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Pricing Electricity in Residential Communities Using Game-Theoretical Billings

IEEE TRANSACTIONS ON SMART GRID 2023年第2期14卷 1621-1631页

作者： Hupez, Martin Toubeau, Jean-Francois Atzeni, Italo De Greve, Zacharie Vallee, Francois Univ Mons Dept Elect Power Engn B-7000 Mons Belgium Univ Oulu Ctr Wireless Commun Oulu 90014 Finland

By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove to be unfair. In this work, we analyze the outcomes of three relevant game-theoretical billing methods, which are innovatively transposed to the day-ahead scheduling of energy exchange within a liberalized residential community dominated by distributed energy resources. The first two approaches rely on a (static) daily billing scheme, while the third considers a multi-temporal (continuous) billing. The Nash equilibria are computed using distributed algorithms, hence ensuring individual decision-making and avoiding third-party dependencies. The cost distributions are assessed using both a qualitative and a quantitative comparison based on various prosumer profiles in a modern smart grid. It is shown that, depending on the billing option, either the contribution towards the entity (i.e., the ability to improve the global solution) or the individual empowerment (i.e., the ability to bargain) can be preferentially incentivized.

关键词： Costs Resource management Load modeling Scheduling Low voltage Games Game theory Billing distributed algorithms energy efficiency energy communities fairness game theory renewable energy smart grid energy storage Vickey-Clarke-Groves

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A General Framework to Distribute Iterative algorithms With Localized Information Over Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第12期68卷 7358-7373页

作者： Timoudas, Thomas Ohlson Zhang, Silun Magnusson, Sindri Fischione, Carlo RISE Res Inst Sweden Div Digital Syst Comp Sci S-16440 Molndal Sweden MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Stockholm Univ Dept Comp & Syst Sci S-11419 Stockholm Sweden KTH Royal Inst Technol Digital Futures S-10044 Stockholm Sweden

Emerging applications in the Internet of Things (IoT) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for optimization or machine learning purposes. While existing work in the literature exhibits similarities, for the tasks of both algorithm design and theoretical analysis, there is still no unified method or framework for accomplishing these tasks. This article develops such a general framework for distributing the execution of (centralized) iterative algorithms over networks in which the required information or data is partitioned between the nodes in the network. This article furthermore shows that the distributed iterative algorithm, which results from the proposed framework, retains the convergence properties (rate) of the original (centralized) iterative algorithm. In addition, this article applies the proposed general framework to several interesting example applications, obtaining results comparable to the state of the art for each such example, while greatly simplifying and generalizing their convergence analysis. These example applications reveal new results for distributed proximal versions of gradient descent, the heavy ball method, and Newton's method. For example, these results show that the dependence on the condition number for the convergence rate of this distributed heavy ball method is at least as good as that of centralized gradient descent.

关键词： Agents and autonomous systems communication networks distributed algorithms optimization algorithms

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Semi-Decentralized Generalized Nash Equilibrium Seeking in Monotone Aggregative Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 140-155页

作者： Belgioioso, Giuseppe Grammatico, Sergio ETH Automatic Control Lab Swiss Fed Inst Technol CH-8092 Zurich Switzerland Delft Univ Technol Delft Ctr Syst & Control NL-2628CD Delft Netherlands

We address the generalized Nash equilibrium seeking problem for a population of agents playing aggregative games with affine coupling constraints. We focus on semi-decentralized communication architectures, where there is a central coordinator able to gather and broadcast signals of aggregative nature to the agents. By exploiting the framework of monotone operator theory and operator splitting, we first critically review the most relevant available algorithms and then design two novel schemes: 1) a single-layer, fixed-step algorithm with convergence guarantee for general (noncocoercive, nonstrictly) monotone aggregative games and 2) a single-layer proximal-type algorithm for a class of monotone aggregative games with linearly coupled cost functions. We also design novel accelerated variants of the algorithms via (alternating) inertial and over-relaxation steps. Finally, we show via numerical simulations that the proposed algorithms outperform those in the literature in terms of convergence speed.

