Evaluation of Energy Market Platforms Potential in Microgrids: Scenario Analysis Based on a Double-Sided Auction

Local energy markets represent a mean for distributed energy resources trading for prosumers and energy management for utilities. In these markets, prosumers either trade or shift their loads to maximize their trading gains via communicating with an energy market platform. The utility considers the trading process as an approach to maximize the autonomy and minimize the peak loads. The benefits of the prosumer and utility can vary depending on several parameters such as the market rules, microgrid configurations, or the lifestyle and social behavior of the market participants. In this paper, selected scenarios are presented that discuss and analyze the major factors influencing the market dynamics and microgrid energy balance based on a forward double-sided auction market model simulation. These scenarios are divided into three scenario groups that consider market design parameters, microgrid configurations, and user behavior. Furthermore, the same scenarios are once more evaluated using a reference model, where no market platform is integrated, so that the results of the energy market can be compared. The results are analyzed based on multiple metrics from the perspective of the prosumer and utility to quantify and compare the benefits of the two major market players.

[1]  Shengwei Mei,et al.  A multi-lateral trading model for coupled gas-heat-power energy networks , 2017 .

[2]  Juan José González de la Rosa,et al.  Weather forecasts for microgrid energy management: Review, discussion and recommendations , 2018, Applied Energy.

[3]  Markus Kraft,et al.  Incorporating seller/buyer reputation-based system in blockchain-enabled emission trading application , 2018 .

[4]  Federico Delfino,et al.  A Dynamic Market Mechanism for Combined Heat and Power Microgrid Energy Management , 2017 .

[5]  Thomas Hamacher,et al.  Coordinating smart homes in microgrids: A quantification of benefits , 2013, IEEE PES ISGT Europe 2013.

[6]  Gregor Verbic,et al.  A study of energy trading in a low-voltage network: Centralised and distributed approaches , 2017, 2017 Australasian Universities Power Engineering Conference (AUPEC).

[7]  Isabel Praça,et al.  Multi-agent Simulation of Continental, Regional, and Micro Electricity Markets , 2012, 2012 23rd International Workshop on Database and Expert Systems Applications.

[8]  Nathan G. Johnson,et al.  Scalable multi-agent microgrid negotiations for a transactive energy market , 2018, Applied Energy.

[9]  Mihai Gavrilas,et al.  Applying a micro-market inside an electric vehicles parking facility , 2014, 2014 49th International Universities Power Engineering Conference (UPEC).

[10]  Peter Tzscheutschler,et al.  Impact of probabilistic small-scale photovoltaic generation forecast on energy management systems , 2018 .

[11]  Yue Zhou,et al.  Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework , 2018, Applied Energy.

[12]  Stamatis Karnouskos,et al.  The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading , 2014, IEEE Transactions on Smart Grid.

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

[14]  Peter Tzscheutschler,et al.  High-resolution dataset for building energy management systems applications , 2018, Data in brief.

[15]  Johannes Jungwirth,et al.  Field Test with Stirling Engine Micro-CHP-Units in Residential Buildings , 2011 .

[16]  M. Hadi Amini,et al.  A Decentralized Trading Algorithm for an Electricity Market with Generation Uncertainty , 2017, ArXiv.

[17]  Andreas Sumper,et al.  Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets , 2013 .

[18]  Peter Tzscheutschler,et al.  Autonomous coordination of smart buildings in microgrids based on a double-sided auction , 2017, 2017 IEEE Power & Energy Society General Meeting.

[19]  Peter Tzscheutschler,et al.  Day-ahead probabilistic PV generation forecast for buildings energy management systems , 2018, Solar Energy.

[20]  Yenhaw Chen,et al.  Stochastic programming and market equilibrium analysis of microgrids energy management systems , 2016 .

[21]  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.

[22]  G. Ledwich,et al.  Auction based energy trading in transactive energy market with active participation of prosumers and consumers , 2017, 2017 Australasian Universities Power Engineering Conference (AUPEC).

[23]  Xiaonan Wang,et al.  Energy Demand Side Management within micro-grid networks enhanced by blockchain , 2018, Applied Energy.

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

[25]  Peter Tzscheutschler,et al.  Experimental Study and Modeling of Ground-Source Heat Pumps with Combi-Storage in Buildings , 2018 .

[26]  Daniel J. Veit,et al.  A Critical Survey of Agent-Based Wholesale Electricity Market Models , 2008 .

[27]  Seunghwan Kim,et al.  Energy Prosumer Business Model Using Blockchain System to Ensure Transparency and Safety , 2017 .

[28]  Wayes Tushar,et al.  Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches , 2018, IEEE Signal Process. Mag..

[29]  Peter Tzscheutschler,et al.  Integration of energy markets in microgrids: A double-sided auction with device-oriented bidding strategies , 2019, Applied Energy.