The phase out of conventional synchronous generators (SGs) and the vigorous development of renewable energy sources (RESs) are indisputably leading to a significant reduction in the inertia of power grids, blowing a hole in frequency security and stability. To address this issue, various fast-acting resources such as battery energy systems (BESS) are being discussed worldwide. Accurate quantifying the relative effectiveness of these resources in arresting frequency decline to conventional methods is, therefore, of great significance to securely operate low-inertia power systems (LIPS). To do so, an analytical model of the equivalent frequency-containment performance ratio (EFCPR) is proposed for heterogeneous resources having different response characteristics. Furthermore, two Sigmoid-function-based approaches are also proposed to extend the EFCPR model to aggregated resources, and a more general scenario taking delivery time instant of instantaneous response BESS into account. Numerical results on the Texas test case and the Great Britain power grids with real operation data (from September 2023 to December 2024) collected from the National Energy System Operator (NESO) website validate the EFCPR model and penetrate many of the parameters' impacts.

Battery energy storage systems (BESS) are crucial for maintaining grid frequency stability, particularly with the increasing integration of intermittent renewable energy sources. However, energy delivered from BESS drifts over time due to asymmetric charging and discharging inefficiencies, posing significant challenges for effective frequency support. In this work, simulations revealed that energy drift accelerated early crossing of the state of charge (SOC) boundaries. A sensitivity analysis of asymmetric inefficiencies showed that improving charging and discharging consistency reduced energy drift and decreased frequency of energy transactions. C-rate constraints highlighted a trade-off between BESS safety and the rapidity of frequency support responses. Simplified dynamic droop control was shown to stabilize early-stage grid operations but required degradation-informed BESS control to mitigate the energy drift. The sizing of BESS showcased a trade-off between energy drift and cost. These findings offer valuable insights into designing safe and robust SOC management strategies for grid-connected BESS. Broadly, this work enhances fundamental understanding of energy drift arising from inherent battery degradation and its impact on the reliable energy storage systems for the stable and sustainable power grid.

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME.

To solve the profit allocation problem among multiagent energy systems (MAESs), this study proposes a cooperative operation strategy for the MAES integrated with wind, hydrogen, and buildings based on the asymmetric Nash bargaining (NB) approach. The modeling of the MAES, including wind farms, hydrogen systems, and buildings is conducted, with detailed electrical and thermal characteristics used to improve operation flexibility while ensuring the users’ comfort. To solve the issue of equal profit allocation in symmetric NB, this article proposes using asymmetric NB to achieve a rational profit allocation. Functions are constructed to quantify the agents’ contributions, which are then used as bargaining power in the asymmetric NB process to realize an asymmetric profit allocation. Furthermore, the proposed distributed algorithm three-stage predictor-corrector accelerated alternating direction method of multipliers (ADMM) algorithm can improve solving efficiency and preserve information privacy. The case studies show that compared with equal profit allocation by symmetric NB, the proposed asymmetric NB-based strategy can acknowledge each agent's contribution and allocate agents with profits that correspond to their respective contributions. The fairness and rationality of the asymmetric profit allocation method can benefit the cooperative relationships among agents.

The multi-agent characteristics of integrated energy systems are becoming increasingly prominent, rendering the energy management problems more intricate. Additionally, the distributed agents are faced with challenges from the communication layer, such as packet losses and privacy disclosure issues. Therefore, a privacy-preserving neurodynamic-based optimization strategy considering packet losses is proposed in this article. A privacy-preserving communication model is constructed based on differential privacy (DP). Unlike traditional DP methods, the privacy preservation mechanism employed in this article can achieve a quantified high level of privacy while ensuring the solution accuracy of the optimization algorithm. Moreover, communication packet loss models based on both the two-state Markov process and the Bernoulli process are established. The proposed fully distributed scheme only requires communication between adjacent agents. The efficiency of the neurodynamic-based approach, the high-level privacy preservation, and the robustness against communication packet loss are demonstrated through several case studies.

