Secondary control in autonomous DC microgrids is essential for bus voltage recovery and ensuring proper current sharing, yet often fails to enforce strict state constraints during large transients. This article proposes a safety-certified secondary controller that systematically integrates a distributed nominal performance law with a novel safety layer based on a proximal-reserve control barrier function. The controller is synthesized in realtime for each dispatch unit (DU) via a computationally efficient quadratic program. A proximal component allows the nominal controller to operate unimpeded when states are far from their limits to preserve transient performance. A reserve component creates the nonoscillatory barrier at the boundary to robustly enforce constraints. This mechanism directly mitigates the chattering and conservatism associated with standard control barrier functions. Rigorous analysis establishes forward invariance of the safe set and closed-loop stability with recursive feasibility under load changes. Hardware-in-the-loop experiments with a four-DUs microgrid validate the proposed method. Results demonstrate that it strictly enforces transient voltage and current limits while eliminating chattering.

The application of federated learning in Virtual Power Plants (VPPs) addresses the data silo issue between VPPs and enhances their ability to cope with nonlinear and stochastic scheduling characteristics, which enables VPPs better accommodate distributed energy resources and flexible loads while participating in frequency regulation services. However, although existing federated learning methods strive to solve privacy protection issues, the plaintext transmission of gradients still exposes sensitive data to the threat of curious power control centers and external inference attacks. Therefore, a privacy-protected horizontal federated reinforcement learning approach for multi-VPP optimal scheduling is proposed in this paper. Firstly, a cost-based global optimization scheduling model for multiple VPPs is constructed, modeling the internal scheduling process of VPPs as a Markov decision process. Then, an improved secure horizontal federated multi-VPP collaborative training method is presented, and local models are trained using the Deep Transformer Q-Network algorithm, with local differential privacy and CKKS homomorphic encryption implemented to ensure privacy protection. Finally, a case study is conducted using frequency regulation ancillary service market data and the IEEE-39 bus system structure. Simulation results show that the proposed approach outperforms similar algorithms, achieving high levels of privacy protection and economic operation for VPPs.

Geographically distributed data centers (DCs) have emerged as significant energy consumers, which has led to the integration of renewable energy sources (RES) into DC power provisioning systems. However, the intermittent nature of RES and the randomness of user requests can cause significant fluctuations in DC operating power. It can be detrimental to the operation of IT equipment and lead to instability in the power grid. In this paper, aiming for tightly coupled interconnection scenarios with multi-data centers in varying regions, a multi-data center tie-line power smoothing method based on demand response is proposed. By modulating the power load of server clusters with workload scheduling, we establish a control model combined with intra-DC temporal task migration and inter-DC spatial task migration to deal with high-frequency power fluctuations. The uninterruptible power supply (UPS) battery control model is established to tackle low-frequency fluctuations. Furthermore, we design the two-stage heuristic power regulation algorithm to achieve the best practice of smoothing effect by real-time tracking of power targets after two-layer filtering. Finally, this paper performs a detailed performance simulation evaluation based on tracking data from a real DC and wind and photovoltaic (PV) new energy generation data, using four interconnected DC parks of different sizes across different regions as examples. The simulation results demonstrate that the proposed method effectively smoothing the multi-data center's tie-line power. Additionally, inter-DC temporal task migration serves as a viable solution to overcome the limitations of task migration response within a single DC, reducing the frequency of UPS battery bank charges and discharges, which in turn prolongs their service life. This approach facilitates the utilization of RES while maintaining power quality, and it also aids in reducing the escalating operation and maintenance expenses of DCs.

With the expansion scale of interconnected power systems and refined state perception, scientific power calculations become more complex and diverse. They need faster computation speed and better scalability to support power flow calculation, reactive power optimization, and static/transient stability analysis for unit scheduling. Therefore, this paper proposes a novel cloud data center task mapping algorithm of the Stoer-Wagner binary tree (SWBT) to support accelerated executions of these calculations. Firstly, based on the block bordered-diagonal form of the admittance matrix, high-time complexity scientific power calculations are transformed into a unified multi-task decomposition-coordination directed acyclic graph (DC-DAG). And then, the critical tasks in this DC-DAG are found and the virtual machines encapsulating them are matched with physical machines in the data center preferentially. Finally, on CloudSim, a cloud computing platform, the multi-job mixed experiments of 118-13659 bus power systems are carried out. In addition, real-time workload performance is enhanced in two very large real-world power systems. Studies illustrate that SWBT can improve the underlying physical machine resource utilization and reduce data interaction transmission hops to achieve better computing acceleration performance.

