Non-cooperative Game Theoretic Approach Research Articles

ELEKTRA: Empowering prosumers with distributed prosumer grouping and game-theoretic energy procurement

In this paper, the ELEKTRA framework is introduced, a novel community energy management system that organizes the prosumers into autonomous communities and determines the prosumers’ optimal energy procurement in a distributed manner. Our motivation stems from the increasing complexity of the energy procurement for prosumers and the need for more efficient and decentralized approaches to optimize their energy consumption. The first element of the ELEKTRA framework implements an autonomous hedonic game-theoretic model at the prosumers’ home energy management controllers, considering the prosumers’ individual energy consumption and generation patterns, as well as the utility-provided rewards for proactive participation. Specifically, the prosumer grouping is modeled as a hedonic game, demonstrating the existence of a Nash-stable and individually-stable prosumer grouping. The second element of the ELEKTRA framework employs a distributed non-cooperative game-theoretic approach. This addresses how prosumers strategically consume energy to meet their needs while maximizing their payoff by procuring additional energy from the utility company. Also, utilizing the theory of n-person concave games allows for a thorough evaluation of accuracy, performance, and complexity in determining optimal energy consumption for prosumers. A comprehensive evaluation of the ELEKTRA framework is conducted using real data from the southwest region of USA. The results demonstrate the operational effectiveness of the ELEKTRA framework, showcasing its superiority in optimizing prosumer payoff compared to existing models. The performance assessment, grounded in real-world data, provides valuable insights into the efficacy of the ELEKTRA framework in contrast to current state-of-the-art.

A Fuzzy Economic Order Quantity Model on Manufacturer-Retailer considering delay in payments: A Non-Cooperative Game Theoretical Approach

A Fuzzy Economic Order Quantity Model on Manufacturer-Retailer considering delay in payments: A Non-Cooperative Game Theoretical Approach

A fuzzy economic order quantity model on manufacturer-retailer considering delay in payments: a non-cooperative game theoretical approach

A fuzzy economic order quantity model on manufacturer-retailer considering delay in payments: a non-cooperative game theoretical approach

Examining B2B channel decision-making within differential quality-level zone: a supply chain design using a non-cooperative strategic game theoretic approach (“Stackelberg Supply Chain for B2B”)

Purpose The consumers want to purchase the target products in the right place, whereas the manufacturers want to allocate their possible products to optimal distribution channels. The manufacturer must know how to handle itself in this business. The study aims to examine the B2B channel decision-making with different product qualities in a non-cooperative supply chain. Design/methodology/approach The authors develop a B2B Manufacturer-Stackelberg game as an analytical framework, combining asymmetric preference of purchase channels choice by the consumers, a continuous quality setting of the manufacturer and differential channel structure to study the manufacturer’s product strategy and channel optimisation. By horizontal comparisons across four channel structures, product variety can be classified into the differential quality-level zone through exogenous quality intervention, and the preference of manufacturers in each quality-level zone within the structures can be ranked. Findings Theoretically and practically, the hybrid-channel structure should be completely neglected when the direct channel dominates the retail channel. In contrast, dual-channel structures dominate single channels irrespective of the channel power, and channel preferences between high-quality and low-quality zones are stable, whereas the preference in medium-quality zone is unstable. In addition, the supply chain system cannot achieve global Pareto improvement without any additional coordination mechanism between the manufacturer and the retailer. Originality/value The extended results by numerical examples suggest that the bigger the area of the medium-quality zone, the more significant the product variety of the manufacturer.

Rate-Splitting Multiple Access-Based Satellite–Vehicular Communication System: A Noncooperative Game Theoretical Approach

Rate-Splitting Multiple Access (RSMA) has recently found favor in high-mobility scenarios due to the benefits of relaxing the accuracy of Channel State Information at the Transmitter (CSIT) while maintaining high spectral efficiency. These benefits are particularly important in Satellite-Vehicular Networks (SVNs), where the mobility of the vehicles significantly affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based satellite-vehicular communication system for reliable data transmission. Specifically, we investigate the outage probability of the RSMA-based satellite-vehicular communication system under the Shadowed-Rician model. Considering the satellite-vehicular communication outage problem, an optimization problem is formulated to maximize the Weighted Sum Rate (WSR) of the communication system. However, due to communication overhead and privacy concerns, not all vehicles are willing to participate in interference management. In this study, we exploit an incentive mechanism to efficiently solve this problem. Specifically, we formulated a non-cooperative game to motivate users to report the CSIT and participate in the power allocation. In addition, we have proved the existence and uniqueness of Nash Equilibrium (NE) in the game formulated. Our simulation results show that the proposed non-cooperative game theoretical approach achieves the effectiveness of the RSMA-based satellite-vehicular system. The game theoretical framework is shown to motivate users to participate in interference management, so as to maximize their transmission rates.

