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.

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.

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.

Widespread advanced metering infrastructure and wide-area monitoring systems generate a significant amount of electricity load consumption data, which can facilitate eliciting end users’ temperature flexibility for demand response programs. However, the direct delivery of users’ load profiles is a threat to users’ privacy. So this paper proposes a privacy-preserving hybrid cloud framework for TCL-based demand response programs, composed of user private clouds and aggregation cloud. User clouds store users’ load profiles and elicit temperature flexibility by the proposed stable temperature-related regression model. In the aggregation cloud, this paper proposes the slope-priority flexibility aggregation method for the mean-variance analysis of aggregate flexibility and the XGBoost-accelerated disaggregation model for real-time selecting users based on users’ fitting coefficients. Hybrid cloud achieves privacy-preserving by separating flexibility eliciting models and aggregation/disaggregation methods into user private clouds and aggregation cloud. Numerical experiments verify that: 1) in user clouds, the stable regression model achieves less predict errors; 2) in aggregation cloud, the slope-priority method can achieve higher aggregate flexibility, and XGBoost-accelerated disaggregating reduces the solving time by nearly three orders of magnitude.

The wide application of phasor measurement units (PMUs) increases the interdependence between the smart grid and wide-area measurement systems (WAMS). However, the end-to-end (ETE) delay that the data experienced from PMUs to the phasor data concentrator (PDC) has become non-negligible. A significant delay may affect the data completeness received by the PDC, thereby affecting the performance of the WAMS application, e.g., state estimation (SE). The previous research focuses on centralized path planning to mitigate the physical distance between PMUs and the PDC. Few studies have considered addressing the data completeness problem using the distributed algorithm from the aspect of the ETE delay. This article proposes a distributed bias min-consensus-based approach in path construction to minimize the ETE delay. Simulation results demonstrate the advantages of the proposed approach in addressing the data completeness and enhancing the SE performance. Also, it is computationally efficient in path reconstruction when the links suffer cyber-attacks.

Eliminating the erroneous state bias from cyberattack is essential to ensure the real-time control and secure operation of the smart grid, especially when cyberattack flourishes in recent years. For this reason, this article,for the first time, proposes a new operating state reconstruction scheme to automatically filter out possible cyberattacks in smart grid. This scheme consists of an attack separation method, a state forecasting algorithm, and a state recovery approach. Based on P-Q decomposition, the attack separation method takes into account the network parameter perturbations and prediction uncertainties to analytically estimate the regular deviation of each state, thereby identifying potential abnormal states. Then, a particle filtering-based state forecasting algorithm is developed to evaluate the original operating level of the detected contaminated states. Finally, we propose a fast bilinear state recovery approach to mitigate the smearing effect of undetected contaminated states due to cyberattacks. The proposed state reconstruction method can not only detect cyberattacks but also realize automatic correction of the erroneous states, thus mitigating the devastating impact of attack on smart grids. The feasibility and effectiveness of our reconstruction scheme are extensively validated on IEEE standard 9-, 14-, 30-, 57- and 118-bus power systems. The obtained results show that the proposed scheme exhibits strong robustness, high stability, and promising performance for automatic attack filtering, indicating a great potential for implementations in deep cyber-penetrated smart grids.

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.

Multiuncertainties in community grids bring enormous challenges to system scheduling. To reply to renewable uncertainties on the prosumer side, this article proposes a trilayer stackelberg game (SG) based robust power management method for a typical community grid. Firstly, in the upper-layer, the supplier acts as a leader to determine the trading prices according to prosumers' power demands in the normal scenario. The scheduling of prosumers before and after uncertainties is formulated as an inner-loop SG model in the middle- and lower-layers, and a linear robust counterpart model is developed to address the nonlinearity and nonconvexity of traditional robust models, thereby ensuring the convergence of the robust equilibrium solution. Second, to solve the trilayer SG model effectively, the existence and uniqueness of the equilibrium solution are proved based on a multistep backward induction method. Third, a novel two-stage distributed iterative algorithm is further exploited to avoid the nested iterations in existing solution methods. Furthermore, the possible oscillation in iterative optimization is also prevented to improve the computational efficiency. Case studies illustrate the effectiveness of the proposed trilayer SG model, and the developed distributed algorithm requires fewer iterations and less solution time comparing to other algorithms. Besides, this method shows better scalability and practicality with the expansion of the grid-scale.

This article is concerned with the event-triggered fault detection problem for discrete-time systems subject to unknown-but-bounded (UBB) process disturbance and measurement noise via zonotope-based residual evaluation. To save communication resources, a novel discrete-time dynamic event-triggered mechanism is proposed. An optimal event-triggered l1/H∞fault detection observer (FDO) design criterion is proposed such that the generated residual is sensitive to system faults while robust against exogenous disturbance and measurement noise. On the basis of the designed FDO, a zonotopebased dynamic threshold for residual evaluation is well constructed by considering the impacts of disturbance, noise, and event-triggered communication. Finally, a vehicle lateral dynamic system is adopted to illustrate the effectiveness of the proposed zonotope-based dynamic eventtriggered fault detection mechanism.

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.

