Abstract:Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis highlights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.

Abstract:Power system optimal dispatch with transient security constraints is commonly represented as transient security-constrained optimal power flow (TSC-OPF). Deep reinforcement learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converging DRL method for optimal dispatch of large-scale power systems under transient security constraints is proposed in this paper. The Markov decision process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved deep deterministic policy gradient algorithm with curriculum learning, parallel exploration, and ensemble decision-making (DDPG-CL-PE-ED) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.

Abstract:Driven by increasing penetration of intermittent renewable energy generation, modern power systems are promoting the integration of energy storage (ES) and advocating high-resolution dynamic security constrained optimal power flow (DSCOPF) models to exploit ES time-shifting flexibility against contingencies and respond promptly to more frequent variations in the system operating status. While pioneering research works explore different methods to solve security constrained optimal power flow (SCOPF) problems at individual time steps, real-time implementation of DSCOPF still faces challenges associated with uncertainty adaptation, complex constraint satisfaction， and computational efficiency. This paper proposes a physics-guided safe policy learning method, featuring an analytical evaluation model to provide both accurate safety and cost-efficiency evaluations. A primal-dual-based learning procedure is developed to guide policy learning, fostering prompt convergence. A spatial-temporal graph neural network is constructed to enhance perception on the spatial-temporal uncertainties and leverage policy generalization. Case studies validate the effectiveness and scalability of the proposed method in safety, cost-efficiency, and computational performance and highlight the value of enhanced perception on IEEE 39-bus and 118-bus test systems.

Abstract:Advanced management algorithms are required in modern power systems to sustain energy supply with the highest availability and lowest cost. These algorithms need to be capable of not only maintaining scalability, tractability, and privacy, but also enabling the utilization of grid-edge aggregated flexibilities in transmission systems. This paper proposes a distributed hierarchical transactive energy management (TEM) scheme to manage peak load and line congestion problems using connected and aggregated flexibilities. In the scheme, resource owners can privately solve their respective preference problems and send their scheduled power to the corresponding node operator (NO). Afterward, NOs solve a coordination problem to harmonize the actions of resource owners at the same node. Meanwhile, the independent system operator (ISO) updates control signals to steer the scheduled power to a feasible and optimal point. To accomplish all these, a hybrid decomposition approach is further proposed based on consensus+exchange alternating direction method of multipliers (CE-ADMM) and dual decomposition (DD) (CE-ADMM+DD). Besides, a dynamically constrained cutting plane (DC-CP) update algorithm is evolved to control the feasibility condition and minimize sensitivity to initialization. The proposed hybrid decomposition approach is verified and its performance is compared with other reported approaches. Application to various networks verifies its scalability, enhanced accuracy, and convergence speed.

Abstract:Transmission expansion planning (TEP) addresses the intricate task of optimizing new transmission infrastructure within an existing grid to meet system objectives. As a critical strategy in power system development, TEP significantly influences the long-term efficiency, reliability, and scalability of the network, with enduring effects on overall system performance. This paper explores the application of unconventional high surge impedance loading (HSIL) lines as a cost-effective alternative to conventional extra high-voltage (EHV) transmission lines. By optimizing the geometry of subconductors, HSIL designs could achieve higher power delivery capacities while operating at reduced voltage levels, addressing the increased demand for sustainable energy infrastructure. Two 500 kV HSIL line configurations are analyzed for their feasibility in replacing the conventional 765 kV transmission lines for the TEP to integrate the large-scale wind energy sources located in far remote areas. The analysis is carried out within the 23-bus EHV test system. This study reveals that both HSIL line configurations successfully meet the technical constraints of the TEP problem, ensuring reliable system operation even under contingency conditions. Therefore, the HSIL lines offer significant cost savings due to infrastructure and accessories at reduced voltage levels with much smaller right of way (ROW) than conventional counterparts. This underscores the potential of unconventional HSIL lines to contribute to more sustainable and cost-effective grid planning strategies for integrating large-scale renewable energy sources.