关键词： distributed algorithms multi-agent systems optimization methods scalability

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Bilateral Peer-to-Peer Energy Trading via Coalitional Games

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第5期19卷 6814-6824页

作者： Raja, Aitazaz Ali Grammatico, Sergio Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

In this article, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, 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.

关键词： Games Contracts Peer-to-peer computing Informatics Game theory Stability analysis Costs distributed algorithms mechanism design multi-agent systems smart grids transactive energy

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Arbitrary pattern formation on infinite regular tessellation graphs

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THEORETICAL COMPUTER SCIENCE 2023年 942卷 1-20页

作者： Cicerone, Serafino Di Fonso, Alessia Di Stefano, Gabriele Navarra, Alfredo Comp Sci & Math Univ Aquila Dept Informat Engn Via Vetoio I-67100 Laquila Italy Comp Sci Univ Perugia Dept Math Via Vanvitelli 1 I-06123 Perugia Italy

Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that |R| = |F|, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible graph discretizing the Euclidean plane only the standard square grid has been considered in the context of the classical OBLOT model. However, it is natural to consider also the other regular tessellation graphs, that are triangular and hexagonal grids. In particular, the former can be considered as the most general in terms of possible symmetries and *** provide a resolution algorithm for APF when the initial configuration is asymmetric and the considered topology is any regular tessellation graph. The algorithm and its correctness are based on a rigorous methodology.(c) 2022 Elsevier B.V. All rights reserved.

关键词： distributed algorithms Mobile robots Asynchrony Pattern formation Graph

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A Decentralized Market Model for a Microgrid With Carbon Emission Rights

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IEEE TRANSACTIONS ON SMART GRID 2023年第2期14卷 1388-1402页

作者： Mu, Chenggang Ding, Tao Zhu, Shanying Han, Ouzhu Du, Pengwei Li, Fangxing Siano, Pierluigi Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Elect Reliabil Council Texas Austin TX 78750 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy

Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The proposed trading model not only satisfies the demand for transactions but also ensures the constraint of total carbon emissions for the microgrid. Energy storage (ES) is introduced to balance loads more economically, and the constraint specifying that the ES cannot be simultaneously charged and discharged is proven to be satisfied automatically, ensuring the convexity of the model. Furthermore, by inserting local trackers to global constraints for each node, a scalable fully distributed algorithm is designed to solve the model locally for both global equality and inequality constraints. The proposed algorithm decomposes the arithmetic demand to each user without intermediate agents, which can effectively reduce the cost and ensure the transparency of trading. The algorithm is also proven to be convergent. Numerical results verify the effectiveness of the proposed market model.

关键词： Carbon dioxide Microgrids Generators Renewable energy sources Production Energy storage distributed algorithms Carbon emission rights decentralized market model microgrid energy storage distributed algorithm

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Totally Asynchronous Primal-Dual Convex Optimization in Blocks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第1期10卷 454-466页

作者： Hendrickson, Katherine R. Hale, Matthew T. Univ Florida Herbert Wertheim Coll Engn Dept Mech & Aerosp Engn Gainesville FL 32611 USA

In this article, we present a parallelized primal-dual algorithm for solving constrained convex optimization problems. The algorithm is "block-based," in which vectors of primal and dual variables are partitioned into blocks, each of which is updated only by a single processor. We consider four behaviors that could be asynchronous: 1) updates to primal variables;2) updates to dual variables;3) communications of primal variables;and 4) communications of dual variables. We show that any amount of asynchrony in the communications of dual variables can preclude convergence, though the other forms of asynchrony are permitted. A first-order primal-dual update law is then developed and shown to be robust to these other forms of asynchrony. We next derive convergence rates to an approximate Lagrangian saddle point in terms of the operations agents execute, without specifying any timing or pattern with which they must be executed. The distance between the approximate solution we obtain and the exact solution is explicitly bounded. Convergence rates include an "asynchrony penalty" that we quantify and present ways to mitigate. Numerical results illustrate these developments.

