Iet Generation Transmission & Distribution最新文献

This paper introduces a novel analytical approach for the identification of the admittance matrix and the generalized short-circuit ratio (gSCR) in power systems integrated with renewable energy sources. The proposed method leverages voltage and current measurements from phasor measurement units (PMUs) to construct a least squares objective function, which is then solved using matrix calculus and partial derivatives. Unlike conventional optimization algorithms, this approach provides an analytical solution that substantially reduces data requirements, enabling the efficient and accurate identification of the gSCR with smaller datasets. Additionally, its fixed computational complexity allows for real-time updates as new data are collected, ensuring continuous refinement of the system of equations and enabling rapid, precise gSCR calculations. The method also exhibits strong robustness against measurement noise, making it well-suited for practical applications in dynamic power systems. The combination of reduced data requirements, real-time adaptability, noise robustness and fixed computational load establishes this method as a highly efficient and reliable tool for real-time power system stability analysis. Case studies on an EPRI 36-bus system demonstrate the method's effectiveness, highlighting its accuracy in closely matching true gSCR values, even under diverse disturbances and noisy conditions.

The interface inverter control system based on virtual synchronous generator (VSG) technology, has been widely used in new power systems due to its ability to provide system inertia. To enhance the robustness of the VSG-based inverter control system against uncertain disturbances, this paper proposes a novel sliding mode control (SMC) strategy consisting of two control layers for VSG-based inverters based on disturbance estimation. Firstly, the VSG-based outer loop control layer is established to mitigate the transient instability during pre-synchronization process, which consists of an active frequency control loop with a phase angle regulator and a reactive voltage control loop with an amplitude regulator. Secondly, an SMC-based inner loop control layer is designed to replace the traditional voltage and current dual loop control for robustness improvement, where the uncertain disturbance can be estimated and fed back to the controlled system for disturbance suppression. Moreover, both of the eigenvalue method based small signal stability and the Lyapunov functional based large signal stability are analysed for different control layers, and the comparative simulations are conducted between the proposed strategy and traditional pre-synchronization control as well as voltage current dual loop control, in scenarios of grid-connected voltage fluctuation and islanded load power variation. The dSPACE based prototype physical experiment further validates the effectiveness of the sliding mode control strategy for VSG-based inverters with disturbance estimation.

With replacement of synchronous generator (SG) by voltage source converter (VSC) interfaced renewables, power system dynamics is undergoing significant changes. This paper investigates the frequency dynamical behaviour of new-generation power systems composed solely of grid-following (GFL) and/or grid-forming (GFM) VSCs, without an infinitely strong bus or SG. It is found that for a system composed solely of GFL-VSC, as it lacks a frequency equilibrium point after a certain disturbance, the whole system exhibits an unusual phenomenon of frequency drifting. On the other hand, for a hybrid system composed of both GFM-VSC and GFL-VSC, as it has a frequency equilibrium point, the system can settle down to a new frequency steady-state, and the GFM-VSC and GFL-VSC show completely different behaviours in the transient process. The GFM-VSC plays a key role in the frequency dynamics, similar to the SG. Based on the inertia-centre frequency dynamics, it is observed that the GFM-VSC determines the equivalent damping of the system, and both GFM-VSC and GFL-VSC contribute to the equivalent inertia. All these findings are well supported and verified by our theoretical analysis and time-domain simulations, and they can provide physical insights in the bulk frequency dynamical behaviour of new-generation power systems dominated by converters.

Demand-side management (DSM) tactics such as load shifting (LSP) and load curtailment (LCP) improve energy efficiency in a low-voltage microgrid (MG). LCP encourages load reduction, whereas LSP directs elastic loads to low-cost times. The MG under consideration includes price-based (PBDR) and incentive-based demand response (IBDR), as well as a hybrid DSM (HDSM) that blends LSP and IBDR. Plug-in hybrid electric vehicles (PHEVs) are also incorporated to reduce expense and pollution. LSP shifts 20% of elastic loads, while IBDR cuts 35 kW to counter PHEV demand. Optimal generator scheduling seeks to reduce costs and emissions across five load profiles, including base load. The minimum generating cost is reduced from $252 (base load) to $234, $246, and $230 for LSP, IBDR, and HDSM, respectively. Emissions remain at 2916 kg for base and LSP-based loads, while IBDR and HDSM reduce them to 2792 kg. Among all techniques, HDSM has the best trade-off, with a $237 generation cost and 3116 kg emissions.

