Abstract:In this paper, a robust method for quantifying the impact of short-circuit faults on microgrids is proposed. Microgrids can operate in both islanded (grid-forming) and grid-connected (grid-following) modes, and the ownership and responsibility for the microgrid operation can vary significantly from distribution system operators (DSOs) to third-party microgrid operators. This necessitates the development of a robust short-circuit calculation (SCC) method that can provide accurate results for all the possible microgrid topologies, operational modes, and ownership models. Unlike previously developed SCC methods for microgrids, the SCC method proposed in this paper provides highly accurate results for all possible microgrid topologies: islanded microgrid, grid-connected microgrid, and utility microgrid as a part of a larger distribution grid. In addition, the proposed SCC method solves the short-circuit faults of any complexity, with the same simplicity. The proposed SCC method is tested on a complete model of a real-life microgrid on the Case Western Reserve University campus, operating in both islanded and grid-connected modes. The computational results show the advantages of the proposed SCC method in comparison to the previous ones for microgrids, regarding the robustness (ability to solve complex short-circuit faults with an arbitrary number of faulted buses and phases that affect a microgrid of any topology), as well as the accuracy of the results.

Abstract:Photovoltaic (PV) inverter, as a promising voltage/var control (VVC) resource, can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage. Meanwhile, active power plays a significant role in microgrid voltage profile. Price-based demand response (PBDR) can shift load demand via determining time-varying prices, which can be regarded as an effective means for active power shifting. However, due to the different characteristics, PBDR and inverter-based VVC lack systematic coordination. Thus, this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method, including day-ahead PBDR price scheduling, hour-ahead reactive power dispatch of PV inverters, and real-time local droop control of PV inverters. Considering their mutual influence, a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages. To solve the bilinear constraints of droop control function, the problem is reformulated into a second-order cone programming relaxation model. Then, the concave constraints are convexified, forming a penalty convex-concave model for feasible solution recovery. Lastly, a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model. The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods. The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.

Abstract:Although power grids have become safer with increased situational awareness, major extreme events still pose reliability and resilience challenges, primarily at the distribution level, due to increased vulnerabilities and limited recovery resources. Information and communication technologies (ICTs) have introduced new vulnerabilities that have been widely investigated in previous studies. These vulnerabilities include remote device failures, communication channel disturbances, and cyberattacks. However, only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat. This paper proposes a communication-aware restoration approach of smart distribution grids, which leverages power-telecom interdependencies to determine the optimal restoration strategies. The states of grid-energized telecom points are tracked to provide the best restoration actions, which are enabled through the resilience resources of repair, manual switching, remote reconfiguration, and distributed generators. As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies, different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations. System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming. Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources. The existing disparity between overhead and underground power line configurations is also quantified.

Abstract:The flexible interconnection of microgrids (MGs) adopting back-to-back converters (BTBCs) has emerged as a new development trend in the field of MGs. This approach enables larger-scale integration and higher utilization of distributed renewable energy sources (RESs). However,their stability characteristics are very different from single MG due to the control characteristics of flexible interconnection. Meanwhile, the uncertainty and stochastic dependence structures of RESs and loads create challenges for stability analysis and cooperative control. In this paper, a probabilistic small-signal stability assessment and cooperative control framework is proposed for interconnected MGs via BTBCs. First, a cooperative control architecture for MGs is constructed. Then, a small-signal model of interconnected MGs via BTBCs containing primary control and secondary control is developed. This model facilitates the analysis of the impacts of BTBCs and various control strategies on the system stability. Subsequently, Copula functions and polynomial chaos expansion (PCE) are combined to achieve the probabilistic small-signal stability assessment. On this basis, the parameters of the cooperative control are optimized, enhancing the robustness of interconnected MGs via BTBCs. Finally, a case of interconnected MGs via BTBCs are built in MATLAB/Simulink to verify the accuracy and effectiveness of the proposed framework.

