Blockchain-Assisted Crowdsourced Energy Systems

Crowdsourcing relies on people’s contributions to meet product- or system-level objectives. Crowdsourcing-based methods have been implemented in various cyber-physical systems and realtime markets. This paper explores a framework for Crowdsourced Energy Systems (CES), where small-scale energy generation or energy trading is crowdsourced from distributed energy resources, electric vehicles, and shapable loads. The merits/pillars of energy crowdsourcing are discussed. Then, an operational model for CESs in distribution networks with different types of crowdsourcees is proposed. The model yields a market equilibrium depicting traditional and distributed generator and load setpoints. Given these setpoints, crowdsourcing incentives are designed to steer crowdsourcees to the equilibrium. As the number of crowdsourcees and energy trading transactions scales up, a secure energy trading platform is required. To that end, the presented framework is integrated with a lightweight Blockchain implementation and smart contracts. Numerical tests are provided to showcase the overall implementation.

[1]  David Menga,et al.  Novel paradigms for advanced distribution grid energy management , 2017, ArXiv.

[2]  Ted S. Sindlinger,et al.  Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business , 2010 .

[3]  Sijie Chen,et al.  Smart contract-based campus demonstration of decentralized transactive energy auctions , 2017, 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[4]  Jeff Howe,et al.  Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business , 2008, Human Resource Management International Digest.

[5]  Steven H. Low,et al.  Branch Flow Model: Relaxations and Convexification—Part I , 2012, IEEE Transactions on Power Systems.

[6]  Jonathan Mather,et al.  Blockchains for decentralized optimization of energy resources in microgrid networks , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[7]  Mohammad Shahidehpour,et al.  Security-constrained unit commitment with volatile wind power generation , 2009, 2009 IEEE Power & Energy Society General Meeting.

[8]  Steven H. Low,et al.  Distributed Optimal Power Flow Algorithm for Balanced Radial Distribution Networks , 2014, 1404.0700.

[9]  Douglas C. Schmidt,et al.  Privacy-Preserving Platform for Transactive Energy Systems , 2017, ArXiv.

[10]  Hanif D. Sherali,et al.  A global optimization algorithm for polynomial programming problems using a Reformulation-Linearization Technique , 1992, J. Glob. Optim..

[11]  C. Langbort,et al.  On Real-Time Pricing for Strategic Agents , 2009 .

[12]  Joshua A. Taylor Convex Optimization of Power Systems , 2015 .

[13]  Steven H. Low,et al.  Optimal Branch Exchange for Distribution System Reconfiguration , 2013 .

[14]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[15]  Sairaj V. Dhople,et al.  Scalable Optimization Methods for Distribution Networks With High PV Integration , 2016, IEEE Transactions on Smart Grid.

[16]  Mircea Lazar,et al.  Real-time control of power systems using nodal prices , 2008 .

[17]  M. E. Baran,et al.  Optimal capacitor placement on radial distribution systems , 1989 .

[18]  Marko Vukolic,et al.  The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication , 2015, iNetSeC.

[19]  Arati Baliga,et al.  Understanding Blockchain Consensus Models , 2017 .

[20]  Minghua Chen,et al.  Crowd-Sourced Storage-Assisted Demand Response in Microgrids , 2017, e-Energy.

[21]  S. Low,et al.  Feeder Reconfiguration in Distribution Networks Based on Convex Relaxation of OPF , 2015, IEEE Transactions on Power Systems.

[22]  Melanie Swan,et al.  Blockchain: Blueprint for a New Economy , 2015 .

[23]  Toru Namerikawa,et al.  Real-Time Pricing Mechanism for Electricity Market With Built-In Incentive for Participation , 2015, IEEE Transactions on Smart Grid.

[24]  Steven H. Low,et al.  Branch Flow Model: Relaxations and Convexification—Part II , 2012 .