Showing papers on "Stackelberg competition published in 2017"

Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach

Abstract: For microgrids with photovoltaic (PV) prosumers, the effective energy sharing management (ESM) is important for the operation. In this paper, a Stackelberg game approach for ESM is proposed. First, according to feed-in-tariff of PV energy, a system model of ESM is introduced, which includes the profit model of microgrid operator (MGO) and the utility model of PV prosumers. Moreover, an hour-ahead optimal pricing model of ESM is proposed. The model is designed based on Stackelberg game, where the MGO acts as the leader and all participating prosumers are considered as the followers. With the proof of equilibrium and uniqueness of the Stackelberg equilibrium, the MGO is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a billing mechanism is designed to deal with the uncertainty of PV energy and load consumption. By using the collected data from realistic PV-roofed buildings, the effectiveness of the model is verified in terms of the profit of MGO, the utilities of prosumers, and the net energy of the microgrid.

Who Should Finance the Supply Chain? Impact of Credit Ratings on Supply Chain Decisions

Abstract: Problem description: We study the impact of credit ratings on operational and financial decisions of a supply chain with a supplier and a retailer interacting via an early payment discount contract. The retailer has a single opportunity to order a product from the supplier to satisfy future uncertain demand. Both the retailer and supplier are capital constrained, and the retailer can use both short-term bank loans and trade credits for his financing needs, while the supplier can use short-term bank loans and/or the retailers early payment. We analyze for all relevant operational decisions (wholesale price, trade credit rates, bank loans, and order quantity) for capital-constrained firms. Academic/practical relevance: We add a framework on who should finance inventories, and at what rates, in the presence of differential credit ratings of the supply chain parties. Methodology: Within a modified selling to the newsvendor Stackelberg game with the supplier as the leader, we derive the equilibrium trade credi...

Data Offloading in 5G-Enabled Software-Defined Vehicular Networks: A Stackelberg-Game-Based Approach

Abstract: Data offloading using vehicles is one of the most challenging tasks to perform due to the high mobility of vehicles. There are many solutions available for this purpose, but due to the inefficient management of data along with the control decisions, these solutions are not adequate to provide data offloading by making use of the available networks. Moreover, with the advent of 5G and related technologies, there is a need to cope with high speed and traffic congestion in the existing infrastructure used for data offloading. Hence, to make intelligent decisions for data offloading, an SDN-based scheme is presented in this article. In the proposed scheme, an SDNbased controller is designed that makes decisions for data offloading by using the priority manager and load balancer. Using these two managers in SDN-based controllers, traffic routing is managed efficiently even with an increase in the size of the network. Moreover, a single-leader multi-follower Stackelberg game for network selection is also used for data offloading. The proposed scheme is evaluated with respect to several parameters where its performance was found to be superior in comparison to the existing schemes.

Cloud/fog computing resource management and pricing for blockchain networks

Abstract: The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.

Distributed Home Energy Management System With Storage in Smart Grid Using Game Theory

Abstract: In this paper, the problem of distributed home energy management system with storage ( HoMeS ) in a coalition, which consists of multiple microgrids and multiple customers, is studied using the multiple-leader–multiple-follower Stackelberg game theoretic model—a multistage and multilevel game. The microgrids, which act as the leaders, need to decide on the minimum amount of energy to be generated with the help of a central energy management unit and the optimum price per unit energy to maximize their profit. On the other hand, the customers, which act as the followers, need to decide on the optimum amount of energy to be consumed, including the energy to be requested for storage. Using the proposed distributed scheme, i.e., HoMeS , the earned profit of the grid improves up to 55%, and the customers consume almost 30.79% higher amount of energy, which, in turn, increases the utilization of the generated energy by the microgrids.