Abstract:Traditional virtual synchronous generator (VSG) suffers from frequency steady-state deviation in islanded microgrids, which negatively affects the frequency-sensitive loads. Moreover, similar to the synchronous generator, VSG introduces active power oscillation, especially under the condition of multiple parallel VSGs, which may cause overload or damage to the VSG because of its low overcurrent capability as a power electronic inverter. To address these issues, a decentralized frequency restoration and power oscillation damping control method is proposed in this paper, in which the global variable characteristic of the microgrid frequency is considered to restore it to the rated value while ensuring precise active power sharing. Moreover, the proposed control method can dampen the power oscillation during load disturbance without affecting the steady-state characteristics. In addition, the fully decentralized manner obviates the requirement for communication networks, thereby considerably reducing the communication burden and improving system reliability. Finally, simulations and experiments are conducted to validate the effectiveness of the proposed control method.

Abstract:Contingencies, such as behavior shifts of microgrid operators (MGOs) and abrupt weather fluctuations, significantly impact the economic operations of multi-microgrids (MMGs). To address these contingencies and enhance the economic and autonomous performance of MGOs, a self-organizing energy management modeling approach is proposed. A second-order stochastic dynamical equation (SDE) is developed to accurately characterize the self-organizing evolution of the operating cost of MGO incurred by contingencies. Firstly, an operating model of MMG relying on two random graph-driven information matrices is constructed and the order parameters are introduced to extract the probabilistic properties of variations in operating cost. Subsequently, these order parameters, which assist individuals in effectively capturing system correlations and updating state information, are incorporated as inputs into second-order SDE. The second-order SDE is then solved by using the finite difference method (FDM) within a loop-structured solution framework. Case studies conducted within a practical area in China validate that the proposed self-organizing energy management model (SEMM) demonstrates spontaneous improvements in economic performance compared with conventional models.

Abstract:Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.

Abstract:Rural electrification is a crucial component of the power system that requires urgent innovation and transformation to enhance electrification levels. However, various challenges hinder the progress in rural electrification, primarily due to remote locations and unique consumption patterns. To effectively coordinate the local energy distribution, an energy management framework utilizing peer-to-peer (P2P) based interactive operations is proposed, which minimizes the reliance on long-distance transmission while enhancing the rural electrification level. The proposed P2P-based energy management framework incorporates various distributed generation resources across rural areas, facilitating direct energy transactions between neighboring community-based villages. Additionally, the P2P energy trading is modeled as a Nash bargaining (NB) problem, which accounts for the allocation of network loss costs and operational state of the rural distribution network. To protect the privacy of individual villages, an improved adaptive alternating direction method of multipliers (AADMM) is proposed to solve the NB problem. The AADMM utilizes a local curvature approximation scheme during parameter updates, allowing for automatic adjustments of the fixed penalty parameter within the standard alternating direction method of multipliers (ADMM). This enhancement improves the convergence rates without requiring central oversight. Simulation results demonstrate significant reductions in operational costs for both the overall network and individual village participants. The proposed P2P-based energy management framework also enhances the bus voltage stability and reduces the line transmission power, thereby further enhancing rural electrification levels. The adaptability and extensibility of this framework are further validated using the IEEE 33-bus and 118-bus distribution systems. Additionally, the AADMM shows higher convergence rates compared with the standard ADMM.

Abstract:The volatility of increasing distributed generators (DGs) poses a severe challenge to the supply restoration of active distribution networks (ADNs). The integration of power electronic devices represented by soft open points (SOPs) and mobile energy storages (MESs) provides a promising opportunity for rapid supply restoration with high DG penetration. Oriented for the post-event rapid restoration of ADNs, a bi-level supply restoration method is proposed considering the multi-resource coordination of switches, SOPs, and MESs. At the upper level (long-timescale), a multi-stage supply restoration model is developed for multiple resources under uncertainties of DGs and loads. At the lower level (short-timescale), a rolling correction restoration strategy is proposed to adapt to the DG and load fluctuations on short timescales. Finally, the effectiveness of the proposed method is verified based on a modified practical distribution network and IEEE 123-node distribution network. Results show that the proposed method can fully utilize the coordination potential of multiple resources to improve load restoration ratio for ADNs with DG uncertainties.

