, 2020, 264: 114745

[59]

Kreuzer K. The open home automation bus. 2019, available at the

[60]

Schoutsen P. Home assistant. 2019, available at the

[61]

Zhang X, Adhikari R, Pipattanasomporn M, Deploying IoT devices to make buildings smart: performance evaluation and deployment experience. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA, 2016

[62]

Cui B, Gao D, Wang S, Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications. Applied Energy, 2015, 147: 523–535

[63]

Zhang X, Wang D, Zhang Y, IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm. Environmental Impact Assessment Review, 2021, 88: 106567

[64]

Yoon Y B, Seo B, Koh B B Heating energy savings potential from retrofitting old apartments with an advanced double-skin façade system in cold climate. Frontiers in Energy, 2020, 14(2): 224–240

[65]

Paul S, Padhy N P. Real-time bilevel energy management of smart residential apartment building. IEEE Transactions on Industrial Informatics, 2020, 16(6): 3708–3720

[66]

Lee S, Kwon B, Lee S. Joint energy management system of electric supply and demand in houses and buildings. IEEE Transactions on Power Systems, 2014, 29(6): 2804–2812

[67]

Xu Z, Guan X, Jia Q, Performance analysis and comparison on energy storage devices for smart building energy management. IEEE Transactions on Smart Grid, 2012, 3(4): 2136–2147

[68]

Wang J, Chen B, Li P, Distributionally robust optimization of home energy management system based on receding horizon optimization. Frontiers in Energy, 2020, 14(2): 254–266

[69]

Zhao P, Henze G P, Plamp S, Evaluation of commercial building HVAC systems as frequency regulation providers. Energy and Building, 2013, 67: 225–235

[70]

Lin Y, Barooah P, Meyn S P. Low-frequency power-grid ancillary services from commercial building HVAC systems. In: 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, Canada, 2013

[71]

Blum D H, Zakula T, Norford L K. Opportunity cost quantification for ancillary services provided by heating, ventilating, and air-conditioning systems. IEEE Transactions on Smart Grid, 2017, 8(3): 1264–1273

[72]

Kim Y J, Blum D H, Xu N, Technologies and magnitude of ancillary services provided by commercial buildings. Proceedings of the IEEE, 2016, 104(4): 758–779

[73]

Qureshi F A, Lymperopoulos I, Khatir A A, Economic advantages of office buildings providing ancillary services with intraday participation. IEEE Transactions on Smart Grid, 2018, 9(4): 3443–3452

[74]

La Q D, Chan Y W E, Soong B H. Power management of intelligent buildings facilitated by smart grid: a market approach. IEEE Transactions on Smart Grid, 2016, 7(3): 1389–1400

[75]

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

[76]

Bilgin E, Caramanis M C, Paschalidis I C, Provision of regulation service by smart buildings. IEEE Transactions on Smart Grid, 2016, 7(3): 1683–1693

[77]

Arun S L, Selvan M P. Dynamic demand response in smart buildings using an intelligent residential load management system. IET Generation, Transmission & Distribution, 2017, 11(17): 4348–4357

[78]

Lu N, Zhang Y. Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves. IEEE Transactions on Smart Grid, 2013, 4(2): 914–921

[79]

Vanouni M, Lu N. Improving the centralized control of thermostatically controlled appliances by obtaining the right information. IEEE Transactions on Smart Grid, 2015, 6(2): 946–948

[80]

Muhssin M T, Cipcigan L M, Jenkins N, Dynamic frequency response from controlled domestic heat pumps. IEEE Transactions on Power Systems, 2018, 33(5): 4948–4957

[81]

Hu J, Cao J, Chen M Z Q, Load following of multiple heterogeneous TCL aggregators by centralized control. IEEE Transactions on Power Systems, 2017, 32(4): 3157–3167

[82]

Ma K, Yuan C, Yang J, Controller design and parameter optimization of aggregated thermostatically controlled loads for frequency regulation. In: 2016 35th Chinese Control Conference (CCC), Chengdu, China, 2016

[83]

Hao H, Lin Y, Kowli A S, Ancillary service to the grid through control of fans in commercial building HVAC systems. IEEE Transactions on Smart Grid, 2014, 5(4): 2066–2074

[84]

Adhikari R, Pipattanasomporn M, Rahman S. Heuristic algorithms for aggregated HVAC control via smart thermostats for regulation service. IEEE Transactions on Smart Grid, 2020, 11(3): 2023–2032

[85]

Mantovani G, Ferrarini L. Temperature control of a commercial building with model predictive control techniques. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2651–2660

