Abstract:The variable nature of solar power has hitherto been regarded as a major barrier preventing large-scale high-penetration solar energy into the power grid. Based on decades of research, particularly those advances made over the recent few years, it is now believed that dispatchable solar power is no longer a conception but will soon become techno-economically feasible. The policy-driven information exchange among the weather centers, grid operators, and photovoltaic plant owners is the key to realizing dispatchable solar power. In this paper, a five-step forecasting framework for enabling dispatchable solar power is introduced. Among the five steps, the first three, namely numerical weather prediction (NWP), forecast post-processing, and irradiance-to-power conversion, have long been familiar to most. The last two steps, namely hierarchical reconciliation and firm forecasting, are quite recent conceptions, which have yet to raise widespread awareness. The proposed framework is demonstrated through a case study on achieving effectively dispatchable solar power generation at plant and substation levels.

Abstract:In this study, a novel parallel converter-based hybrid high-voltage direct current (HVDC) system is proposed for the integration and delivery of large-scale renewable energy. The rectifier uses the line commutated converter (LCC) and low-capacity modular multilevel converter (MMC) in parallel, while the inverter uses MMC. This configuration combines the economic advantages of LCC with the flexibility of MMC. Firstly, the steady-state control strategies are elaborated. The low-capacity MMC operates in the grid-forming mode to offer AC voltage support. It also provides active filtering for the LCC and maintains the reactive power balance of the sending-end system. The LCC efficiently transmits all active power at the rectifier side, fully exploiting its bulk-power transmission capability. Secondly, the fault ride-through strategies of both the AC faults at two terminals and the DC fault are proposed, in which the MMCs at both terminals can remain unblocked under various faults. Thus, the proposed system can mitigate the impact of the faults and ensure continuous voltage support for the sending-end system. Finally, simulations in PSCAD/EMTDC verify the effectiveness and performance of the proposed system.

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:Distribution networks have been experiencing significant changes under the pressure of the energy transition. The high integration of renewable energy sources combined with the electrification process introduces new challenges in managing distribution networks. Innovative solutions aimed at optimizing the control of complex problems, starting from historical data instead of a detailed system model, have been growing due to rapid development in artificial intelligence and machine learning. This paper proposes a Q-learning algorithm to control the tap setting of the on-load tap changer installed in primary substation transformers. The ultimate goal is to maintain voltage magnitudes at all busses of the medium-voltage distribution network within a safe range, simultaneously optimizing on-load tap changer operations. As a case study, the effectiveness of the proposed algorithm is assessed using a real medium-voltage distribution network with high penetration of renewable energy sources that supplies more than 2500 users/prosumers. The ability of the proposed algorithm to control bus voltages is tested in several scenarios characterized by significant variability and uncertainty. Outcomes show that the proposed algorithm is suitable for optimizing voltage control in distribution networks using a data-driven approach.

Abstract:To maximize conversion efficiency, photovoltaic (PV) systems generally operate in the maximum power point tracking (MPPT) mode. However, due to the increasing penetration level of PV systems, there is a need for more developed control functions in terms of frequency support services and voltage control to maintain the reliability and stability of the power grid. Therefore, flexible active power control is a mandatory task for grid-connected PV systems to meet part of the grid requirements. Hence, a significant number of flexible power point tracking (FPPT) algorithms have been introduced in the existing literature. The purpose of such algorithms is to realize a cost-effective method to provide grid support functionalities while minimizing the reliance on energy storage systems. This paper provides a comprehensive overview of grid support functionalities that can be obtained with the FPPT control of PV systems such as frequency support and volt-var control. Each of these grid support functionalities necessitates PV systems to operate under one of the three control strategies, which can be provided with FPPT algorithms. The three control strategies are classified as ① constant power generation control (CPGC), ② power reserve control (PRC), and ③ power ramp rate control (PRRC). A detailed discussion on available FPPT algorithms for each control strategy is also provided. This paper can serve as a comprehensive review of the state-of-the-art FPPT algorithms that can equip PV systems with various grid support functionalities.

