为了缓解当前化石能源使用危机的问题,在考虑了储冷、储热和储电设备的基础上,构建了区域综合能源系统(regional integrated energy system,RIES)的经济性模型。将天然气购买的成本、购电成本和设备运行成本之和作为目标函数,将系统综...为了缓解当前化石能源使用危机的问题,在考虑了储冷、储热和储电设备的基础上,构建了区域综合能源系统(regional integrated energy system,RIES)的经济性模型。将天然气购买的成本、购电成本和设备运行成本之和作为目标函数,将系统综合运行成本考虑的更为周全,约束条件中不仅包括各设备的能量交互损耗、储能设备的功率和容量约束,还引入了重要负荷备用,提高了RIES的供电可靠性。利用改进的模拟退火-粒子群算法对所提模型进行求解,大大加快了求解算法的收敛速度。案例分析表明,提出的求解算法求解速度更快,进一步减少了系统综合运行成本。展开更多
In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
文摘为了缓解当前化石能源使用危机的问题,在考虑了储冷、储热和储电设备的基础上,构建了区域综合能源系统(regional integrated energy system,RIES)的经济性模型。将天然气购买的成本、购电成本和设备运行成本之和作为目标函数,将系统综合运行成本考虑的更为周全,约束条件中不仅包括各设备的能量交互损耗、储能设备的功率和容量约束,还引入了重要负荷备用,提高了RIES的供电可靠性。利用改进的模拟退火-粒子群算法对所提模型进行求解,大大加快了求解算法的收敛速度。案例分析表明,提出的求解算法求解速度更快,进一步减少了系统综合运行成本。
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