Abstract:Short-term voltage stability (STVS) assessment is a critical monitoring technology in modern power systems. During daily operations, transmission lines may switch on or off due to scheduled maintenance or unexpected faults, which poses challenges to the STVS assessment under varying topology change conditions. To adapt the STVS assessment to the system topology changes, we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies. The topology-adaptive voltage dynamic feature, extracted from streaming time-series data of phasor measurement units (PMUs), is used to characterize transient voltage stability. The voltage dynamic features depend on the balance of reactive power flow and system topology, effectively revealing both spatio-temporal patterns of post-disturbance system dynamics. The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance, with an accuracy of 99.65% in predicting voltage stability compared with the existing deep learning methods. The proposed model also performs well when applied to the larger IEEE 145-bus power system. The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems. It achieves an accuracy of 99.50% in predicting the STVS for the New England 39-bus power system with the transmission line alternation. Furthermore, the proposed model demonstrates strong robustness to noisy and missing data.

Abstract:The utilization of high-voltage direct current (HVDC) lines for the segmentation of the European power grid has been demonstrated to be a highly effective strategy for the mitigation of the risk of cascading blackouts. In this study, an accurate and efficient method for determining the optimal power flow through HVDC lines is presented, with the objective of minimizing load shedding. The proposed method is applied to two distinct scenarios: first, the segmentation of the power grid along the Pyrenees, with the objective of segmenting the Iberian Peninsula from the rest of Europe; and second, the segmentation of the power grid into Eastern and Western Europe, approximately in half. In both scenarios, the method effectively reduces the size of blackouts impacting both sides of the HVDC lines, resulting in a 46% and 67% reduction in total blackout risk, respectively. Furthermore, we have estimated the cost savings from risk reduction and the expenses associated with converting conventional lines to HVDC lines. Our findings indicate that segmenting the European power grid with HVDC lines is economically viable, particularly for segmenting the Iberian Peninsula, due to its favorable cost-risk reduction ratio.

Abstract:The widespread penetration of wind power has introduced challenges in managing the rotor angle stability characteristics of the power system, affecting both small- and large-disturbance rotor angle stabilities due to its uncertain steady-state power output and inverter-based grid interfacing. Traditionally, the two stability criteria are separately analyzed and improved via preventive control, e.g., generation rescheduling. However, they may have conflicting relationship during the preventive control optimization. Therefore, this paper firstly integrates both small- and large-disturbance rotor angle stabilities and proposes an optimization model for preventive generation rescheduling to simultaneously improve them while considering wind power uncertainty. The stability constraints are linearized using trajectory sensitivity analysis, while the wind power fluctuation is represented by employing a scenario-based Taguchi’s orthogonal array testing (TOAT) method. An iterative solution method is proposed to efficiently solve the optimization model. The proposed optimization model is established on the New England 10-machine 39-bus system and a large Nordic system, demonstrating its robustness and effectiveness in addressing wind power fluctuations.

Abstract:Existing sub-/super-synchronous oscillation stability control methods are primarily focused on specific operating conditions at discrete frequencies, limiting their adaptation to varying oscillation ​scenarios in the power system connected with direct-drive permanent magnet synchronous generator (PMSG)-based wind farms. Based on supplementary dissipation compensation, this paper proposes an oscillation stability control method incorporating equipment-level and farm-level cooperative optimization ​to enhance the system-level stability. First, the effects of dynamic self-dissipation and dynamic coupled dissipation on system stability are analyzed, ​establishing the foundational principle of supplementary dissipation compensation. Subsequently, the optimal locations for supplementary dissipation compensation are identified based on critical control designed to enhance the dynamic self-dissipation effect and suppress the dynamic coupled dissipation effect. Furthermore, by considering energy requirements under the combined wind farm-grid interaction and inter-PMSG interactions and balancing the wind farm-grid interaction dissipation energy with inter-PMSG interaction dissipation energy distribution, an equipment-level control parameter optimization algorithm and a farm-level power cooperative optimization algorithm are established. Finally, the simulation results demonstrate that dynamic coupled dissipation constitutes the ​root cause of oscillation inception and progression. Through equipment-level and farm-level cooperative optimization, the proposed method can reliably compensate dynamic dissipation energy, while adapting to the variation of oscillation frequency and the oscillation scenario. It can maximize the energy dissipation effect of the interconnected system, achieving rapid suppression of sub-/super-synchronous oscillations.

Abstract:Adequacy is a key concern of power system planning, which refers to the availability of sufficient facilities to meet demand. The capacity value (CV) of variable renewable energy (VRE) generation represents its equivalent contribution to system adequacy, in comparison to conventional generators. While VRE continues to grow and increasingly dominates the generation portfolio, its CV is becoming non-negligible, with the corresponding impact mechanisms becoming more complicated and nuanced. In this paper, the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level. A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system. An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation. Through the explicit CV expression, several critical impact factors, including the VRE generation variance, source-load correlation, and system adequacy level, are identified and discussed. Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods. In the IEEE-RTS79 test system, the CV of a 2500 MW wind farm (with 40% renewable energy penetration level) is found to be 6.8% of its nameplate capacity. Additionally, the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.