[86]

Ma Y, Matuško J, Borrelli F. Stochastic model predictive control for building HVAC systems: complexity and conservatism. IEEE Transactions on Control Systems Technology, 2015, 23(1): 101–116

[87]

Wang Z, Hu G, Spanos C J. Distributed model predictive control of bilinear HVAC systems using a convexification method. In: 2017 11th Asian Control Conference (ASCC). Gold Coast, QLD, Australia, 2017

[88]

Wu Z, Jia Q, Guan X. Optimal control of multiroom HVAC system: an event-based approach. IEEE Transactions on Control Systems Technology, 2016, 24(2): 662–669

[89]

Zhang Z, Chong A, Pan Y, Whole building energy model for HVAC optimal control: a practical framework based on deep reinforcement learning. Energy and Building, 2019, 199: 472–490

[90]

Nazari S, Borrelli F, Stefanopoulou A. Electric vehicles for smart buildings: a survey on applications, energy management methods, and battery degradation. Proceedings of the IEEE, 2021, 109(6): 1128–1144

[91]

Zhang G, Tan S T, Wang G G. Real-time smart charging of electric vehicles for demand charge reduction at non-residential sites. IEEE Transactions on Smart Grid, 2018, 9(5): 4027–4037

[92]

Liu Z, Wu Q, Shahidehpour M, Transactive real-time electric vehicle charging management for commercial buildings with PV on-site generation. IEEE Transactions on Smart Grid, 2019, 10(5): 4939–4950

[93]

Afshari S, Mishra S. A plug-and-play realization of decentralized feedback control for smart lighting systems. IEEE Transactions on Control Systems Technology, 2016, 24(4): 1317–1327

[94]

Al-Ghaili A M, Kasim H, Al-Hada N M, A review: buildings energy savings-lighting systems performance. IEEE Access : Practical Innovations, Open Solutions, 2020, 8: 76108–76119

[95]

Lee C K, Liu H, Fuhs D, Smart lighting systems as a demand response solution for future smart grids. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2020, 8(3): 2362–2370

[96]

Liu J, Zhang W, Liu Y. Primary frequency response from the control of LED lighting loads in commercial buildings. IEEE Transactions on Smart Grid, 2017, 8(6): 2880–2889

[97]

Amini M, Almassalkhi M. Optimal corrective dispatch of uncertain virtual energy storage systems. IEEE Transactions on Smart Grid, 2020, 11(5): 4155–4166

[98]

Hao H, Sanandaji B M, Poolla K, Aggregate flexibility of thermostatically controlled loads. IEEE Transactions on Power Systems, 2015, 30(1): 189–198

[99]

Mathieu J L, Kamgarpour M, Lygeros J, Energy arbitrage with thermostatically controlled loads. In: 2013 European Control Conference, Zurich, Switzerland, 2013

[100]

Hao H, Sanandaji B M, Poolla K, Aggregate flexibility of thermostatically controlled loads. IEEE Transactions on Power Systems, 2015, 30(1): 189–198

[101]

Martínez G, Liu J, Li B, Enabling renewable resource integration: the balance between robustness and flexibility. In: 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, USA, 2015

[102]

Vrakopoulou M, Li B, Mathieu J L. Chance constrained reserve scheduling using uncertain controllable loads part I: formulation and scenario-based analysis. IEEE Transactions on Smart Grid, 2019, 10(2): 1608–1617

[103]

Chakraborty I, Nandanoori S P, Kundu S. Virtual battery parameter identification using transfer learning based stacked autoencoder. In: 2018 17th IEEE International Conference on Machine Learning and Applications, 2018: 1269–1274

[104]

Madjidian D, Roozbehani M, Dahleh M A. Energy storage from aggregate deferrable demand: fundamental trade-offs and scheduling policies. IEEE Transactions on Power Systems, 2018, 33(4): 3573–3586

[105]

Zhu X, Yang J, Liu Y, Optimal scheduling method for a regional integrated energy system considering joint virtual energy storage. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 138260–138272

[106]

Zhao D, Wang H, Huang J, Virtual energy storage sharing and capacity allocation. IEEE Transactions on Smart Grid, 2020, 11(2): 1112–1123

[107]

Hansen E K, Hammershøj Olesen G G, Mullins M. Home smart home: a Danish energy-positive home designed with daylight. Proceedings of the IEEE, 2013, 101(11): 2436–2449

[108]

Jia R, Jin B, Jin M, Design automation for smart building systems. Proceedings of the IEEE, 2018, 106(9): 1680–1699

