Rising spatiality of transient frequency dynamics in grid-connected renewable energy systems is necessitating complex cooperative inertia allocation and frequency control. To this end, a physics-informed deep reinforcement learning (PI-DRL) control strategy is proposed. First, a nodal rate of change of frequency constrained virtual inertia allocation is proposed upon improved frequency divider and synchronizing power coefficient. It prompts adaptability of the entire scheme in varying unit commitment. Then, upon a recent ASF model, a learning-augmented spatial average system frequency (LA-SASF) model is devised to reduce order of frequency dynamics. It deconstructs the center of inertia frequency into nodal frequencies, such that tractable physics of spatial frequency can be analyzed to train PI-DRL. Thereafter, an alternating training architecture is tailored to concomitantly evolve the PI-DRL and the LA-SASF model. The training scheme is finally developed on the environment with ongoing stochastic disturbances to help learn spatial patterns of frequency responses. Case studies on IEEE 39-bus system manifest that, our approach beats conventional control strategy regarding the effective-ness of nodal frequency security, with 36.7% lower nodal frequency deviation, and the necessity of cooperative inertia allocation and frequency control is verified through ablation tests.

Topology identification lays out the essential foundation for the operation monitoring and management of distribution networks. In this article, a novel two-stage data-driven topology identification approach is proposed for unbalanced three-phase distribution networks utilizing the measurements of smart meters. In the first stage, the phase sequence of each bus is recovered sequentially using the proposed phase identification method, where the similarity criteria are employed to reduce the influence of line impedance on voltage correlation. In the second stage, based on the phase identification results, the buses with relatively low active power injections are grouped into different clusters. By regarding each cluster as an aggregated node, the simplified system topology and the local topology of each cluster (i.e., each aggregated node) are identified sequentially using the ridge regression method. The full-scale topology of system is obtained by integrating the simplified system topology and the local topology of each cluster. Moreover, to further improve the identification efficiency, a novel optimal input design approach is proposed to select rich-information data from historical records. Various case studies are conducted to demonstrate the effectiveness and advantages of the proposed topology identification approach.

Existing ac/dc power flow computations necessitate sequential convergence-oriented trial-and-error under various dc control modes, rising computational burden. This article thus proposes a physics-guided multiagent graph learning (PG-MAGL) method toward real-time power flow analysis with dc control mode adaptation. The tailored graph structure with built-in dc control modes and state variables is first advanced to ensure topology adaptability. Then, MAGL is proposed to enable adaptive jump over dc control modes. The trick is to organize multiagents to parameterize power flow solutions under various dc control modes and set aside trigger signals according to the operational violations of converters for the agent switching to the follow-up agent. To clarify the trigger signals, an augmented Lagrangian method-based PG-MAGL method is finally designed. It relaxes the control boundaries into the violation minimizers and enforces other constraints, such that dc control switching can be identified by the only violation signal. Utilizing inductive biases to rectify experiential biases in pure data-driven models, PG-MAGL enables precise inference of dc control mode feasibility. Case study shows that, relative to the other seven data-driven rivals, only the proposed method matches the performance of the model-based baseline, also beats it in efficiency beyond ten times.

It becomes apparent that time-series dynamic prediction for carbon consumption in sintering production process holds immense significance in the steel industry, as it plays a pivotal role in determining the efficiency and environmental impact of the operation. Given the complexities of the sintering process, encompassing multiple operating conditions, numerous parameters, nonlinearities, etc., this article proposes a time-series dynamic modeling method for carbon consumption based on an improved just-in-time learning (JITL) and a gated recurrent unit-based temporal cascade broad learning system (GRU-TCBLS). First, the data correlation analysis method is employed to determine the process parameters affecting carbon consumption. Further, an improved JITL method incorporating moving window and JITL is developed to obtain relevant training data in real-time for model training. Finally, based on these relevant training data, the GRU-TCBLS is formulated to construct a carbon consumption prediction model. Experiments based on actual production data demonstrate the superiority of the proposed method with respect to some state-of-the-art modeling methods.