With the massive deployment of geographically distributed data centers (DCs) and the demand surge for cloud services, their high energy consumption and carbon pollution are increasingly problematic. Therefore, mitigating the harmful effects of the carbon footprint of DCs has become a critical challenge. This paper takes the first step toward analyzing the complementary characteristics of global multi-regional renewable energy sources (RES). There is an opportunity to schedule workloads flexibly and track RES to reduce emissions. Integrating DCs and inter-DC network to form a refined emission model for the trade-off between emission cutting effects from scheduling and carbon costs of workload migration, we propose the spatio-temporal task migration mechanism to pursue low carbon in dual-dimension: This paper shifts intensive workloads to locations sufficient in RES at a coarse scale, and adjust the execution time of the workload in response to real-time RES fluctuations at a fine scale. Thus, the emission overage is shifted and offset by RES in a complementary manner; meanwhile, the acceptance of RES is enhanced. Finally, experiments with real-world data show that our method can optimally coordinate demand with RES and mitigate carbon pollution in geographically distributed DCs, and verify the performance and applicability with various-parameter scenarios.

In recent years, renewable energy, such as wind and photovoltaic electric power has been increasingly integrated into data center power provisioning systems to address high energy consumption of data centers. However, in reality, the intermittency and randomness of renewable energy (power supply fluctuation) is detrimental to the reliable operation of sophisticated IT equipment in those so-called green data centers. In this article, we address the problem of data center power regulation explicitly taking into account the unreliability and instability of renewable energy sources. To this extent, we design a novel data center power control framework that smoothens the power fluctuation and instability of renewable energy sources. The core of our framework is two power regulation optimization algorithms. In particular, a server workload scheduling algorithm deals with high frequency fluctuations while an Uninterruptable Power Supply (UPS) power regulation algorithm handles low frequency and large extent power fluctuations. These algorithms are also designed to satisfy service level agreement (SLA) and standby power supply capacity. We have conducted an extensive evaluation study using trace data of a real data center of 30000-node cluster with 50 x 250 UPS battery groups and 24-hour power generation data from real wind farm and photovoltaic power station. The experimental results show our framework effectively smoothens fluctuations of data center power supply, more effective use of renewable energy, and extend the UPS batteries’ lives to reduce the skyrocketed data center operating expenses.

Sustainable energy management systems have been increasingly studied in recent years. Non-intrusive load monitoring (NILM), as a key component, estimates the power consumption of individual appliances from the main readings only. However, most NILM approaches are computationally expensive, and their generality is negatively affected by the data drift occurred when the models are used across domains. Besides, the threats of privacy violation will rise in the model transfer due to the possible leakage of the personal information of the users from the source domain. To address all these challenges, we designed a cost-efficient learning method using LightGBM for energy disaggregation. We also proposed a model-based transfer learning algorithm using feature importance analysis, which enhances the generalisation capability of tree-based ensemble models applied in different domains while protecting privacy. We conducted experiments with real-world data sets. The performance of our approach is superior to the state-of-the-art solutions.

With the Internet of Things continuously penetrating into all spheres of our daily lives, the increasing use of smart devices enabled the emergence of the edge computing paradigm. To meet the needs of saving energy and reducing electricity bills for each household, solar energy is exploited by using photovoltaic (PV) panels that can be integrated into an edge computing platform based on a cost-effective scheduling scheme. However, it is still a major challenge to determine the optimal energy allocation of renewable energy due to the intermittent nature of renewable energy generation. In this paper, we propose a unified clustering-based prediction framework with two tree-based algorithms to provide short-term prediction of PV power output. We also provide the in-terpretability analysis for our approach to reveal the features that are important for the prediction. The experimental results show our proposed framework is superior to other benchmark machine learning algorithms.

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

Advance metering and reliable communication are two crucial technologies in Internet of Things, especially in the industrial application – smart grid, with which electric power system become more intelligent and easier to be controlled. And then the system can provide better quality electric power service for users, great efficiently utilize renewable energy, and decrease carbon emission. Integrated the multi-sensor fusion technology, self-organization technology and information processing technology in cloud, Cyber Physical Systems (CPS) are proposed and can apply in smart grid. Based on Time Sensitive Networking (TSN) and distributed agent technologies, this paper proposes a novel multi-QoS information flows control algorithm (MIFCA) applying in cyber power physical systems. With the algorithm, multiple electrical information data from CPS terminals can be controlled accurately, and achieve the effectively exchanging in this special IoTs. Theory analysis proves that the algorithm is convergence and validity. Using the simplified topology from "CERTS Micro-grid System" as simulation case, proposed algorithm's performance is evaluated in different traffic load levels. Bandwidth utilization ratios in light and high load levels are analyzed. The experiment results show that the proposed algorithm makes resource used more balanceable, and congestion can be avoided effectively.

There is a growing trend for employing cyber-physical systems to help smart homes improve the comfort of residents. However, a residential cyber-physical system is different from a common cyber-physical system since it directly involves human interaction, which is full of uncertainty. The existing solutions could be effective for performance enhancement in some cases when no inherent and dominant human factors are involved. Besides, the rapidly rising interest in the deployments of cyber-physical systems at home does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this article, we propose a cyber-physical-system-based energy management framework to enable a sustainable-edge computing paradigm while meeting the needs of home energy management and residents. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented using real-world hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of home appliances most of the time and electricity bills could be cut by up to 60% when our proposed framework was employed.

