Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

Energy Storage Economic Optimization Scheduling Method for Multi-Scene Demand of Peak and Frequency Modulation

energy storage (ES) only contributes to a single-scene (peak or frequency modulation (FM)) control of the power grid, resulting in low utilization rate and high economic cost. Herein, a coordinated control method of peak modulation and FM based on the state of ES under different time scales is proposed. Firstly, for monotone peak and FM control scenarios, the ES configuration and scheduling model is constructed with the goal of maximizing net profit. Secondly, to further improve the ES utilization rate and optimize the operating cost of ES, a cooperative control method of peak modulation and FM is proposed. This method can realize the switch between peak modulation and FM control of ES and improve the ES utilization rate and system economy. Finally, the simulation results show that, compared with that of mono-peak and single-FM control, the ES efficiency of the peak-FM multiscenario optimization scheduling method is improved by 16.25% and 37.29%, respectively. The annual net income is increased by €28,021.50, the investment recovery period is shortened by 0.27 years, and the ES configuration economy is effectively improved.

Optimal Planning of Hotel Renovation Projects

This paper presents the development of a novel model for optimizing the planning of hotel renovation projects to maximize hotel revenues during renovation work while minimizing project cost. The model is developed in three main modules: optimization, scheduling, and hotel profit modules. The model integrates an innovative methodology that enables renovation planners to select which hotels to renovate during any fiscal year based on an allocated renovation budget and identify an optimal floor renovation start date, optimal overtime hours usage and number of assigned crews for each renovation activity, and an optimal floor renovation order in each hotel. An application example of three hotels is analyzed to illustrate the use of the model and demonstrate its capabilities. The results of this analysis illustrate the novel contributions of the model and its original capability in generating optimal plans for hotel renovation projects that enable hotel owners to maximize revenues of their hotels during renovation work while minimizing hotel renovation costs.

Simultaneous optimization scheduling with two agents on an unbounded serial-batching machine

This paper considers a class of simultaneous optimization scheduling with two competitive agents on an unbounded serial-batching machine. The cost function of each agent depends on the completion times of its jobs only. According to whether the jobs from different agents can be processed in a common batch, compatible model and incompatible model are investigated. For the incompatible model, we consider batch availability and item availability. For each problem, we provide a polynomial-time algorithm that can find all Pareto optimal schedules.

Design of Intelligent Security Management and Control System for a Large Nuclear Facility

There are many kinds of safety control sub-systems of a large nuclear facility, and their information is isolated from each other, so the overall safety control efficiency is low. To solve this problem, based on the original security control sub-system, an intelligent security management and control system is designed by using the technology of system integration and multi-sensor cooperative scheduling. Firstly, the web service interfaces of each security control sub-system are developed, and the interconnection and information sharing of each security control sub-system are realized through these interfaces. Then, based on the multi-sensor system cooperative optimization scheduling technology and the improved ant colony algorithm, the information synthesis and security cooperative optimization scheduling of heterogeneous multi-sensor messages are realized. The highly integrated intelligent security management and control system reduces the alarm omission rate and false alarm rate of the safety control and improves the efficiency of the security control.