Abstract:In this paper, a set of distributed secondary controllers is introduced that provide active regulation for both steady-state and transient-state performances of an islanded DC microgrid (MG). The secondary control for distributed converter interfaced generation (DCIG) not only guarantees that the system converges to the desired operating states in the steady state but also regulates the state variations to a prescribed transient-state performance. Compared with state-of-the-art techniques of distributed secondary control, this paper achieves accurate steady-state secondary regulations with prescribed transient-state performance in an islanded DC MG. Moreover, the applicability of the proposed control does not rely on any explicit knowledge of the system topology or physical parameters. Detailed controller designs are provided, and the system under control is proved to be Lyapunov stable using large-signal stability analysis. The steady-state and transient-state performances of the system are analyzed. The paper proves that as the perturbed system converges, the proposed control achieves accurate proportional power sharing and average voltage regulation among the DCIGs, and the transient variations of the operating voltages and power outputs at each DCIG are regulated to the prescribed transient-state performance. The effectiveness of the proposed control is validated via a four-DCIG MG system.

Abstract:This paper proposes a novel parallel hybrid deep reinforcement learning (DRL) approach to address the real-time energy management problem for microgrid (MG). As the proposed approach can directly approximate a discrete-continuous hybrid policy, it does not require the discretization of continuous actions like regular DRL approaches, which avoids accuracy degradation and the curse of dimensionality. In addition, a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness. Finally, a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility. Comparative numerical simulations with several existing MG real-time energy management approaches (i.e., myopic policy, model predictive control, and regular DRL approaches) demonstrate the effectiveness and superiority of the proposed approach.

Abstract:As the penetration of intermittent renewable energy resources in microgrids (MGs) continues to grow globally, optimal operation management becomes increasingly crucial due to the variability of these sources. One potential solution to this challenge is the use of demand response (DR) programs, which are practical and relatively low-cost options. However, ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables. Additionally, in real-world operational decision-making problems, there is a simultaneous optimization of multiple objectives, including the maximization of system flexibility and the minimization of system cost. This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid, which are subject to volatile market prices. The model is based on the feasibility analysis of the uncertain space of wind power generation and load, and it also investigates the level of inflexibility present in the system. The impact of the DR program on the flexibility of MGs is quantified through a case study. The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.

Abstract:This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system (MMGS) that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborative market trading within the MMGS for economic and environmental benefit improvement. Initially, an operation model of each microgrid is developed by synthetically considering electricity-carbon operational constraints related to generation units and energy storage units. Then, a collaborative optimization strategy of the MMGS is established according to the Nash bargaining game (NBG) model with the objective of maximizing overall operational revenue. To determine the trading schedule, an accelerated prediction-correction-based alternating direction method of multipliers (PCB-ADMM) algorithm is employed to derive the optimal scheduling strategy of MMGS in a distributed manner, ensuring the privacy preservation of individual microgrids. For electricity-carbon pricing, a supply-demand ratio (SDR) based pricing strategy is proposed to dynamically update electricity and carbon allowance prices, which fundamentally guides and incentivizes each microgrid to trade within the MMGS preferentially rather than with an upstream distribution network. Finally, a study case verifies the effectiveness of the proposed framework in enhancing the operation economy and environmental friendliness of the entire MMGS.

Abstract:In modern microgrids (MGs) with high penetration of distributed energy resources (DERs), system reconfiguration occurs more frequently and becomes a significant issue. Fixed-parameter controllers may not handle these tasks effectively, as they lack the ability to adapt to the dynamic conditions in such environments. This paper proposes an intelligence-driven grid-forming (GFM) converter control method for islanding MGs using a robustness-guided neural network (RNN). To enhance the adaptability of the proposed method, traditional proportional-integral controllers in the GFM primary control loops are entirely replaced by the RNN. The RNN is trained by a robustness-guided strategy to replicate their robust behaviors. All the training stages are purely data-driven methods, which means that no system parameters are required for the controller design. Consequently, the proposed method is an intelligence-driven model-less GFM converter control. Compared with traditional methods, the simulation results in all testing scenarios show the clear benefits of the proposed method. The proposed method reduces overshoots by more than 71.24%, which keeps all damping ratios within the stable region and provides faster stabilization. In comparison to traditional methods, at the highest probability, the proposed method improves damping by over 14.7% and reduces the rates of change of frequency and voltage by over 59.97%. Additionally, the proposed method effectively suppresses the interactions between state variables caused by inverter-based resources, with frequencies ranging from 1.0 Hz to 1.422 Hz. Consequently, these frequencies contribute less than 19.79% to the observed transient responses.

