Abstract:In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global interpretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.

Abstract:Bulk power systems show increasingly significant frequency spatial distribution characteristics (FSDCs), leading to a huge difference in the frequency response between regions. Existing uniform-frequency models based on analytical methods are no longer applicable. This paper develops a reduced-order bus frequency response (BFR) model to preserve the FSDC and describe the frequency response of all buses. Its mathematical equation is proved to be isomorphic to the forced vibration of a mass-spring-damper system, and the closed-form solution (CFS) of the BFR model is derived by the modal analysis method and forced decoupling method in vibration mechanics. The correlation between its mathematical equation and the state equation for small-signal stability analysis is discussed, and related parameters in the CFS are defined by the eigen-analysis method without any additional devices or tools. Case studies show that the proposed reduced-order BFR model and its CFS can improve the solution accuracy while keeping the solution speed within milliseconds, which can preserve the significant FSDC of bulk power systems and represent a normalized mathematical description of distinct-frequency models.

Abstract:The significant increase in the proportion of renewable energy sources (RESs) has elevated risks of extreme ramp events and frequency instability in power systems. In recent years, frequency stability events have occurred in several countries/regions worldwide due to flexibility deficiencies. Generation flexibility has emerged as a critical factor influencing the frequency stability of power systems. This paper proposes a domain of attraction (DOA)-based quantitative method to assess the frequency stability region of power systems with a high proportion of RESs, considering generation flexibility constraints. First, ramp rate is adopted as the core indicator to characterize generation flexibility within automatic generation control (AGC) timescale, through which a nonlinear AGC model with rate saturation constraints is established. Second, the concept of DOA is introduced to define the stability region of the nonlinear AGC. Third, a quadratic Lyapunov-based estimation method is employed to quantitatively analyze the DOA of the nonlinear AGC at different generation flexibility levels. Simulation results demonstrate that increased generation flexibility expands the estimated DOA of the nonlinear AGC, whereas generation flexibility deficiency induces AGC instability. Moreover, state trajectory and time-domain simulation verify that the proposed estimation method accurately represents the stability region of the nonlinear AGC.

Abstract:With the load growth and the power grid expansion, the problem of short-circuit current (SCC) exceeding the secure limit in large-scale power grids has become more serious, which poses great challenge to the optimal secure operation. Aiming at the SCC limitations, we use multiple back-to-back voltage source converter based (B2B VSC) systems to separate a large-scale AC power grid into two asynchronous power grids. A multi-objective robust optimal secure operation model of large-scale power grid with multiple B2B VSC systems considering the SCC limitation is established based on the AC power flow equations. The decision variables include the on/off states of synchronous generators, power output， terminal voltage, transmission switching, bus sectionalization, and modulation ratios of B2B VSC systems. The influence of inner current sources of renewable energy generators on the system SCC is also considered. To improve the computational efficiency, a mixed-integer convex programming (MICP) framework based on convex relaxation methods including the inscribed N-sided approximation for the nonlinear SCC limitation constraints is proposed. Moreover, combined with the column-and-constraint generation (C&CG) algorithm, a method to directly solve the compromise optimal solution (COS) of the multi-objective robust optimal secure operation model is proposed. Finally, the effectiveness and computational efficiency of the proposed solution method is demonstrated by an actual 4407-bus provincial power grid and the modified IEEE 39-bus power grid, which can reduce the consumed CPU time of solving the COS by more than 90% and obtain a better COS.

Abstract:We propose a quasi-deterministic proxy for the network-constrained stochastic unit commitment (SUC) problem. The proposed proxy can identify very similar commitment decisions as those obtained by solving the SUC problem with a large scenario set. Its computational performance, though, is close to that of a deterministic unit commitment problem. The proposed proxy has the same formulation as the SUC problem but only includes one or two envelope scenarios, generated based on the original scenario set. The two envelope scenarios capture the maximum and minimum net-load conditions in the original scenario set. We use a systematic method to assess the quality of commitment decisions obtained by the proposed proxy. The considered case study is based on the Illinois 200-bus system.