Abstract:This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic (PV) power prediction that arises due to insufficient data samples for new PV plants. First, a time-series generative adversarial network (TimeGAN) is used to learn the distribution law of the original PV data samples and the temporal correlations between their features, and these are then used to generate new samples to enhance the training set. Subsequently, a hybrid network model that fuses bi-directional long-short term memory (BiLSTM) network with attention mechanism (AM) in the framework of deep & cross network (DCN) is constructed to effectively extract deep information from the original features while enhancing the impact of important information on the prediction results. Finally, the hyperparameters in the hybrid network model are optimized using the whale optimization algorithm (WOA), which prevents the network model from falling into a local optimum and gives the best prediction results. The simulation results show that after data enhancement by TimeGAN, the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Abstract:The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources. However, the existing calculation methods suffer from outliers caused by the zero-crossing of the denominator. These outliers can result in inaccuracy and unreliability of harmonic source location. To address this issue, this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid outliers. The correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring data. The results show that the proposed method is not only simple and easy to implement but also highly accurate.

Abstract:The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These prosumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.

Abstract:Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

Abstract:This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic (PV) array, using external measurements of current and voltage at the inverter level. The estimated model is of interest to predict the power output of PV plants, in both planning and operation scenarios, and thus improves the efficient operation of power systems with high penetration of renewable energy. The proposed technique has been assessed in several simulated scenarios under different operating conditions. The results show that accurate estimates are provided for the model parameters, even in the presence of measurement noise and abrupt variations under the external conditions.

Abstract:The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.

Abstract:As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.

Abstract:In flexible DC grids, the rapid rise of fault current requires that the line protection must complete the fault identification within a few milliseconds. Dynamic state estimation based protection (DSEBP) provides a new idea for flexible DC line protection with good performance. However, the operating frequency in the DC grid is 0 Hz. When the DC grid is operating normally, it is difficult to identify the line parameters online to improve the performance of the protection method. This paper proposes a method to identify the frequency-dependent parameters of flexible DC grids based on the characteristic signal injection of half-bridge modular multilevel converter (HB-MMC). The characteristic signal is extracted by the Prony algorithm to calculate the line parameter under different frequencies. Afterwards, the number and position of residues and poles of frequency-dependent parameters are determined using the vector fitting method. Finally, an improved DSEBP is proposed. The simulation shows that the frequency-dependent parameters obtained by the proposed parameter identification method can be used in the improved DSEBP normally, and the identified parameters have better precision.

Abstract:The output power variability of photovoltaic (PV) power plants （PVPPs） is one of the major challenges for the operation and control of power systems. The short-term power variations, mainly caused by cloud movements, affect voltage magnitude and frequency, which may degrade power quality and power system reliability. Comprehensive analyses of these power variations are crucial to formulate novel control approaches and assist power system operators in the operation and control of power systems. Thus, this paper proposes a simulation-based approach to assessing short-term power variations caused by clouds in PV power plants. A comprehensive assessment of the short-term power variations in a PV power plant operating under cloud conditions is another contribution of this paper. The performed analysis evaluates the individual impact of multiple weather condition parameters on the magnitude and ramp rate of the power variations. The simulation-based approach synthesizes the solar irradiance time series using three-dimensional fractal surfaces. The proposed assessment approach has shown that the PVPP nominal power, timescale, cloud coverage level, wind speed, period of the day, and shadow intensity level significantly affect the characteristics of the power variations.

Abstract:The performance of photovoltaic (PV) systems is influenced by various factors, including atmospheric conditions, geographical locations, and spatial and temporal characteristics. Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. In this paper, we adopt virtual reality (VR) technology to visualize PV entities and simulate their performances. The integration of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition (SPR) of PV systems, thereby enhancing their descriptive capabilities. Furthermore, we introduce an interactive GMPPT (IGMPPT) method based on VR technology. This method leverages interactive search techniques to narrow down search regions, thereby enhancing the search efficiency. Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improving the efficiency of GMPPT.

Abstract:Mid- and high-frequency resonance (MHFR) is highly likely to occur at the sending end of voltage source converter-based ultra-high voltage direct current (VSC-UHVDC) for large-scale renewable energy transmission. It is of great importance to investigate the resonance characteristics and the corresponding suppression strategies. Firstly, this paper introduces the overall control scheme of VSC-UHVDC for large-scale renewable energy transmission. Then, the impedance models of VSC under grid-forming control with AC voltage coordinated control are established. The mid- and high-frequency impedance characteristics of VSC-UHVDC are analyzed. The key factors affecting the impedance characteristics have been revealed, including the AC voltage control, the voltage feedforward, the inner current loop, the positive-sequence and negative-sequence independent control (PSNSIC), and the control delay. The MHFR characteristics at the sending-end system are analyzed in the whole operation process, including the black start and the normal power transmission operation. An integrated control scheme is proposed to address the MHFR problems. Finally, extensive case studies are conducted on a planned VSC-UHVDC project to verify the theoretical analysis.