Distortions of National Policies to Renewable Energy Cooperation Mechanisms

Facing more and more severe global warming problems, renewable energy, as an alternative to traditional fossil fuels, is attracting more and more attentions due to its capability of reducing carbon emission. This paper considers two-tier HetNets with orthogonal frequency division multiple access (OFDMA), where the macro base station (MBS) is powered by power grid and small base stations (SBSs) have hybrid energy supplies. Through smart grid, SBSs can share their renewable energy with each other. We consider the problem of cross-layer interference caused by spectrum reuse, the burst of user data, and the randomness of renewable energy arrivals. Through energy cooperation, this paper investigates maximizing the time-average energy efficiency of SBSs. Based on user data queue and SBS energy queue, the optimal problem is decoupled into two subproblems by Lyapunov optimization: resource allocation subproblem and energy scheduling and energy cooperation subproblem. By solving two subproblems, the online solution to the optimization problem is obtained. Through theoretical analysis and simulations, both user data queues and energy queues have an upper bound, the network is stable, and the proposed algorithm performs better than the basic algorithm without energy cooperation.

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Energy Cooperation in Ultradense Network Powered by Renewable Energy Based on Cluster and Learning Strategy

Wireless Communications and Mobile Computing ◽

10.1155/2017/2794324 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10

Author(s):

Chunhong Duo ◽

Baogang Li ◽

Yongqian Li ◽

Yabo Lv

Keyword(s):

Renewable Energy ◽

Learning Strategy ◽

Learning Algorithm ◽

Transmission Rate ◽

Energy Utilization ◽

Base Stations ◽

New Method ◽

Utilization Rate ◽

Arrival Times ◽

Energy Cooperation

A new method about renewable energy cooperation among small base stations (SBSs) is proposed, which is for maximizing the energy efficiency in ultradense network (UDN). In UDN each SBS is equipped with energy harvesting (EH) unit, and the energy arrival times are modeled as a Poisson counting process. Firstly, SBSs of large traffic demands are selected as the clustering centers, and then all SBSs are clustered using dynamic k-means algorithm. Secondly, SBSs coordinate their renewable energy within each formed cluster. The process of energy cooperation among SBSs is considered as Markov decision process. Q-learning algorithm is utilized to optimize energy cooperation. In the algorithm there are four different actions and their corresponding reward functions. Q-learning explores the action as much as possible and predicts better action by calculating reward. In addition, ε greedy policy is used to ensure the algorithm convergence. Finally, simulation results show that the new method reduces data dimension and improves calculation speed, which furthermore improves the utilization of renewable energy and promotes the performance of UDN. Through online optimization, the proposed method can significantly improve the energy utilization rate and data transmission rate.

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Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽

10.3390/s22010270 ◽

2021 ◽

Vol 22 (1) ◽

pp. 270

Author(s):

Mari Carmen Domingo

Keyword(s):

Renewable Energy ◽

Reinforcement Learning ◽

Energy Harvesting ◽

Power Allocation ◽

Cellular Networks ◽

High Energy ◽

Propagation Loss ◽

Throughput Maximization ◽

Energy Cooperation ◽

Multi Agent

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.

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A Critical Survey on Renewable Energy Applications in the Philippines and China: Present Challenges and Perspectives

Frontiers in Energy Research ◽

10.3389/fenrg.2021.724892 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xin Li ◽

Haizhi Wang ◽

Yuanru Lu ◽

Wanlin Li

Keyword(s):

Renewable Energy ◽

The Philippines ◽

Critical Survey ◽

Energy Cooperation ◽

Energy Applications ◽

Advantages And Disadvantages ◽

Development Framework ◽

Depth Analysis ◽

New Ideas ◽

Belt And Road

China’s Belt and Road (B&R) initiative provides new ideas and opportunities for international cooperation. Renewable energy plays a crucial role not only in the national sustainable development framework of China and the Philippines but also in bilateral cooperation between them. However, some obstacles still need to be addressed because renewable energy cooperation between China and the Philippines has not been thoroughly and comprehensively studied to date. Based on an in-depth analysis of current renewable energy cooperation between China and the Philippines, this paper employs PESTEL analysis to fully investigate the cooperative advantages and disadvantages by considering politics (P), economy (E), society (S), technology (T), environment (E), and legislation (L) and proposes several constructive suggestions. The ultimate purpose was to design feasible schemes to ensure the sufficient utilization of renewable energy and the construction of integrated power grid systems to meet shortages of electricity supply especially in the isolated small islands in the Philippines through cooperation with China. In particular, it offers valuable advice concerning the U.S.-China trade war and COVID- 19 pandemic, outlining how cooperation in the exploitation of potential renewable energy is vital.

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