Abstract:Multiterminal low-frequency transmission system (LFTS) has promising potential for large-scale offshore wind power integration. Nevertheless, the existing protection suffers from low sensitivity, and even operates incorrectly because the converters connected to both ends of cables change fault characteristics substantially. To address this problem, this paper firstly inspects the adaptability of current differential protection, revealing the manner in which control strategies after fault impact the sensitivity of the existing protection. Then, based on the characteristics of armored three-core cable, phase-mode transformation is utilized to decouple the fault information and the specific moduli are selected to reflect all kinds of fault types. The expression of backward traveling-wave (BTW) voltage based on interpolation is derived under the condition of low sampling frequency. Finally, a pilot protection based on BTW voltage difference for submarine cables of LFTS with integrated offshore wind power is proposed, which has higher sensitivity because the difference between BTW calculated from local information and the one from remote information is considerable during fault transient period. Simulation tests compare the performance of the existing protection with that of the proposed protection. Extensive simulations corroborate that the proposed protection reliably identifies the fault cable in various fault scenarios.

Abstract:The development of low-carbon energy systems and renewable energy sources (RESs) are critical to solving the energy crisis around the world. However, renewable energy generation control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion. Focusing on large-scale renewable energy transmission lines, the sensitivity of traditional current differential protection and distance protection may be reduced, and there is even the risk of maloperation. Therefore, a suitable transmission line model is established, which considers the distributed capacitance. Afterward, a novel dynamic state estimation based protection (DSEBP) for large-scale renewable energy transmission lines is proposed. The proposed DSEBP adopts instantaneous measurements and additional protection criteria to ensure the quick action and reliability. Finally, faults are identified by checking the matching degree between the actual measurements and the established transmission line model. The performance of the proposed DSEBP is verified through PSCAD/EMTDC and real-time digital simulator (RTDS) hardware-in-loop tests. The results demonstrate that the proposed DSEBP can identify various types of faults quickly and reliably. Meanwhile, the proposed DSEBP has a better capability to withstand fault resistance and disturbance.

Abstract:The phenomenon of sub-synchronous oscillation (SSO) poses significant threats to the stability of power systems. The advent of artificial intelligence (AI) has revolutionized SSO research through data-driven methodologies, which necessitates a substantial collection of data for effective training, a requirement frequently unfulfilled in practical power systems due to limited data availability. To address the critical issue of data scarcity in training AI models, this paper proposes a novel transfer-learning-based (TL-based) Wasserstein generative adversarial network (WGAN) approach for synthetic data generation of SSO in wind farms. To improve the capability of WGAN to capture the bidirectional temporal features inherent in oscillation data, a bidirectional long short-term memory (BiLSTM) layer is introduced. Additionally, to address the training instability caused by few-shot learning scenarios, the discriminator is augmented with mini-batch discrimination (MBD) layers and gradient penalty (GP) terms. Finally, TL is leveraged to fine-tune the model, effectively bridging the gap between the training data and real-world system data. To evaluate the quality of the synthetic data, two indexes are proposed based on dynamic time warping (DTW) and frequency domain analysis, followed by a classification task. Case studies demonstrate the effectiveness of the proposed approach in swiftly generating a large volume of synthetic SSO data, thereby significantly mitigating the issue of data scarcity prevalent in SSO research.

Abstract:The increasing penetration of wind power poses challenges to the power grid operation and scheduling. Yet, if the uncertainty of wind power can be economically and effectively managed on the source side, it can drive the power grids towards renewable-dominant future. In this paper, an enhanced scheduling strategy for wind farm - flexible load joint operation system (WF-FLJOS) is proposed. The proposed strategy is designed to manage the uncertainty of wind power on the generation side when integrated into a large-scale power grid. Moreover, it can contribute to saving energy costs on the load side. Compared with the current wind farm operation rules, more stringent assessment requirements are put forward for wind power output accuracy, and the internal organization framework of WF-FLJOS is designed. For potential power violations of wind farms and flexible loads, the violation penalty mechanisms are developed to regulate the behavior of the participants. The joint operation model of the WF-FLJOS is proposed and the submission and tracking approach of the generation schedule for the wind farm is investigated. Numerical results indicate that the proposed strategy can not only improve the ability of the wind farm to track the generation schedule, but also consider the benefits of both the farm side and the load side. Meanwhile, the proposed strategy effectively reduces the schedule adjustment pressure on the main grid caused by the rolling correction mode of the intraday schedule for wind farms.

