Mixed Game Research Articles

Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory

In the context of global warming and energy scarcity, regional integrated energy system (RIES) can effectively improve energy utilization efficiency and reduce carbon emissions. However, this leads to the increased complexity of the market transactions. To this end, based on the mixed game strategy, a multi-agent low-carbon optimal dispatch model of RIES is proposed in this paper. Firstly, to fully consider the low-carbon objective of the system, a reward and punishment ladder carbon trading mechanism and a ladder integrated demand response (IDR) model are developed. Then, the decision models of the energy management operator (EMO), multiple energy hub agents (EHAs) and the user load aggregator (ULA) are constructed respectively with the goal of economy, and carbon emission flows are introduced as constraints. The interactive trading process of various stakeholders is simulated by a mixed game with master-slave game nested bidding strategy. Finally, the economics and carbon emissions of the system in different scenarios are analyzed by examples, and the effectiveness of the proposed model is verified. Numerical simulation results show that the proposed method not only has good economic and low-carbon environmental protection benefits, but also ensures the interests of various stakeholders.

Research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation

Introduction: It is significant for energy sharing to study the complementary utilization of multiple energy sources, such as water, electricity and gas, and the interaction among multiple stakeholders.Methods: We propose a research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation. Firstly, this paper describes the relationship and functions of all stakeholders under the research framework, and establishes the mathematical model of each unit in the water-electric-gas complementary IES. Secondly, the internal roles are layered based on the relationship between stakeholders in the system. Then a non-cooperative game model for the distribution network operator and multiple subsystems is established according to the theory of Stackelberg game, and a cooperative game model for multiple subsystems is further established based on the theory of Nash bargaining. In the next step, the complexity of the problem is analyzed, followed by the description of the specific algorithm and process of solving the model.Results: Finally, the results of example analysis show that the model proposed in this paper not only balances the interests of stakeholders at the upper and lower layers of the system, but also allocates the interests of multiple subsystems at the lower layer.Discussion: Thus effectively improving the energy utilization of the system.

Sosialisasi Teknik Dasar Pukulan pada Permainan Bulutangkis di SMK Negeri Kecamatan Biau

Badminton is a sport that uses racquets in a game that is played by two people or four people with positions in different fields of the field which are bounded by a net (net). Two people for the singles game category, and for four people for the doubles and mixed game categories. The aim of the game of badminton is to try to drop the shuttlecock in the opponent's playing area and try so that the opponent cannot hit the shuttlecock in the opponent's playing area and try so that the opponent cannot hit the shuttlecock and drop it in his own playing area. The basic techniques of badminton sports must be studied first so that when playing they can do the basic techniques well so that they can support athletes to win from the techniques they do. Because one of the determinants of victory in a match is determined by mastering the basic techniques of playing in badminton games.

Optimal scheduling of data centers based on multiple games

With the increasing dominance of electricity retailers in the electricity market, it has become a new trend for the data center (DC) to participate in sales-side transactions. However, data center electricity retailers (DCERs) and DCs that purchase electricity by DCERs, as different stakeholders, will inevitably face conflicts of interest. To promote the benefit distribution of DCs and DCERs to achieve a win–win situation, our study proposes an optimal scheduling method based on multiple games and establishes a mixed game model by integrating the master–slave game method and the cooperative game method, in which DCERs take profit maximization as the optimization goal, while Internet DCs take the lowest total cost as the optimization goal. The master–slave game is adopted between the DCER and the DC, and the cooperative game is adopted among the members of the DC. The benefits are distributed through Nash bargaining. The model is solved by using the particle swarm optimization algorithm combined with the alternating direction method of multipliers. To demonstrate the effectiveness of our proposed method, we provide an illustrative example that showcases its ability to not only increase DCER revenue by 136.04% but also decrease total DC costs by 9.39%. As a result, our method facilitates a more equitable distribution of cooperation revenues.

