International Journal of Electrical Power & Energy Systems最新文献

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper’s unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.

In the context of increasing distributed flexibility, enhanced TSO-DSO coordination is needed when procuring and activating flexibility. The literature shows that pricing the changes in the power flow over the TSO-DSO interfacing substation leads to optimal flexibility procurement cost in sequential TSO-DSO flexibility markets. This paper proposes a bilevel model, considering a TSO leader which sets interface flow prices freely, and DSO-followers in a Stackelberg game. This game-theoretical approach allows for the identification of regulatory risks and the testing of regulatory mechanisms. Based on two case studies, results show that, if left unregulated, the strategic TSO creates significant cost allocation distortions, creating unwanted financial transfers from DSOs to the TSO. However, when acting strategically, the TSO also activates (or leads to the activation of) economical flexibility providers, having as a reference the first-best option, namely the Common Coordination Scheme (CS). Leveraging on these results, a cap and floor mechanism is proposed, limiting unwanted cost allocation distortions and retaining incentives for efficient flexibility activations. Results showcase that a Fragmented CS with regulated interface flow prices could be an efficient second-best compared to the Common CS, outperforming other regulatory options found in the literature.

To the oscillation and stability problem caused by multi-scale and broadband electromagnetic dynamics among many isomerized power electronic devices in a microgrid, a small-signal model of hierarchical control structure microgrid and stability analysis method based on oscillation trajectories are proposed. Moreover, a hierarchical control structure is used as the research object in microgrid, and the analysis is performed based on the small-signal model and Hopf bifurcation theory. First, the small-signal model of the hierarchical control structure microgrid is established, combining the dominant eigenvalue and participation factor analysis methods, to analyze the influence of the controller and related sensitive parameters on the dynamic performance of the system. Then, based on the small-signal model and Hopf bifurcation theory, a stability analysis method based on oscillation trajectories is proposed. The relationship between different oscillation modes and limit cycles in the microgrid system and the influence of the sag control parameter values on the oscillation trajectory and stability domain of the system are researched on. Combining Hopf bifurcation theory and oscillation trajectory to delineate the stable domain of parameter trajectories, the influence of secondary control on the system stability is analyzed under different oscillation trajectories and load disturbances, revealing the corresponding relationship between the state trajectories of different oscillation modes and system stability of a hierarchical control structure microgrid. Based on the proposed oscillating trajectories, the stability analysis method has sufficient universality for studying the impact of system stability, establishing the parameter selection standards for the design of microgrids. Finally, according to the verification results, the correctness and applicability of above methods are verified.

The instability of the inverse problem is caused by its nonlocal and non-causal nature. This study addresses the inverse problem of determining the physical parameters of semiconductor devices. Based on statistical inversion theory, the probability distribution (posterior distribution) of the SBHs has been estimated by convolutional neural networks. Regularization techniques were then applied to such a distribution to accurately determine the SBHs of semiconductor devices. The results reveal that the fluctuations in the predicted SBHs by convolutional neural networks are similar to the amplitude between the upper and lower envelopes of the free decay curve. The method achieves a maximum relative error below 3.4% when using theoretical diode current–voltage data as input and maintains a relative error of less than 7% when compared to traditional methods when using experimental current–voltage data. Furthermore, the proposed method offers a mathematical interpretation of the inverse problem and demonstrates the capability of the proposed method to extract the physical parameters of semiconductor devices with a small amount of data.

Doubly-fed induction generator (DFIG) is susceptible to unbalanced grid voltage and mismatched motor parameters during grid-connected operation. The conventional model predictive control (MPC) has low complexity and fast dynamic response, which is widely used in the control of DFIG. However, it has a high steady-state ripple, large computation, and poor robustness. This paper proposes a three-vector model predictive power control based on linear extended state observer (TVMPPC-LESO) to solve the above problems. The method introduces linear extended state observer (LESO) to estimate the system’s lumped disturbance, which makes the calculation of the rotor reference voltage less dependent on the motor parameters to improve the robustness of the MPC. On this basis, the number of switches is decreased and the steady-state ripple is lowered by applying three voltage vectors in a control period and optimizing the switching sequence acting on the rotor-side converter (RSC). By adding a flexible power compensation value to the original power reference value, the TVMPPC-LESO can be extended to unbalanced grids and improve the grid-connected performance of the DFIG. The simulation and experimental results validate its effectiveness by comparing it with conventional MPC, direct power control with space vector modulation based on extended power theory (EXDPC-SVM), and three-vector-based model predictive power control (TV-MPPC).

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.

