Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In t...
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Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company's expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted is an element of-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure.
We present a new exact approach for solving bi-objective integer linear programs. The new approach employs two of the existing exact algorithms in the literature, including the balanced box and the is an element of-co...
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We present a new exact approach for solving bi-objective integer linear programs. The new approach employs two of the existing exact algorithms in the literature, including the balanced box and the is an element of-constraint methods, in two stages. A computationally study shows that the new approach has three desirable characteristics. (1) It solves less single-objective integer linear programs. (2) Its solution time is significantly smaller. (3) It is competitive with the two-stage algorithm proposed by Leitner et al. (2016). (C) 2017 Elsevier B.V. All rights reserved.
Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather ...
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Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform. In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate is an element of-constraint method, tested on a set of instances and compared with the weighted sum (WS) method. The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions. (C) 2013 Elsevier B.V. All rights reserved.
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