Energy efficiency of data centers (DCs) has become a major concern as DCs continue to grow large often-Energy efficiency of data centers (DCs) has become a major concern as DCs continue to grow large often hosting tens of thousands of servers or even hundreds of thousands of them. Clearly, such a volume of DCs implies scale of data center network (DCN) with a huge number of network nodes and links. The energy consumption of this communication network has skyrocketed and become the same league as computing servers' costs. With the ever-increasing amount of data that need to be stored and processed in DCs, DCN traffic continues to soar drawing increasingly more power. In particular, more than one-third of the total energy in DCs is consumed by communication links, switching and aggregation elements. In this paper, we concern the energy efficiency of data center explicitly taking into account both servers and DCN. To this end, we present VPTCA, as a collective energy-efficiency approach to data center network planning, which deals with virtual machine (VM) placement and communication traffic configuration. VPTCA aims particularly to reduce the energy consumption of DCN by assigning interrelated VMs into the same server or pod, which effectively helps reduce the amount of transmission load. In the layer of traffic message, VPTCA optimally uses switch ports and link bandwidth to balance the load and avoid congestions, enabling DCN to increase its transmission capacity, and saving a significant amount of network energy. In our evaluation via NS-2 simulations, the performance of VPTCA is measured and compared with two well-known DCN management algorithms, Global First Fit and ElasticTree. Based on our experimental results, VPTCA outperforms existing algorithms in providing DCN more transmission capacity with less energy consumption.

Distributed storage systems, e.g., Hadoop Distributed File System (HDFS), have been widely used in datacenters for handling large amounts of data due to their excellent performance in terms of fault tolerance, reliability and scalability. However, these storage systems usually adopt the same replication and storage strategy to guarantee data availability, i.e., creating the same number of replicas for all data sets and randomly storing them across data nodes. Such strategies do not fully consider the difference requirements of data availability on different data sets. More servers than necessary should thus be used to store replicas of rarely-used data, which will lead to increased energy consumption. To address this issue, we propose an energy-efficient storage strategy for cloud datacenters based on a novel hypergraph coverage model. According to users' requirements of data availability in different applications, our proposed algorithm can selectively determine the corresponding minimum hyperedge coverage, which represents the minimum set of data nodes required in the datacenter. Hence, some other data nodes can be turned off for the purpose of energy saving. We have also implemented our proposed algorithm as a dynamic runtime strategy in a HDFS based prototype datacenter for performance evaluation. Experimental results show that the variable hypergraph coverage based strategy can not only reduce energy consumption, but can also improve the network performance in the datacenter.

Data Center (DC), the underlying infrastructure of cloud computing, becomes startling large with more powerful computing and communication capability to satisfy the wide spectrum of composite applications. In a large scale DC, a great number of switches connect servers into one complex network. The energy consumption of this communication network has skyrocketed and become the same league as the computing servers' costs. More than one-third of the total energy in DCs is consumed by communication links, switching and aggregation elements. Saving Data Center Network (DCN) energy to improve data center efficiency (power usage effectiveness or PUE) become the key technique in green computing. In this paper, we present VPTCA as an energy-efficient data center network planning solution that collectively deals with virtual machine placement and communication traffic configuration. VPTCA aims to reduce the DCN's energy consumption. In particular, interrelated VMs are assigned into the same server or pod, which effectively helps to reduce the amount of transmission load. In the layer of traffic message, VPTCA optimally uses switch ports and link bandwidth to balance the load and avoid congestions, enabling DCN to increase its transmission capacity, and saving a significant amount of network energy. In our evaluation via NS-2 simulations, the performance of VPTCA is measured and compared with two well-known DCN management algorithms, Global First Fit and Elastic Tree. Based on our experimental results, VPTCA outperforms existing algorithms in providing DCN more transmission capacity with less energy consumption.

The cloud computing paradigm is attracting an increased number of complex applications to run in remote data centers. Many complex applications require parallel processing capabilities. Parallel applications of certain nature often show a decreasing utilization of CPU resources as parallelism grows, mainly because of the communication and synchronization among parallel processes. It is challenging but important for a data center to achieve a certain level of utilization of its nodes while maintaining the level of responsiveness of parallel jobs. Existing parallel scheduling mechanisms normally take responsiveness as the top priority and need nontrivial effort to make them work for data centers in the cloud era. In this paper, we propose a priority-based method to consolidate parallel workloads in the cloud. We leverage virtualization technologies to partition the computing capacity of each node into two tiers, the foreground virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). We provide scheduling algorithms for parallel jobs to make efficient use of the two tier VMs to improve the responsiveness of these jobs. Our extensive experiments show that our parallel scheduling algorithm significantly outperforms commonly used algorithms such as extensible argonne scheduling system in a data center setting. The method is practical and effective for consolidating parallel workload in data centers.