[109]

Duerr S, Ababei C, Ionel D M. SmartBuilds: an energy and power simulation framework for buildings and districts. IEEE Transactions on Industry Applications, 2017, 53(1): 402–410

[110]

Li Y, Wang Y, Hu S. Online generative adversary network based measurement recovery in false data injection attacks: a cyber-physical approach. IEEE Transactions on Industrial Informatics, 2020, 16(3): 2031–2043

[111]

Patel A, Purwar S. Switching attacks on smart grid using non-linear sliding surface. IET Cyber-Physical Systems: Theory & Applications, 2019, 4(4): 382–392

[112]

Khalid H M, Muyeen S M, Peng J C H. Cyber-attacks in a looped energy-water nexus: an inoculated sub-observer-based approach. IEEE Systems Journal, 2020, 14(2): 2054–2065

[113]

Lyu X, Ding Y, Yang S. Bayesian network based C2P risk assessment for cyber-physical systems. IEEE Access: Practical Innovations, Open Solutions, 2020, 8: 88506–88517

[114]

Cardenas D J S, Hahn A, Liu C C. Assessing cyber-physical risks of IoT-based energy devices in grid operations. IEEE Access : Practical Innovations, Open Solutions, 2020, 8: 61161–61173

[115]

Bhuiyan M Z A, Anders G J, Philhower J, Review of static risk-based security assessment in power system. IET Cyber-Physical Systems: Theory & Applications, 2019, 4(3): 233–239

[116]

Lyu X, Ding Y, Yang S. Safety and security risk assessment in cyber-physical systems. IET Cyber-Physical Systems: Theory & Applications, 2019, 4(3): 221–232

[117]

Roberts C, Scaglione A, Jamei M, Learning behavior of distribution system discrete control devices for cyber-physical security. IEEE Transactions on Smart Grid, 2020, 11(1): 749–761

[118]

Venkataramanan V, Hahn A, Srivastava A C P S A M. Cyber-physical security assessment metric for monitoring microgrid resiliency. IEEE Transactions on Smart Grid, 2020, 11(2): 1055–1065

[119]

Zhang Y, Krishnan V V G, Pi J, Cyber physical security analytics for transactive energy systems. IEEE Transactions on Smart Grid, 2020, 11(2): 931–941

[120]

Oozeer M I, Haykin S. Cognitive risk control for mitigating cyber-attack in smart grid. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 125806–125826

[121]

Liu G, Xu Y, Tomsovic K. Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Transactions on Smart Grid, 2016, 7(1): 227–237

[122]

Yang Z, Wu R, Yang J, Economical operation of microgrid with various devices via distributed optimization. IEEE Transactions on Smart Grid, 2016, 7(2): 857–867

[123]

Ding T, Liu S, Yuan W, A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks. IEEE Transactions on Sustainable Energy, 2016, 7(1): 301–311

[124]

Zheng W, Wu W, Zhang B, A fully distributed reactive power optimization and control method for active distribution networks. IEEE Transactions on Smart Grid, 2016, 7(2): 1021–1033

[125]

Chen S, Chen Q, Xu Y. Strategic bidding and compensation mechanism for a load aggregator with direct thermostat control capabilities. IEEE Transactions on Smart Grid, 2018, 9(3): 2327–2336

[126]

Hu J, Cao J, Guerrero J M, Improving frequency stability based on distributed control of multiple load aggregators. IEEE Transactions on Smart Grid, 2017, 8(4): 1553–1567

[127]

Zhang G, Jiang C, Wang X, Bidding strategy analysis of virtual power plant considering demand response and uncertainty of renewable energy. IET Generation, Transmission & Distribution, 2017, 11(13): 3268–3277

[128]

Zhang G, Jiang C, Wang X. Comprehensive review on structure and operation of virtual power plant in electrical system. IET Generation, Transmission & Distribution, 2019, 13(2): 145–156

[129]

Awerbuch S, Preston A. The Virtual Utility: Accounting, Technology and Competitive Aspects of the Emerging Industry. Boston, MA: Springer US, 1997

[130]

Yang Z, Wu R, Yang J, Economical operation of microgrid with various devices via distributed optimization. IEEE Transactions on Smart Grid, 2016, 7(2): 857–867

[131]

Lin J, Wan C, Song Y, Situation awareness of active distribution network: roadmap, technologies, and bottlenecks. CSEE Journal of Power and Energy Systems, 2016, 2(3): 35–42

[132]