Abstract:Oscillation accidents emerge in power systems integrated with increasing penetration of renewable energy sources. The impedance of electromagnetic dynamics is investigated in recent years, where the mechanical dynamics are neglected. So far, the low-frequency oscillations are not well addressed with the impedance analysis method. A novel analytical impedance is formulated and implemented for wind energy conversion system consisting of wind turbine generators (WTGs) and wind farm, which fills the gap in the mechanical dynamics of the impedance. Instead of assuming constant values, the electromechanical dynamics of the rotor speed and the pitch angle are involved in the WTG impedance. Besides, the impedance framework is generally and modularly designed and is adaptive to different operating regions. With the developed analytical impedance, the stability assessment can cover the low-frequency oscillations, providing an in-depth insight into the mechanical parameters influencing the small-signal stability performance. As an application, the impedance characteristic and stability performance of systems with active power reserve for grid supporting are analyzed and optimized. Furthermore, the shafting torsional vibrations of WTGs in wind farms are analyzed with modal decomposition and the low-frequency impedance model. The improved accuracy of the developed analytical impedance is illustrated by comparison with commonly used impedance, which ignores the coupling between the electrical and mechanical dynamics. It is proven that the mechanical dynamics have a significant influence on the impedance, particularly in the low-frequency range. Experimental validation is carried out to validate the low-frequency impedance model and the stability performance.

Abstract:Energy storage can smooth the fluctuations of wind power integrated into the grid. Due to the strong adaptability of the empirical mode decomposition (EMD) algorithm to non-stationary signals, it is widely used in wind power smoothing control strategies. However, traditional EMD algorithms cannot guarantee that the upper and lower areas of the calculated intrinsic mode functions (IMFs) are equal, which tends to result in imbalanced calculated energy storage power and thus exceeding the limit of energy storage capacity. Focusing on wind power smoothing control by energy storage, this paper proposes a strategy based on the area-equilibrium EMD, which modifies the upper and lower areas of the IMFs to achieve a more balanced distribution. As a result, the IMFs contain less energy, and consequently, the energy contained in the calculated smoothing power is also reduced. This makes the energy storage capacity less likely to exceed the limit, thereby achieving better wind power smoothing performance under given energy storage capacity. Case studies show that the proposed strategy results in more balanced upper and lower areas of the IMFs, reduces the fluctuating range of calculated energy storage, and improves the wind power smoothing effectiveness.

Abstract:The integrated electricity-heat-hydrogen system (IEHHS) facilitates the efficient utilization of multiple energy sources, while the operational flexibility of IEHHS is hindered by the high heat inertia of alkaline electrolyzers (AELs) and the variations of renewable energy. In this paper, we propose a robust scheduling of IEHHS considering the bidirectional heat exchange (BHE) between AELs and district heating networks (DHNs). First, we propose an IEHHS model to coordinate the operations of AELs, active distribution networks (ADNs), and DHNs. In particular, we propose a BHE that not only enables the waste heat recovery for district heating but also accelerates the thermal dynamics in AELs. Then, we formulate a two-stage robust optimization (RO) problem for the IEHHS operation to consider the variability of renewable energy in ADNs. We propose a new solution method, i.e., multi-affine decision rule (MADR), to solve the two-stage RO problem with less conservatism. The simulation results show that the operational flexibility of IEHHS with BHE is remarkably improved compared with that only with unidirectional heat exchange (UHE). Compared with the traditional affine decision rule (ADR), the MADR effectively reduces the IEHHS operating costs while guaranteeing the reliability of scheduling strategies.

