Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

The use of Distributed Ledger Technologies such as Blockchain for certifying Demand Response services allows for the creation of a distributed system in which customers can communicate with the system operator to provide their flexibility, in a secure, transparent and traceable way. Blockchain technology also supports incentive mechanisms for users taking part in the service through the generation of utility tokens to recognize the user’s contribution. This paper presents the experimental test of a novel methodology for Demand Response programs implementation by using the Blockchain technology. The latter is employed for defining a distributed Demand Response service and a new system for its tracing and certification. For this work, a Smart Contract has been conceived and written to execute Demand Response events, calculate users’ baseline, compute the support provided by each user towards the fulfilment of the requested load curve modification and remunerate each user with utility tokens proportionally to their contribution. To test the methodology, a Hyperledger Fabric network and a Smart Contract were deployed on four nodes of the Microgrid Laboratory of the Department of Energy Technology at Aalborg University (DK). Subsequently, a realistic scenario comprising two consumer nodes was developed using power electronic converters for generating the household profiles and Smart Meters for the measurement of the consumption profiles. Theoretical and experimental results show the feasibility of Distributed Ledger Technologies in smart grids management with a minimum investment in new hardware while enabling the active participation of customers in Demand Response more transparently and fairly.

[1]  Yuan-Yih Hsu,et al.  Dispatch of direct load control using dynamic programming , 1991 .

[2]  Gerald B. Sheblé,et al.  Direct load control-A profit-based load management using linear programming , 1998 .

[3]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[4]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[5]  J. Torriti,et al.  A review of the costs and benefits of demand response for electricity in the UK , 2013 .

[6]  Nico Keyaerts,et al.  Shift, not drift : towards active demand response and beyond , 2013 .

[7]  Karl Aberer,et al.  When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers , 2014, IEEE Transactions on Smart Grid.

[8]  U. Holmgren,et al.  Evaluation of Demand Response Systems for Smart Grids: state of the art, value potential and the Hyllie case , 2014 .

[9]  Ramazan Bayindir,et al.  The path of the smart grid -the new and improved power grid , 2016, 2016 International Smart Grid Workshop and Certificate Program (ISGWCP).

[10]  Zancanella Paolo,et al.  Demand response status in EU Member States , 2016 .

[11]  A. Yassine Implementation challenges of automatic demand response for households in smart grids , 2016, 2016 3rd International Conference on Renewable Energies for Developing Countries (REDEC).

[12]  Pedro Faria,et al.  Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs , 2016, IEEE Transactions on Industrial Informatics.

[13]  P. Charpentier,et al.  Statistical Estimation of the Residential Baseline , 2016, IEEE Transactions on Power Systems.

[14]  A. Moreno-Muñoz,et al.  Using smart meters data for energy management operations and power quality monitoring in a microgrid , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[15]  Giacomo Verticale,et al.  The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case , 2017 .

[16]  Ailin Asadinejad,et al.  Error Analysis of Customer Baseline Load (CBL) Calculation Methods for Residential Customers , 2017, IEEE Transactions on Industry Applications.

[17]  Marilyn A. Brown,et al.  Smart meter deployment in Europe: A comparative case study on the impacts of national policy schemes , 2017 .

[18]  Ying Zhong,et al.  M2M Blockchain: The Case of Demand Side Management of Smart Grid , 2017, 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS).

[19]  Cheng Wang,et al.  Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers , 2017, IEEE Transactions on Power Systems.

[20]  Fei Wang,et al.  A Baseline Load Estimation Approach for Residential Customer based on Load Pattern Clustering , 2017 .

[21]  Ilenia Tinnirello,et al.  Overgrid: A Fully Distributed Demand Response Architecture Based on Overlay Networks , 2017, IEEE Transactions on Automation Science and Engineering.

[22]  Zibin Zheng,et al.  An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[23]  Joy Dalmacio Billanes,et al.  Aggregation Potentials for Buildings—Business Models of Demand Response and Virtual Power Plants , 2017 .

[24]  D. H. Vu,et al.  Customer reward‐based demand response program to improve demand elasticity and minimise financial risk during price spikes , 2018, IET Generation, Transmission & Distribution.

[25]  Yang Li,et al.  A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer , 2018 .

[26]  Marcel Antal,et al.  Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids , 2018, Sensors.

[27]  Dimitrios Tzovaras,et al.  A Secured and Trusted Demand Response system based on Blockchain technologies , 2018, 2018 Innovations in Intelligent Systems and Applications (INISTA).

[28]  Marko Vukolic,et al.  Hyperledger fabric: a distributed operating system for permissioned blockchains , 2018, EuroSys.

[29]  Gaetano Zizzo,et al.  DEMAND Project: Bottom-Up Aggregation of Prosumers in Distribution Networks , 2018, 2018 AEIT International Annual Conference.

[30]  Ioannis Karamitsos,et al.  Design of the Blockchain Smart Contract: A Use Case for Real Estate , 2018 .

[31]  Zita Vale,et al.  Methods for Aggregation and Remuneration of Distributed Energy Resources , 2018, Applied Sciences.

[32]  Karen Scarfone,et al.  Blockchain Technology Overview , 2018, ArXiv.

[33]  Gaetano Zizzo,et al.  A Technical Approach to the Energy Blockchain in Microgrids , 2018, IEEE Transactions on Industrial Informatics.

[34]  Muhammad Faizan Tahir,et al.  Demand Response Programs Significance, Challenges and Worldwide Scope in Maintaining Power System Stability , 2018 .

[35]  Shai Halevi,et al.  Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation , 2018, 2018 IEEE International Conference on Cloud Engineering (IC2E).

[36]  Gerhard Schwabe,et al.  To Token or not to Token: Tools for Understanding Blockchain Tokens , 2018, ICIS.

[37]  Manlu Liu,et al.  How Will Blockchain Technology Impact Auditing and Accounting: Permissionless versus Permissioned Blockchain , 2019, Current Issues in Auditing.

[38]  Katinka Wolter,et al.  Performance Prediction for the Apache Kafka Messaging System , 2019, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[39]  Dimitrios Tzovaras,et al.  Permissioned Blockchains and Virtual Nodes for Reinforcing Trust Between Aggregators and Prosumers in Energy Demand Response Scenarios , 2019, 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[40]  Gaetano Zizzo,et al.  Ancillary Services in the Energy Blockchain for Microgrids , 2019, IEEE Transactions on Industry Applications.

[41]  Mohammad A. Hoque,et al.  Blockchain Consensus Algorithms: A Survey , 2020, 2001.07091.

[42]  Josep M. Guerrero,et al.  Blockchain for power systems: Current trends and future applications , 2020 .