Xu Y, Xie L, Singh C. Optimal scheduling and operation of load aggregators with electric energy storage facing price and demand uncertainties. In: 2011 North American Power Symposium. Boston, MA, USA, 2011

[133]

Wang L, Zhu Z, Jiang C, Bi-level robust optimization for distribution system with multiple microgrids considering uncertainty distribution locational marginal price. IEEE Transactions on Smart Grid, 2021, 12(2): 1104–1117

[134]

Wang L, Jiang C, Gong K, Data-driven distributionally robust economic dispatch for distribution network with multiple microgrids. IET Generation, Transmission & Distribution, 2020, 14(24): 5712–5719

[135]

Sharma S, Verma A, Xu Y, Robustly coordinated Bi-level energy management of a multi-energy building under multiple uncertainties. IEEE Transactions on Sustainable Energy, 2021, 12(1): 3–13

[136]

Wen S, Zhang C, Lan H, A hybrid ensemble model for interval prediction of solar power output in ship onboard power systems. IEEE Transactions on Sustainable Energy, 2021, 12(1): 14–24

[137]

Zolezzi J M, Rudnick H. Transmission cost allocation by cooperative games and coalition formation. IEEE Transactions on Power Systems, 2002, 17(4): 1008–1015

[138]

Molina Y P, Saavedra O R, Amarís H. Transmission network cost allocation based on circuit theory and the aumann-shapley method. IEEE Transactions on Power Systems, 2013, 28(4): 4568–4577

[139]

Li B, Wang X, Shahidehpour M, Robust bidding strategy and profit allocation for cooperative DSR aggregators with correlated wind power generation. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1904–1915

[140]

Zhang Z, Li R, Li F. A novel peer-to-peer local electricity market for joint trading of energy and uncertainty. IEEE Transactions on Smart Grid, 2020, 11(2): 1205–1215

[141]

Nezamabadi H, Vahidinasab V. Arbitrage strategy of renewable-based microgrids via peer-to-peer energy-trading. IEEE Transactions on Sustainable Energy, 2021, 12(2): 1372–1382

[142]

AlAshery M K, Yi Z, Shi D, A blockchain-enabled multi-settlement quasi-ideal peer-to-peer trading framework. IEEE Transactions on Smart Grid, 2021, 12(1): 885–896

[143]

Hamouda M R, Nassar M E, Salama M M A. A novel energy trading framework using adapted blockchain technology. IEEE Transactions on Smart Grid, 2021, 12(3): 2165–2175

[144]

Jin Y, Wang H, Chugh T, Data-driven evolutionary optimization: an overview and case studies. IEEE Transactions on Evolutionary Computation, 2019, 23(3): 442–458

[145]

Huang P, Wang H, Ma W. Stochastic ranking for offline data-driven evolutionary optimization using radial basis function networks with multiple kernels. In: 2019 IEEE Symposium Series on Computational Intelligence, Xiamen, China, 2019

[146]

Huang P, Wang H, Jin Y. Offline data-driven evolutionary optimization based on tri-training. Swarm and Evolutionary Computation, 2021, 60: 100800

[147]

Wang X, Shahidehpour M, Jiang C, Resilience enhancement strategies for power distribution network coupled with urban transportation system. IEEE Transactions on Smart Grid, 2019, 10(4): 4068–4079

[148]

Wang X, Li Z, Shahidehpour M, Robust line hardening strategies for improving the resilience of distribution systems with variable renewable resources. IEEE Transactions on Sustainable Energy, 2019, 10(1): 386–395

[149]

Gong K, Wang X, Jiang C, Security-constrained optimal sizing and siting of BESS in hybrid AC/DC microgrid considering post-contingency corrective rescheduling. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2110–2122

[150]

Tao M, Ota K, Dong M. Foud: integrating fog and cloud for 5G-enabled V2G networks. IEEE Network, 2017, 31(2): 8–13

[151]

Zhang Y, Li J, Zheng D, Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice. Journal of Network and Computer Applications, 2018, 122: 50–60

[152]

Zhang Y, Zhao J, Zheng D. Efficient and privacy-aware power injection over AMI and smart grid slice in future 5G networks. Mobile Information Systems, 2017, 2017: 1–11

[153]

Zhou Y, Li L. The 5G communication technology-oriented intelligent building system planning and design. Computer Communications, 2020, 160: 402–410

[154]

Kumar A, Dhanagopal R, Albreem M A, A comprehensive study on the role of advanced technologies in 5G based smart hospital. Alexandria Engineering Journal, 2021, 60(6): 5527–5536

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (2590KB)

4585

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/