Abstract:The mutual impedance between doubly-fed induction generator (DFIG) system and weak grid may cause a resonance, which yields to undesirable distortions and harmonics. The equivalent impedance of DFIG systems is high, which creates high-frequency resonance (HFR) in interaction with weak grids. Although several studies are conducted to mitigate HFRs, more improvements are needed in terms of damping and phase-margin. Accordingly, an active damping control strategy based on virtual admittance is proposed, which properly mitigates the disturbances. The proposed strategy is accurate as it considers the dynamic high-frequency model of DFIG system to effectively reduce the HFR. The performance of the proposed strategy is verified by using different case studies on a 2 MW DFIG system with time-domain simulations in MATLAB/Simulink environment.

Abstract:The reduced frequency regulation capability in low-inertia power systems necessitates enhanced frequency support from photovoltaic (PV) systems. However, the regulation capability of PV system under conventional control scheme is limited, which requires flexible power control and support from battery energy storage systems (BESSs). This paper proposes an energy management strategy of PV-BESS to provide stable frequency support to the grid. The proposed strategy initially develops a maximum power point tracking (MPPT)-based power reserve control (PRC) for PV power reserve. At this stage, the BESS is manipulated to buffer the power fluctuations caused by MPPT execution and environmental variability. To enhance the regulation capability of BESS and mitigate battery aging, the proposed strategy incorporates a flexible PRC for state of charge (SOC) recovery. Subsequently, the battery can be rationally charged or discharged during the PRC process to maintain SOC. Simulation results obtained from MATLAB/Simulink and experimental results from the StarSim platform validate that the proposed strategy achieves stable PV power reserve, mitigates fluctuations induced by environmental uncertainties, enables efficient SOC recovery, and improves grid frequency quality. Overall, the proposed strategy ensures stable operation of PV-BESS both under steady-state conditions and during frequency events.

Abstract:This paper proposes a novel control scheme for inverter-based resources (IBRs) based on the complex frequency (CF) concept. The control objective is to maintain a constant CF of voltage at the terminals of IBR by adjusting its current reference. This current is imposed based on the well-known power flow equation, the dynamics of which are calculated through estimating the CFs for the voltages of adjacent buses. The performance is evaluated by analyzing the local variations in frequency and voltage magnitude, as well as the frequency of center of inertia (CoI), and then compared with conventional frequency droop, proportional-integral (PI) voltage controllers, and virtual inertia. The case study utilizes a modified version of WSCC 9-bus system and a 1479-bus model of the Irish transmission grid and considers various contingencies and sensitivities such as the impact of current limiters, delays, noise, R/X ratio, and electromagnetic transient (EMT) dynamics. Results show that the proposed control scheme consistently outperforms the conventional controllers, leading to significant improvements in the overall dynamic response of the system.

Abstract:The data-driven approaches have been extensively developed for multi-operation impedance modeling of the renewable power generation equipment (RPGE). However, due to the black box of RPGE, the dataset used for establishing impedance model lacks theoretical guidance for data generation, which reduces data quality and results in a large amount of data redundancy. To address this issue, this paper proposes an impedance dataset optimization method for data-driven modeling of RPGE considering multi-operation conditions. The objective is to improve the data quality of the impedance dataset, thereby reflecting the overall impedance characteristics with a reduced data amount. Firstly, the impact of operation conditions on impedance is evaluated to optimize the selection of operating points. Secondly, at each operating point, the frequency distribution is designed to reveal the impedance characteristics with fewer measurement points. Finally, a serial update method for measured datasets and the multi-operation impedance model is developed to further refine the dataset. The experiments based on control-hardware-in-loop (CHIL) are conducted to verify the effectiveness of the proposed method.

Abstract:This paper demonstrates a new type of sub-synchronous oscillation of the grid-forming (GFM) converter, which occurs at low rather than high power levels. To reveal the intrinsic mechanism, a simplified analytical small-signal impedance model of the GFM converter is derived. It is found that the reactive power control loop (RPCL) can have a significant impact on the system stability. In particular, the voltage matrix introduced by the RPCL is the key factor causing instability in the GFM grid-connected system at low operating points. Therefore, this paper proposes a control strategy that reshapes the RPCL to counteract the negative effect of the voltage matrix by introducing a q-axis current feedforward, ensuring stable operation at any operating point. Finally, experimental results validate the correctness and effectiveness of the proposed control strategy.

