PDF(2386 KB)
使用IEEE 2030.5 标准在电网边缘进行可交易需求响应操作
Javad Fattahi, Mikhak Samadi, Melike Erol-Kantarci, Henry Schriemer
工程（英文） ›› 2020, Vol. 6 ›› Issue (7) : 801-811.
PDF(2386 KB)
PDF(2386 KB)
使用IEEE 2030.5 标准在电网边缘进行可交易需求响应操作
Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard
本文提出一种针对拥有发电资产的住宅客户网络的可交易需求响应（TDR）方案，该方案强调了交易能源架构内的互操作性。完整的基于实验室的实施（据我们所知）首次实现了全面的TDR案例，该案例完全符合电气与电子工程师协会（IEEE）2030.5标准，解决了网络安全智能能源规范（SEP）应用协议的互操作性。通过使用基于Internet协议（IP） [局域网（LAN）和Wi-Fi]的通信协议和传输层安全性（TLS）1.2加密协议的商业硬件的完整系统集成来提供验证，而实证通过大量住宅智能电表数据仿真提供。需求响应（DR）方案旨在兼顾隐私问题，允许客户选择其DR响应水平，并提供激励措施以最大程度地提高其参与度。本文提出的TDR方案通过在交易代理（TA）和家庭能源管理系统（HEMS）代理之间实施SEP 2.0通信协议来解决隐私问题。客户响应由一个多入多出（MIMO）模糊控制器处理，该控制器管理客户代理和TA之间的协商。我们采用多代理系统方法实现邻域协调，通过TA在一个公共变压器上为多个客户提供服务，并在基于事件的优化过程中利用激励机制最大化客户的参与度。基于在较长时间内获取的一组智能电表数据，我们参与了多个TDR场景，并通过符合IEEE 2030.5标准的全功能实现证明了我们的方案可以在现实条件下将网络峰值功耗降低22%。
This paper presents a transactive demand response (TDR) scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture. A complete laboratory-based implementation provides the first (to our knowledge) realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers (IEEE) 2030.5 standard, which addresses interoperability within a cybersecure smart energy profile (SEP) context. Verification is provided by a full system integration with commercial hardware using IP-based (local area network (LAN) and Wi-Fi) communication protocols and transport layer security (TLS) 1.2 cryptographic protocol, and validation is provided by emulation using extensive residential smart meter data. The demand response (DR) scheme is designed to accommodate privacy concerns, allows customers to select their DR compliance level, and provides incentives to maximize their participation. The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent (TA) and home energy management system (HEMS) agents. Customer response is handled by a multi-input multi-output (MIMO) fuzzy controller that manages negotiation between the customer agent and the TA. We take a multi-agent system approach to neighborhood coordination, with the TA servicing multiple residences on a common transformer, and use a reward mechanism to maximize customer engagement during the event-based optimization. Based on a set of smart meter data acquired over an extended time period, we engage in multiple TDR scenarios, and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22% under realistic conditions.
可交易需求响应 / IEEE 2030.5 / 智能电网 / 多代理系统 / 邻域协调
Transactive demand response / IEEE 2030.5 / Smart grid / Multi-agent system / Neighborhood coordination
| [1] |
Gellings CW. The concept of demand-side management for electric utilities. Proc IEEE 1985;73(10):1468–70.
|
| [2] |
Masoum AS, Deilami S, Moses PS, Masoum MAS, Abu-Siada A. Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimisation considering voltage regulation. IET Gener Transm Distrib 2011;5(8):877–88.
|
| [3] |
Erol-Kantarci M, Mouftah HT. Wireless sensor networks for cost-efficient residential energy management in the smart grid. IEEE Trans Smart Grid 2011;2(2):314–25.
|
| [4] |
River C. Primer on demand-side management with an emphasis on priceresponsive programs. Technical report. Washington, DC: The World Bank by Charles River Associates; 2005.
|
| [5] |
US Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them. Technical report. Washington, DC: US Department of Energy; 2006.
|
| [6] |
Nadel S. Utility demand-side management experience and potential—a critical review. Annu Rev Energy Environ 1992;17(1):507–35.
|
| [7] |
Vardakas JS, Zorba N, Verikoukis CV. A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun Surv Tutorials 2015;17(1):152–78.
|
| [8] |
Li R, Wang Z, Gu C, Li F, Wu H. A novel time-of-use tariff design based on Gaussian Mixture Model. Appl Energy 2016;162:1530–6.
|
| [9] |
Henley A, Peirson J. Time-of-use electricity pricing: evidence from a British experiment. Econ Lett 1994;45(3):421–6.
