Non-cooperative Game Research Articles

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

Generalized Nash Equilibrium Seeking for Noncooperative Game With Different Monotonicities by Adaptive Neurodynamic Algorithm.

This article proposes a novel adaptive neurodynamic algorithm (ANA) to seek generalized Nash equilibrium (GNE) of the noncooperative constrained game with different monotone conditions. In the ANA, the adaptive penalty term, which acts as trajectory-dependent penalty parameters, evolves based on the degree of constraints violation until the trajectory enters the action set of noncooperative game. It is shown that the trajectory of the ANA enters the action set in finite time benefited from the adaptive penalty term. Moreover, it is proven that the trajectory exponentially (or polynomially) converges to the unique GNE when the pseudo-gradient of cost function in noncooperative game satisfies strong (or "generalized" strong) monotonicity. To the best of our knowledge, this is the first time to study the polynomial convergence of GNE seeking algorithm. Furthermore, when the pseudo-gradient mentioned above satisfies monotonicity in general, based on Tikhonov regularization method, a new ANA for finding its ε -generalized Nash equilibrium ( ε -GNE) is proposed, and the related exponential convergence of the algorithm is established. Finally, the river basin pollution game and 5G base station location game are given as examples to showcase the algorithm's effectiveness.

Is high-intensity conflict escalation inevitable in the future? A two-level game analysis on the causes of US-Iran risky rivalry

In recent years, there appears to be a growing consensus among policymakers in both the East and the West, that the decades of global peace after the Cold War has now come to an end, and that the world is facing mounting security risks to the future of humanity. Among those challenges, the ongoing U.S.-Iran conflict poses particularly significant risks to regional and global security, and is closely related to many other contemporary international crises involving European states and their allies, particularly the Russo-Ukrainian War, the nuclear proliferation in Europe, and the military disputes between the Iran-backed Houthis and EU forces. But why has the U.S.-Iran relationship deteriorated so dramatically over the past years, and what are the future risks its presents to the international community? While extensive scholarly works have been conducted to examine the ideological, historical, and geopolitical variables that fuel this observed escalating antagonism, no study to date has formal-modelled the complex interactions between Tehran and Washington into a two-level game. Utilizing an infinitely-repeated game theory approach, this research presents a comprehensive analytical framework that explains how interrelated political factors at both the international and the domestic level jointly shape the dynamic of this bilateral relationship. We contend that the domestic rally effect created by risk-oriented diplomacy, incompatible ideological stances and political values, the increasing polarization within U.S. Congress, and the mounting rivalry between Israel and the Iran-led Axis of Resistance convince policymakers from both sides that their utilities can be maximized in a non-cooperative game. Based on our models, it can be forecasted that there is hardly any chance Washington and Tehran will concede to the other’s security demands in the foreseeable future. Thus, the risks of high-intensity conflict escalation due to miscalculation will continue to upsurge in the future.

Secure Edge Server Placement With Non-Cooperative Game for Internet of Vehicles in Web 3.0

Secure Edge Server Placement With Non-Cooperative Game for Internet of Vehicles in Web 3.0

A carbon integrated energy pricing strategy based on non-cooperative game for energy hub in seaport energy system

Seaports are significant energy consumers and sources of carbon emissions within the maritime industry. To address this problem, an energy hub (EH) can be established as an intensive energy interconnection center. This paper proposes a carbon emission flow (CEF) model based on the energy hub in seaport, which monitors the carbon emissions generated by the internal energy conversion unit operating within the hub. Additionally, a carbon integrated energy pricing strategy based on non-cooperative game theory is proposed. Based on duality theory and gradient descent method, an iterative algorithm is designed to seek the global optimal solution satisfying the Nash equilibrium point. The aim of the strategy is to encourage seaport energy consumers to adjust their energy consumption strategy according to the optimal benefits and reduce carbon emissions. A comparative study of three different strategy cases are conducted to evaluate the effectiveness of the proposed strategy. Numerical simulation results show the reduction of the total energy consumption and carbon emissions of the system, which is conducive to realizing the low-carbon operation of the seaport energy system.

Mixed deterministic and stochastic disturbances in a discrete-time Nash game

This paper considers a discrete-time non-cooperative M-players linear affine quadratic game of pre-specified fixed duration, affected by stochastic noise and deterministic disturbances. The last one is seen as a pernicious fictitious player looking to maximise the expected cost functions of each player. Inspired in the discrete-time robust dynamic programming, sufficient conditions for the existence of a type of robust feedback NE are given in the solution of a set of discrete-time difference equations. A formal induction proof is provided for the closed form of the obtained robust set of strategies. Two illustrative simulation examples are included, one related to the problem of coordination of a two-echelon supply chain with uncertain seasonal demand. The goal of the agents is to reduce the expected cost of storage while satisfying a partially known demand. The second example is related to a game of government debt stabilisation. Comparing the simulation with the standard Nash strategy, the robust one achieves a better performance.