It is generally accepted that the impact of weather variation is gradually increasing in modern distribution networks with the integration of high-proportion photovoltaic (PV) power generation and weather-sensitive loads. This article analyzes power flow using a novel stochastic weather generator (SWG) based on statistical machine learning (SML). The proposed SML model, which incorporates generative adversarial networks (GANs), probability theory, and information theory, enables the generation and evaluation of simulated hourly weather data throughout the year. The GAN model captures various weather variation characteristics, including weather uncertainties, diurnal variations, and seasonal patterns. Compared to shallow learning models, the proposed deep learning model exhibits significant advantages in stochastic weather simulation. The simulated data generated by the proposed model closely resemble real data in terms of time-series regularity, integrity, and stochasticity. The SWG is applied to model PV power generation and weather-sensitive loads. Then, we actively conduct a power flow analysis (PFA) on a real distribution network in Guangdong, China, using simulated data for an entire year. The results provide evidence that the GAN-based SWG surpasses the shallow machine learning approach in terms of accuracy. The proposed model ensures accurate analysis of weather-related power flow and provides valuable insights for the analysis, planning, and design of distribution networks.

The increasing penetration of distributed energy resources (DERs) has facilitated the development of Peer-to-Peer (P2P) trading mechanism. An efficient P2P trading market framework is essential to integrate various kinds of DERs into new power systems while ensuring network security constraints (NSCs). This paper proposes a carbon-aware P2P trading market to realize joint energy and reserve trading for prosumers while satisfying NSCs of the distribution network (DN) simultaneously. A geometric series acceleration (GSA) method accelerated algorithm based on the consensus alternating direction method of multipliers (C-ADMM) is proposed to solve the distributed P2P trading problem. A data-driven method based on the two-sided distributionally robust chance constraint (TS-DRCC) is adopted to tackle with the uncertainty problem associated with DERs. Numerical tests on the IEEE 15-Bus distribution system and IEEE 141-Bus distribution system verify the advantages and effectiveness of the proposed method.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-, park-, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

As one of the most important infrastructures, the energy system covers electricity, heating, cooling, natural gas, oil, coal, hydrogen, etc. Energy security, energy equity, and environmental sustainability are the well-known energy trilemma. The energy system is the largest source of carbon emissions; therefore, the goal for carbon neutrality puts forward very high requirements. Renewable energy will account for the majority of generation in the future, while the current energy system is not ready.

The popularization of electric vehicles (EVs) and charging stations has been threatening the distribution network’s reliability and efficiency. The prediction of EV charging demand can benefit the optimization of the operation of energy-transportation nexus and improve social welfare toward a low carbon future. In this article, a short-term probabilistic charging demand forecast model is proposed to estimate the quantiles of future charging demand of a charging station 15 min ahead, i.e., the self-attention-based machine theory of mind (SAMToM). The SAMToM has considered both the users’ historical charging habits (schedules) and the current trend of charging demand variation using the framework of machine theory of mind (MToM), and real-world-data-based case studies have verified its superiority in EV charging demand forecast over state-of-the-arts. Moreover, analyses show that the advantage of SAMToM lies in the following aspects. 1) The self-attention layers have mitigated the long-range forgetting in SAMToM. 2) The MToM architecture enables SAMToM to balance historical charging habits and current charging demand variation trends well. 3) Using a quantile forecast evaluation metric as the loss function, i.e., the continuous ranked probability score (CRPS), enables SAMToM to aim directly at the highest quality of forecasted quantiles.

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

The uncertainties of renewable energy, loads, and electricity prices pose significant challenges to the economical and secure energy management of smart grids. In this article, a hybrid stochastic/robust (HSR) optimization method is developed to minimize the overall cost of all units. The proposed approach takes advantage of stochastic programming, robust optimization, and distributed optimization methods while considering various system constraints. First, stochastic electricity price scenarios are selected by the Latin hypercube sampling method. Second, the uncertainties of renewable energy generation and loads are managed by the proposed robust optimization method under each price scenario. Then, an improved distributed optimization method is proposed to solve the formulated HSR optimization problem, which considerably enhances the convergence with the accelerated gradient method. Numerical case studies of both small-scale and large-scale power systems demonstrate the accuracy, effectiveness, and scalability of the proposed distributed HSR approach. Additionally, the optimality and convergence of this proposed distributed algorithm are mathematically proven and analyzed.

Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges: the inefficiency of harnessing high dimensionality and the severe lack of fraudulent samples. To overcome them, an NTL detection model based on deep learning and anomaly detection is proposed in this article, namely bidirectional Wasserstein GAN and support vector data description-based NTL detector (BSBND). Motivated by the powerful ability of generative adversarial networks (GANs) to learn deep representation from high-dimensional distributions of data, in the BSBND, we utilized a BiWGAN for feature extraction from high-dimensional raw consumption records, and a one-class classifier trained only on benign samples-SVDD-is adopted to map features into judgments. Moreover, a novel alternate coordinating algorithm is proposed to optimize the cooperation between the upstream BiWGAN and the downstream SVDD, and also, an interpreting algorithm is proposed to visualize the basis of each fraudulent judgment. Case studies have demonstrated the superiority of the BSBND over the state of the arts, the powerful feature extraction ability of BiWGAN, and also the effectiveness of the proposed coordinating and interpreting algorithms.

As the conventional automatic generation control (AGC) is inadequate to deal with the strong random disturbance issues induced by the ever-increasing penetration of renewable energy to the power grids, this article proposes a deep-reinforcement-learning-based three-network double-delay actor-critic (TDAC) control strategy for AGC to handle the above problem, which is mainly developed by multiple neural networks to fit the system action strategies and evaluate the value. The proposed strategy can increase the exploration efficiency and the quality of AGC and improve the system control performance using the modified actor-critic (AC) method with incentive heuristic mechanism, while a novel iterative way of the value function is also used to reduce the bias of optimization effectively for achieving optimal coordinated control of the power grid. The simulations are provided in the work to show the control performance of the strategy. Compared with other smart methods, the simulation study demonstrates that TDAC has excellent exploratory stability and learning ability. Meanwhile, it also can improve the dynamic performance of the power system and achieve the regional optimal coordinated control.

In an integrated energy system, the growing number of distributed heat and electric power generation units will bring new technical challenges to the existing centralized economic dispatch strategies. This paper proposes a distributed optimization approach for the economic system operation in a multienergy system by considering various equality and inequality constraints to accommodate the integration of intermittent renewable generations. The proposed distributed neurodynamic-based approach only requires the information exchange among neighboring units and offers flexibility, adaptivity, scalability, faster convergence, and lower communication burden compared with some traditional centralized methods. The simulation results of two integrated energy systems validate the effectiveness of the proposed distributed approach. Comparisons with other centralized and distributed optimization methods quantify the advantages of the proposed distributed approach in terms of convergence speed and computation complexity.

This paper investigates the issue of power management networked ac-dc microgrids (MGs) interconnected by interlinking converters with the consideration of unbalanced single-/three-phase ac MGs as well as power quality improvement. An integrated hierarchical distributed coordinated control approach is developed, which mainly consists of an up-layer event-triggered method of power sharing among MGs, and an event-triggered dynamic power flow routing approach to navigate the power flow among phases of the single-/three-phase ac MGs to balance the power of the MG. With the proposed control method, balanced output phase powers for the three-phase distributed generation (DGs) and enhanced voltage quality at the point of common coupling and DG terminals can be achieved besides proportional active power sharing among MGs and reduced communication. Simulation results are presented to demonstrate the proposed control method.

