Renewable Energy Sharing Among Base Stations as a Min-Cost-Max-Flow Optimization Problem

Abstract

Limited work has been done to optimize the power sharing among base stations (BSs) while considering the topology of the cellular network and the distance-dependent power loss (DDPL) in the transmission lines. In this paper, we propose two power sharing optimization algorithms for energy-harvesting BSs: the max-flow (MF) algorithm and the min-cost-max-flow (MCMF) algorithm. The two proposed algorithms minimize the power drawn from the main grid by letting BSs with power surpluses transmit harvested power to BSs with deficits. The MCMF algorithm has an additional DDPL cost associated with each transmission line. Hence, the MCMF algorithm shares the harvested power over shorter distances and loses less power during the transmission than the MF algorithm. Our numerical results show that for a fully connected cellular network, i.e., every pair of BSs can share power, with a moderate power loss coefficient per l (∈ R + ) meters of transmission line, the MCMF algorithm saves up to 10%, 22%, and 30% more main grid power than the MF algorithm for 5, 10, and 15 BSs uniformly distributed in a square area of l 2 square meters, respectively.

Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand

“…is algorithm is well-known and widely used in cloud resource allocation [38], energy sharing management [39], resource management [40,41], and network traffic management [42,43].…”

Renewable Energy-Enabled Cellular Networks

“…For each work in more detail, D. Niyato et al [123] investigated the performance of a solar-powered wireless sensor/mesh network in terms of packet blocking and dropping probabilities with the presented queuing model. Given the fact that as the period of BS's off state increases, the packet blocking probability also increases while the packet dropping [95], [113], [114] [82], [100], [102] [103], [115] [84], [95], [100], [103] [107], [114] [38], [79]- [81] [84], [88], [89] [107] [116], [117] Minimizing the total energy (on-grid and RE) cost [118] Minimizing the net energy cost in smart grid [21] Maximizing the network utility [55], [99], [101] [76], [99] [76] Minimizing the network latency [39], [85] [57], [58] Maximizing the weighted sum of on-grid consumption and user QoS metric [40] [40], [41] [42]- [44] [86], [87], [108] [41], [108] Maximizing the utilization of RE [104] [82], [104] Maximizing the energy efficiency [96], [105] [47], [105] Minimizing the cost of using conventional energy source [106] [83], [93] [36], [37], [91] [6...…”

“…The regarding works determine the amount of wired energy transaction among the BSs or between BS and smart grid or both. With the optimal amount, grid power consumption can be minimized [89], [107], [116], [117]. In the case of energy sharing through physical power line between two BSs, it was shown that 80% of grid power consumption can be saved [89].…”

“…Nevertheless, the work did not consider the distance between two BSs which affects power losses. Considering the topology of the cellular network, D. Benda et al proposed two algorithms and compared their results [116]. First one was aware of the distance-dependent power loss in the transmission line and the other one was not.…”

Toward Energy Efficiency Aware Renewable Energy Management in Green Cellular Networks With Joint Coordination

“…The amount of solar power generation is may be higher or lower in accordance with tempo-spatial traffic diversity and solar radiation intensity which often causes a mismatch with the BS load consumption. The concept of sharing surplus electricity among neighboring BSs via smart grid or external physical transmission lines has been recognized as an emerging solution in some literature [26]- [28]. An efficient energy cooperation framework among collocated BSs via smart grid based on the level of a priori knowledge about RE generation has been proposed in [28].…”