Block-CPS: Blockchain and Non-Cooperative Game-Based Data Pricing Scheme for Car Sharing

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.

Sharing unused storage in local energy markets utilizing physical storage rights: A non-cooperative game theoretic approach

This paper proposes a local market framework to allocate physical storage rights (PSRs). As a market product, PSRs are provided by the storage owners and enable the local market participants (including arbitrageurs, renewable producers, consumers and prosumers) to access the storage. That is, they can book storage in the form of PSRs and dispatch it at a given time aiming to maximize their utility function. Storage owners can monetize their unused capacity in the form of PSRs, while the rest of the remaining market participants aiming at additional flexibility, compete to obtain the PSRs on a short-term basis. To design a functional mechanism capable of handling the periodical PSR auctions efficiently, two different business options have been investigated: (i) market participants offer part of their unused storage, keeping however the priority on utilizing their resource, and (ii) storage is exclusively granted by an external supplier that makes profit by selling storage services solely in the form of PSRs. Considering the context above, we propose two distinct local market-clearing mechanisms, consisting of two equilibrium and two optimization models. We prove that the equilibrium models can be substituted with the equivalent optimization ones ensuring the same desirable market properties, such as efficiency and revenue adequacy. Numerical results from case studies show that storage presence in the energy community decreases the total system cost up to almost 10%, demonstrating at the same time the benefits of the proposed PSR allocation mechanism for the individual market participants. That is, the individual revenue is increased up to 39%, depending on the market participant.

A Game Theoretic Horizon Decomposition Approach for Real-Time Demand-Side Management

This article introduces two instances of rolling horizon algorithms, viz, shrinking horizon proximal decomposition algorithm (SHPDA) and distributed horizon proximal decomposition algorithm (DHPDA), to solve the demand-side management problem in real time. Both these algorithms are comprised of “two-step”. The number of time-slots to be considered in each iteration is decided in first step. In second step, the solution for considered time-slots is obtained through a non-cooperative game-theoretic approach using the proximal decomposition algorithm (PDA). An investigation is carried out to establish the existence and multiplicity of Nash equilibrium for the formulated game. The convergence of PDA has a strong dependency on system parameters and is unique for a system. Hence an analysis is performed to derive the convergence criteria for the formulated problem. The system model includes distributed energy storage, distributed dispatchable generation, time-shiftable, power-shiftable (PSD), and essential devices. The reduction in power consumption of PSD caused due to the optimization process creates discomfort to users. Therefore, we have incorporated an objective to minimize the discomfort and reduce consumers’ energy bills. Simulation is carried out to show the efficacy of developed algorithms in terms of the energy bill, peak reduction, and solution time. Also, eight scenarios have been analyzed to show the impact of adding discomfort cost on the above-mentioned system parameters.

Design and selection of government policies for electric vehicles adoption: A global perspective

Regardless of increased attention in electric vehicles (EV) market expansion, the actual penetration of EVs remains low globally. Almost all major OEMs have announced investment plans to ensure that EVs constitute a major, if not complete, chunk of their product portfolios. On their part, governments worldwide (e.g., China, Poland, India, USA, etc.) have used various policy measures to facilitate EV adoption. In this paper, we study how incentives offered in terms of subsidy and differential taxation schemes could increase the market penetration of EVs. We analyze different models under uniform and differential taxation policies with and without subsidy, using a non-cooperative game-theoretic approach. Our analysis reveals that the government can follow any of the three tax-subsidy mixes that could maximize social welfare, i.e., differential taxation with and without subsidy, and identical tax with a subsidy. Surprisingly, the manufacturer's profit, the government's income, and consumer surplus for these three models are also the same and are better than the other two models depending on the consumer's green sensitivity, i.e., for higher green sensitivity, these three models can provide a win–win outcome. From an environmental perspective, levying tax on gasoline vehicles (GV) without subsidy to the manufacturer minimizes the overall environmental impact. In contrast, levying the same tax for both types of vehicles without subsidy to the manufacturer generates the maximum overall environmental impact. Furthermore, an increase in the unit environmental impact of vehicles attracts higher taxes. We portray that the increase in the cost-difference between EV and GV increases GV demand and is detrimental for EV acceptance. In addition, multifaceted insights are drawn for manufacturers and policymakers to envisage electric mobility. We extend our models and show that our main results hold under the implementation of mandate on EV manufacturers under subsidy and non-subsidy model, and inclusion of hassle cost for consumers due to lack of infrastructure in terms of charging facilities and maintenance.

