The vision of the future Smart Grid considers end-users connected to it as both consuming and generating energy. Equipped with small-scale renewable energy generators and storage systems, end-users, also known as prosumers, engage in a local energy market for procuring and selling energy, in turn disrupting the traditional utility model. The appeal of this vision lies in the engagement of end-users, in facilitating the introduction and optimization of renewable energy sources, with the overall expectation of optimizing the global energy generation and distribution process. To handle the peer-to-peer energy exchange and distributed energy generation in the digitalized Smart Grid, we proposed an optimization strategy. In the present work, we propose a Monte Carlo based simulation model to investigate the role of the topology in facilitating the peer-to-peer energy exchanges and distributed energy generation. We consider a 37-node distribution network and evaluate four topological models: radial, complete graph, random graph, and small-world. The results indicate that the random graph model is better than other models in reducing the average delivery path length and energy losses in the energy transfer between providers and consumers. The small-world model has higher efficiency than other models in reducing the maximum power load in the distribution network and the cost of buying energy for end-users. We scale up the investigation by considering a 100-node network and evaluate the random graph and the small-world models by varying the rewiring probabilities. The results show that the small-world model outperforms the random graph model on most efficiency metrics, even when considering infrastructural costs. This work provides the foundation for a decision support system for analysis and high level planning of the distribution network.

中文翻译：

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智能电网中对等能量交换和分布式能量产生的拓扑注意事项

未来智能电网的愿景认为与之相连的最终用户既在消耗能源，又在发电。配备了小型可再生能源发电器和存储系统的最终用户（也称为生产者）进入当地的能源市场以获取和出售能源，从而破坏了传统的实用新型。该愿景的吸引力在于最终用户的参与，这将促进可再生能源的引入和优化，并总体上期望优化全球能源的生产和分配过程。为了处理数字化智能电网中的对等能源交换和分布式能源发电，我们提出了一种优化策略。在目前的工作中，我们提出了一个基于蒙特卡洛的仿真模型，以研究拓扑在促进对等能量交换和分布式能量产生中的作用。我们考虑一个由37个节点组成的分布网络，并评估四个拓扑模型：径向，完整图，随机图和小世界。结果表明，随机图模型在减少提供商和消费者之间的能量传递中的平均传递路径长度和能量损失方面优于其他模型。在减少配电网络中的最大功率负载以及为最终用户购买能源的成本方面，小世界模型比其他模型具有更高的效率。我们通过考虑一个100节点的网络来扩大调查范围，并通过改变重新布线的概率来评估随机图和小世界模型。结果表明，即使考虑基础设施成本，小世界模型在大多数效率指标上也优于随机图模型。这项工作为决策支持系统提供了基础，该决策支持系统用于配电网络的分析和高级规划。