With the widespread application of Internet of thingstechnology in smart grid, the integration of numerous intelligent terminals exacerbates network congestion and data loss, leading to increased load tracking deviations. Simultaneously, the automatic generation control (AGC) employed for maintaining supply-demand balance faces high costs, slow response rates, and limited adaptability to renewable energy. Employing gas-to-power technology in conjunction with AGC can enhance overall system efficiency and stability. This paper proposes a power allocation and pricing strategy utilizing a two-level Stackelberg game framework to reduce utility costs while boosting profits for telecom operator and gas company. We develop an electricity cost model for utilities considering regulation errors from direct load control in smart grid. Using an iterative algorithm and backward induction, we derive the Nash equilibrium for the Stackelberg game. Simulation results show that this strategy reduces utility costs and increases profits for telecom operator and gas company.

Smart Grid needs real-time monitoring of power equipment status and optimization of power distribution. In order to meet the needs of mass communication and data transmission, smart grid needs sufficient spectrum resources to ensure fast and reliable data transmission. However, the limitation of spectrum resources and the interference caused by multisystem coexistence have become the bottleneck of the development of smart grid. This article pointed out the defect of the existing spectrum resources management mode. Based on the Taguchi Quality Assessment Theory, we developed a cost model for a utility company, which used a decoding and forward cooperative relay strategy to transmit downlink horizontal propagation to a data aggregation unit. This article analyzed the bilateral auction process between utility company and licensed user to maximize social welfare, and determined the optimal spectrum transaction to improve the utilization of spectrum resources.

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and further increase the cost of utility companies due to the purchase of additional automatic generation control services. This article considers a cooperative communication network consisting of multiple data aggregation units (DAUs) and multiple relays in smart grid, and each relay can forward data for all DAUs. We optimize the transmission power allocation of the relays to reduce the demand-side regulation errors and the cost of utility companies. However, additional cost is incurred due to the rental of relay in commercial networks. In order to minimize the total costs of utility companies, a two-layer game model is proposed and an iterative algorithm is developed. Simulation results show that the cost of utility companies can be reduced under the proposed scheme.

Owing to the openness and diversification of heterogeneous communication network, communication security becomes a pressing problem. In this paper, we consider a heterogeneous communication network in which spectrum resources are shared by electric power communication network and licensed network. First, we establish the utility companies’ cost model based on Taguchi loss function. Next, we utilize cooperative relay strategy to enhance the transmission quality and achieve high-speed information transmission in smart grids. Under the premise of ensuring high-quality transmission of electric power communication services, we propose a secure transmission strategy for information resource sharing and interference price trading that utilizes smart grid infrastructure and relay to interfere with eavesdroppers to improve the security rate of licensed user (LU), which achieves mutual benefits. Furthermore, the bernstein approximation method and the successive convex approximation are adopted to obtain the open-form expression of the constraint and transform the non-convex problem into the convex problem, respectively. A distributed robust power control algorithm is then proposed to obtain the optimal solutions. Finally, numerical results verify that the proposed secure scheme and algorithm can increase the secrecy rate at LU, reduce the total electricity cost, and improve both the profit of relay and the social welfare.

In order to improve the profits of electricity utility company (EUC) and gas utility company (GUC) and reduce the electricity and heat cost of users, an energy trading and pricing scheme based on three-stage game is proposed. Firstly, a three-stage optimization problem is established, and the conversion between electricity and gas is considered. Meanwhile, the conversion factor is introduced and coordinated with the energy price to adjust the balance of supply and demand. Then, the equilibrium solution of the game is obtained by using Lagrange function and backward induction method. In addition, an iterative algorithm is developed to obtain the optimal conversion factor between electricity and natural gas. Numerical results show that the profits of EUC and GUC are increased by 31.6% and 14.4%, the electricity and heat profits of energy hubs (EHs) are increased by 3% and 6.4%, and the electricity and heat cost of users are reduced by 9.25% and 14.05%. Note to Practitioners—In the multi-energy market, trading and pricing strategies have attracted more and more attention. Based on this, many scholars only studied pricing strategy to balance the supply and demand. However, in the context of multi-energy coupling, the previous pricing strategy is not effective. In this paper, we propose a new pricing strategy, with which the conversion factor can cooperate with the energy price to achieve the balance between supply and demand. The new pricing strategy can increase the profits of electricity utility companies and gas utility companies and reduce the costs of users.