Research on Game Strategy and Enlightenment Based on the Perspective of Hybrid War

Following Russia's military conflict in Crimea and eastern Ukraine in 2014, with the background of the strategic game of great powers and the interweaving of various interests, in 2022, the Russian-Ukrainian conflict once again broke out and intensified, showing a typical hybrid war mode. Based on the perspective of hybrid war, this paper studies the mixed game strategy in modern war in terms of combat subjects, combat styles, combat means, combat environment, etc., explores the characteristics of mixed cognitive combat practice, and puts forward relevant countermeasures for China's reunification.

Two-stage electric vehicle charging optimization model considering dynamic virtual price-based demand response and a hierarchical non-cooperative game

This paper proposes a two-stage bi-layer game charging optimization model based on the background of non-coordination between a network operator (NO), a distributed generation operator (DGO), and a charging agent (CA). In the first stage, a dynamic virtual price-based demand response (DVPBDR) model is constructed to pre-optimize the charging load with the virtual charging cost as the objective. In the second stage, a strategy for adjusting output deviations based on a bi-layer Stackelberg game model is established, with the economic benefits of each participant as the objectives. Full cooperation mode and bi-layer mixed game are introduced to compare with the bi-layer Stackelberg game in Simulation Analysis. The calculation results show that: (1) the DVPBDR is not constrained by the actual electricity price system and mechanism, and reflect the real influence of price changes on charging demands, thus effectively reducing energy abandonment by 41.76% and net load fluctuation by 53.50%; (2) in the full cooperation mode, there is a conflict of interests and CA suffers financial loss, thus resulting in a reduction in comprehensive benefits by at least 61.08%, compared to the non-cooperative cases; (3) in the bi-layer mixed games, the cooperative gain of DGO and NO is superior than that of the cooperation between DGO and CA, so a relative win-win is achieved in the bi-layer mixed game with DGO-CA cooperation, and the comprehensive benefits is increased by 3.32%; (4) in the bi-layer Stackelberg game, each participant has a completely independent awareness of decision-making and establishes strategies for maximizing its own interests, which results in achieving the optimal comprehensive benefits (increases by at least 44.18% compared to other cases). Therefore, multi-dimensional benefits are realized in the multi-participant charging system with a bi-layer Stackelberg game.

Optimal operation of distribution networks and multiple community energy prosumers based on mixed game theory

Community energy systems are currently transitioning from conventional consumers to prosumers. Under this context, the coordinated management of multiple community energy prosumers (CEPs) is explored this study in accordance with the same distribution network. The aim of this study is to facilitate the coordination of unit operations, demand response, and peer-to-peer (P2P) energy trading among CEP coalitions based on the distribution network operator (DSO) by setting electricity prices. Thus, a mixed game-based optimal operation model of DSO and CEP alliances is developed, integrating Stackelberg and cooperative games. The upper layer aims at maximizing DSO revenue, whereas the lower layer minimizes the operation costs of CEP cooperative alliances while allocating benefits in accordance with the Nash-Harsanyi bargaining theory. The model is solved with a two-stage distributed algorithm that integrates the bisection method and the alternating direction method of multipliers (ADMM). Furthermore, cloud energy storage (CES) is introduced to optimize the economics of CEP. The effectiveness of the proposed method is verified through a case study, demonstrating the collaborative optimal scheduling of multiple CEPs and reasonable benefit distribution. Accordingly, this study is conducive to expediting the effective and economical management of CEPs under distribution networks.

Fixed-Time Nash Equilibrium Seeking in Time-Varying Networks

In this paper we introduce first-order and zeroth-order Nash equilibrium seeking dynamics with fixed-time convergence certificates for non-cooperative games having finitely many players. The first-order algorithms achieve exact convergence to the Nash equilibrium of the game in a finite time that can be additionally upper bounded by a constant that is independent of the initial conditions of the actions of the players. Moreover, these fixed-time bounds can be prescribed a priori by the system designer under an appropriate tuning of the parameters of the algorithms. When players have access only to measurements of their cost functions, we consider a class of distributed zeroth-order model-free adaptive dynamics that achieve semi-global practical fixed-time stability, qualitatively preserving the fixed-time bounds of the first-order dynamics. Moreover, by leveraging the property of fixed-time input-to-state stability, further results are obtained for mixed games where some of the players implement different seeking dynamics. Fast and slow switching communication graphs are also incorporated using tools from hybrid dynamical systems theory. We consider potential games as well as gener