In the context of power systems increasingly reliant on renewable energy sources, the consideration of uncertainty becomes paramount for year-round hourly operational simulations aimed at assessing the efficacy of power grid development strategies. While multi-stage stochastic programming has been effective in capturing multi-scale power fluctuations, its adoption faces challenges related to computational complexity and convergence performance. To address these issues, this paper presents a novel fast multi-stage stochastic unit commitment method tailored for year-round hourly operational simulation. This method strategically incorporates the expectations of a limited number of future stages to expedite the iteration process, thereby mitigating computational burdens. The annual time-series data is adaptively segmented based on the fluctuation characteristics of power and load, ensuring a balanced sub-problem scale aligned with the number of stages. Results from rigorous testing across multiple standard cases demonstrate that the proposed method consistently achieves optimal lower bounds within 6-8 iterations, resulting in significant computational time savings of up to 50%. Furthermore, the efficacy of the proposed method is showcased through its application in the annual operational simulation of a real-world provincial high-voltage power grid in China.

This paper analyzes the factors affecting the stability of phase-locked loops (PLLs) in weak power grids. By establishing a PLL model in weak power grids, it is found that line impedance, grid frequency disturbances, and output power all have an impact on PLL stability. The influence of line impedance, grid frequency disturbances, and output power on PLL is analyzed using the phase plane method. Additionally, it is observed that increasing the PLL damping ratio can enhance PLL stability, but when the damping ratio exceeds the critical damping ratio, PLL instability may occur. Therefore, this paper divides the controllable range of PLL into two cases: damping ratio greater than the critical damping ratio and damping ratio less than the critical damping ratio. For the case where the damping ratio is less than the critical damping ratio, a transient virtual inductance control method is proposed to enhance the PLL damping ratio and improve PLL stability without introducing power coupling. For the case where the damping ratio is greater than the critical damping ratio, PLL adaptive parameter adjustment control is proposed to ensure that the PLL trajectory does not diverge by increasing the PLL adjustment time without increasing the damping ratio, thus improving VSC stability. Finally, a comparison with conventional methods is conducted, and the feasibility and correctness are analyzed through time-domain simulations, followed by presenting the result analysis.

Modern electrical grids have intelligent electronic devices (IEDs) such as protective relays that use internal logic to detect the types of electrical faults. The increasing integration of distributed energy sources and the resulting complexity of electrical grid communication architectures necessitates enhanced robustness of IEDs’ monitoring while maintaining security against potential cyber threats. In this study, a backup electrical faulted phase detection method with a distributed ledger technology (DLT) platform was implemented. Cyber Grid Guard software was developed to collect phase currents and voltages transmitted through IEC 61850 GOOSE messages, detect faulted phases from the IEDs using the GOOSE data, and validate the data by hashing them and storing them in the distributed ledger. In this way, the hashed data were run into an electrical faulted phase algorithm based on using a current threshold for detecting the faulted phases in the medium-voltage main feeder of an electrical substation. The detection of the electrical faulted phases was assessed in a real-time simulator with protective relays, meters, the software framework, and DLT in the loop. The proposed method provides secure and reliable backup detection external to the IEDs, and DLT validation enhances system security and trust.

To fully utilize the voltage regulation capacity of flexible load and distributed generations (DGs), we propose a novel Approximatively Completed Observed Markov Decision Process-based (ACOMDP-based) Reinforcement Learning (RL) (namely, ACMRL) scheme for a multi-objective Automatic Voltage Control (AVC) problem considering Differential Increment Incentive Mechanism (DIIM)-based Incentive-Based Demand Response (IBDR). Firstly, we propose a DIIM to motivate high-flexibility consumers to achieve maximum potential in real-time voltage control while ensuring the best economy. Secondly, we characterize the multi-objective AVC problem as an ACOMDP model, transformed from the Partially Observable Markov Decision Process (POMDP) model, by introducing a novel hidden system state vector that incorporates the belief state, and the high confidence probability vector. The belief state and the high-confidence probability vector describe the probability distribution extracted from the historical observed state, portraying the precise state and the uncertainty existing in the state update process. Then, the ACOMDP block is inputted into the RL block, which adopts a modified underlying network architecture with the Asynchronous Advantage Actor-Critic (MA3C) algorithm embedded with the Shared Modular Policies(SMP) module. The MA3C-based RL block, characterized by enhanced communication efficiency, enables expedited generation of optimal decision-making actions even in the face of substantial uncertainty. Case studies are conducted in a practical district in Suzhou, China, and simulation results validate the superior performance of the proposed methodology.