In smart grid, serious packet loss often occurs in the process of information interaction between the Utilities and customers, which results in supply-demand deviation and further increases the cost of the Utilities. In order to improve the information transmission performance of communication networks, the Utilities purchase relay service from telecom operator to help data aggregator units (DAU) transfer information to gateway (GW), so as to improve communication quality and reduce the cost of the Utilities. Second, in order to solve the problem of telecom operators’ energy reduction and reduce the cost of purchasing energy, we utilize energy supply point (ESP) to collect the surplus energy of retail customers for energy supply, and telecom operator pays a certain amount of remuneration to ESP in exchange for ESP to continuously supply energy to telecom operator. Then, we establish a three-stage game method and system model between the Utilities, telecom operator and ESP, and propose the relay power allocation and energy transaction pricing strategy. Due to the real-time change of energy demand, we consider two situations of energy oversupply and conservative supply, and use the backward induction method and iterative algorithm to obtain the equilibrium solution of the Stackelberg game, including the unit energy price of ESP, total power of telecom operator, the proportion of transmission power allocated to the relay service, and payment scheme of the Utilities. Simulation results show that the proposed algorithm can quickly and accurately converge to the optimal solution of the problem, and the method can improve the stability of demand-side regulation, reduce the cost of the Utilities and increase the profit of telecom operator.

The consensus based distributed frequency control strategy makes all measurements and control units in microgrids vulnerable to false data injection (FDI) attacks. This paper presents an observer-based resilient frequency control for estimating and compensating for FDI attacks on sensors and actuators in AC microgrids. Firstly, attacks of sensors and actuators are estimated online simultaneously by observers. Then a distributed H ∞ output feedback protocol is used to ensure that the frequency consensus tracking is achieved with cooperative uniform ultimate boundedness. The control strategy is completely distributed and does not require any quantitative information about attacks. Furthermore, there is no limit to the number and location of attacked units. Several case studies are provided to verify the effectiveness of the proposed resilient frequency control strategy.

This article considers both the influence of relay power and load control period on the load tracking performance in smart grid communication. First, based on regulation errors caused by load control with imperfect channel state information (CSI), the load tracking cost model integrated of load control period and relay power is established in smart grid communication. Then, a coordination mechanism of cost-sharing contract (CSC) is presented. In the CSC, the utility companies select the preferred contractual terms offered by the telecom operator (TO) to reduce their costs and coordinate the whole network system simultaneously. Finally, the theoretical analysis and simulation demonstrate that the simultaneous consideration of the relay power and load control period can reduce the costs of the utility companies, increase the profit of the TO, and improve the social welfare. Besides, the proposed coordination mechanism of CSC can coordinate the whole network system.

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

The fast-growing demand of refueling electric vehicles (EVs) blocks the application and popularization of EVs. Battery swapping provides the EV users with a quick and convenient refueling way. In this article, an aggregative shared battery station (SBS) model is proposed, which is composed of a control center and a group of SBSs. With the SBS, the customers can rent the battery and pay a corresponding fee based on the swapped energy and satisfaction level. In order to enhance the SBS system responsiveness and reconfiguration to meet the changeable customers' battery demand and peak shaving and valley filling task, a two-stage framework for the multi-SBS is designed based on a self-adaptive dispatching strategy. On behalf of the SBS operator, an optimization objective function is established to maximize the operating revenue by optimizing the charging, discharging, and sleeping process of the batteries. Using the genetic algorithm, we perform extensive simulations to validate the optimization model and demonstrate the efficiency of the self-adaptive dispatching strategy. The results suggest that the proposed dispatching strategy is effective for scheduling SBSs to satisfy the EV refueling demand, provide peak shaving and valley filling service, and achieve the revenue maximization.

In this article, we study an interference management and power allocation problem when electrical power communication (EPC) networks are densely deployed in the coverage of licensed networks. The purpose is to reduce the electricity cost and improve the licensed operator's profit subject to the quality-of-service (QoS) of licensed users (LUs). First, the electricity cost is modeled based on the Taguchi loss function, which links the cost to the communication errors in the EPC networks. The operator's profit is formulated by introducing a bonus-penalty mechanism, and a rational interference threshold (IT) of the licensed base station (LBS) is set to ensure the QoS of the LU. Second, we formulate the interference management and power allocation problem as a Stackelberg game, and a successive convex approximation algorithm is used to solve this problem to achieve the optimal IT and relay power. The simulation results indicate that the cost to the utility company is reduced and the profit of the LBS increases.

