To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

Smart grid integrates the distributed energy resources such as renewable energy with massive information to facilitate the flow of energy in the industries. The renewable energy accommodation is one of the key issues to achieve the energy efficiency in smart grid, which is difficult to obtain dynamic optimal policies due to the intermittency of renewables. To capture statuses of renewable energy for decision-making, large amounts of information in heterogeneous forms are collected by massive end devices deployed in smart grid. Such information not only provides fruitful features for existing learning based algorithms but also incurs high computation complexity. Besides, such heterogeneous data may also contain missing values, which may result in wrong policies by existing algorithms. In this article, a novel cloud-edge-end orchestrated computing scheme is proposed to efficiently repair missing values and obtain optimal policies in two separate layers. In the first layer, deep learning based algorithms deployed can perceive the characteristics and repair the missing values. In the second layer, deep reinforcement learning based algorithms are employed to obtain optimal policies. Simulations on the real power grid dataset illustrate the effectiveness of proposed fault-tolerant renewable energy accommodation algorithm.

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.