Secondary School Students’ Cognitive Structures Regarding Educational Games

To employ educational games in education as intended, it is required to show students’ cognitive structures for this concept. As a result, the purpose of this research was to reveal the cognitive structures of secondary school students regarding educational games. The research was a survey-based descriptive study. A total of 340 secondary school students were included for this research, which included fifth graders (n=80), sixth graders (n=95), seventh graders (n=69), and eighth graders (n=96). Simple random sampling was used to choose the participants. Data were gathered using the Word Association Test (WAT) and a drawing test. Six categories are used to organize the students' responses to the educational game's concept. ‘Individual gains,’ ‘lessons,’ ‘sports,’ ‘lesson tools/applications,’ ‘games,’ and ‘features of games’ were all of these. Four categories were used to categorize the drawings made by the students to represent the concept of educational games: ‘physical ability games,’ ‘intelligent and strategy games,’ ‘puzzle,’ and ‘mixed games.’ As a result of this research, it is possible to conclude that students’ cognitive structures for the concept of educational games include games played in classes, and educational games highlight the game aspect more. Besides that, there was little vocabulary diversity in their cognitive structures for the educational dimension. To enhance students' cognitive structures toward educational games, focus should be given to the instructional feature of educational games in research to be undertaken in the relevant field.

Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach

In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme.

Integrated Energy System Systems and Game Theory A Review (ITALIC)

As mankind expands quickly, there is a shortage of energy worldwide. Instead of focusing on the production, transmission, and provision of a particular power source, China has proposed an To address the energy crisis, an integrated energy system is being developed. This system links energy resources and utilizes their complementary benefits. With the development of integrated energy systems, involvement and Engagement is getting increasingly difficult. Game theory, which can successfully address issues arising from multi-agent commerce, is a natural application of the System of integrated energy. This publication gives in-depth information analysis of how the integrated power system has undergone game theory analysis. Initial game situations in integrated power systems are provided, followed by a brief introduction to the evolution of integrated energy, The design and deployment of interconnected power systems issues are outlined, along with game scenarios that take into account the energy production side, supply chain, demand side, and all of these factors. Second, a summary of the fundamental for the integrated energy system, use game theory models is provided. The last topic is examined along with the challenges involved in game theory's potential application to integrated power systems. When applying the newly created game models to the integrated power system, a mixed game is recommended to solve these issues. This study should prove to be an invaluable resource for upcoming scholars in the subject.

Integrated Energy System Systems and Game Theory A Review

As mankind expands quickly, there is a shortage of energy worldwide. Instead of focusing on the production, transmission, and provision of a particular power source, China has proposed an To address the energy crisis, an integrated energy system is being developed. This system links energy resources and utilizes their complementary benefits. With the development of integrated energy systems, involvement and Engagement is getting increasingly difficult. Game theory, which can successfully address issues arising from multi-agent commerce, is a natural application of the System of integrated energy. This publication gives in-depth information analysis of how the integrated power system has undergone game theory analysis. Initial game situations in integrated power systems are provided, followed by a brief introduction to the evolution of integrated energy, The design and deployment of interconnected power systems issues are outlined, along with game scenarios that take into account the energy production side, supply chain, demand side, and all of these factors. Second, a summary of the fundamental for the integrated energy system, use game theory models is provided. The last topic is examined along with the challenges involved in game theory's potential application to integrated power systems. When applying the newly created game models to the integrated power system, a mixed game is recommended to solve these issues. This study should prove to be an invaluable resource for upcoming scholars in the subject.

