Integrating P2P Energy Trading With Probabilistic Distribution Locational Marginal Pricing

This paper proposes a new local energy market design for distribution systems, which integrates peer-to-peer (P2P) energy trading and probabilistic locational marginal pricing. Distribution locational marginal pricing and P2P energy trading have each been proposed as potential alternatives to traditional retail pricing, to improve coordination between prosumers with distributed energy resources. Unidirectional locational pricing provides a scalable approach for coordinating demand, considering constraints and losses; while P2P energy trading allows prosumers to negotiate mutually beneficial bilateral energy transactions that increase the utilisation of their flexible energy resources. This paper proposes a market design combining the benefits of these two strategies. First, a new strategy for day-ahead locational marginal pricing is developed, which manages the uncertainty associated with local generation, demand and upstream prices by introducing a spread between the prices charged for energy imports and paid for energy exports. Then, local P2P energy trading platforms are integrated to additionally enable direct prosumer-to-prosumer trading, with transaction fees penalising energy transfers according to probabilistic differential locational marginal prices. Case studies are presented for a multi-phase low voltage distribution network, showing how the design can create value for prosumers, and the system as a whole, by reducing the curtailment of renewable generation.

[1]  Hoay Beng Gooi,et al.  Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model , 2019, IEEE Transactions on Industrial Electronics.

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

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

[4]  Vassilios G. Agelidis,et al.  Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview , 2018, IEEE Transactions on Smart Grid.

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

[6]  Anthony Papavasiliou,et al.  Analysis of Distribution Locational Marginal Prices , 2018, IEEE Transactions on Smart Grid.

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

[8]  Wenbo Wang,et al.  Probabilistic Calculation of Locational Marginal Price Taking Account of Correlated Loads , 2012, 2012 Asia-Pacific Power and Energy Engineering Conference.

[9]  Jianhui Wang,et al.  Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Jan Ossenbrink How feed-in remuneration design shapes residential PV prosumer paradigms , 2017 .

[11]  Zhong Fan,et al.  A Mean Field Game Theoretic Approach to Electric Vehicles Charging , 2016, IEEE Access.

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

[13]  Pablo Hernandez-Leal,et al.  Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems , 2019, IEEE Transactions on Power Systems.

[14]  Jianhui Wang,et al.  Blockchain-Assisted Crowdsourced Energy Systems , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[15]  Vassilios G. Agelidis,et al.  Network Topology Independent Multi-Agent Dynamic Optimal Power Flow for Microgrids With Distributed Energy Storage Systems , 2018, IEEE Transactions on Smart Grid.

[16]  Sanjoy Das,et al.  A day-ahead market energy auction for distribution system operation , 2017, 2017 IEEE International Conference on Electro Information Technology (EIT).

[17]  Ian A. Hiskens,et al.  Decentralized charging control for large populations of plug-in electric vehicles , 2010, 49th IEEE Conference on Decision and Control (CDC).

[18]  Samuela Franceschini,et al.  Point estimate methods based on Taylor Series Expansion – The perturbance moments method – A more coherent derivation of the second order statistical moment , 2012 .

[19]  C. Dent,et al.  Decentralized Multi-Period Economic Dispatch for Real-Time Flexible Demand Management , 2016, IEEE Transactions on Power Systems.

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

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

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

[23]  Nicholas Jenkins,et al.  Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid , 2018, Applied Energy.

[24]  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).

[25]  Thomas Morstyn,et al.  Matching Markets with Contracts for Electric Vehicle Smart Charging , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[26]  Yan Zhang,et al.  Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains , 2017, IEEE Transactions on Industrial Informatics.

[27]  R. Hakvoort,et al.  Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design , 2016 .

[28]  Lorenzo Kristov,et al.  A Tale of Two Visions: Designing a Decentralized Transactive Electric System , 2016, IEEE Power and Energy Magazine.

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

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

[31]  Qiuwei Wu,et al.  Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming , 2018, IEEE Transactions on Smart Grid.

[32]  Ivana Kockar,et al.  Dynamic Optimal Power Flow for Active Distribution Networks , 2014, IEEE Transactions on Power Systems.

[33]  Goran Strbac,et al.  Price-Based Schemes for Distributed Coordination of Flexible Demand in the Electricity Market , 2017, IEEE Transactions on Smart Grid.

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

[35]  Georgios Giasemidis,et al.  Short term load forecasting and the effect of temperature at the low voltage level , 2019, International Journal of Forecasting.

[36]  Christophe Defeuilley,et al.  Retail competition in electricity markets , 2009 .

[37]  Thomas Morstyn,et al.  Constructing Prosumer Coalitions for Energy Cost Savings Using Cooperative Game Theory , 2018, 2018 Power Systems Computation Conference (PSCC).

[38]  Vassilios G. Agelidis,et al.  Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems , 2017, IEEE Transactions on Control Systems Technology.

[39]  Xiaochen Zhang,et al.  A Data-Driven Approach for Detection and Estimation of Residential PV Installations , 2016, IEEE Transactions on Smart Grid.

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

[41]  Fushuan Wen,et al.  Discussion on “Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management” , 2014 .

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

[43]  Andrey Bernstein,et al.  Linear power-flow models in multiphase distribution networks , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

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

[45]  Mohammad S. Obaidat,et al.  Distributed Home Energy Management System With Storage in Smart Grid Using Game Theory , 2017, IEEE Systems Journal.

[46]  Walid Saad,et al.  Economics of Electric Vehicle Charging: A Game Theoretic Approach , 2012, IEEE Transactions on Smart Grid.

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