Trust-less electricity consumption optimization in local energy communities

Optimizing energy consumption in local energy communities is one of the key contributions to the so-called smart grid. Such communities are equipped with rooftop photovoltaic power plants or other forms of small power plants for local energy production. In addition, a number of appliances allow for shiftable energy consumption, e.g., heat pumps or electric vehicle charging stations. The ability to shift is, however, dependent on customer preferences. In this paper, we present a trust-less approach for optimizing the electricity consumption in a local energy community given forecasts of energy production and customer demands, along with constraints for shiftable loads. In larger communities, appointing a single party for managing load curtailment requires a high level of trust. In the proposed trust-less approach, all parties can independently propose optimal solutions for this optimization problem and then globally agree one one solution that meets the defined requirements to the greatest extent.

[1]  Federico Matteo Benvci'c,et al.  Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph , 2018 .

[2]  Stephen B. Wicker,et al.  Vegvisir: A Partition-Tolerant Blockchain for the Internet-of-Things , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

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

[4]  Jamil Y. Khan,et al.  A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network , 2013, Comput. Networks.

[5]  Pierre Pinson,et al.  Consensus-Based Approach to Peer-to-Peer Electricity Markets With Product Differentiation , 2018, IEEE Transactions on Power Systems.

[6]  Wolf Fichtner,et al.  Load shift potential of electric vehicles in Europe , 2014 .

[7]  G. Walker,et al.  Community energy systems , 2012 .

[8]  Alfred Menezes,et al.  The Elliptic Curve Digital Signature Algorithm (ECDSA) , 2001, International Journal of Information Security.

[9]  Lei Shen,et al.  Structural design of a universal and efficient demand-side management system for Smart Grid , 2012, 2012 Power Engineering and Automation Conference.

[10]  Paulien M. Herder,et al.  Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems , 2016 .

[11]  Christof Weinhardt,et al.  Designing microgrid energy markets , 2018 .

[12]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[13]  Petr Musilek,et al.  Optimization of photovoltaic power self-consumption using linear programming , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[14]  Tineke van der Schoor,et al.  Power to the people: Local community initiatives and the transition to sustainable energy , 2015 .

[15]  Hussein T. Mouftah,et al.  Wireless Sensor Networks for domestic energy management in smart grids , 2010, 2010 25th Biennial Symposium on Communications.

[16]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[17]  Christof Weinhardt,et al.  A blockchain-based smart grid: towards sustainable local energy markets , 2017, Computer Science - Research and Development.

[18]  George Kesidis,et al.  Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[19]  M. J. van der Kam,et al.  Increasing Self-consumption of Photovoltaic Electricity by Storing Energy in Electric Vehicle using Smart Grid Technology in the Residential Sector - A Model for Simulating Different Smart Grid Programs , 2014, SMARTGREENS.

[20]  Florian Skopik,et al.  The social smart grid: Dealing with constrained energy resources through social coordination , 2014, J. Syst. Softw..

[21]  Emin Gün Sirer,et al.  Majority Is Not Enough: Bitcoin Mining Is Vulnerable , 2013, Financial Cryptography.

[22]  Jay Taneja,et al.  Towards Cooperative Grids: Sensor/Actuator Networks for Renewables Integration , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[23]  Elaine Shi,et al.  On Scaling Decentralized Blockchains - (A Position Paper) , 2016, Financial Cryptography Workshops.

[24]  Emin Gün Sirer,et al.  Bitcoin-NG: A Scalable Blockchain Protocol , 2015, NSDI.

[25]  Björn Scheuermann,et al.  Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies , 2016, IEEE Communications Surveys & Tutorials.

[26]  Ivana Podnar Žarko,et al.  Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[27]  Christof Weinhardt,et al.  Designing microgrid energy markets A case study: The Brooklyn Microgrid , 2018 .

[28]  Sanna Syri,et al.  Electrical energy storage systems: A comparative life cycle cost analysis , 2015 .

[29]  M. Ilic,et al.  Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets , 2011, IEEE Transactions on Power Systems.

[30]  Canbing Li,et al.  An Optimized EV Charging Model Considering TOU Price and SOC Curve , 2012, IEEE Transactions on Smart Grid.

[31]  Frances M. T. Brazier,et al.  Social networking for Smart Grid users , 2015, 2015 IEEE 12th International Conference on Networking, Sensing and Control.

[32]  Maryline Chetto,et al.  Optimal Scheduling for Real-Time Jobs in Energy Harvesting Computing Systems , 2014, IEEE Transactions on Emerging Topics in Computing.

[33]  Enis Karaarslan,et al.  Blockchain Based DNS and PKI Solutions , 2018, IEEE Communications Standards Magazine.