Game-Theoretical Energy Management for Energy Internet With Big Data-Based Renewable Power Forecasting

Abstract: Energy internet, as a major trend in power system, can provide an open framework for integrating equipment of energy generation, transmission, storage, consumption, and so on, so that global energy can be managed and controlled efficiently by information and communication technologies. In this paper, we focus on the coordinated management of renewable and traditional energy, which is a typical issue on energy connections. We consider a conventional power system consisting of the utility company, the energy storage company, the microgrid, and electricity users. First, we formulate the energy management problem as a three-stage Stackelberg game, and every player in the electricity market aims to maximize its individual payoff while guaranteeing the system reliability and satisfying users’ electricity demands. We employ the backward induction method to solve the three-stage non-cooperative game problem, and give the closed-form expressions of the optimal strategies for each stage. Next, we study the big data-based power generation forecasting techniques, and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Furthermore, we prove the properties of the proposed energy management algorithm including the existence and uniqueness of Nash equilibrium and Stackelberg equilibrium. Simulation results show that accurate prediction results of wind power is conducive to better energy management.

When Social Network Effect Meets Congestion Effect in Wireless Networks: Data Usage Equilibrium and Optimal Pricing

Abstract: The rapid growth of online social networks has strengthened wireless users’ social relationships, which in turn has resulted in more data traffic due to network effect in the social domain. Nevertheless, the boosted demand for wireless services may challenge the limited wireless capacity. To build a thorough understanding, we study mobile users’ data usage behavior by jointly considering the network effect due to their social relationships in the social domain and the congestion effect in the physical wireless domain. Specifically, we develop a Stackelberg game for socially aware data usage: in Stage I, a wireless provider first decides the data pricing to all users in order to maximize its revenue, and then in Stage II, users decide their data usage, for the given price, subject to mutual interactions under both social network effect and congestion effect. We analyze the two-stage game via backward induction. In particular, for Stage II, we first provide conditions for the existence and the uniqueness of a user demand equilibrium (UDE). Then, we propose algorithms to find the UDE and for users to reach the UDE in a distributed manner. We further investigate the impact of different system parameters on the UDE. Next, for Stage I, we develop an optimal pricing algorithm to maximize the wireless provider’s revenue. We numerically evaluate the performance of our proposed algorithms using real data, and thereby draw useful engineering insights for the operation of wireless providers: 1) when social network effect dominates congestion effect, the marginal gain of the total usage increases with the social ties and the number of users, or decreases with the congestion coefficient; in contrast, when congestion effect dominates social network effect, the marginal gain decreases (or increases, respectively) with these parameters and 2) when social network effect is strong, a lower price should be set to increase the total revenue; in contrast, when congestion effect is strong, a higher price is preferred.

Competitive Charging Station Pricing for Plug-In Electric Vehicles

Abstract: This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs’ charging station selection decisions, and the other station’s pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a 1-D system with two competing charging stations and Poisson arriving PEVs. We propose a multileader-multifollower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in stage I and the PEVs (followers) make their charging station selections in stage II. We show that there always exists a unique charging station selection equilibrium in stage II, and such equilibrium depends on the charging stations’ service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.

A Multi-Leader Multi-Follower Stackelberg Game for Resource Management in LTE Unlicensed

Abstract: It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs’ interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

Optimal decisions of price, quality, effort level and return policy in a three-level closed-loop supply chain based on different game theory approaches

Abstract: Over the last few decades, the closed loop supply chain (CLSC) has been examined because of concerns over the environment and social liability. In this paper, we propose a joint optimisation model of pricing strategies, quality levels, effort decisions, and return policies by considering the reference price effect in a three-level supply chain under different channel power structures. To investigate the impact of different scenarios on optimal decisions and performance of a CLSC, we address five different channel power structures: centralised, vertical Nash, manufacturer Stackelberg, retailer Stackelberg, and third party Stackelberg. We present a numerical example to demonstrate the theoretical results of the developed model, and we also compare the optimal decisions to determine the best channel power structures considered. Then, to examine the impact of the key parameters on the model's behaviour, we conduct a sensitivity analysis on the main parameters, and finally, we provide a conclusion. [Received 5 October 2016; Revised 9 March 2017; Accepted 24 March 2017]

A Hierarchical Learning Solution for Anti-Jamming Stackelberg Game With Discrete Power Strategies