Abstract:In extreme events, microgrid (MG) formation has drawn attention due to its potential to assist in load restoration in the distribution network by utilizing the distributed generations (DGs). However, most of the state-of-the-art studies pay attention to the steady constraints without considering the transient performance during MG formation process. Power fluctuations caused by line switch operations can lead to frequency overruns in low-inertia DG-based systems, thus tripping protective relays. This paper proposes an MG formation method for load restoration in the distribution network with dynamic frequency constraints during the load restoration process. Firstly, considering the frequency constraints, a frequency nadir formula is derived based on the aggregated model. The proposed MG formation method offers two solutions to ensure the frequency safety. One solution is to incorporate the dynamic frequency constraints into the MG formation optimization model to satisfy the frequency requirements if the load restoration amount is preferred. Another alternative solution is to introduce an inertia-adjustable control strategy using virtual synchronous generators (VSGs), which is aimed to improve the frequency nadir during MG formation process. This solution is implemented without changing the MG formation result that is subject to only steady constraints when the load restoration speed is privileged. Theoretical validity is verified through the simulation results. Case study results prove the effectiveness of proposed solutions under various demands in the aspect of frequency improvement.

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: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:The high share of intermittent wind power jeopardizes system frequency security in isolated offshore field microgrids (IOFMs). Existing scheduling strategies, mainly focusing on stable energy supply and demand, fail to ensure frequency security due to the limited flexible and dispatchable resources in the IOFM. Thus, this paper proposes an optimal scheduling model of wind power generators with unified frequency response and spinning reserve constraints to assist operators in efficiently managing turbine generators. Frequency security indices are introduced to quantify the impact of both sudden wind power shortages and continuous wind power fluctuations on the frequency dynamics under different control modes. Based on these indices, unified frequency response and spinning reserve constraints are analytically derived to support the optimization of the control mode and on/off status of wind power generators. These highly nonlinear unified constraints are then reformulated as mixed-integer linear constraints, which are integrated into the scheduling model with operating costs as the objective. The proposed model is tested using a modified real-world IOFM. The results demonstrate that the proposed model not only ensures system frequency security but also reduces operating costs and carbon emissions.

Abstract:For providing a reliable power supply in an islanded AC microgrid, it is crucial to ensure that the operating states meet the safety constraints. However, conventional distributed secondary control methods often struggle to handle voltage and frequency constraints, limiting their practical applicability. To address these limitations, this paper proposes a novel prescribed performance control (PPC) based distributed secondary coordination method for islanded AC microgrids. The proposed method introduces a systematic framework that integrates bijective transformation with distributed secondary control. In this framework, the constrained distributed voltage and frequency control problems can be transformed into unconstrained ones, enabling strict adherence to predefined performance boundaries. Additionally, Lyapunov-Krasovskii functional analysis is employed in this paper to ensure asymptotic stability and calculate the stable boundaries in the transformed error space under time-delay conditions. The effectiveness of stability recovery and prescribed performance is tested in both MATLAB/Simulink and RTLab-based hardware-in-the-loop (HIL) experimental environments.

Abstract:This study introduces a distributed specified-time control mechanism (DSTCM) for secondary control in islanded microgrids (MGs) operating under directed switching communication topologies. The proposed mechanism ensures convergence properties that are independent of initial conditions, enabling the design of an exact offline settling time to reduce power losses and limit the upper bound of convergence time. By employing a piecewise function-based communication approach and directed switching graphs, the proposed mechanism effectively reduces computational and communication demands on the system. Moreover, the proposed mechanism significantly enhances power system performance while minimizing adjustment costs, delivering precise control actions under various operating conditions. The accuracy and effectiveness of the proposed mechanism are validated through extensive MATLAB simulations, demonstrating its ability to regulate MG voltages and frequencies, achieve accurate proportional active power sharing, and maintain state-of-charge (SoC) balancing. Its superiority over previously established mechanisms is also confirmed by a comparative analysis.