Optimization of dynamic trading strategies in carbon markets: A decision framework based on Stackelberg game and prospect theory

Non-cooperative Optimization of Charging Scheduling for Electric Vehicle via Stackelberg Game and Matching Theory

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

With the development of institutional investors, the game between institutional investors and individual investors has become an important factor affecting the stock price. Exploring the buying and selling decisions made by different investors in a certain stock market can help to predict stock price. In this paper, we use the bidder to represent institutional investor and the sellers to represent individual investors. We propose a Stackelberg game model which achieves the optimal income utility of both parties. We also explore the equilibrium point and conditions for the establishment of equilibrium. Finally, we tested the feasibility of the Mechanism through simulation.

Computing power network (CPN) is a new architecture proposed in recent years. It has integrated multidimensional resources, such as computing, network and storage resources in cloud, edge and end to realize more flexible resource allocation and task scheduling. Consumers in CPN want to purchase some resources to meet their service demands and gain some profits, while operators would like to obtain payments by selling their idle resources to others. However, consumers usually prefer to purchase resources from large and public cloud operators in reality, while small and private operators who lack the recognition struggle to obtain the opportunity to join in the sales. Therefore, both parties may lack the passion and enthusiasm to participate in CPN transaction and there is a great need for a resource trading incentive mechanism. In this paper, we propose a three-tier joint optimization framework to increase their motivations and establish a centralized computing service platform (CSP). We use the Stackelberg game to describe the resource-purchasing process of computing service demanders (CSDs) and use the auction theory to describe the task-scheduling process of computing service operators (CSOs). In this way, both parties can gain their profits so that they will have a greater willingness to participate in the transaction. The simulation results demonstrate the feasibility and superiority of our scheme.

A variable neighborhood search based algorithm and game theory models for green supply chain design

Negotiation Theory is a research area with emphasis from three different research streams being game theory, psychology and negotiation analysis. Recently, negotiation theory research has moved towards the combination of game theory and psychology negotiation theory models that could be called Integrated Negotiation Theory (INT). As, negotiations are often impacted by external factors, there is risk associated with achieving the expected outcomes. Prospect theory and Negotiation theory are combined in this paper to incorporate the risk associated within negotiations. Negotiation Decision Support Systems (NDSS) is an information technology tool using negotiation theory and artificial intelligence to assist disputants in a negotiation to obtain better negotiated outcomes than they can obtain otherwise. Due primarily to the limitations of bounded rationality and differing mental or physical state of the disputant. In the past, NDSS have primarily used game theory within their negotiation and decision making framework to assist disputants resolve negotiations, as game theory is easier to incorporate. This paper shows how INT and Risk Factors (Prospect Theory) can be incorporated into an NDSS to improve negotiation decision making.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

Multi-objective robust dynamic pricing and operation strategy optimization for integrated energy system based on stackelberg game

In this paper, we have introduced two models for spectrum management (spectrum trading and spectrum competition) in cognitive radio network. The first model is without game theory and the second one is with game theory. The first model for spectrum management without game theory, called SMWG, which has been designed to provide an efficient and dynamic equations to enhance the network performance. SMWG provides a novel function, called QoS function, to support three levels of QoS. In addition, it provides a novel factor, called Competition Factor, to control the behaviors among spectrum owners (primary users). In the second model, we have applied the game theory concept into spectrum management (SMG) to compare the network performance in both cases (SMWG and SMG). SMG provides two games, the first one is to model the dynamic behavior of spectrum competition among primary users, a Bertrand game is formulated where the Nash equilibrium is considered as the solution. The second game is to model the spectrum trading between the primary user and the secondary user, a Stackelberg game is formulated where the Nash equilibrium is again considered as the solution. Basically, the solution is found in terms of the size of the offered spectrum to the secondary users with regards to the offered spectrum price. We compare SMWG with conventional scheme, also compare SMG with conventional scheme, and finally compare SMWG with SMG in terms of network performance.