Strategic coopetition among transportation service providers considering supply–demand congestion effects and asymmetric bargaining power

The phenomenon of transportation service providers (TSPs) engaging in both competition and cooperation, commonly referred to as coopetition, has become increasingly prevalent in the transportation market. This is driven by the rapid advancements in information technology and sharing economy. In practice, competitive TSPs can engage in a resource exchange scheme to share their resources to improve their service quality. However, such a resource exchange scheme may not be sustained since the service prices and profits will be further changed due to the competition in the end market. This study proposes a two-stage sequential-move game to characterize the coopetition problem between TSPs, wherein the first-stage resource exchange problem is modeled with a Nash bargaining game, and the second-stage pricing problem is modeled with a non-cooperative Nash game. Different from prior studies, our model incorporates the supply–demand congestion effects and the asymmetric bargaining power of TSPs. The subsequent impacts on social welfare, TSPs, and end users are investigated. Analytical results show that only when the unit price of the exchanged resources decreases in the exchanged resource quantity will the resource-exchange scheme succeed. Furthermore, we find that TSPs prefer to leave some “buffer zone” in between to avoid fierce competition with price wars.

Correspondence of Fixed-Point Theorem in \(T_2, T_3\)-SPACE

Fixed-point theory (FPT) has lot of applications not only in the field of mathematics but also in various other disciplines. Fixed Point Theorem presents that if T:X \(\to\) X is a contraction mapping on a complete metric space (X, d) then there exists a unique fixed point in X. FPT is also essential in game theory, in this case Brower Fixed Point has an application in game theory specifically in non-cooperative games and existence of Equilibrium. In particular, a game is a set of actions done by the participants defined by a set of rules. This is commonly described using mathematical concepts, which offers a concrete model to describe a variety of situations. On the other hand, the separation axioms Ti, i = 0,1,2,3,4 are vital properties that describes the topological spaces T0,T1,T2,T3 and T4. It is noted that a T3 - space is a generalized version of T2- space and since various results on application of fixed point theory in game theory on an arbitrary locally convex T2 - space has been established, in this study we sort to extend this concept to the general T3 - space. The utilization of a symmetric property of Hausdorff space established that if two continuous commutative mappings are defined on a T3 - space, then the two maps achieves unique fixed points.

Optimal consumption and investment in pooled annuity funds with and without fund managers

In this paper, we study two schemes of pooled annuity funds: pools with and without fund managers. In the pool without managers, there are no artificial rules or management cost and we introduce Nash equilibrium to depict the simultaneous non-cooperative game among participants. Given others' strategies, participants choose optimal consumption and investment strategies to maximize their own utility. In the pool with managers, the fund manager makes artificial rules for consumption and investment, ensuring cooperation among participants. The manager then determines optimal consumption withdrawal and investment strategies to maximize the total utility of all participants. By using variational methods and recursive algorithms, semi-analytical solutions in both pools are obtained. Due to prisoner's dilemma, larger pools without managers induce more intense competition in consumption and reduce utility. Thus, there exists an optimal pool size and only homogeneous participants form the pool. Meanwhile, larger pools with managers diversify investment and longevity risks more sufficiently and the subsidy effect among participants is mitigated. Therefore, the perfect pool with infinite participants is recommended as the optimal pool. Moreover, the preference between the two optimal pools depends on risk aversion degree, management cost and flexibility in selecting the strategies.

Pareto optimal provisions as outcomes of voluntary public good supply

It is shown that a large set of public good supply levels that arise in a Pareto optimal allocation can also be attained as public good provisions in the Nash equilibrium of a non-cooperative provision game. With a not very far-fetched assumption on agents’ preferences this result even holds for all Pareto supply levels.

Potential and challenges of Peer-To-Peer (P2P) electrical energy trading: investigation and case study

This paper explores the potential and challenges associated with Peer-to-Peer (P2P) electrical energy trading, focusing on both theoretical investigation and practical application through a comprehensive case study. By examining the interactions between prosumers (those who both produce and consume energy) and consumers, we employ utility functions to model these interactions. Optimization is achieved using a non-cooperative game theory approach coupled with real-time pricing mechanisms to reflect market dynamics accurately. Our analysis ensures feasibility and reliability by considering crucial factors such as energy balance and grid capacity constraints, which are essential for maintaining system stability. The simulation results demonstrate the effectiveness of our proposed method. These results highlight the significant potential of P2P trading to enhance overall energy efficiency, promote decentralized energy management, and reduce reliance on traditional energy sources. Furthermore, this study provides detailed insights into maximizing total utility by determining optimal prices and energy trades, ultimately showcasing the transformative impact of P2P trading on real-time energy market operations.