Existing decoupled energy and carbon trading market leads to an inefficient and suboptimal operation of the distribution networks regarding economic interests and emission reduction. Corresponding to these issues, this paper designs a novel peer-to-peer (P2P) trading market of both energy and carbon emission allowance (CEA). It factors the value of transactive CEA into prosumers’ energy trading and leads to a cost-efficient decarbonization. The P2P coupled trading market is modelled as a risk-averse stochastic Stackelberg game to account for the competitive relationships between prosumers. Moreover, the approach enables prosumers to deal with risks in profits due to uncertainties from solar, load, and upstream price according to their different subjective perception of risks. Rather than directly enforcing prosumers to behave carbon-efficiently and grid-friendly, we impose a carbon-aware network charge to incentivize prosumer to adopt trading strategies that are optimal for both prosumers and the network. We illustrate that the proposed decentralized market-clearing algorithm yields a unique Stackelberg equilibrium without disclosing sensitive information of prosumers concerning operation costs and emission pattern. Results demonstrate that the proposed coupled market outperforms the traditional decoupled market in self-interest, social welfare, and emission reduction.

In this article, a novel data-driven robust voltage regulation method employing the multiagent soft actor–critic algorithm for photovoltaic-rich distribution grids considering storage lifetime and topology flexibility is proposed. In the proposed scheme, the active and reactive power from distributed energy storage system (ESS) are coordinated to deliver effective voltage support. To account for the long-term influence of ESS behavior on its lifetime, the life costs associated with the energy throughput are firstly formulated into the reward function of the Markov game-based voltage regulation model. Then, the topology status is represented by continuous variables transformed via Gumbel-softmax and embedded into the local observation of ESS agents for being aware of topology variations due to operational reconfiguration. In addition, to enhance the robustness of the voltage regulation method against imperfect measurements, the designed state space incorporates solely partially observed information from the entire distribution networks. Numerical simulations on IEEE 69-bus and IEEE 141-bus test systems confirm the outperforming of the proposed method over the previously implemented voltage regulation approaches.

Electricity prices behave more irregular patterns due to uncertainties and effects of mixed-temporal primary energy markets. Thus, it is challenging to precisely forecast them. To conquer this barrier, an interpretable interval prediction method that seamlessly unifies cross-energy and electricity markets is proposed. At the outset, to clarity the feature rising the aberrant electricity prices, several exogenous, and multitemporal features from other primary energy markets, such as natural gas and coal markets, are unified to settle our database. Then, a Gaussian mixture model (GMM)-lightweight gradient boosting machine hybrid detector is presented to isolate and foresee the outlier sequence of electricity prices. A hybrid LSTNet-kernel density estimation (LSTNet-KDE) method is further proposed to enable outlier-adaptive interpretable interval prediction. Specifically, the LSTNet contributes to amalgamating multitemporality across markets and predicting the principal trends, and the KDE serves to encapsulate the uncertainty for the GMM-foreseen outliers. The method further merges with the Shapley additive explanations technique, such that exogenous latent features that induce electricity prices outliers can be finally comprehended. The numerical study on the real-world Danish electricity market verifies that, our proposed method beats other rivals in terms of precision, especially notable in forecasting outliers of electricity prices.

Larger-scale stochastic power systems urge the development of real-time alternating current optimal power flow, artificial intelligence (AI) thus becomes an alternative. However, traditional AI only imitates experiences, and cannot follow in-depth physics. This may cause an undesired nongeneralizability and topology intractability. To address this issue, a physics-guided graph neutral network (PG-GNN) is proposed. The PG-GNN firstly capture the physical constraints by a dual Lagrangian. Besides, the branch features of power grids are fully exploited to allow the PG-GNN to master tremendous topological patterns. To further manage the out-of-distribution topology, stability property of the PG-GNN is proved, then upon this evidence, an online transfer learning is proposed to allow the PG-GNN to fast master the unexpected topology. Numerical tests on benchmarks show that, the proposed method holds well topology-transferability, enables near or even better solutions than conventional optimizer, but merits much more than 100 times efficiency.

Real-time topology estimation in distribution grid with high penetration of distributed energy resources remains a challenging task due to the insufficient high-precision measurements and frequent topology variations. This article proposes a real-time distribution system topology estimation approach building on the graph theory and Bayesian networks with sparse measurements. The graph theory develops the topology graph bank to effectively leverage the prior knowledge of topology models, including the topology structure and the switching relationship between different topologies. This allows the development of the Bayesian networks for topology tracking using real-time voltage and power injection measurements. A novel discrete method considering the similarity of data correlation information is proposed for the optimal placement ofμPMUs to ensure the performance of topology estimation. Numerical results on the IEEE 33-node and 123-node systems show that the BN-based topology estimation model has better performance against incomplete information, i.e., missing data, than other alternatives.

