This paper studies the task offloading problem for ground users in remote areas in satellite edge computing. Each user can offload computation tasks to either the Geosynchronous Earth Orbit (GEO) satellite, forward th...
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ISBN:
(纸本)9789819707973;9789819707980
This paper studies the task offloading problem for ground users in remote areas in satellite edge computing. Each user can offload computation tasks to either the Geosynchronous Earth Orbit (GEO) satellite, forward them to the ground cloud computing center, or offload them to a Low Earth Orbit (LEO) satellite which is constantly moving relative to the ground. To obtain the optimal task offloading plan and resource allocation plan that minimize system computing delay, we formulate this problem as a mixedintegernonlinearprogramming (MINLP) problem and propose a low complexity solution algorithm for it. Through mathematical derivation, we can organize the MINLP problem into three separate solutions: optimal allocation of computing resources, optimal transmission power control, and optimal offloading plan. In our algorithm, we apply the Lagrange multiplier method and binary search to obtain the optimal allocation of computing resources and optimal transmission power control under a given offloading plan. Then, using our proposed method based on the idea of greedy algorithm, we obtain an approximate optimal solution for task offloading. Compared to other algorithms, our proposed algorithm significantly reduces the system cost with a low computation complexity.
In the era of smart grids and the Internet of Things, demand side management, which aims to reduce electricity bills while increasing user satisfaction by scheduling appliances properly, becomes imperative for residen...
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In the era of smart grids and the Internet of Things, demand side management, which aims to reduce electricity bills while increasing user satisfaction by scheduling appliances properly, becomes imperative for residential consumers. As a result of the conflict between the two objectives, it is impossible to optimize them simultaneously. Nevertheless, using multi-objective optimization approaches, trade-off solutions can be obtained. In this paper, a novel demand-side management method is presented to manage the operation of residential appliances. In the beginning, appliances are divided into interruptible, non-interruptible, and power-shiftable types according to their operating characteristics and the user's preferences. And the mathematical models are built accordingly. Then, a multi-objective optimization problem is formulated to minimize the electricity cost and user dissatisfaction, in which residents' tolerance to discomfort is considered. Since it is a multi-objective mixed integer nonlinear programming problem, a hybrid meta-heuristic algorithm is proposed to solve it efficiently. The experiment results have confirmed the effectiveness of the optimization model and the higher efficiency of the hybrid algorithm. Furthermore, a case study has been performed to demonstrate the effectiveness of the scheduling method.
This study proposes a stochastic optimisation model for interconnected distribution network planning (IDNP) in the presence of renewable sources (RSs) considering power transfer capability after an N − 1 contingency. ...
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This study proposes a stochastic optimisation model for interconnected distribution network planning (IDNP) in the presence of renewable sources (RSs) considering power transfer capability after an N − 1 contingency. Two types of scenarios, feeder contingency, and substation transformer contingency, are formulated in the IDNP problem. Uncertainties of the RS output and load fluctuation are also integrated. The planning method provides planners with the decisions of feeder reformation, new tie lines, new feeders, substation expansion, new substations and new load allocation. The IDNP model is a chance-constrained mixedinteger non-linear programmingproblem, which is solved by dynamic niche differential evolution algorithm. A case study carried out on a modified 104-bus distribution network demonstrates the effectiveness of those techniques. Compared with the traditional planning approach, the IDNP method could exploit asset utilisation by optimising existing networks efficiently as well as improve system economy and security simultaneously.
Urban traffic congestion has already become an urgent problem. Artificial societies, Computational experiments, and Parallel execution (ACP) method is applied to urban traffic problems. In ACP framework, optimization ...
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ISBN:
(纸本)9781479960798
Urban traffic congestion has already become an urgent problem. Artificial societies, Computational experiments, and Parallel execution (ACP) method is applied to urban traffic problems. In ACP framework, optimization for urban road networks achieves remarkable effect. Optimization for urban road networks is a problem of nonlinear and non-convex programming with typical large-scale continual and integer variables. Due to the complicated urban traffic system, this paper focuses on the ACP-based Computational experiments modeling. It hopes to find an optimization model that is further accord with the practical situation. To this end, we use a mixed integer nonlinear programming problem (MINLP) and an genetic algorithm (GA) for urban road networks optimization. The systemic simulation experiments show that the approach is more effective in improving traffic status and increasing traffic safety.
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