|
| [10] |
Aslam S, Iqbal Z, Javaid N, Khan ZA, Aurangzeb K, Haider SI. Towards efficient energy management of smart buildings exploiting heuristic optimization with real time and critical peak pricing schemes. Energies 2017;10(12):2065.
|
| [11] |
Herter K. Residential implementation of critical-peak pricing of electricity. Energy Policy 2007;35(4):2121–30.
|
| [12] |
Baughman ML, Siddiqi SN. Real-time pricing of reactive power: theory and case study results. IEEE Trans Power Syst 1991;6(1):23–9.
|
| [13] |
Mortaji H, Ow SH, Moghavvemi M, Almurib HAF. Smart grid demand response management using internet of things for load shedding and smart-direct load control. In: Proceedings of the 2016 IEEE Industry Applications Society Annual Meeting; 2016 Oct 2–6; Portland, OR, USA; 2016. p. 1–7.
|
| [14] |
Luo F, Zhao J, Dong ZY, Tong X, Chen Y, Yang H, et al. Optimal dispatch of air conditioner loads in southern China region by direct load control. IEEE Trans Smart Grid 2016;7(1):439–50.
|
| [15] |
Vale ZA, Ramos C, Faria P, Soares JP, Canizes B, Khodr HM. Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods. In: Proceedings of the 2010 IEEE PES General Meeting; 2010 Jul 25– 29; Providence, RI, USA; 2010. p. 1–6.
|
| [16] |
Martinez VJ, Rudnick H. Active participation of demand through a secondary ancillary services market in a smart grid environment. IEEE Trans Smart Grid 2013;4(4):1996–2005.
|
| [17] |
Ela E, Kirby B, Navid N, Smith JC. Effective ancillary services market designs on high wind power penetration systems. In: Proceedings of the 2012 IEEE Power and Energy Society General Meeting; 2012 Jul 22–26; San Diego, CA, USA; 2012. p. 1–8.
|
| [18] |
Olivares DE, Cañizares CA, Kazerani M. A centralized optimal energy management system for microgrids. In: Proceedings of the 2011 IEEE Power and Energy Society General Meeting; 2011 Jul 24–28; Detroit, MI, USA; 2011. p. 1–6.
|
| [19] |
Ruiz N, Cobelo I, Oyarzabal J. A direct load control model for virtual power plant management. IEEE Trans Power Syst 2009;24(2):959–66.
|
| [20] |
Tsikalakis AG, Hatziargyriou ND. Centralized control for optimizing microgrids operation. In: Proceedings of the 2011 IEEE Power and Energy Society General Meeting; 2011 Jul 24–28; Detroit, MI, USA; 2011. p. 1–8.
|
| [21] |
Schneider KP, Fuller JC, Chassin D. Analysis of distribution level residential demand response. In: Proceedings of the 2011 IEEE/PES Power Systems Conference and Exposition; 2011 Mar 20–23; Phoenix, AZ, USA; 2011. p. 1–6.
|
| [22] |
Chen S, Liu CC. From demand response to transactive energy: state of the art. J Mod Power Syst Clean Energy 2017;5(1):10–9.
|
| [23] |
Ghamkhari M. Transactive energy versus demand response in cutting wholesale electricity prices. In: Proceedings of the 2019 3rd International Conference on Smart Grid and Smart Cities; 2019 Jun 25–28; Berkley, CA, USA; 2019. p. 182–7.
|
| [24] |
Huang P, Kalagnanam J, Natarajan R, Sharma M. Analytics and transactive control design for the pacific northwest smart grid demonstration project. In: Proceedings of the 2010 1st IEEE International Conference on Smart Grid Communications; 2010 Oct 4–6; Gaithersburg, MD, USA; 2010. p. 449–54.
|
| [25] |
Behboodi S, Chassin DP, Djilali N, Crawford C. Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets. Appl Energy 2018;210:1310–20.
|
| [26] |
Hao H, Corbin CD, Kalsi K, Pratt RG. Transactive control of commercial buildings for demand response. IEEE Trans Power Syst 2017;32 (1):774–83.
|
| [27] |
Daneshvar M, Pesaran M, Mohammadi-ivatloo B. Transactive energy integration in future smart rural network electrification. J Clean Prod 2018;190:645–54.
|
| [28] |
Yan Y, Qian Y, Sharif H, Tipper D. A survey on cyber security for smart grid communications. IEEE Commun Surv Tutorials 2012;14(4):998–1010.