Mixed Game-Based AoI Optimization for Combating COVID-19 With AI Bots

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

Cluster head selection method of multiple UAVs under COVID-19 situation

As COVID-19 continues to spread, people are unable to move freely when their residence region is temporarily lockdown, supplies cannot normally enter into such zones, leading to the shortage of supplies in these areas. Thus to ensure the delivery of supplies while reducing contact, the unmanned aerial vehicle (UAV) deliveries have become a common way. In order to efficiently use UAV resources and reduce energy loss in data transmission while performing the tasks, clustering is often used for achieving the above objectives, where the selected cluster heads centrally plan tasks so that reduce the communication times. However, problems such as unreasonable clustering, high energy consumption of cluster heads, and high mortality of cluster heads, directly lead the low cooperation efficiency and short life cycle of UAVs. Considering the nodes often died earlier through the k-means algorithm and ant colony algorithm, and highly dependent on the base station, these factors affect the working cycle and coordination efficiency of the UAVs. Facing the issues above, the cluster head selection algorithm of UAV based on game (CHSA) is proposed, where the mixed game model is adopted to select cluster heads for each region after regional division, and selecting the representative node to perform the cluster head selection algorithm, which help to reduce the energy consumption of each round of communication between nodes. Moreover, the key properties of the CHSA algorithm are proved, and the comparison experiment are conducted to prove the CHSA algorithm can effectively reduce energy consumption and prolong the network life cycle.

Multi-stakeholders energy management and control method of integrated energy system considering carbon trading mechanism

With the proposal of global energy conservation and emission reduction policies, the scale of grid connection of Integrated Energy System (IES) is gradually increasing. How to formulate a carbon trading mechanism to guide the system’s carbon emissions and realize the operation of the system’s low-carbon economy has become the main problem to be solved urgently. Based on this background, this paper proposes a multi-stakeholders energy management and control method of IES considering carbon trading scheme. First, a dynamic multi-energy coupling energy flow model of electricity, heat and gas in IES is established. Second, carbon emissions are limited by introducing the carbon trading scheme with a punishment mechanism into the IES control mode. Finally, a hierarchical model is established based on the system trading framework, and energy flow management and the control model of IES under multi-stakeholders is established by quoting mixed game theory. A simulation example is given to verify that the proposed control method can make IES run in a low-carbon economy.

Evolution of cooperation driven by diversity on a double-layer square lattice

Understanding the potential promotion mechanism for the formation and maintenance of cooperation is a pivotal issue in the development of human society and the natural world. Evolutionary games provide a forceful theoretical tool to probe into the essence of cooperative behaviors. It is considered that diversity (heterogeneity) is a common attribute of individuals and their interaction environment. We explore the propagation of cooperation under the influence of diversity and identify the similarities and discrepancies in the impact of internal and external sources of heterogeneity. The results show that heterogeneity, regardless of its origin, can promote the diffusion of cooperative behaviors. However, our researches further demonstrate that diversity of individual internal attributes is more conducive to the formation of cooperative behaviors than environmental heterogeneity. Moreover, we discuss the impact of asymmetry on the results and verify the robustness of the conclusions in different mixed game models. Our explorations shed some light on the promotion mechanism of cooperation from the perspective of sources of heterogeneity and highlight the role of diversity in the spread of altruistic behaviors.

New Game-Theoretic Approach to Decentralized Path Selection and Sleep Scheduling for Mobile Edge Computing

Network function virtualization (NFV) implements mobile edge computing (MEC) services as software appliances, and allows resources to be adaptively allocated to accommodate demand variations. Scalability and network cost (including operational cost and response latency) are key challenges. This paper presents a new game-theoretic approach to minimizing the network cost, where access points (APs) select MEC servers and routes in a decentralized manner, and unloaded routers and links are deactivated for cost saving. The key idea is that we interpret the minimization of network cost as a mixed game with a non-monotonic cost function capturing both the operational cost and response latency. We prove that the game is conditionally an ordinary potential game and converges to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -approximate equilibriums. A closed-form expression is derived for the convergence delay. Another important aspect is that we integrate Stackelberg routing into the proposed mixed game to avoid inefficient equilibriums (with high cost or latency). We prove that the mixed game can converge faster to better equilibriums under linear response latency models. Extensive simulations corroborate the new game-theoretic approach can significantly outperform existing techniques in terms of efficiency, convergence, and scalability.