The widely used transient stability-constrained optimal power flow (TSC-OPF) method for power system preventive control is very time-consuming and thus not applicable for large-scale systems. This article proposes a new deep learning-enabled surrogate model that can significantly improve computational efficiency while maintaining high accuracy. To achieve that, the deep belief network (DBN) is strategically integrated with the reference-point-based nondominated sorting genetic algorithm (NSGA-III) to develop a new preventive control framework. The DBN allows us to identify the mapping relationship between the transient stability index and system operational features. The identified functional mapping relationship is further used as the surrogate to connect the DBN results with TSC-OPF for preventive control. The integrated NSGA-III and surrogate model enable the multiobjective optimization to consider various constraints and objectives, such as minimization of costs of generation dispatch cost and load shedding while maintaining the system stability. Extensive simulation results on several IEEE test systems show that the proposed method can achieve highly efficient control solutions and outperform other alternatives in terms of computational efficiency and economic benefits.

In this paper, brake response time of 110 near-crash cases with cyclist is researched. Cyclists include bicyclist, electric bicyclist, motorcyclist and tricyclist. This paper refers to the time interval from the moment a collision threat appears to the moment the vehicle begins to decelerate to avoid the collision as brake response time (BRT). Values of BRT range from 0.47s to 2.13s with a mean of 1.016s and a standard deviation of 0.3875s. Influence of seven factors on BRT is analyzed using one-way Analysis of Variance and path analysis. Factors include occurrence time of near-crash, visibility, number of potential threat vehicles, intersection or not, road type, moving status and velocity of the vehicle. The results show that visibility, intersection or not and number of potential threat vehicles are significant factors. Better visibility in the darkness can significantly shorten BRT. BRT decreases with the increase of potential threat vehicles. However, when there are too many (more than three) potential threat vehicles ahead, drivers show significantly longer BRT. Drivers brake significantly more lately at intersection.

Summary The Common Information Model (CIM) has been heavily used in electric power grids for data exchange among a number of auxiliary systems such as communication systems, monitoring systems, and marketing systems. With a rapid deployment of digitalized devices in electric power networks, the volume of data continuously grows, which makes verification of CIM data a challenging issue. This paper presents a parallelK‐meansclustering algorithm for large‐scale CIM data verification. The parallelK‐meansbuilds on the MapReduce computing model which has been widely taken up by the community in dealing with data‐intensive applications. A genetic algorithm‐based load‐balancing scheme is designed to balance the workloads among the heterogeneous computing nodes for a further improvement in computation efficiency. The performance of the parallelK‐meansis initially evaluated in a small‐scale in‐house MapReduce cluster and subsequently evaluated in a commercial cloud computing platform. Finally, the parallelK‐meansis evaluated in large‐scale simulated MapReduce environments. Both the experimental and simulation results show that the parallelK‐meansreduces the CIM data‐verification time significantly compared with the sequentialK‐meansclustering, while generating a high level of precision in data verification. Copyright © 2015 John Wiley & Sons, Ltd.

The operational environment of today’s smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring challenges and even threats to control centers. New techniques are anticipated to help dispatchers become aware of the accurate situations as they manipulate and navigate the situations as quickly as possible. To address the issues, we first introduce the background for this topic as well as the emerging technical demands of situational awareness in the dispatcher’s environment. The general concepts and technical requirements of situational awareness are then summarized, aimed at offering an overview for readers to understand the state-of-the-art progress in this area. In addition, we discuss the importance of integrating the architecture of support tools in accordance with the dispatcher’s thought process, which in fact guides correct and swift reactions in real-time operations. Finally, the prospects for situational awareness architecture are investigated with the goal of presenting situational awareness modules in an advanced and visualized manner.

A virtual power plant (VPP) can realize the aggregation of distributed generation in a certain region, and represent distributed generation to participate in the power market of the main grid. With the expansion of VPPs and ever-growing heat demand of consumers, managing the effect of fluctuations in the amount of available renewable resources on the operation of VPPs and maintaining an economical supply of electric power and heat energy to users have been important issues. This paper proposes the allocation of an electric boiler to realize wind power directly converted for supplying heat, which can not only overcome the limitation of heat output from a combined heat and power (CHP) unit, but also reduce carbon emissions from a VPP. After the electric boiler is considered in the VPP operation model of the combined heat and power system, a multi-objective model is built, which includes the costs of carbon emissions, total operation of the VPP and the electricity traded between the VPP and the main grid. The model is solved by the CPLEX package using the fuzzy membership function in Matlab, and a case study is presented. The power output of each unit in the case study is analyzed under four scenarios. The results show that after carbon emission is taken into account, the output of low carbon units is significantly increased, and the allocation of an electric boiler can facilitate the maximum absorption of renewable energy, which also reduces carbon emissions from the VPP.