<gh_stars>10-100
@INPROCEEDINGS{mengelkamp2017, 
author={E. {Mengelkamp} and P. {Staudt} and J. {Garttner} and C. {Weinhardt}}, 
booktitle={{2017 14th International Conference on the European Energy Market (EEM)}}, 
title={Trading on local energy markets: A comparison of market designs and bidding strategies}, 
year={2017}, 
volume={}, 
number={}, 
pages={1-6}, 
abstract={Increasing renewable energy sources and innovative information and communication systems open up new challenges and opportunities to integrate distributed generation into the energy supply system. Formerly centralized energy systems need to be adapted to take full advantage of the immense potential of decentralized energy generation and smart, interconnected energy end users. We introduce a local electricity market on which prosumers and consumers of a community are able to trade electricity directly amongst each other. This local electricity market supports the local integration of renewable energy generation. It facilitates a local balance of energy supply and demand and hence reduces the need for extensive electricity transmission. We introduce, evaluate and compare two local market designs, a direct peer-to-peer market and a closed order book market, as well as two agent behaviors, zero-intelligence agents and intelligently bidding agents. We derive four scenarios by combining each market design with each agent behavior, respectively. All market scenarios offer similar economic advantages for the market participants. However, the peer-to-peer market with intelligent agents appears to be the most advantageous as it results in the lowest average overall electricity price.}, 
keywords={power markets;tendering;local energy markets;renewable energy sources;distributed generation;energy supply system;decentralized energy generation;energy end users;local electricity market;renewable energy generation;direct peer-to-peer market;closed order book market;zero-intelligence agents;intelligently bidding agents;Electricity supply industry;Peer-to-peer computing;Renewable energy sources;Supply and demand;Intelligent agents;Distributed power generation;Electric potential;Local energy markets;market design;agent behavior}, 
doi={10.1109/EEM.2017.7981938}, 
ISSN={2165-4093}, 
month={June},}

@INPROCEEDINGS{horta2017, 
author={<NAME>} and D. {Kofman} and D. {Menga} and A. {Silva}}, 
booktitle={{2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)}}, 
title={Novel market approach for locally balancing renewable energy production and flexible demand}, 
year={2017}, 
volume={}, 
number={}, 
pages={533-539}, 
abstract={Future electricity distribution grids will host a considerable share of variable renewable energy sources and local storage resources. Moreover, they will face new load structures due for example to the growth of the electric vehicle market. These trends raise the need for new paradigms for distribution grids operation, in which Distribution System Operators will increasingly rely on demand side flexibility and households will progressively become prosumers playing an active role on smart grid energy management. However, in present energy management architectures, the lack of coordination among actors limits the capability of the grid to enable the mentioned trends. In this paper we tackle this problem by proposing an architecture that enables households to autonomously exchange energy blocks and flexibility services with neighbors, operators and market actors. The solution is based on a blockchain transactive platform. We focus on a market application, where households can trade energy with their neighbors, aimed to locally balancing renewable energy production. We propose a market mechanism and dynamic transport prices that provide an incentive for households to locally manage energy resources in a way that responds to both prosumer and operator needs. We evaluate the impact of such markets through comprehensive simulations using power flow analysis and realistic load profiles, providing valuable insight for the design of appropriate mechanisms and incentives.}, 
keywords={demand side management;energy management systems;power markets;pricing;renewable energy sources;smart power grids;renewable energy production;flexible demand;variable renewable energy sources;local storage resources;load structures;electric vehicle market;distribution grids operation;Distribution System Operators;demand side flexibility;households;smart grid energy management;energy management architectures;energy blocks;flexibility services;market actors;market application;energy resources;operator needs;market approach;electricity distribution grids;Renewable energy sources;Batteries;Production;Contracts;Smart grids;Measurement;Conferences}, 
doi={10.1109/SmartGridComm.2017.8340728}, 
ISSN={}, 
month={Oct},}

@INPROCEEDINGS{kiedanski2019,
author={<NAME>} and D. {Kofman} and J. {Horta} and D. {Menga}},
booktitle={{2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT)}},
title={Strategy-proof local energy market with sequential stochastic decision process for battery control},
year={2019},
volume={},
number={},
pages={1-5},
abstract={Low voltage distribution networks were not designed to support massive deployment of distributed energy resources (DER) such as solar panels, which is currently hindering the Energy Transition. Recent research contributions have shown that local energy markets improve the capacity of distribution grids to host DER. In parallel, distributed models were created to deal with the control of batteries in presence of stochastic demand and production, as well as variable electricity prices. The combination of the two techniques has received little attention until now, from both the literature and the industry. In this paper we extend the traditional approach to sequential stochastic decision processes by also modeling the interaction with the neighborhood in addition to the utility. The model is then solved using reinforcement learning techniques. For the local energy market we use MUDA: a strategy-proof multi-unit double auction. The performance of the proposed system is evaluated through simulations, demonstrating its capacity to effectively decrease the overall exchange of energy with the grid and the monetary cost for users.},
keywords={learning (artificial intelligence);power engineering computing;power grids;power markets;secondary cells;stochastic processes;strategy-proof local energy market;sequential stochastic decision process;battery control;distribution grids;distributed models;stochastic demand;variable electricity prices;strategy-proof multiunit double auction;energy transition;reinforcement learning techniques;Batteries;Stochastic processes;Production;Renewable energy sources;Reinforcement learning;Optimization;Mathematical model;ADP;Battery;Smart Grid;Energy Market;Auction},
doi={10.1109/ISGT.2019.8791585},
ISSN={2167-9665},
month={Feb},}