The uncertainties caused by sources [such as wind power and photovoltaic (PV)], load switchings, and the equivalent negative impedance of constant power loads (CPLs) commonly exist in microgrids and often undermine the system stability and damping. In this article, a distributed secondary H∞consensus approach with an eventtriggered communication scheme is proposed for dc microgrids to achieve accurate current sharing and satisfactory performance in the presence of CPLs and uncertainties. Different from many existing works, the proposed eventtriggered communication scheme only requires the information at every fixed sampled interval without the Zenobehavior and continuous-time information. Then, global large-signal stability of the dc microgrid with CPLs and uncertainties under the proposed distributed control is analyzed, where a primary plug-and-play (PnP) voltage controller is considered for each distributed generator (DG). Furthermore, effects of key controller parameters and CPLs on the dynamic performance is analyzed, and a PnP design method is presented for the primary-secondary controllers. With the proposed method, full PnP operation of the dc microgrid can be realized and communication burden can be considerably reduced. Finally, simulation results are presented to validate the proposed method.

Probability density forecast offers the whole distributions of forecasting targets, which brings greater flexibility and practicability than the other probabilistic forecast models such as prediction interval (PI) and quantile forecast. However, existing density forecast models have introduced various constraints on forecasted distributions, which has limited their ability to approximate real distributions and may result in suboptimality. In this paper, a distribution-free density forecast model based on deep learning is proposed, in which the real cumulative density functions (CDFs) of forecasting target are approximated by a large-capacity positive-weighted deep neural network (NN). Benefiting from the universal approximation ability of NNs, the range of forecasted distributions has been proven to contain all the distributions with continuous CDFs, which is superior to existing models' considering both width and accordance with reality. Three tests from different scenarios were implemented for evaluation, i.e., very-short-term wind power, wind speed, and day-ahead electricity price forecast, in which the proposed density forecast model has shown superior performance over the state of the art.

This paper proposes an optimal distributed control strategy for the coordination of multiple distributed generators in an islanded microgrid (MG). A finite-time secondary frequency control approach is developed to eliminate the frequency deviation and maintain accurate active power sharing in a finite-time manner. It is demonstrated that the traditional distributed control approach with asymptotical convergence is just a special case of the proposed finite-time control strategy under the specific control parameter settings. Then, a secondary voltage control approach is presented to regulate the average voltage magnitude of all distributed generators to the desired value and achieve accurate reactive power sharing. The implementation of the proposed distributed control strategy only requires information exchange among neighboring local controllers through a sparse communication network. Simulations with an islanded MG testbed built in MATLAB/Simulink are conducted to validate the effectiveness of the proposed distributed control strategy.

The transient stability control of power systems with growing penetration of renewable energy resources is challenging due to inherent small damping of generators and complicated operating conditions. To address the drawbacks of existing control approaches which need accurate systemwide network parameters, a model-free fuzzy controller is proposed to enhance the transient and frequency stability of power systems. Also, an adaptive parameter estimation scheme is developed to eliminate the fuzzy approximation errors and compensate the external disturbances. The proposed strategy is implemented based on the multiagent framework, which enables the sharing of communication and computation burdens among local controllers for fast and coordinated response. The convergence of the proposed distributed control approach is rigorously proved using the Graph theory and Lyapunov stability theory. Simulation studies validate the effectiveness of the proposed distributed control approach.

As nontechnical losses in power systems have recently become a global concern, electricity fraud detection models attracted increasing academic interest. The wide application of smart meters has offered more possibility to detecting fraud from user's consumption patterns. However, the performances of existing consumption-based electricity fraud detection models are still not satisfactory enough for practice, partly due to their limited ability to handle high-dimensional data. In this paper, a deep-learning-based model is developed for detecting electricity fraud in the advanced metering infrastructure, namely, the multitask feature extracting fraud detector (MFEFD). The deep architecture has brought MFEFD a powerful ability to handle high-dimensional input, through which consumption patterns inside load profiles can be effectively extracted. Another challenge is that the insufficiency of labeled data has restricted the generalization of existing models since they are mostly based on supervised learning and labeled data. MFEFD is trained in a semisupervised manner, in which multitask training was implemented to combine the supervised and unsupervised training, so that both the knowledge from unlabeled and labeled data can be made use of. Real-world-data-based case studies have demonstrated MFEFD's high detection performance, robustness, privacy preservation, and practicability.

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.