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In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator. 11 pages, 8 figures. Published in IEEE Transactions on Smart Grid, 2022

We formulate for the first time the economic dispatch problem in an integrated electrical and gas distribution system as a game equilibrium problem between distributed prosumers. Specifically, by approximating the non-linear gas-flow equations either with a mixed-integer second order cone or a piece-wise affine model and by assuming that electricity and gas prices depend linearly on the total consumption we obtain a potential mixed-integer game. To compute an approximate generalized Nash equilibrium, we propose an iterative two-stage method that exploits a problem convexification and the gas flow models. We quantify the quality of the computed solution and perform a numerical study to evaluate the performance of our method. 13 pages

IEEE Transactions on Control Systems Technology, 25 (6) ISSN:1063-6536 ISSN:1558-0865

<p>In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br> We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br> The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability.<br> We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br> In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br> </p> <p>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</p> <p> </p> <p>Postprint accepted for publication in <em>IEEE Transactions on Control Systems Technology</em></p> <p><br></p> <p><strong>How to cite: </strong>P. Scarabaggio, S. Grammatico, R. Carli and M. Dotoli, (2021) "Distributed Demand Side Management With Stochastic Wind Power Forecasting, "<em>IEEE Transactions on Control Systems Technology</em>, 2022. <strong>DOI</strong>: <a href="http://doi.org/10.1109/TCST.2021.3056751" target="_blank">http://doi.org/10.1109/TCST.2021.3056751</a></p> <h4><strong>© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</strong><br> </h4>

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, and a special class of coalitional games. {The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of ``stable" and ``fair" bilateral contracts which encourages prosumer participation.} The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.