Abstract:A mathematical programming approach rooted in distributionally robust optimization (DRO) provides an effective data-driven strategy for battery energy storage system (BESS) planning. Nevertheless, the DRO paradigm often lacks interpretability in its results, obscuring the causal relationships between data distribution characteristics and the outcomes. Furthermore, the current approach to battery type selection is not included in traditional BESS planning, hindering comprehensive optimization. To tackle these BESS planning problems, this paper presents a universal method for BESS planning, which is designed to enhance the interpretability of DRO. First, mathematical definitions of interpretable DRO (IDRO) are introduced. Next, the uncertainties in wind power, photovoltaic power, and loads are modeled by using second-order cone ambiguity sets (SOCASs). In addition, the proposed method integrates selection, sizing, and siting. Moreover, a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems. Finally, the effectiveness of the proposed method is demonstrated through case studies, offering planners richer decision-making insights.

Abstract:Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.

Abstract:Stray currents from DC metro systems intrude into the grounded neutrals of large power transformers, posing a major threat to the differential relay protection of transformer. In this paper, the performance of harmonic blocking based differential relay protection considering neutral stray currents (NSCs) from DC metro systems is thoroughly investigated. The findings reveal that relays may fail to clear internal faults in some scenarios because they are blocked due to NSC-induced harmonic currents. To improve the reliability of differential relay protection, a method for preventing incorrect operation is proposed using a skewness-based criterion to detect the presence of NSCs. Then, the relay is unblocked when an internal fault is simultaneously detected by the novel internal fault detection block. The proposed method is resistant to current transformer saturation and accounts for NSC fluctuations. Various time-domain simulations conducted in PSCAD/EMTDC verify the effectiveness of the proposed method.

Abstract:This paper constructs a synthetic framework for the operational reliability evaluation and risk mitigation of asynchronous grids coupled through flexible high-voltage DC (HVDC) systems (AGs-FDCSs). First of all, an analytical model for the unavailability of DC units is reformulated to refine and facilitate the reliability modeling of such flexible HVDC systems considering their time-dependent features as well as the impacts of converter station configurations. Subsequently, the operational risk associated with the redispatch procedure is extended to the reliability evaluation of composite power system, and the risk is mitigated through an optimal power flow (OPF) based short-term state assessment model. In addition, some new reliability indices like expected DC transmission power (EDCTP) and DC terminal outage probability (DCTOP) are defined to quantify the impact of the reliability of flexible HVDC systems on the entire grid. The effectiveness of the proposed framework on a modified IEEE RTS-79 system is validated with the elaborate discussions on the time-dependent reliability of AGs-FDCSs as well as the impacts of the converter station configurations.

Abstract:As power systems scale up and uncertainties deepen, traditional centralized optimization approaches impose significant computation burdens on large-scale optimization problems, introducing new challenges for power system scheduling. To address these challenges, this study formulates a distributionally robust optimization (DRO) scheduling model that considers source-load uncertainty and is solved using a novel distributed approach that considers the distribution of tie-line endpoints. The proposed model includes a constraint related to the transmission interface, which consists of several tie-lines between two subsystems and is specifically designed to ensure technical operation security. In addition, we find that tie-line endpoints enhance the speed of distributed computation, leading to the development of a power system partitioning approach that considers the distribution of these endpoints. Further, this study proposes a distributed approach that employs an integrated algorithm of column-and-constraint generation (C&CG) and sub-gradient descent (IACS) to address the proposed model across multiple subsystems. A case study of two IEEE test systems and a practical provincial power system demonstrates that the proposed model effectively ensures system security. Finally, the scalability and effectiveness of the distributed approach in accelerating problem-solving are confirmed.