Abstract:In volt/var control (VVC) for active distribution networks, it is essential to integrate traditional voltage regulation devices with modern smart photovoltaic inverters to prevent voltage violations. However, model-based multi-device VVC methods rely on accurate system models for decision-making, which can be challenging due to the extensive modeling workload. To tackle the complexities of multi-device cooperation in VVC, this paper proposes a two-timescale VVC method based on reinforcement learning with hybrid action space, termed the hybrid action representation twin delayed deep deterministic policy gradient (HAR-TD3) method. This method simultaneously manages traditional discrete voltage regulation devices, which operate on a slower timescale, and smart continuous voltage regulation devices, which function on a faster timescale. To enable effective collaboration between the different action spaces of these devices, we propose a variational auto-encoder based hybrid action reconstruction network. This network captures the interdependencies of hybrid actions by embedding both discrete and continuous actions into the latent representation space and subsequently decoding them for action reconstruction. The proposed method is validated on IEEE 33-bus, 69-bus, and 123-bus distribution networks. Numerical results indicate that the proposed method successfully coordinates discrete and continuous voltage regulation devices, achieving fewer voltage violations compared with state-of-the-art reinforcement learning methods.

Abstract:The multi-voltage-level DC distribution network (MVL-DC-DN) is a promising network for efficiently integrating rapidly growing DC loads, and fast-growing load demand would bring a challenge to the MVL-DC-DN in terms of the maximum loadability. This paper considers the DC electric spring (DC-ES) as a novel candidate flexible resource for enhancing the maximum loadability of the MVL-DC-DN, and proposes an evaluation method for the maximum loadability. Firstly, with the consideration of device constraints, the impact that the DC-ES on the maximum loadability of the DC distribution network (DC-DN) is analyzed via a simplified equivalent circuit. Subsequently, the power flow (PF) model of an MVL-DC-DN with DC-ESs is established. Finally, a method based on continuation power flow (CPF) for evaluating the maximum loadability of an MVL-DC-DN with DC-ESs is proposed. During the evaluation, limitations of the DC-ES and the DC transformer (DCT) are considered. The consideration of the practical constraints avoids the overestimation of the maximum loadability. The case study verifies the effectiveness of the proposed method.

Abstract:With the progressive exhaustion of fossil energy and growing concerns about climate change, it has been observed that distributed energy resources such as photovoltaic (PV) systems and electric vehicles (EVs) are being increasingly integrated into distribution systems. This underscores the increasing imperative for a thorough analysis to evaluate reliability from the perspectives of distribution systems and EV charging services, taking into account the stochastic nature of PV and EV load demands. This paper presents an approach for the reliability assessment of distribution systems that incorporate PV and EVs considering reliability models for both PV systems and EV battery systems. It also defines new indices to investigate the adequacy and customer-side reliability for EV charging services. The developed methodology utilizes a Monte Carlo simulation-based approach and is showcased using the modified Roy Billinton Test System (RBTS) Bus 4 distribution system. The results illustrate that reliability indices for EV charging services, such as percentage of charging energy not supplied (PCENS), average EV interruption frequency index (AEVIFI) and average EV interruption duration index (AEVIDI), are improved under the proposed approach.

Abstract:With the rapid expansion of urban road networks and the increasing ownership of vehicles in many countries and regions, the greenhouse gas and pollutant emissions from road travels have become a global concern. The introduction of electric vehicles (EVs) with dynamic charging into road systems, which is defined as electric road systems (ERSs), has been widely recognized as a viable solution to address this problem. This paper presents a comprehensive study on the reliability of power supply systems integrated with renewables for ERS (ERS-PSRs), which interface with both road traffic and power networks. First, a brief introduction to the charging modes of EVs demonstrates the coupling of the two networks. A simplified traffic model is then built, based on which the reliability indices of the system considering the influence of the dynamic charging and static charging modes of EVs are proposed. Further, a simplified trip chain based Monte Carlo reliability assessment method of ERS-PSRs is proposed. Case studies based on the IEEE Roy Billinton Test System (RBTS) show that the dynamic charging mode of EVs can not only effectively balance the supply and demand of the power network at different time (shaving peaks and filling valleys), but also significantly improve the reliability of ERS-PSRs. The case studies also examine the effects of the ratio of EVs with dynamic charging, wind generation penetration rate, additional wind power, and battery energy storage systems (BESSs) on the reliability of ERS-PSRs.