Abstract: This letter investigates the anti-jamming problem with discrete power strategies, and then a Stackelberg game is formulated to model the competitive interactions between the user and jammer. Specifically, the user acts as the leader, whereas the jammer is the follower. Based on their own utilities, the user and jammer select their power strategies and determine their respective optimal strategies. Also, a hierarchical power control algorithm (HPCA) is proposed to obtain the Stackelberg equilibrium, and the asymptotic convergence is analyzed. In addition, we consider the impact of the imperfect information due to the jammer’s bounded rationality and inaccurate observation of the user’s action. Finally, simulations are conducted to show the effectiveness of the proposed HPCA algorithm, and simulation results demonstrate that the jammer’s bounded rationality and limited observation lead to the increase of the user’s utility.

Economic Analysis of Network Effects on Sponsored Content: A Hierarchical Game Theoretic Approach

Abstract: Sponsored content policy enables a content provider to pay a network operator, and thereby their users access contents from the content provider through network services from the network operator with lower charge. In this paper, we study the interaction among three entities under the sponsored content policy, namely, the network operator or service provider, the content provider and the end-users. We consider a hierarchical three-stage setting to formulate the game theoretic model to analyze the interaction. Using the game model, we derive the user content demand, optimal sponsoring of content provider, and pricing of service provider based on backward induction. The model incorporates the network effects in social domain and congestion in network domain which enables us to obtain insights from the sponsored content policy. We derive the closedform solution, i.e., equilibrium, and prove its existence and uniqueness in each stage of the game. Additionally, we develop an iterative algorithm to obtain the Stackelberg equilibrium of the entire three-stage game. The simulation results indicate that the revenue, profit, and utility of the service provider, content provider, and end-users have been improved to a large extent under the sponsored content policy because of the network effects.

Low Carbon Strategy Analysis of Competing Supply Chains with Different Power Structures

Abstract: This paper investigates the emission reduction performance for supply chain members in both single-channel and exclusive dual-channel cases Two game scenarios (Manufacturer Stackelberg and Retailer Stackelberg) are examined under different channel structures Furthermore, we introduce government subsidies as an impact factor of low-carbon strategy adoption In the single-channel (Case 1), we mainly examine the influence of consumers’ price-sensitivity on channel members’ optimal decisions In the dual-channel (Case 2), we focus on the joint impact of product substitutability and different channel power structures on the optimal decisions under asymmetric related channel status The analysis suggests that the Stackelberg leaders always perform better than their corresponding followers before emission reduction, while they may not necessarily yield more benefits after emission reduction The implementation of low-carbon strategy depends on parameters like product substitutability and channel base demand Finally, all the supply chain members will encounter a Prisoner’s Dilemma when the product substitutability is relatively high

A Collaborative DDoS Defence Framework Using Network Function Virtualization

Abstract: High-profile and often destructive distributed denial of service (DDoS) attacks continue to be one of the top security concerns as the DDoS attacks volumes are increasing constantly Among them, the SYN Flood attack is the most common type Conventional DDoS defense solutions may not be preferable, since they demand highly capable hardware resources, which induce high cost and long deployment cycle The emerging of network function virtualization (NFV) technology introduces new opportunities to decrease the amount of proprietary hardware that is needed to launch and operate network services In this paper, we propose a DDoS defense mechanism named CoFence, which facilitates a “domain-helps-domain” collaboration network among NFV-based domain networks CoFence allows domain networks to help each other in handling large volume of DDoS attacks through resource sharing Specifically, we design a dynamic resource allocation mechanism for domains so that the resource allocation is fair, efficient, and incentive-compatible The resource sharing mechanism is modeled as a multi-leader-follower Stackelberg game In this game, all domains have a degree of control to maximize their own utility The resource supplier domains determine the amount of resource to each requesting peer based on optimizing a reciprocal-based utility function On the other hand, the resource requesting domains decide the level of demand to send to the resource supplier domains in order to reach sufficient support Our simulation results demonstrate that the designed resource allocation game is effective, incentive compatible, fair, and reciprocal under its Nash equilibrium