• Energy Research

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

<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>

We propose a novel frequency control approach in between centralized and distributed architectures, that is a generalized continuous-time feedback control version of the dual decomposition optimization method. Specifically, a convex combination of the frequency measurements is centrally aggregated and followed by an integral control, which is broadcast as the control signal, and then by the optimal local allocations. We show that our controller comprises many previously proposed strategies for specific parameter sets. Under mild parametric assumptions, we prove local asymptotic stability of the closed-loop equilibria of the power system, which is modeled as a nonlinear, differential-algebraic, dynamical system that includes traditional generators, frequency-responsive as well as passive loads, where the sources are already equipped with primary droop control. Our feedback control is designed such that the closed-loop equilibria of the power system solve the optimal economic dispatch problem.

In the last years there is a growing interest in electric and hybrid electric propulsions due to environmental concerns. In particular industrial vehicles are a promising field of application for their duty cycles characterized by low velocities, frequent start and stop jobs, long periods of idling and material-handling tool power peaks. In this paper a series-parallel hybrid electric powertrain for wheel loaders is investigated with the goal to reduce the fuel consumption. The developed architecture exploits the benefits of a series hybrid electric powertrain at low traction requirements and progressively takes advantage from the typical power coupling of a parallel hybrid drivetrain at increasing power demands, according to the ordinary employment of a loader. In the drivetrain, a planetary gear unit and the electrical subsystem (generator/motor and battery) decouple the wheels from the engine operating point. Therefore the supervisory regulator can control the engine in its high efficiency regions. The drivetrain is modeled and a control algorithm is derived. Simulation results confirm the attractiveness of the proposed series-parallel hybrid electric powertrain for loaders.

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

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.