关键词： Convergence Program processors Delays Optimization Network systems Faces Convex functions distributed algorithms gradient methods intelligent systems multiagent systems optimization

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Wake up and join me! An energy-efficient algorithm for maximal matching in radio networks

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distributed COMPUTING 2023年第3期36卷 373-384页

作者： Dani, Varsha Gupta, Aayush Hayes, Thomas P. Pettie, Seth Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Univ Michigan Comp Sci & Engn Ann Arbor MI 48109 USA

We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is O ((log n)(log Delta)), and the time complexity is O(Delta log n). Here n is any upper bound on the number of nodes, and Delta is any upper bound on the maximum degree;n and Delta are parameters of our algorithm that we assume are known a priori to all the processors. We note that there exist families of graphs for which our bounds on energy cost and time complexity are simultaneously optimal up to polylog factors, so any significant improvement would need additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of eventual node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present an efficient decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog (n) factor bigger that the optimum.

关键词： distributed algorithms Energy-aware computation Radio networks Maximal matching Sensor networks

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From Stars to Diamonds: Counting and Listing Almost Complete Subgraphs in Large Networks

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COMPUTER JOURNAL 2023年第6期67卷 2151-2161页

作者： Finocchi, Irene Garcia, Renan Leon Sinaimeri, Blerina Luiss Univ Viale Romania 32 I-00197 Rome Italy Sapienza Univ Rome Via Salaria 113 I-00139 Rome Italy

Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on $k$-cliques, i.e. complete subgraphs on $k$ nodes. However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called $k$-diamonds and defined as cliques of size $k$ with one missing edge. We first provide a sequential algorithm that, in $O(nm<^>{(k-1)/2})$ time, counts and lists all the $k$-diamonds in large graphs, for any constant $k \geq 4$. A parallel extension of the sequential algorithm is then proposed and analyzed in a MapReduce-style model, achieving the same local and total space usage of the state-of-the-art algorithms for $k$-cliques. The running time is optimal on dense graphs and $O(\sqrt{m})$ larger than $k$-clique counting if the graph is sparse. Our algorithms compute induced diamonds by analyzing the structure of directed stars formed by the graph nodes and their neighbors.

关键词： clique relaxations dense subgraphs distributed algorithms enumeration algorithms graph mining MapReduce

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A Privacy-Preserving Blockchain-Based Method to Optimize Energy Trading

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IEEE TRANSACTIONS ON SMART GRID 2023年第2期14卷 1148-1157页

作者： Ping, Jian Yan, Zheng Chen, Sijie Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Noncarbon Energy Convers & Utilizat Inst Shanghai 200240 Peoples R China

It is always desired for optimizing energy trading to disable manipulation and preserve individual privacy. These two features become increasingly appealing for an energy market where interest parties do not mutually trust each other, such as peer-to-peer energy trading. Traditional centralized or hierarchical optimization schemes are vulnerable to an untrusted coordinator who may dishonestly broadcast coordination results or be curious about individual privacy. Recent blockchain-based optimization schemes resist dishonesty but increase the risk of privacy leakage. This paper proposes a privacy-preserving blockchain-based method to optimize energy trading. In the proposed method, participants submit encrypted bids/offers based on a bid/offer encryption algorithm to preserve their privacy. A privacy-preserving-Byzantine-fault-tolerance-based coordination algorithm is proposed to ensure the correctness of trading results with considering dishonesty. Numerical results in a peer-to-peer energy trading case demonstrate the performance of our method on convergence, resisting dishonesty, preserving privacy, and scalability.

关键词： Privacy Optimization Convergence Resists Blockchains Power transmission distributed algorithms Blockchain privacy-preserving consensus algorithm energy trading distributed optimization

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