In this study, the authors propose a user association scheme with quality of service support for load balancing in heterogeneous cellular networks (HCNs), which jointly considers user's achievable rate and load le...
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In this study, the authors propose a user association scheme with quality of service support for load balancing in heterogeneous cellular networks (HCNs), which jointly considers user's achievable rate and load level of each BS instead of only utilising the former. To reveal how HCNs should self-organise, the authors formulate it as a network-wide weighted utility maximisation problem. Note that the formulated problem is a non-linear mixed-integer one, and its optimal solutions may be very difficult to be found when it is large-scale. To solve the proposed problem, the authors design a low-complexity distributed algorithm via dual decomposition. Numerical results show that, compared with the range expansion association (REA) and best power association (BPA), the strategy has a higher load balancing level (LBL) and a lower call blocking probability (CBP). Meanwhile, the proposed algorithm occupies a very fast convergence rate when its parameters are set properly.
The electricity industry has always been interested in expanding investment in the transmission sector of the industry. As load demand increases and generation expands to meet the need, transmission expansion becomes ...
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ISBN:
(纸本)9781424404926
The electricity industry has always been interested in expanding investment in the transmission sector of the industry. As load demand increases and generation expands to meet the need, transmission expansion becomes important in order to increase social welfare by reducing total system operating cost, and to make the system more reliable. A methodology for contemporary transmission expansion planning using mixed-integernonlinear Multi-Objective Optimization to reduce total system operating cost (congestion alleviation) and line construction/investment cost is explored here. The mixed-integernonlinear Multi-Objective Optimization includes network constraints (line thermal limits, voltage limits, and generator limits). Contingency analysis is performed after the optimization. The methodology is applied to an IEEE 30-bus system, an IEEE 118-bus system, and the results are presented.
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