A two-sided equilibrium model of Vehicle-to-Vehicle charging platform

A novel mobile charging service utilizing vehicle-to-vehicle (V2V) charging technology has been proposed as a complement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study develops a two-sided equilibrium model for a V2V charging platform, where the demand-side charging vehicles (CVs) choose between charging piles and the V2V platform, and the supple-side discharging vehicles (DVs) decide whether to provide discharging services. The V2V platform matches CVs with DVs, receiving compensation from CV owners and reimbursing DV owners. To explore the strategic interactions between the V2V platform and fixed CI operators, we construct an analytical bi-level model that examines different operation regimes, including competitive and cooperative scenarios, i.e., non-cooperative game and Nash Bargaining game. The lower-level model is a two-sided equilibrium model that quantifies charging and discharging choices of EV owners, while the upper-level model optimizes the pricing decisions of the V2V platform and fixed CI operator. Numerical results indicate that the introduction of the V2V platform can reduce the overall charging costs, thus encourage the use of public charging services. Furthermore, it is suggested that the V2V platform be managed by independent enterprises, rather than existing fixed CI operators, to promote the EVs at relatively low charging costs.

Model-Based Online Adaptive Inverse Noncooperative Linear-Quadratic Differential Games via Finite-Time Concurrent Learning

Model-Based Online Adaptive Inverse Noncooperative Linear-Quadratic Differential Games via Finite-Time Concurrent Learning

Extremum-seeking Based Approach for Distributed Noncooperative N-coalition Games

Extremum-seeking Based Approach for Distributed Noncooperative N-coalition Games

Distributed Non-Cooperative Games and Distributed Learning in Linear and Nonlinear Systems: An Overview

Distributed Non-Cooperative Games and Distributed Learning in Linear and Nonlinear Systems: An Overview

Knowledge sharing decision-making under stochastic factors in platform ecosystems: the diversified participants' perspective

PurposeKnowledge sharing is critical to creating value in platform ecosystems. However, participants refrain from sharing knowledge and even engage in free-riding behavior, thereby causing the value co-destruction of the platform ecosystems. To encourage knowledge sharing among participants, it is essential to analyze the influencing factors and decision-making mechanisms of knowledge sharing in the platform ecosystems.Design/methodology/approachThe study investigated the issue of knowledge sharing among participants in platform ecosystems, based on the stochastic differential game model. Considering the uncertain factors, the Nash non-cooperative game, Stackelberg leader-follower game, and cooperative game models were proposed. By utilizing system dynamics and numerical simulations, the key influencing factors and mechanisms of knowledge sharing were deeply explored, consequently providing game solutions to achieve the Pareto optimality of the ecosystem.FindingsParticipants' innovation capability and the marginal benefits of knowledge-sharing positively impact knowledge-sharing decisions, while the environmental knowledge decay rate has a negative influence. The platform subsidy mode enhances the knowledge-sharing effect, and the collaborative cooperation mode can realize the Pareto optimization of the system.Practical implicationsThe research findings will provide theoretical support for fostering knowledge innovation and sustainable development of platform ecosystems. Managers should cultivate an innovative environment, establish fair reward mechanisms, and utilize subsidies to promote knowledge sharing, leading to higher value creation.Originality/valueUtilizing the stochastic differential game model, the study proposed various game-theoretic frameworks to analyze participants' knowledge-sharing strategies. The integration of system dynamics and numerical simulations provides a practical approach to understanding the key influencing factors and decision-making processes.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach

In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive nature of this technology has raised concerns about its long-term sustainability and environmental footprint. To address this challenge, we explore the potential of Peer-to-Peer Federated Learning (P2P-FL), a distributed machine learning approach that allows multiple nodes to collaborate without sharing raw data. We present a novel integration of P2P-FL with blockchain technology, aimed at enhancing the sustainability and efficiency of blockchain networks. The basic idea of our approach is the use of distributed learning mechanisms to find the optimal performance parameters of blockchain without relying on centralized control. These parameters are then used by a load-balancing mechanism that prioritizes energy efficiency to distribute loads on different blockchains. Furthermore, we formulate a non-cooperative game theory model to align the individual node strategies with the collective objective of energy optimization, ensuring a balance between self-interest and overall network performance. Our work is exemplified through a case study in the renewable energy sector, demonstrating the application of our model in creating an efficient marketplace for energy trading. The experimentation and results indicate a significant improvement in the execution times and energy consumption of blockchain networks. Therefore, the overall sustainability of the network is enhanced, making our framework practical and applicable in real-world scenarios.

Anti-Rollover Trajectory Planning Method for Heavy Vehicles in Human–Machine Cooperative Driving

The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles.