Multiscale design for system-wide peer-to-peer energy trading

Summary The integration of renewable generation and the electrification of heating and transportation are critical for the sustainable energy transition toward net-zero greenhouse gas emissions. These changes require the large-scale adoption of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has gained attention as a new approach for incentivizing the uptake and coordination of DERs, with advantages for computational scalability, prosumer autonomy, and market competitiveness. However, major unresolved challenges remain for scaling out P2P trading, including enforcing network constraints, managing uncertainty, and mediating transmission and distribution conflicts. Here, we propose a novel multiscale design framework for P2P trading, with inter-platform coordination mechanisms to align local transactions with system-level requirements, and analytical tools to enhance long-term planning and investment decisions by accounting for forecast real-time operation. By integrating P2P trading into planning and operation across spatial and temporal scales, the adoption of large-scale DERs is tenable and can create economic, environmental, and social co-benefits.

[1]  D. T. Nguyen,et al.  Pool-Based Demand Response Exchange—Concept and Modeling , 2011 .

[2]  Thomas Morstyn,et al.  Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences , 2019, IEEE Transactions on Power Systems.

[3]  Archie C. Chapman,et al.  Peer-to-Peer Energy Systems for Connected Communities: A Review of Recent Advances and Emerging Challenges , 2020, Applied Energy.

[4]  Henri van Soest,et al.  Peer-to-peer electricity trading: A review of the legal context , 2018, Competition and Regulation in Network Industries.

[5]  Thomas Olofsson,et al.  Combined Environmental and Economic Assessment of Energy Efficiency Measures in a Multi-Dwelling Building , 2019, Energies.

[6]  Thomas Morstyn,et al.  Integrating P2P Energy Trading With Probabilistic Distribution Locational Marginal Pricing , 2020, IEEE Transactions on Smart Grid.

[7]  Munther A. Dahleh,et al.  Advancing systems and control research in the era of ML and AI , 2018, Annu. Rev. Control..

[8]  R. Madlener,et al.  Technology, Business Model, and Market Design Adaptation Toward Smart Electricity Distribution: Insights for Policy Making , 2018, Energy Policy.

[9]  Sergey Karabasov,et al.  Multiscale modelling: approaches and challenges , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Duncan S. Callaway,et al.  Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads , 2015, IEEE Transactions on Power Systems.

[11]  Constance Crozier,et al.  The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems , 2020, Applied Energy.

[12]  Nicholas Good,et al.  Review and classification of barriers and enablers of demand response in the smart grid , 2017 .

[13]  Diana Neves,et al.  Peer-to-peer energy trading potential: An assessment for the residential sector under different technology and tariff availabilities , 2020 .

[14]  Leonardo Meeus,et al.  DSO-TSO cooperation issues and solutions for distribution grid congestion management , 2018, Energy Policy.

[15]  Benjamin Sovacool,et al.  Electricity market design for the prosumer era , 2016, Nature Energy.

[16]  Dimitra Apostolopoulou,et al.  Residential load variability and diversity at different sampling time and aggregation scales , 2017, 2017 IEEE AFRICON.

[17]  Fangxing Li,et al.  Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support , 2018, IEEE Transactions on Power Systems.

[18]  Marko Aunedi,et al.  Value of integrating Distributed Energy Resources in the UK electricity system , 2010, IEEE PES General Meeting.

[19]  Gregor Verbic,et al.  Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network , 2018, IEEE Transactions on Smart Grid.

[20]  Pierre Pinson,et al.  Peer-to-peer and community-based markets: A comprehensive review , 2018, Renewable and Sustainable Energy Reviews.

[21]  M. Thring World Energy Outlook , 1977 .

[22]  S. Low,et al.  Some Emerging Challenges in Electricity Markets , 2018, Smart Grid Control.

[23]  Claudio Vergara,et al.  Renewable energy curtailment: A case study on today's and tomorrow's congestion management , 2018 .

[24]  Lang Tong,et al.  Probabilistic Forecasting of Real-Time LMP and Network Congestion , 2015, IEEE Transactions on Power Systems.

[25]  Stamatis Karnouskos,et al.  Smart Houses in the Smart Grid: Developing an interactive network. , 2014, IEEE Electrification Magazine.

[26]  Duncan S. Callaway,et al.  Distributed Resources Shift Paradigms on Power System Design, Planning, and Operation: An Application of the GAP Model , 2019, Proceedings of the IEEE.

[27]  Paul Cuffe,et al.  Embracing an Adaptable, Flexible Posture: Ensuring That Future European Distribution Networks Are Ready for More Active Roles , 2016, IEEE Power and Energy Magazine.

[28]  Paul Cuffe,et al.  A Three-Tier Framework for Understanding Disruption Trajectories for Blockchain in the Electricity Industry , 2020, IEEE Access.

[29]  A. Meystel,et al.  Multiscale models and controllers , 1994, Proceedings of IEEE Symposium on Computer-Aided Control Systems Design (CACSD).

[30]  Thomas Morstyn,et al.  Incentivizing Prosumer Coalitions With Energy Management Using Cooperative Game Theory , 2019, IEEE Transactions on Power Systems.

[31]  Jarosław Milewski,et al.  Virtual Power Plants - general review : structure, application and optimization , 2012 .

[32]  H. Vincent Poor,et al.  Grid Influenced Peer-to-Peer Energy Trading , 2019, IEEE Transactions on Smart Grid.

[33]  Xiwei Xu,et al.  Quantifying the Cost of Distrust: Comparing Blockchain and Cloud Services for Business Process Execution , 2018, Information Systems Frontiers.