A New Cooperative Game—Theoretic Approach for Customer-Owned Energy Storage

The increasing demand for energy storage systems (ESSs) alongside the continuous enhancements to storage technology have been of great positive impact on the electric grid. Their unceasing development has been driven by the need to accommodate increased penetration of renewable energy resources and defer capital investments, among other benefits. Moreover, ESSs have played a key role in the grid’s ability to cope with its ever-shifting load profiles, resulting in large economic gain for ESS owners. For this reason, this prospective study was designed to investigate privately-owned energy storage hubs (ESHs) and their interactions with potential customers as well as with the electric grid. This research examined two contrasting interaction approaches for customer-owned stationary energy storage hubs: a cooperative and a non-cooperative game-theoretic approach. The goal of the cooperative technique is to conduce to a correlated equilibrium increasing the social welfare of all players involved using a regret matching algorithm. On the other hand, in the non-cooperative approach, modeled as an ascending price-clinching auction, each player acts greedily, maximizing only their individual welfare. Implementing both case studies resulted in important insights into ESH players’ interactions and provided contrasting methods of modeling their behaviors. Finally, depending on the application at hand, the choice of one approach may be more realistic than the other.

Evaluation of cooperative and non-cooperative game theoretic approaches for water allocation of transboundary rivers

Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work’s results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development.

Coordination mechanisms for digital and sustainable textile supply chain

PurposeSupply chain network is complicated to manage due to the involvement of a number of agents. Formation of virtual organization using Industry 4.0 (I4.0) is an approach to improve the efficiency and effectiveness and to overcome the complexities of the channel. However, the task of managing the channel further becomes complicated after incorporating sustainability into the supply chain. To fill this gap, this paper focuses on designing of mechanism and demonstration of I4.0-based virtual organization to coordinate sustainable supply chain.Design/methodology/approachIn this paper, we model and compare I4.0-based virtual organization models using four other traditional contracts with centralized supply chain. The non-cooperative game theoretic approach has been used for the analysis of models.FindingsOur game-theoretic analysis shows that investment in I4.0 and sustainable innovation are beneficial for the overall supply chain. Our results show that linear two-part tariff contract and I4.0-based virtual organization model can perfectly coordinated with the supply chain.Research limitations/implicationsThis study consider deterministic model settings with full information game. Therefore researchers are encouraged to study I4.0-based coordination models under information asymmetry and uncertain situations.Practical implicationsThe paper includes implications for the development of I4.0-based coordination model to tackle the problems of channel coordination.Originality/valueThis study proposes I4.0-based game-theoretic model for the sustainable supply chain coordination.

Energy Efficient Handover for Heterogeneous Networks: A Non-Cooperative Game Theoretic Approach

Energy Efficient Handover for Heterogeneous Networks: A Non-Cooperative Game Theoretic Approach

Decentralized Energy Management System for LV Microgrid Using Stochastic Dynamic Programming With Game Theory Approach Under Stochastic Environment

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways-in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach.

A Non-Cooperative Game-Theoretic Approach for Distributed Voltage Regulation in DC Grids with a High Penetration of Renewable Energies

This manuscript deals with the situation in which the different agents or buses in a power network have access to local renewable resources and must manage its use in a distributed fashion. The buses distributedly decide the amount of power to be generated using their local renewable power plants, and that to be demanded from the grid. The decisions are made according to the optimization of a cost function that considers both economic and technical factors. The problem is approached resorting to a game-theoretical framework that requires a negotiation process among the neighboring buses. An iterative algorithm is proposed in order to solve this problem, providing existence and convergence conditions under which the buses reach a suitable equilibrium. The algorithm performance is tested in simulations over a modification of the IEEE 14-bus system, in which the lines are modeled as resistances and distributed generation is considered. Simulations on a network of 44 buses are also included to show the scalability of the method.

