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.

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.

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.

This paper proposes a three-stage multi-energy sharing strategy for a gas-electricity integrated energy system (IES). It aims to solve the multi-energy imbalance problem among energy hubs (EHs) based on the peer-to-peer (P2P) trading mode. First, considering the characteristics of multi-energy coupling and conversion, the quantity of shareable energy is determined for EHs that participate in the P2P trading. Furthermore, EHs conduct multi-bilateral negotiations based on the Raiffa-Kalai-Smorodinsky bargaining solution (RBS) to determine the optimal energy trading price. Finally, the buyer agent and the seller agent will design the optimal energy sharing trading strategy for all EHs. Moreover, the results show that the pricing mechanism improves the fairness of satisfaction obtained by the EHs from the utility distribution, and the social welfare of the system is improved, which proves that the three-stage multi-energy sharing trading strategy is effective.

A multiagent system based distributed cooperative control is proposed for a dc microgrid. First, the multiagent system based control scheme is built, where each distributed energy resource unit is associated with a first-level unit control agent to deal with primary control and a secondary-level distributed cooperative control agents to implement secondary control. For the secondary control purposes of global voltage regulation and proportional current sharing, both voltage and current regulators are installed in each secondary-level agent. Their consensus secondary control protocols are designed by using the state information exchanged among only adjacent secondary-level control agents based on a switching topology communication network with time-vary delays. Then these protocols are applied into the associated first-level agent to structure an improved outer-loop droop controller. According to the reference set points corrected by the improved droop controller, an inner-loop voltage/current robust controller is designed in place of the conventional PI control to regulate the voltage and current of distributed energy resource unit. Finally, the validity of the proposed control is testified by means of simulation results.