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:With the rapid integration of communication and information technology into substations, the risk of cyber attacks has significantly increased. Attackers may infiltrate substation networks, manipulate switches, and disrupt power lines, potentially causing severe damage to the power system. To minimize such risks, this paper proposes a three-layer defender-attacker-defender (DAD) model for optimally allocating limited defensive resources to substations. To model the uncertainty surrounding the knowledge of defender of potential attacks in real-world scenarios, we employ a fuzzy analytic hierarchy process combined with the decision-making trial and evaluation laboratory (FAHP-DEMATEL). This method accounts for the attack resource uncertainty by utilizing intelligence data on factors potentially influenced by attackers, which serves as an evaluation metric to simulate the likelihood of various attack scenarios. These uncertainty probabilities are then incorporated into the substation DAD model consisting three layers of agents: the decision-maker, the attacker, and the operator. The decision-maker devises a defense strategy before the attack, while the attacker aims to identify the strategy that causes the maximum load loss. Meanwhile, the operator seeks to minimize the load loss through optimal power flow scheduling. To solve the model, the original problem is transformed into a two-layer subproblem and a single-layer master problem, which are solved iteratively using a column-and-constraint generation algorithm. Case studies conducted on the IEEE RTS-96 system and the IEEE 118-node system demonstrate the effectiveness and practicality of the proposed model. Comparative experiments further highlight the advantages of the proposed model.

Abstract:Public buildings present substantial demand response (DR) potential, which can participate in the power system operation. However, most public buildings exhibit a high degree of uncertainties due to incomplete information, varying thermal parameters, and stochastic user behaviors, which hinders incorporating the public buildings into power system operation. To address the problem, this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation. Firstly, the DR evaluation is developed based on the equivalent thermal parameter (ETP) model, actual outdoor temperature data, and air conditioning (AC) consumption data. To quantify the uncertainties of public buildings, the interval evaluation is given employing the linear regression method considering the confidence bound. Utilizing the evaluation results, the risk dispatch model is proposed to allocate public building reserve based on the chance constrained programming (CCP). Finally, the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming (MISOCP) for its solution. The proposed evaluation method and the risk dispatch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.

Abstract:Demand response (DR) is a practical solution to overcoming the challenges posed by the volatility and intermittency of the renewable generation in power systems. Industrial electricity demand is growing rapidly, which makes the DR potential estimation of industrial user critical for the DR implementation. In this paper, a unified model for estimating DR potential in the production processes of aluminum, cement, and steel is proposed on the basis of their unique operational characteristics. Firstly, considering the typical characteristic constraints of different industrial users, a DR potential estimation model is developed to capture typical industrial user response behavior under various operational and economic factors. The proposed estimation model is further refined to account for the uncertain and subjective factors present in the actual estimation environment. Secondly, a virtual data acquisition method is introduced to obtain the private virtual parameters required in the estimation process. Then, an industrial user participation threshold is presented to determine whether industrial users may participate in DR at a given time with consideration of their response characteristics. The industrial users may not always act with perfect rationality, and the response environment remains uncertain. In addition ， the subjective factor in this paper includes the proposed threshold and the bounded rationality. Finally, an improved DR potential estimation model is proposed to reduce the difficulties in the actual estimation process. The simulation results validate the effectiveness of the proposed estimation model and the improved DR potential estimation model across multiple cases.

Abstract:Over the past decade, bidding in electricity markets has attracted widespread attention. Reinforcement learning (RL) has been widely used for electricity market bidding as a powerful artificial intelligence (AI) tool to make decisions under real-world uncertainties. However, current RL-based bidding methods mostly employ low-dimensional bids (LDBs), which significantly diverge from the N price-power pairs commonly used in current electricity markets. The N-pair bid format is denoted as high-dimensional bid (HDB) format, which has not been fully integrated into the existing RL-based bidding methods. The loss of flexibility of current RL-based bidding methods could greatly limit the bidding profits and make it difficult to address the increasing uncertainties caused by renewable energy generation. In this paper, we propose a framework for fully utilizing HDBs in RL-based bidding methods. First, we employ a special type of neural network called the neural network supply function (NNSF) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov decision process (MDP) to make it compatible with most existing RL algorithms. Finally, the experiments on energy storage systems (ESSs) in the Pennsylvania-New Jersey-Maryland (PJM) real-time electricity market show that the proposed bidding method with HDBs can increase the bidding flexibility, thereby increasing the profits of state-of-the-art RL-based bidding methods.

