A Hierarchical Game Theory Based Demand Optimization Method for Grid-Interaction of Energy Flexible Buildings

Building demand-side management is an effective solution for relieving the peak and imbalance problems of electrical grids. How to explore the energy flexibility of buildings and to coordinate a variety of buildings with different energy flexibilities for effective interactions with smart grids are a great challenge. This paper proposes a game theory–based hierarchical demand optimization method for energy flexible buildings for achieving better grid interactions. This method consists of two optimization strategies at the grid and building levels. At the grid level, a demand-price interaction model for buildings and the grid is established to identify the Nash equilibrium solutions based on game theory; these solutions are used to determine the optimized energy demand of buildings and the associated electricity prices by accommodating the interests of all participants involved. At the building level, three types of buildings with different energy flexibilities are investigated to analyze the influence of building management strategies on grid interactions. The effectiveness of the proposed method is verified in a simulated case study. The results show that the optimization method can reduce building operational cost by 3–18%, reduce the fluctuation of the power grid by 30–50%, and ensure that the power grid increases income by 8–20%.

A Method to Select Best Among Multi-Nash Equilibria

A non-zero sum bimatrix game may yield numerous Nash equilibrium solutions while solving the game. The selection of a good Nash equilibrium from among the many options poses a dilemma. In this article, three methods have been proposed to select a good Nash equilibrium. The first approach identifies the most payoff-dominant Nash equilibrium, while the second method selects the most risk-dominant Nash equilibrium. The third method combines risk dominance and payoff dominance by giving due weights to the two criteria. A sensitivity analysis is performed by changing the relative weights of criteria to check its effect on the ranks of the multiple Nash equilibria, infusing more confidence in deciding the best Nash equilibrium. JEL Codes: C7, C72, D81

Applying the noncooperative game model for compensation concept in contractor selection process for large-scale projects

Nowadays, the scale of construction projects has been larger and more complex, the tender preparation is often costly to the bidder thus. It is becoming one of the primary barriers for attracting bidder’s involvement, as well as contractor's encouraging high effort. Bid compensation concept is proposed as a reward to foster the bidder participating in a higher endeavor. Game theory is ideal for modeling the dynamics and deriving high-effort strategies for bid compensation. The experiment results have demonstrated the owner can gain benefit by using rational compensation. The sensitivity analysis also shows the interest correlation between the owner and bidders. By choosing a proper strategy based on Nash Equilibrium solutions, both the owner and bidders can reach to win-win situation.

Stochastic Differential Game-Based Malware Propagation in Edge Computing-Based IoT

Internet of Things (IoT) has played an important role in our daily life since its emergence. The applications of IoT cover from the traditional devices to intelligent equipment. With the great potential of IoT, there comes various kinds of security problems. In this paper, we study the malware propagation under the dynamic interaction between the attackers and defenders in edge computing-based IoT and propose an infinite-horizon stochastic differential game model to discuss the optimal strategies for the attackers and defenders. Considering the effect of stochastic fluctuations in the edge network on the malware propagation, we construct the Itô stochastic differential equations to describe the propagation of the malware in edge computing-based IoT. Subsequently, we analyze the feedback Nash equilibrium solutions for our proposed game model, which can be considered as the optimal strategies for the defenders and attackers. Finally, numerical simulations show the effectiveness of our proposed game model.

Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

Modeling and Computation of Transboundary Pollution Game Based on Joint Implementation Mechanism

In the paper, we use the differential game method to test the impact of joint implementation (JI) mechanism on pollution control in two bilateral countries. The Hamilton-Jacobi-Bellman (HJB) equations of the models are obtained by using the dynamic programming principle. We obtain the optimal emissions, optimal local and foreign investments in environment projects, optimal revenues, and optimal trajectories of carbon stock under three situations, namely, situation without JI, with JI (noncooperative), and with JI (cooperative), of the two countries by solving these equations. We also compare their optimal Nash equilibrium solutions. We find that the introduction of JI mechanism can slow down the growth of the carbon stocks by reducing emissions or increasing investment in emission reduction projects, compared to the situation without JI mechanism. However, the JI mechanism does not reduce the revenue of the two countries under certain conditions. Finally, some numerical tests are provided to illustrate the theoretical results.

A Differential Game Approach to Dynamic Competitive Decisions Toward Human-Computer Collaboration

Partnership between humans and computers has a significant potential to extend the ability of humans to address complex design problems. This paper presents a decision-making process for computers to effectively collaborate with humans in the solution of complex problems under dynamic competition. In the proposed process, the computers learn strategies and objectives from prior experimental data and provide strategy suggestions to human collaborators. The study integrates clustering and sequential learning methods from machine learning with a differential game formulation based on model predictive control to find dynamic Nash equilibrium solutions to zero-sum games. The application of the proposed approach is demonstrated on the real-time strategy game Starcraft II that offers a dynamic competitive problem comparable in complexity to real-world applications. The results show that the proposed approach can successfully identify a variety of opening strategies in the experimental data for the initial phase of the process. The game-theoretic strategies in the later phases provide useful suggestions for low-performing players but are unnecessarily conservative for high-performing players where there is little opportunity for improvement. These results suggest a need for an assessment of the opponent expertise and a human intuition to judge the appropriateness of the game-theoretic suggestions for further improvement.