The H∞filter design for a class of switched Takagi-Sugeno (T-S) fuzzy systems with persistent dwell time (PDT) is investigated in this paper. The considered switched fuzzy systems contain a limited number of subsystems and each local subsystem is represented by the well-known T-S fuzzy model. Compared with the dwell time (DT) switching or average DT switching that attracted quantities of interests over the last decade, the PDT switching considered in this paper is known to be more general. The stability and £2-gain analysis for switched systems with PDT switching are derived first, based on which a set of full-order H∞filter is designed to guarantee the global uniform asymptotic stability with a prescribed non-weighted H∞noise attenuation performance for the resulting filtering error system. Finally, the effectiveness of the provided method is illustrated with an example.

This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multiarea power system. SVC minimizes the voltage deviation of the load buses while CS deals with the problem of the limited bandwidth capacity of the communication channel by reducing the size of massive data output from the phasor measurement unit (PMU) based monitoring system. The proposed strategy further incorporates the application of a morphological median filter (MMF) to reduce noise from the output of the PMUs. To keep the control area secure and protected locally, mathematical singular entropy (MSE) based fault identification approach is utilized for fast discovery of faults in the control area. Simulation results with 27-bus and 486-bus power systems show that CS can reduce the data size up to 1/10th while the MSE-based fault identification technique can accurately distinguish between fault and steady-state conditions.

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.

In this paper, a fully distributed coordination control based on electricity trading system (ETS) is proposed. It aims at simulating price trading mechanism and the interaction process of the electricity price bargain among multiple users in a microgrid. Each user with an intelligent module is considered as an agent and each agent carries out its own price response and decision. The proposed approach adopts the ZigBee technology for communication. Each agent contains a ZigBee module based on the chip CC2530 and embedded C as the development language. The distributed coordination algorithm is proposed for the agents to obtain the optimal power generation or consumption and maintain the supply demand-balance within a microgrid. In the proposed ETS, only local data and information exchange with other adjacent nodes are required, so the computation and communication burdens are evenly allocated to multiple local agents. Hardware simulation results verify the effectiveness of the proposed distributed control strategy.

Recently, more and more small-scale renewable generation sources based distributed generators are integrated to the existing power network through power electronic-based converters. Microgrid has been proposed as a solution to meet the challenges posed by highly intermittent renewable generations. To address the fast response and complex operating conditions of various inverters in an autonomous microgrid, this paper proposes a robust finite-time control algorithm for frequency/voltage regulation and active/reactive power control. The major advantages of the proposed control algorithm include, being robust and stable against various load disturbances, unmodeled dynamics and system parameter perturbations; enabling flexible convergence time according to user preferences and different operating conditions' requirements. The finite-time convergence of the robust control algorithm is guaranteed through rigorous analysis and the balance between control accuracy and chattering suppression is investigated. Simulation results demonstrate the effectiveness of the proposed robust finite-time control algorithm.

In this paper, the discrete economic dispatch problem is formulated as a knapsack problem. An effective distributed strategy based on distributed dynamic programming algorithm is proposed to optimally allocate the total power demand among different generation units considering the generation limits and ramping rate limits. The proposed distributed strategy is implemented based on a multiagent system framework which only requires local computation and communication among neighboring agents. Thus, it enables the sharing of computational and communication burden among distributed agents. In addition, the proposed strategy can be implemented with asynchronous communication, which may lead to simpler implementation and faster convergence speed. Simulation results with a four-generator system and the IEEE 162-bus system are presented to demonstrate the effectiveness of the proposed distributed strategy.

Traditionally, economic dispatch and demand response (DR) are considered separately, or implemented sequentially, which may degrade the energy efficiency of the power grids. One important goal of optimal energy management (OEM) is to maximize the social welfare through the coordination of the suppliers' generations and customers' demands. Thus, it is desirable to consider the interactive operation of economic dispatch and DR, and solve them in an integrated way. This paper proposes a fully distributed online OEM solution for smart grids. The proposed solution considers the economic dispatch of conventional generators, DR of users, and operating conditions of renewable generators all together. The proposed distributed solution is developed based on a market-based self-interests motivation model since this model can realize the global social welfare maximization among system participants. The proposed solution can be implemented with multiagent system with each system participant assigned with an energy management agent. Based on the designed distributed algorithms for price updating and supply-demand mismatch discovery, the OEM among agents can be achieved in a distributed way. Simulation results demonstrate the effectiveness of the proposed solution.

This paper proposes a fully distributed multiagent-based reinforcement learning method for optimal reactive power dispatch. According to the method, two agents communicate with each other only if their corresponding buses are electrically coupled. The global rewards that are required for learning are obtained with a consensus-based global information discovery algorithm, which has been demonstrated to be efficient and reliable. Based on the discovered global rewards, a distributed$Q$-learning algorithm is implemented to minimize the active power loss while satisfying operational constraints. The proposed method does not require accurate system model and can learn from scratch. Simulation studies with power systems of different sizes show that the method is very computationally efficient and able to provide near-optimal solutions. It can be observed that prior knowledge can significantly speed up the learning process and decrease the occurrences of undesirable disturbances. The proposed method has good potential for online implementation.