The purpose of this paper is to study the rough concept lattice and use the information flow to construct a second-order cone programming model for big data. Through the construction of the model, attribute reduction ...
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The purpose of this paper is to study the rough concept lattice and use the information flow to construct a second-order cone programming model for big data. Through the construction of the model, attribute reduction is performed on the original data of the noise in the formal background. Then, construct the concept lattice according to the reduced formal background, and then analyze the big data in the form of information flow. Then, based on the advantages of the beta-upper and lower distribution reduction algorithms of the variable-precision rough set, combine the rough concept. The characteristics of the background of the lattice form, the second-ordercone thought method theory is applied, and then a second-ordercone calculation model is constructed. The rough concept lattice is applied to the processing of big data, and then it is analyzed and researched through concrete examples. The time required in traditional mode is between 118.3 min and 123.6 min, while the time required for second-ordercone and concept lattice fitting is 92.4 min and 98.5 min. Experimental data show that the rough concept lattice uses information flow to construct a second-order cone programming model for big data, which results in a greatly reduced number of nodes in the rough concept lattice and an enhanced anti-noise capability of the system, which saves data statistics and calculation time. The traditional concept lattice algorithm can be traced back to the purification of the formal background, and the purification of the formal background can simplify the concept connotation and study attribute reduction from the perspective of lattice isomorphism. Experimental data show that the rough concept lattice uses information flow to construct a second-order cone programming model for big data, which greatly guarantees the integrity and security of the data by about 15%, and saves 20% of the data processing time compared with traditional and algorithms. It has guiding significance for t
Container slot allocation for liner shipping services is to allocate the limited container slots of ships to different segments of demands in order to maximize the total revenue over a shipping network. This study foc...
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Container slot allocation for liner shipping services is to allocate the limited container slots of ships to different segments of demands in order to maximize the total revenue over a shipping network. This study focuses on a planning-level container slot allocation problem with uncertain demand, which is essential in container shipping revenue management. Due to the challenge of calibrating/formulating a specific probability distribution of uncertain container slot demand, we can rely on its fundamental descriptive statistics, namely, mean, upper/lower bounds as well as variance to tackle the container slot allocation problem. We, therefore, develop a robust optimization model using the fundamental descriptive statistics, which is approximated by a solvable second-order cone programming model. A numerical example based on a real shipping network demonstrates that the optimal solution from the second-order cone programming model outperforms the models using the expectation of uncertain demand data and the normally distributed demand. The numerical results indicate that the robust optimization model can well deal with the large fluctuations of uncertain container slot demand.
With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In thi...
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With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In this study, the optimal allocation of distributed ESSs is studied to maximise the benefit of the distribution system operator. The ESS allocation problem is divided into two stages: the mixed integer investment problem as the first stage and the optimal operation problems considering daily charging/discharging schedule of ESSs as the second stage. To tackle the uncertainties of distributed generation output and base load, typical days (scenarios) are firstly obtained by the clustering method and thereafter the operation problems include a number of scenarios, each with the corresponding possibility. Then each second stage problem is relaxed to a second-order cone programming model. To efficiently solve the whole problem considering multiple scenarios, the generalised Benders decomposition (GBD) algorithm is adopted, which is further accelerated by relaxing and rebinding integer constraints. Numerical experiments are conducted on a 17-bus test system to demonstrate the effectiveness of the proposed method. Additionally, comparisons between different algorithms are performed to verify the merits of the proposed acceleration method with respect to the original GBD and the branch-and-bound algorithm.
This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, dem...
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This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on-load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non-linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second-order cone programming model through distflow equations and constraints relaxation. A modified 50-bus application example is used to verify the proposed model.
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