This work investigates a two‐tier supply chain in which a supplier adjusts product quality and prices and distributes the products through a retailer or directly to consumers with the reference quality (RQ) effect. The supplier can choose from three, that is, traditional sales (TS), dual‐channel (DC), and online‐to‐offline (O2O), sales modes. In the O2O sales mode, the retailer is allowed to open an online channel. The market demands are determined by the consumer utility consisting of two parts, a part depending on the consumer RQ effect and the other depending on the retail price and product quality. The profit maximization, that is, the Stackelberg game, models involving the consumer RQ effect are developed using prospect theory, wherein the supplier has the first mover advantage. The analytical optimal solutions are obtained through backward induction. The optimal quality and pricing decisions and the optimal profits of the supply chain members are compared among the three sales modes. A practical application is analyzed to verify the theoretical results of the proposed models. The results show that the consumer RQ effect benefits the two supply chain members under certain conditions. The optimal quality and pricing decisions are significantly affected by the consumer RQ effect except those of the supplier under the O2O sales mode. When consumers have the RQ effect, the DC sales mode is the best choice for the supplier, is a win–win mode for the two supply chain members under certain conditions, and benefits the consumers the most among the three sales modes.

In supply chain, the key problem and difficulty in information-based construction lies in coordinating the benefits of various participating enterprises so that they may always maintain a positive attitude towards participation. This paper, from the hypothesis of the rational people, studies the financing for supply chain information-based construction between member enterprises in the supply chain, and then combines the features of distributed control supply chain to propose the corresponding financing package based on Stackelberg game theory, and last analyzes the approach to financing balance between information-based construction enterprises in the decentralized supply chain. It is found in this study that the expected return is the most important factor to coordinate the financing member enterprises for supply chain informationbased construction

We investigate the spectrum sharing problem of a radio frequency (RF)-powered cognitive radio network, where a multi-antenna secondary user (SU) harvests energy from RF signals radiated by a primary user (PU) to boost its available energy before information transmission. In this paper, we consider that both the PU and SU are rational and self-interested. Based on whether the SU helps forward the PU's information, we develop two different operation modes for the considered network, termed as non-cooperative and cooperative modes. In the non-cooperative mode, the SU harvests energy from the PU and then use its available energy to transmit its own information without generating any interference to the primary link. In the cooperative mode, the PU employs the SU to relay its information by providing monetary incentives and the SU splits its energy for forwarding the PU's information as well as transmitting its own information. Optimization problems are respectively formulated for both operation modes, which constitute a Stackelberg game with the PU as a leader and the SU as a follower. We analyze the Stackelberg game by deriving solutions to the optimization problems and the Stackelberg Equilibrium (SE) is subsequently obtained. Simulation results show that the performance of the Stackelberg game can approach that of the centralized optimization scheme when the distance between the SU and its receiver is large enough.

Evolutionary game is a method to explain the strategy-choices of decision-makers through calculating the replication dynamic equations. In the study, three-party evolutionary game and prospect theory are used to analyze the dynamics strategy changes of the government, the App store, and the App software under the government privacy regulation. MATLAB is used to do the simulation analysis. According to the results, the best evolutionary stability point will be changed when considering prospect theory. The sensitivity of government’s perceived value and loss avoidance are the key impact factors. The conclusions provide suggestions for further exploration of privacy regulation.

Geographic Profiling of Lone Wolf Terrorists: The Appication of Economics, Game Theory and Prospect Theory

Providing user satisfaction is a major concern for on-demand multimedia service providers and Internet carriers in Wireless Communications. Traditionally, user satisfaction was measured objectively in terms of throughput and latency. Nowadays the user satisfaction is measured using subjective metrices such as Quality of Experience (QoE). Recently, Smart Media Pricing (SMP) was conceptualized to price the QoE rather than the binary data traffic in multimedia services. In this research, we have leveraged the SMP concept to chalk up a QoE-sensitive multimedia pricing framework to allot price, based on the user preference and multimedia quality achieved by the customer. We begin by defining the utility equations for the provider-carrier and the customer. Then we translate the profit maximizing interplay between the parties into a two-stage Stackelberg game. We model the user personal preference using Prelec weighting function which follows the postulates Prospect Theory (PT). An algorithm has been developed to implement the proposed pricing scheme and determine the Nash Equilibrium. Finally, the proposed smart pricing scheme was tested against the traditional pricing method and simulation results indicate a significant boost in the utility achieved by the mobile customers.