Abstract:With the increasing integration of uncertain distributed renewable energies (DREs) into distribution networks (DNs), communication bottlenecks and the limited deployment of measurement devices pose significant challenges for advanced data-driven voltage control strategies such as deep reinforcement learning (DRL). To address these issues, this paper proposes an offline-training online-execution framework for volt-var control in DNs. In the offline-training phase, a graph convolutional network (GCN)-based denoising autoencoder (DAE), referred to as the deep learning (DL) agent, is designed and trained to capture spatial correlations among limited physical quantities. This agent predicts voltage values for nodes with missing measurements using historical load data, DRE outputs, and global voltages from simulations. Furthermore, the dual-timescale voltage control problem is formulated as a multi-agent Markov decision process. A DRL agent employing the multi-agent soft actor-critic (MASAC) algorithm is trained to regulate the tap position of on-load tap changer (OLTC) and reactive power output of photovoltaic (PV) inverters. In the online-execution phase, the DL agent supplements the limited measurement data, providing enhanced global observations for the DRL agent. This enables precise equipment control based on improved system state estimation. The proposed framework is validated on two modified IEEE test systems. Numerical results demonstrate its ability to effectively reconstruct missing measurements and achieve rapid, and accurate voltage control even under severe measurement deficiencies.

Abstract:The probabilistic short circuit analysis provides relevant information for power system planning and power quality assessment tasks. Traditional Monte Carlo methods (TMCMs) are usually applied to consider the randomness affecting short circuit operating conditions, but they require numerous iterations to properly characterize the network conditions. This paper proposes a mixed Taguchi-based method (MTBM) as a new alternative tool to account for the uncertainties affecting the inputs of probabilistic short circuit analysis. The MTBM significantly reduces the number of iterations required to properly address the randomness of inputs (environmental conditions, pre-fault conditions, fault characteristics), and allows diversifying the representation of inputs through a quantile-based selection of their levels. The proposed method is applied to unbalanced three-phase four-wire low-voltage (LV) distribution systems with photovoltaic systems (PVSs) operating in low voltage ride- through (LVRT) during the fault. Numerical applications related to a test system are presented, and the proposed MTBM is compared with the TMCM, the unmixed Taguchi-based method (UTBM), and the point estimate method (PEM). The proposed MTBM returns values very close to those of the TMCM (with average deviations ranging from 0.01% to 3.12%) and enables a fast and accurate analysis of faulted LV distribution systems with PVSs operating in LVRT.

Abstract:In cyber-physical distribution systems (CPDSs), the complex coupling between cyber and physical components poses significant challenges to system resilience. When extreme weather disasters occur, these coupling relationships greatly increase the complexity of recovery decisions, which prolongs recovery time and increases recovery costs. In this paper, a collaborative recovery method for CPDS considering multiple coupling constraints is proposed to avoid large-scale outages. First, a fictitious flow based model is established to describe the functional availability of cyber nodes. Second, three typical components are analytically modeled to describe the energy-control coupling relationships. Then, a collaborative recovery method is proposed for post-disaster crew dispatch, network reconfiguration, and fault repair to restore critical loads, considering both the cyber availability constraints and cyber-physical coupling constraints. Finally, the effectiveness of the proposed recovery method is verified by the DCPS-160 test system.

Abstract:Wide-area damping controllers (WADCs) help in damping poorly damped inter-area oscillations (IAOs) using wide-area measurements. However, the vulnerability of the communication network makes the WADC susceptible to malicious dynamic attacks. Existing cyber-resilient WADC solutions rely on accurate power system models or extensive simulation data for training the machine learning (ML) model, which are difficult to obtain for large-scale power system. This paper proposes a novel non-intrusive hybrid two-stage detection framework that mitigates these limitations by eliminating the need for real-time access to large system data or attack samples for training the ML model. In the first stage, an autoencoder is deployed at the actuator location to detect dynamic attacks with sharp gradient variations, e.g., triangular, saw-tooth, pulse, ramp, and random attack signals. In the second stage, an unscented Kalman filter with unknown input estimation at the control center identifies smoothly varying dynamic attacks by estimating the control signal received by the actuator using synchrophasor measurements. A modified cosine similarity (MCS) metric is proposed to compare and quantify the similarity between the estimated control signal and the control signal sent by the WADC placed at the control center to detect any dynamic attacks. The MCS is designed to differentiate between events and dynamic attacks. The performance of the proposed framework has been validated on a hardware-in-the-loop (HIL) cyber-physical testbed built by using the OPAL-RT simulator and industry-grade hardware.