A Game-Theoretic Approach to the Freight Transportation Pricing Problem in the Presence of Intermodal Service Providers in a Competitive Market

This article studies a competitive freight transportation pricing problem in the presence of two Intermodal Service Providers (ISPs) and a Direct Transportation System (DTS). The ISPs apply both rail and road transportation modes to carry the demands of a network of customers. The DTS uses only roads to carry the demands, without any transhipment at a distribution center. Each customer chooses its best transportation service based on the prices offered by the ISPs and the expenses of using the DTS. The ISPs determine their prices to maximize their profits, considering the customers’ choice behaviour. In order to determine the equilibrium decisions, a non-cooperative game-theoretic approach based on Stackelberg leader-follower competition is applied. Mixed-integer linear programming models are proposed to formulate this competition. A real-life case study is also conducted to demonstrate the validity of the models. We find that a barrier pricing strategy from the leader to deter the entrance of the follower ISP is not recommended for both of them because it may even lead to a negative value of profits for the leader. Finally, some sustainability objectives of the government, as the strategic decision maker, are examined. The results could help the government assess the effects of its policies on the transportation market, the environment, and the society.

Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.

A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning

This paper presents FENOCOP, a game-theoretic approach for solving non-cooperative planning problems that involve a set of self-interested agents. Each agent wants to execute its own plan in a shared environment but the plans may be rendered infeasible by the appearance of potential conflicts; agents are willing to coordinate their plans in order to avoid conflicts during a joint execution. In order to attain a conflict-free combination of plans, agents must postpone the execution of some of their actions, which negatively affects their individual utilities. FENOCOP is a two-level game approach: the General Game selects a Nash equilibrium among several combinations of plans, and the Scheduling Game generates, for a combination of plans, an executable outcome by introducing delays in the agents’ plans. For the Scheduling Game, we developed two algorithms that return a Pareto optimal and fair equilibrium from which no agent would be willing to deviate.

Spectrum Resource Sharing in Heterogeneous Vehicular Networks: A Noncooperative Game-Theoretic Approach With Correlated Equilibrium

In this paper, with the aims of alleviating the pressure from the shortage of spectrum resource and addressing the inefficient spectrum utilization, we investigate the spectrum sharing for moving vehicles in Heterogeneous Vehicular Networks (HVNs) consisting of the macrocells and the Road Side Units (RSUs) with Cognitive Radio (CR) technology. We first propose an incentive mechanism for encouraging macrocells to share spectrum resource with vehicle users, in which the CR-enabled RSUs perform sensing the spectrum availability in the surrounding urban environments. Furthermore, the downlink resource allocation for vehicle users associated with different RSUs is modeled as an n-person game and solved by designing a noncooperative game theoretic approach. By considering transmission power constraint of RSU and inter-RSU interference, the resource allocation and interference mitigation among RSUs are formulated via maximizing the overall utility in the HVNs. We design a game theoretical strategy optimization algorithm based on regret-matching and then derive the correlated equilibrium solution. Moreover, we propose a heuristic power control algorithm for further mitigating the inter-RSU interference in the noncooperative game based resource allocation. Simulation results demonstrate that the proposed approach can achieve the

Energy Splitting for Swipt in Qos-Constraint Mtc Network: A Non-Cooperative Game Theoretic Approach

This paper studies the emerging wireless energy harvesting algorithm dedicated for machine type communication (MTC) in a typical cellular network where one transmitter (e.g. the base station, a hybrid access point) with constant power supply communicates with a set of users (e.g. wearable devices, sensors). In the downlink direction, the information transmission and power transfer are conducted simultaneously by the base station. Since MTC only transmits several bits control signal in the downlink direction, the received signal power can be split into two parts at the receiver side. One is used for information decoding and the other part is used for energy harvesting. Since we assume that the users are without power supply or battery, the uplink transmission power is totally from the energy harvesting. Then, the users are able to transmit their measured or collected data to the base station in the uplink direction. Game theory is used in this paper to exploit the optimal ratio for energy harvesting of each user since power splitting scheme is adopted. The results show that this proposed algorithm is capable of modifying dynamically to achieve the prescribed target downlink decoding signal-to-noise plus interference ratio (SINR) which ensures the high reliability of MTC while maximizing the uplink throughput.