## 2 Related Work and Contributions  
The survey carried out by authors in [16] highlights the predominant contribution of BSs to the total power consumption of cellular networks. This work also studies the integration of smart grids to the RE-powered cellular networks, including analyses on architecture and challenges that could be faced in the implementation; however, BS-Sw is not considered as an additional factor to enhance the EE of the system. Similarly, research conducted in [17] discusses the feasibility and benefits of integrating RE resources into the smart grid but again disregarding BS-Sw. There are also several works that study real-time BS control or BS-Sw to maximise energy savings. For instance, in [18], the authors employed the Markov decision process (MDP)-based algorithm to develop such a system. The operational mode of the BSs switches according to the capricious state (arrival and departure) of the active UEs; however, frequent and unexpected switching of BSs may not be efficient in practice [19]. BS-Sw is also studied from the perspective of energy cost in [20] where the energy expenditure can be reduced at the expense of poorer QoS. Moreover, the main concern in this work is communication cost reduction based on real-time energy pricing. Similarly, the studies in [21] focus on monetary-related issues such as operational and energy cost saving among the mobile network operators. In this work, a Markovian model is used as an RE generator and manager. The work in [5] studies BS coordination-based switching design for a green cellular network by making use of a self-adaptive scheduling algorithm without utilising energy harvesting. This work also suggests that BSs are the prime contributors to the total power consumption of the cellular network through the chart shown in Figure 1. Authors in [22] also studied the methods to reduce the grid power consumption of cellular networks through renewable energy without affecting user service quality by utilising a coalitional technique to manage the association between the user and the BS; however, BS-Sw was not considered in this work. Thus far, the existing works related to BS-Sw for HetNets have not been integrated with RE, smart grid, power control and data cooperation under a single objective. A general review of some recent relevant works that employ BS-Sw is presented in Table 1. The work in [23] optimises energy performance and offloads macrocell traffic without considering power allocation and energy sharing. In [24], the system EE is maximised by incorporating power control and data cooperation but there is no RE harvesting facility. On the other hand, the work in [25] looks into minimising the total power consumption by utilising power management and RE sharing disregarding data cooperation. The work in [26] contributes to maximising on-grid energy savings through energy sharing but no data cooperation or power allocation has been considered. Furthermore, the authors of [27] have enhanced the network EE by employing data cooperation discounting energy harvesting and sharing. Unlike the existing work, the proposed algorithm in this paper maximises the energy efficiency by utilising data cooperation and power allocation that mitigates the interference while employing renewable energy source to reduce the grid power consumption. Subsequently, excess energy is shared among the BSs through smart-grid and BS-Sw is applied to manage the underutilised BSs, which causes unnecessary energy and resource wastage. Motivated by the above mentioned problems, in this paper, we propose an algorithm to efficiently allocate transmit powers to all BSs and/or switch-off an optimal set of BSs to improve the EE of a HetNet consisting of CoMP-enabled hybrid-powered BSs subject to a minimum quality-of-service (QoS) for all mobile users. Hybrid power in this context implies that the BSs are powered by both the grid power source as well as the energy harvesting source.