The coevolving behavior of games and strategies under different network structures

In this paper, we study the coevolving behavior of mixed games when agents have a relationship represented by a fully connected network or a square lattice. Under the imitation update rule, whether the system will evolve to a state of pure game or mixed games and what the level of cooperation of the population will finally be are dependent on the initial fraction of mixed games, the game parameters and the network structures. We find that agents prefer to afford the prisoner’s dilemma (PD) game than the snowdrift game in the full connected network or in the square lattice and thus the cooperation is greatly suppressed. When the PD game mixes with the stag hunt game initially, they will coexist during evolution and a bistable phenomenon is observed. Meanwhile, the fraction of cooperation is enhanced when agents compete in a square lattice by comparison with the case of a fully connected network. If the PD game mixes with the harmony game (HG) initially, which one will dominate the other is related to the game parameters. The cooperation prevails in the population if the HG dominates the PD game. We also analyze the case of a fully connected network by a theory and the theoretical results are in good agreement with the simulation data.

Coordinated Control Strategy of Electricity-Heat-Gas Integrated Energy System Considering Renewable Energy Uncertainty and Multi-Agent Mixed Game

The Integrated Energy System (IES) can promote social energy transformation and low-carbon development, which is also an effective means to make contributions to energy structure optimization, energy consumption reduction, and new energy consumption. However, the IES has the characteristics of complex energy flow, and strong uncertainty with multi-agents. Therefore, traditional mathematical optimization models are difficult to comprehensively and accurately reflect the interest needs of different entities in the integrated energy microgrid. Aiming at this problem, a two-level collaborative control strategy model of “electricity-heat-gas” IES based on multi-agent deep reinforcement learning is proposed in this paper. The upper layer of this model is a multi-agent hybrid game decision-making model based on the Multi-Agent Deep Deterministic Policy Gradient algorithm (MADDPG), and the lower layer contains the power and gas flow calculation model. The lower model provides the upper model with the energy flow data of the IES and the upper layer rewards the decision-making behavior of the agent based on the energy flow data provided by the lower layer. Effectively solving the high-dimensional nonlinear optimization problem existing in the complex coupling network, this method can improve the convergence and training speed of the model. In this paper, the IEEE 33-node distribution network and 20-node gas network coupling system are provided to verify the model. The simulation results show that the proposed collaborative control strategy method can provide effective decision-making for electric-agent and gas-agent and realize the efficient and economic operation of the integrated energy system.

Implementation strategy and emission reduction effectiveness of carbon cap-and-trade in heterogeneous enterprises

In this research, a carbon emission reduction supply chain mechanism formed by government and enterprises is developed. Using a mixed game model, we study the impact of enterprise heterogeneity and social willingness-to-pay for environmental measures on the implementation strategy and effectiveness of cap-and-trade mechanism. The choice and effectiveness of the strategies are cross-influenced by the aforementioned two factors. The former has thresholds, which serve as a basic guarantee and adjustment; the latter is a key factor affecting government and enterprise decision-making and the fairness of the sharing of emission reduction responsibilities. The quotas allocation in compliance with the rule mainly affects the allocation of industry profits and consumers’ active payment among enterprises, and is an effective tool for achieving two-way incentives. The difference between quotas and the voluntary reduction of emissions is the main reason for the surplus or shortage of quotas. We propose an emission reduction integration mechanism, which is based on the social willingness-to-pay for environmental measures, the difference of carbon emissions per unit and the quota allocation schemes, can avoid negative incentives and lack of fairness. The emission reduction mechanism can only reduce the loss of social welfare, but the government can launch appropriate emission reduction practices based on the conception of carbon neutrality to improve social welfare.