[34]  Andrea Michiorri,et al.  Incorporating flexibility options into distribution grid reinforcement planning: A techno-economic framework approach , 2019, Applied Energy.

[35]  Stephen Hall,et al.  Prosumers in the post subsidy era: an exploration of new prosumer business models in the UK , 2019, Energy Policy.

[36]  Ruggero Schleicher-Tappeser,et al.  How renewables will change electricity markets in the next five years , 2012 .

[37]  Qiuwei Wu,et al.  Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management , 2014, IEEE Transactions on Power Systems.

[38]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[39]  Gregor Verbic,et al.  Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading , 2020 .

[40]  P. Pinson,et al.  Negotiation Algorithms for Peer-to-Peer Electricity Markets: Computational Properties , 2018, 2018 Power Systems Computation Conference (PSCC).

[41]  Thomas Morstyn,et al.  Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery , 2020, 2001.04283.

[42]  Yury Dvorkin,et al.  A P2P-Dominant Distribution System Architecture , 2019, IEEE Transactions on Power Systems.

[43]  Kenneth Train,et al.  Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes , 2000 .

[44]  Ian Richardson,et al.  Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .

[45]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[46]  Ignacio J. Pérez-Arriaga,et al.  Improved Regulatory Approaches for the Remuneration of Electricity Distribution Utilities with High Penetrations of Distributed Energy Resources , 2017 .

[47]  Evangelos G. Kardakos,et al.  Modelling the energy transition: A nexus of energy system and economic models , 2018 .

[48]  Kirsten E H Jenkins,et al.  Energy justice : a conceptual review , 2016 .

[49]  Ioannis Lampropoulos,et al.  A methodology for modeling the behavior of electricity prosumers within the smart grid , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[50]  Stephen P. Boyd,et al.  Dynamic Network Energy Management via Proximal Message Passing , 2013, Found. Trends Optim..

[51]  Victor M. Zavala,et al.  A multi-scale optimization framework for electricity market participation , 2017 .

[52]  E. McKenna,et al.  Keep it simple: time-of-use tariffs in high-wind scenarios , 2015 .

[53]  Thomas M. Overman,et al.  High-Assurance Smart Grid: A Three-Part Model for Smart Grid Control Systems , 2011, Proceedings of the IEEE.

[54]  Tao Chen,et al.  Local Energy Trading Behavior Modeling With Deep Reinforcement Learning , 2018, IEEE Access.

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

[56]  Antonio Vicino,et al.  A Community Microgrid Architecture with an Internal Local Market , 2018, Applied Energy.

[57]  Decentralizing Energy for a High-Demand, Low-Carbon World , 2019 .

[58]  H. Vincent Poor,et al.  Challenges and prospects for negawatt trading in light of recent technological developments , 2020, Nature Energy.

[59]  S. Binato,et al.  Large scale transmission network planning using optimization and heuristic techniques , 1995 .

[60]  Thomas Morstyn,et al.  Bilateral Contract Networks for Peer-to-Peer Energy Trading , 2019, IEEE Transactions on Smart Grid.

[61]  Ram Rajagopal,et al.  A model for the effect of aggregation on short term load forecasting , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[62]  Sanjoy Das,et al.  Fairness-Regularized DLMP-Based Bilevel Transactive Energy Mechanism in Distribution Systems , 2018, IEEE Transactions on Smart Grid.

[63]  Julian D Marshall,et al.  Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States , 2014, Proceedings of the National Academy of Sciences.

[64]  Meysam Qadrdan,et al.  Energy system impacts from heat and transport electrification , 2014 .

[65]  E. Verdolini,et al.  Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments , 2021, Nature Climate Change.

[66]  Benjamin K. Sovacool,et al.  Energy Justice: Conceptual Insights and Practical Applications , 2015 .

[67]  S. Galloway,et al.  The Impact of Distribution Locational Marginal Prices on Distributed Energy Resources: An Aggregated Approach , 2018, 2018 15th International Conference on the European Energy Market (EEM).

[68]  Access Rights and Consumer Protections in a Distributed Energy System , 2017 .

[69]  Deepjyoti Deka,et al.  Is Machine Learning in Power Systems Vulnerable? , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[70]  Z. Vale,et al.  Coordination of transmission and distribution planning and operations to maximise efficiency in future power systems , 2005, 2005 International Conference on Future Power Systems.

[71]  Furong Li,et al.  A Novel Peer-to-Peer Local Electricity Market for Joint Trading of Energy and Uncertainty , 2020, IEEE Transactions on Smart Grid.

[72]  J. Carmeliet,et al.  Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential , 2017 .

[73]  Nikolaos Gatsis,et al.  Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix , 2017, IEEE Transactions on Power Systems.

[74]  Thomas Morstyn,et al.  Designing Decentralized Markets for Distribution System Flexibility , 2019, IEEE Transactions on Power Systems.

[75]  Thomas Morstyn,et al.  Peer-to-Peer Energy Trading , 2020 .

[76]  Yi Wang,et al.  Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices , 2021, IEEE Transactions on Smart Grid.

[77]  Michel Bierlaire,et al.  Decision support for strategic energy planning: A robust optimization framework , 2020, Eur. J. Oper. Res..

[78]  Thomas Morstyn,et al.  Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants , 2018, Nature Energy.

[79]  David Shipworth,et al.  Consumer Demand for Blockchain-Enabled Peer-to-Peer Electricity Trading in the United Kingdom: An Online Survey Experiment , 2019 .

[80]  P. Pinson,et al.  Exogenous Cost Allocation in Peer-to-Peer Electricity Markets , 2019, IEEE Transactions on Power Systems.