Abstract:Meter encoding, as a side-effect-free scheme, has been proposed to detect false data injection (FDI) attacks without significantly affecting the operation of power systems. However, existing meter encoding schemes either require encoding lots of measurements from different buses to protect a substantial proportion of a power system or are unhidden from alert attackers. To address these issues, this paper proposes a smart inverter enabled meter encoding scheme for detecting FDI attacks in distribution system state estimation. The proposed scheme only encodes the measurements from the existing programmable smart inverters. Meanwhile, this scheme can protect all the downstream buses from the encoded inverter bus. Compared with existing schemes, the proposed scheme encodes fewer meters when protecting the same number of buses, which decreases the encoding cost. In addition, by following the physical power flow laws, the proposed scheme is hidden from alert attackers who can implement the state estimation-based bad data detection (BDD). Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed scheme can mislead the attacker’s state estimation on all the downstream buses from the encoded bus without arousing the attacker’s suspicion. FDI attacks that are constructed based on the misled estimated state are very likely to trigger the defender’s BDD alarm.

Abstract:The increasing focus on carbon neutrality has led to heightened interest in multiple microgrids (MGs) due to their potential to significantly reduce emissions by the integrated electricity-heat-carbon sharing among them. In this paper, a decentralized peer-to-peer (P2P) framework for integrated electricity-heat-carbon sharing is proposed to optimize the trading process of multi-energy and carbon among multiple MGs. The proposed framework considers certified emission reductions (CERs) of photovoltaic (PV) systems in each MG, and carbon allocation and trading among multiple MGs. The P2P trading behaviors among multiple MGs are modelled as a non-cooperative game. A decentralized optimization method is then developed using a price-based incentive scheme to solve the non-cooperative game and optimize the transactions of the electricity-heat-carbon jointly. The optimization problem is solved using sub-gradient in a decentralized manner. And the Nash equilibrium of the non-cooperative game is proven to exist uniquely, ensuring the convergence of the model. Furthermore, the proposed decentralized optimization method safeguards the private information of the MGs. Numerical results show that the total operational cost of the MGs and the carbon emissions can be reduced significantly.

Abstract:Distributed secondary control has been proposed to maintain frequency/voltage synchronization and power sharing for distributed energy sources in AC microgrids (MGs). The cyber layer is susceptible to time delays and cyber failures and thus, a distributed resilient secondary control should be investigated. This paper proposes a distributed multi-scale attention and predictor-based control (DMAPC) strategy to address false data injection attacks and packet loss failures with time delays. The multi-scale attention mechanism enables the system to selectively focus on neighbors ’ states with higher confidence evaluated in different time scales, while the data-driven predictor compensates for lost neighbors’ states in the nonlinear controller. The DMAPC does not impose strict limitations on the number of false communication links or upper bound for false data. Besides, the DMAPC is formulated as an uncertain system with time delays and is proven to be uniformly ultimately bounded. Extensive experiments on a hardware-in-the-loop MG testbed have validated the effectiveness of DMAPC, which successfully relaxes restrictions on cyber failures compared to existing strategies.

Abstract:Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.

Abstract:The proliferation of electric vehicles (EVs) introduces transformative opportunities and challenges for the stability of distribution networks. Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid, while regulated EV charging will alleviate such imbalance. To systematically address this challenge, this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators (EVAs). By constructing a coordinated framework that integrates the day-ahead and real-time markets, the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system, with a particular focus on dynamic compensation for deviations between scheduled and real-time operations. A bi-level Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance, with Karush-Kuhn-Tucker (KKT) conditions and mixed-integer second-order cone programming (MISOCP) ensuring feasible solutions. The proposed coordinated framework is validated with different bidding modes includes independent bidding, full price acceptance, and cooperative bidding modes. The proposed two-stage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.

Abstract:Peer-to-peer (P2P) energy trading enables an efficient regulation of distributed renewable energy among prosumers, implicitly promoting low-carbon operation. This study proposes a novel P2P energy trading scheme with coupled electricity-carbon (E/C) market that co-optimizes both power and carbon emission flows. To facilitate the low-carbon operations in the market, we introduce a prosumer-driven carbon-aware distribution locational marginal price (PDC-DLMP) to serve as a pricing signal for the distribution system operator (DSO). To efficiently determine the optimal trading solutions, we adopt a two-layer data-driven approach. The first layer employs a reinforcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient (MATD3); the second layer uses a deep neural network (DNN) driven surrogate model, which is designed to map the PDC-DLMP signals, thereby eliminating the need for direct DSO intervention during market operation. This approach protects the physical model parameters of the distribution network and ensures multi-level privacy protection. Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market, demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.