@article{blouin2001decentralized,
    author = {<NAME>. and <NAME>},
    title = "{A Decentralized Market with Common Values Uncertainty: Non-Steady States}",
    journal = {The Review of Economic Studies},
    volume = {68},
    number = {2},
    pages = {323-346},
    year = {2001},
    month = {04},
    abstract = "{We analyse a market where (i) trade proceeds by random and anonymous pairwise meetings with bargaining; (ii) agents are asymmetrically informed about the value of the traded good; and (iii) no new entrants are allowed once the market is open. We show that information revelation and efficiency never obtain in equilibrium, even as discounting is removed. This holds whether the asymmetry is two-sided or one-sided. In some cases there exist equilibria where a substantial amount goes untraded. This contrasts with the earlier literature, which was based on the steadystate equilibria of a model where agents enter the market every period.}",
    issn = {0034-6527},
    doi = {10.1111/1467-937X.00171},
    url = {https://doi.org/10.1111/1467-937X.00171},
    eprint = {https://academic.oup.com/restud/article-pdf/68/2/323/4369413/68-2-323.pdf},
}



@inproceedings{segal2018muda,
  title={MUDA: a truthful multi-unit double-auction mechanism},
  author={<NAME> <NAME>},
  booktitle={{Thirty-Second AAAI Conference on Artificial Intelligence}},
  year={2018}
}

@article{huangauction,
author = {<NAME>},
title = {Design of a Multi–Unit Double Auction E–Market},
journal = {Computational Intelligence},
volume = {18},
number = {4},
pages = {596-617},
keywords = {electronic market, auction, mechanism design, multi–agent system, market clearing},
doi = {10.1111/1467-8640.t01-1-00206},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-8640.t01-1-00206},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-8640.t01-1-00206},
abstract = {We envision a future economy where e–markets will play an essential role as exchange hubs for commodities and services. Future e–markets should be designed to be robust to manipulation, flexible, and sufficiently efficient in facilitating exchanges. One of the most important aspects of designing an e–market is market mechanism design. A market mechanism defines the organization, information exchange process, trading procedure, and clearance rules of a market. If we view an e–market as a multi–agent system, the market mechanism also defines the structure and rules of the environment in which agents (buyers and sellers) play the market game. We design an e–market mechanism that is strategy–proof with respect to reservation price, weakly budget–balanced, and individually rational. Our mechanism also makes sellers unlikely to underreport the supply volume to drive up the market price. In addition, by bounding our market’s efficiency loss, we provide fairly unrestrictive sufficient conditions for the efficiency of our mechanism to converge in a strong sense when (1) the number of agents who successfully trade is large, or (2) the number of agents, trading and not, is large. We implement our design using the RETSINA infrastructure, a multi–agent system development toolkit. This enables us to validate our analytically derived bounds by numerically testing our e–market.},
year = {2002}
}

@Article{simulation,
author = { <NAME> and <NAME> },
title = {A simulation analysis of the microstructure of double auction markets},
journal = {Quantitative Finance},
volume = {2},
number = {5},
pages = {346-353},
year = {2002},
publisher = {Routledge},
doi = {10.1088/1469-7688/2/5/303},
URL = {https://www.tandfonline.com/doi/abs/10.1088/1469-7688/2/5/303},
eprint = {https://www.tandfonline.com/doi/pdf/10.1088/1469-7688/2/5/303},
abstract = { Abstract We introduce an order-driven market model with heterogeneous agents trading via a central order matching mechanism. Traders set bids and asks and post market or limit orders according to exogenously fixed rules. We investigate how different trading strategies may affect the dynamics of price, bid-ask spreads, trading volume and volatility. We also analyse how some features of market design, such as tick size and order lifetime, affect market liquidity. The model is able to reproduce many of the complex phenomena observed in real stock markets. *Paper presented at Applications of Physics in Financial Analysis (APFA) 3, 5–7 December 2001, Museum of London, UK. }}

@Article{builder,
author = { <NAME> },
title = {A builder's guide to agent-based financial markets},
journal = {Quantitative Finance},
volume = {1},
number = {2},
pages = {254-261},
year = {2001},
publisher = {Routledge},
doi = {10.1088/1469-7688/1/2/307},
URL = { https://doi.org/10.1088/1469-7688/1/2/307},
eprint = { https://doi.org/10.1088/1469-7688/1/2/307},
abstract = { This paper is intended to guide researchers interested in building their own agent-based financial markets. Key design questions are outlined, along with some of the major controversies about which directions to take. }
}