|
| [29] |
Zhou K, Yang S, Shao Z. Household monthly electricity consumption pattern mining: a fuzzy clustering-based model and a case study. J Clean Prod 2017;141:900–8.
|
| [30] |
Tyagi R, Chen W, Black J, Tiwari P, Lecours B, Shaver J. Transforming electrical load from an operational constraint to a controllable resource. INFORMS J Appl Anal 2017;47(4):292–304.
|
| [31] |
Council TGA. Gridwise transactive energy framework version 1.1. Technical report. Richland: The GridWise Architecture Council; 2019.
|
| [32] |
Nunna HK, Srinivasan D. Multiagent-based transactive energy framework for distribution systems with smart microgrids. IEEE Trans Ind Inf 2017;13 (5):2241–50.
|
| [33] |
Good N, Martínez Ceseña EA, Heltorp C, Mancarella P. A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems. Energy 2019;184:165–79.
|
| [34] |
Zia MF, Benbouzid M, Elbouchikhi E, Muyeen SM, Techato K, Guerrero JM. Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis. IEEE Access 2020;8:19410–32.
|
| [35] |
Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, et al. Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inf 2011;7(4):529–39.
|
| [36] |
2030.5-2018-IEEE standard for smart energy profile application protocol. New York: COM/PLC- Power Line Communications; 2018.
|
| [37] |
McArthur SDJ, Davidson EM, Catterson VM, Dimeas AL, Hatziargyriou ND, Ponci F, et al. Multi-agent systems for power engineering applications—part I: concepts, approaches, and technical challenges. IEEE Trans Power Syst 2007;22(4):1743–52.
|
| [38] |
Han DM, Lim JH. Smart home energy management system using IEEE 802.15.4 and zigbee. IEEE Trans Consum Electron 2010;56(3):1403–10.
|
| [39] |
Uslar M, Specht M, Rohjans S, Trefke J, González JM. The common information model CIM: IEC 61968/61970 and 62325—a practical introduction to the CIM. Berlin: Springer; 2012.
|
| [40] |
Richardson L, Ruby S. RESTful web services. Cambridge: O’Reilly Media Inc.; 2007.
|
| [41] |
Santesson S, Myers M, Ankney R, Malpani A, Galperin S, Adams C. X.509 internet public key infrastructure online certificate status protocol—OCSP. RFC Editor 2013;6960:1–41.
|
| [42] |
tabuchiamerica.com [Internet]. Osaka: Tabuchi Electric CO., LTD.; c2020 [cited 2020 Jun 5]. Available from: http://www.tabuchiamerica.com.
|
| [43] |
Ho¯lHo¯m solution overview [Internet]. Ottawa: Energate; [cited 2020 May 8]. Available from: http://www.energateinc.com/solutions/hhplus-solution-overview/.
|
| [44] |
Parker LE. Distributed intelligence: overview of the field and its application in multi-robot systems. J Physical Agents 2008;2(1):5–14.
|
| [45] |
Ramchurn SD, Vytelingum P, Rogers A, Jennings N. Agent-based control for decentralised demand side management in the smart grid. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent System; 2011 May; Taipei, China; 2011. p. 5–12.
|
| [46] |
Zhang J, Tang C, Guo L, Xu H. A longitudinal investigation of customer cooperation in services: the role of appraisal of cooperation behaviors. Psychol Mark 2018;35(12):957–67.
|
| [47] |
Tu KY, Lee TT, Wang WJ. Design of a multi-layer fuzzy logic controller for multi-input multi-output systems. Fuzzy Sets Syst 2000;111(2): 199–214.
|
| [48] |
Kacprzyk J, Pedrycz W, editors. Springer handbook of computational intelligence. Berlin: Springer-Verlag; 2015.
|
| [49] |
hydroottawa.com [Internet]. Ottawa: Hydro Ottawa; c2020 [cited 2020 Mar 5]. Available from: https://hydroottawa.com/.
|
| [50] |
Alpaydin E. Introduction to machine learning. 2nd ed. Cambridge: The MIT Press; 2009.
|
| [51] |
Bemporad A, Borrelli F, Morari M. Model predictive control based on linear programming—the explicit solution. IEEE Trans Autom Contr 2002;47 (12):1974–85.
|
| [52] |
Camacho EF, Alba CB. Model predictive control. Berlin: Springer Science & Business Media; 2013.
|
| [53] |
Han SH, Lee JH. An overview of peak-to-average power ratio reduction techniques for multicarrier transmission. IEEE Wireless Commun 2005;12 (2):56–65.
|
/
| 〈 |
|
〉 |