The electricity costs of the Utilities are increased with the deviations from the forecasted demand, which are caused by forecast errors and demand fluctuations. The forecast errors are deterministic and can be avoided by the Utilities with long-term operations in electricity markets, whereas the demand fluctuations are stochastic and unavoidable. Demand response can be used for mitigating the demand fluctuations by measuring the electricity usage of consumers and publishing control commands periodically. The performance of demand response is dependent on the quality of communications between the control center and the consumers. The two-way communications are established based on the data aggregator units (DAU) deployed by the Utilities. To improve the quality of communications, we utilize the base stations in telecom networks to forward the metering and control data for the DAUs. The telecom operators maximize their profits and decide the fractions of transmission power allocated for relaying, and then the Utilities select the payments to obtain the corresponding transmission power allocated for relaying. We characterize the electricity costs of the Utilities based on Taguchi loss function and establish a Stackelberg game between the telecom operators and the Utilities. We prove the existence and uniqueness of Nash equilibrium for the follower-level payment selection game and develop an iterative algorithm to search for the equilibrium. Then, the Stackelberg equilibrium can be obtained by a backward induction method. Numerical results show that the cost of the Utilities can be reduced and the profits of the telecom operators are increased.

In this paper, a pricing mechanism is proposed for the electricity supply chain, which is consisting of one generation company (GC), multiple consumers, and competing utility companies (UCs). The UC participates in electricity supply chain management by a revenue sharing contract (RSC). In the electricity supply chain, the electricity real-time balance has an important role in the stable operation of the power system. Therefore, we introduce the demand response into the electricity supply chain to match supply with demand under forecast errors. Hence, we formulate a noncooperative game to characterize the interactions among the multiple competing UCs, which set the retail prices to maximize their profits. Besides, the UCs select their preferred contractual terms offered by the GC to maximize its profits and coordinate the electricity supply chain simultaneously. The existence and uniqueness of the Nash equilibrium (NE) are examined, and an iterative algorithm is developed to obtain the NE. Furthermore, we analyze the RSC that can coordinate the electricity supply chain and align the NE with the cooperative optimum under the RSC. Finally, numerical results demonstrate the superiority of the proposed model and the influence of market demand disruptions on the profits of the UCs, GC, and supply chain.

In this paper, we optimize power and spectrum allocation simultaneously to improve the demand-side communication quality in smart grid, to further reduce the cost of utility companies. The electricity cost is first modeled based on regulation errors caused by direct load control in the smart grid. Then the subbands are allocated to different data aggregator units according to the band confidence levels and the utility company's maximum cost. An algorithm is designed to optimize transmission power of the relay and refine the spectrum allocation to reduce the cost of utility companies. Simulation results demonstrate that the packet loss rate and cost of utility companies can be significantly reduced.

This paper presents an energy management method to optimally control the energy supply and the temperature settings of distributed heating and ventilation systems for residential buildings. The control model attempts to schedule the supply and demand simultaneously with the purpose of minimizing the total costs. Moreover, the Predicted Percentage of Dissatisfied (PPD) model is introduced into the consumers' cost functions and the quadratic fitting method is applied to simplify the PPD model. An energy management algorithm is developed to seek the optimal temperature settings, the energy supply, and the price. Furthermore, due to the ubiquity of price oscillations in electricity markets, we analyze and examine the effects of price oscillations on the performance of the proposed algorithm. Finally, the theoretical analysis and simulation results both demonstrate that the proposed energy management algorithm with price oscillations can converge to a region around the optimal solution.

Demand response can improve the stability of power system and reduce the operation cost. This paper design a pricing scheme to balance the energy demand and supply based on demand response in smart grid. In the demand side, a Bayesian game is formulated to model the interaction of multiple consumers because each consumer's utility function is private. The utility company announces a regulation price which can change the Bayesian Nash equilibrium and the energy demand of consumers. We develop an algorithm to update the regulation price until the energy demand match the energy supply. Numerical results demonstrate that the algorithm make the regulation price converge to a stable state and balance the energy supply and demand.