Abstract:The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems. Meanwhile, integrating small-capacity battery energy storage systems into distribution network is a growing trend in the construction of virtual power plants (VPPs), which offer great potential advantages in improving the system frequency regulation capabilities. However, the process of power dispatch for VPPs may be hindered by imperfections in the communication network, which affects their frequency control performance. Simultaneously, the economic benefits associated with their frequency control services are often overlooked. As such, we propose a co-design method of power dispatch with dynamic power regulation and communication transmission optimization for frequency control in VPPs. First, a joint design scheme of power dispatch and routing optimization under cloud-edge collaborations is proposed. This scheme encompasses a power dispatch method considering the influences of communication network and a routing optimization policy based on graph convolutional neural networks, both of which are designed to ensure the accurate and real-time frequency control service. Further, we propose a dynamic power regulation strategy under edge-edge collaborations. Specifically, according to the established correction control objective, an adaptive distributed auction algorithm (ADAA) based dynamic power regulation control method is designed to determine the optimal regulation power of VPPs, thereby improving the economic benefits of frequency control service. Finally, the simulation results validate the feasibility and superiority of the proposed co-design method for frequency control.

Abstract:The difficulty in capital recovery for distributed renewable energy operators (DREOs) and the high charging costs at electric vehicle charging stations (EVCSs) have long been significant challenges in power systems. Collaborative operation of DREOs and EVCSs can effectively address these challenges, yet few studies have approached incentivizing collaboration from the perspective of profit allocation. Therefore, this paper proposes a fair and efficient profit allocation method. Incorporating the Gauss-Legendre quadrature formula into the Aumann-Shapley value (GL-AS) method enables efficient calculation of the profit allocation of cooperative members. However, existing literature only discusses the profit allocation method of conventional power generation units, limiting its applicability. This paper addresses the problem of energy storage system (ESS) switching between charging and discharging in any time interval and the time-varying problem of renewable energy power output, thereby ensuring the efficiency of the solution process. Furthermore, a novel profit allocation adjustment model is provided through the adoption of triangular fuzzy comprehensive evaluation (TFCE). Finally, the effectiveness of the proposed profit allocation method is validated through numerical simulations in various scenarios.

Abstract:The increasing integration of renewable energy sources poses great challenges to the power system frequency security. However, the existing electricity market mechanism lacks integration and incentives for emerging frequency regulation (FR) resources such as wind power generators (WPGs), which may reduce their motivation to provide frequency support and further deteriorate the frequency dynamics. In this paper, a market scheduling and pricing method for comprehensive frequency regulation services (FRSs) is proposed. First, a modeling approach for flexible FR capabilities of WPGs is proposed based on the mechanism of inertia control and power reserve control. Subsequently, considering the differences in inverter control strategies, a novel system frequency response model with grid-following and grid-forming inverters is established. Combined with the automatic generation control, the frequency security constraints of the whole FR process are derived, and integrated into the market scheduling model to co-optimize the energy and FRSs. Finally, by distinguishing the contributions of various types of resources in different FR stages, a differentiated pricing scheme is proposed to incentivize producers with various regulation qualities to provide FRSs. The effectiveness of the proposed method is verified on the modified IEEE 6-bus system and the IEEE RTS-79 system.

Abstract:In recent years, transmission system operators have started requesting converter-interfaced generators (CIGs) to participate in grid services such as power oscillation damping (POD). As power systems are prone to topology changes because of connection and disconnection of generators and lines, one of the most important requirements in the design of POD controller is to account for these changes. This can be done by either adjusting the controller structure during the operation or applying a fixed structure designed to address changes in the system. The fixed structure is usually preferred by transmission system operators since it is easier to determine its impact on the system. In this paper, a design procedure is proposed for network-reconfiguration-aware POD controller with fixed structure for CIG-based power plants that considers network configurations with any one line disconnected. The design procedure is based on frequency-response techniques, so it is suitable for application in CIG-based power plants, even in cases when a detailed small-signal model of the system is not available. Designs of a POD controller for the damping of critical system modes can be obtained by using active power, reactive power, or both power components simultaneously. The application to the design of a POD controller for a CIG-based power plant connected to the IEEE 39-bus system is presented as an example. Simulations performed in MATLAB and SimPowerSystems are used to validate the proposed design procedure. The validation includes an analysis of system performance with changes considered in the proposed designed procedure. Also, the system performance under unconsidered changes is examined, covering variations in load and inertia values, as well as disconnection of synchronous generators.

Abstract:Fault current limiting is a critical technology to ensure the safe operation of modular multilevel converter based multi-terminal direct current (MMC-MTDC) grids. This paper proposes a fault severity classification based coordination control strategy of fault current limiter (FCL) and MMC for adaptive fault current limiting. The proposed strategy reduces the investment in FCL, and keeps the bus voltages of non-faulty lines at reasonable values. Firstly, a rapid fault circuit parameter estimation (FCPE) method using initial fault current information is proposed. With this method, the fault distance and fault transition resistance can be quickly estimated, which are used for a quantitative indication of the fault severity. Subsequently, the coordination control strategy of FCL and MMC is proposed, in which the FCL action is prioritized, while the control of MMC is complementary for current limiting. Based on the proposed strategy, fault severity phase planes (FSPPs) are constructed to assess fault severity and calculate the activation time of FCL and voltage regulation factor of MMC. Therefore, the FCL activation and MMC control are matched to the fault severity. The effectiveness and advantages of the proposed strategy are validated by the simulations in PSCAD/EMTDC.

Abstract:This paper develops a detailed equivalent model for modular multilevel converters with partially-integrated battery energy storage. The proposed model gains computational efficiency in two ways. Firstly, it markedly reduces the large number of nodes in the conventional switching model of the converter, thereby shrinking the size of its admittance matrix. Secondly, it avoids computationally expensive re-triangularization of the admittance matrix during the normal operation of the converter and restricts it only to the rare occasions of converter blocking. Mathematical derivation of the model is carried out using differential equations of the converter. The computational efficiency and accuracy of the proposed model are confirmed by comparison of the results from its implementation in the PSCAD/EMTDC simulator against conventional detailed switching models and measurements from a single-phase scaled-down laboratory setup. This paper also shows a case study wherein a converter with partially-integrated batteries is included in the CIGRE B4-5 benchmark system.

Abstract:The self-synchronizing voltage source inverter (SSVSI) is widely studied because of its grid-forming capability. However, the slow response of the active power control loop (APCL) under the weak grid makes it difficult for the SSVSI to quickly support the frequency of a low-inertia grid. In this paper, a grid framework is established to analyze the frequency support service process of the SSVSI, and the shortcomings of the regulation of the damping coefficient and virtual inertia coefficient for frequency support are analyzed. Then, an adaptive additional damping control method is proposed to optimize the ability of SSVSI to support the grid frequency. The proposed control method adjusts the damping of the APCL without affecting the system steady-state characteristics, which improves the active power response speed of the SSVSI. Besides, the proposed control method adaptively adjusts the additional damping coefficient based on the active power response without measuring the grid parameters. Compared with other forms of control, the proposed control method excels in minimizing the rate of change of frequency (RoCoF) and the frequency deviation (FD) within the grid, without succumbing to the constraints posed by unknown grid parameters. Furthermore, the analysis of the system stability is also presented. Finally, the experimental hardware results obtained from a miniaturized grid prototype are presented, corroborating the effectiveness of the proposed control method.

Abstract:This paper proposes a continuous control set model predictive control (CCS-MPC) algorithm of a modular multilevel matrix converter (M3C) for low-frequency AC transmission (LFAC), via which the offshore wind farm (OWF) is integrated. The M3C is operated with a 16.7 Hz frequency at the OWF side and a 50 Hz frequency at the onshore grid side. The balance of the capacitor voltages and the regulation of circulating currents in the M3C are performed using the proposed CCS-MPC algorithm, which is based on the online solution of a cost function with constraints. Simulation and experimental work (with a 5 kW M3C prototype) are provided, showing the performance of the LFAC system to operate with symmetrical and asymmetrical voltage dips, active and reactive power steps, and optimal limitation of currents and voltages using constraints. Unlike previous publications, the predictive control system in this paper allows seamless operation under balanced and unbalanced conditions, for instance, during asymmetrical voltage dips.