With the increasing proportion of distributed energy resources, optimisation in the active distribution network becomes highly complex and challenging. To protect user privacy and perform efficient calculations, this paper proposes an online distributed optimisation between the user and the operator. To overcome the constraint non-linearity, a new linear power flow model is adopted, which can be updated online. In addition, to solve the time-coupled conundrum of energy storage, the Lyapunov drift plus penalty method is employed to transform the long-time scale optimisation problem into an instantaneous problem. Furthermore, a novel proportional integral derivative (PID)–Lagrange algorithm is proposed to improve the efficiency, where the distributed optimisation algorithm is considered as a control process, and a PID controller is used to control it. The proposed PID–Lagrange algorithm has an explicit parameter tuning strategy and significantly improves the efficiency by 54.23% in the case study.

As the number of wind turbines increase, those units like others would contribute to active power in a competitive market. Also, with development in power electronics, those units like others are able to produce reactive power and therefore can participate in reactive power market. Meanwhile, high degrees of uncertainty and prediction error are associated with this type of producing units. Due to the relationship between active and reactive power generation, this uncertainty affects both active and reactive power markets and deprives those units of the opportunity to participate in both markets. Therefore, providing an accurate model of participation in the active and reactive power market, considering the existing uncertainty and its related forecasting errors, will help those units to be able to compete with others in the active and reactive power market. In addition, the proposed model should consider the interaction between active and reactive power markets and the conditions for their simultaneous implementation. In addition, the ability of wind units to impose market power is decreased by presenting a new market power index for reactive power generation. The effectiveness of the proposed model is evaluated by implementing on 24-node IEEE RTS grid.

The increasing integration of renewable energy sources is making power systems evolve, driving the necessity to interconnect different power systems to improve the robustness and operation of grids. Here, the so-called virtual power line (VPL) control for interlinking converters (ICs) is presented, whose purpose is to couple different electric systems analogously to a classical transmission line. The VPL control employs local measurements, and it does not require any communication link to operate. Two VPL control variants are presented: the dual grid-supporting VPL control and the single grid-forming VPL (SGF-VPL) control. Both support the frequency and/or voltage of the interconnected grids, and the latter provides grid-forming capabilities for one of the interconnected systems. The performance of VPL controllers show how the frequency and voltage nadir values are improved by $��36�\%�$ (when comparing low and strong coupling results), while the rate of change of frequency and voltage can be decreased by $��49.6�\%�$, with a $��40�\%�$ faster settling time. Furthermore, it is demonstrated how the SGF-VPL is able to set the grid when other grid-forming units fail, and the flow of power happens naturally through ICs from generation to consumption areas as if all devices were part of the same electric system.

The Wasserstein distributionally robust optimization has become the preferred method for addressing the uncertainties in optimal power flow problems caused by renewable energy sources. However, when the system involves high-dimensional random variables, such as multiple solar or wind farms, the curse of dimensionality associated with this method leads to a slow convergence rate of Wasserstein ambiguity sets. Therefore, it is essential to explore novel ambiguity sets which can effectively address the dimensionality problem. This paper proposes a distributionally robust optimal power flow model based on a multi-transport hyperrectangle ambiguity set to tackle the uncertainties in wind power. First, this paper presents the multi-transport hyperrectangle, which resolves the curse of dimensionality issue associated with Wasserstein ambiguity sets. Furthermore, the wind power curtailment cost in the objective function is reformulated into a tractable form using duality theory, enabling commercial solvers to provide efficient solutions. Finally, tests conducted on the modified IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed ambiguity set maintains a stable convergence rate under high-dimensional random variables without rapid deterioration as the sample size increases. Moreover, the model achieves significant cost reductions while ensuring system stability.

This research introduces an advanced adaptive control framework utilizing deep reinforcement learning, specifically the Asynchronous Advantage Actor-Critic algorithm, to optimize the operation of photovoltaic-enriched microgrids integrated with solar electric vehicles. The integration of solar electric vehicles within microgrids not only addresses transportation needs and energy sustainability by acting as dynamic energy storage systems. The inherent intermittency of solar power and the dynamic energy demands of solar electric vehicles pose significant operational challenges, requiring robust, flexible control systems capable of real-time optimization. Our framework leverages the Asynchronous Advantage Actor-Critic algorithm for its efficiency in handling high-dimensional state spaces and its capability for rapid, concurrent learning processes, making it well-suited for the dynamic and complex environment of photovoltaic-enriched microgrids. The proposed model innovatively combines solar energy generation with solar electric vehicle energy storage and consumption dynamics, providing a holistic approach to microgrid management that optimizes energy flows, reduces reliance on traditional energy sources, and minimizes environmental impact.

The aggregation models of renewable energy power stations are difficult to apply to the stability research of the fault inside the station or the oscillation analysis between the station and the grid-side system, and the high dimensional characteristics of their detailed model will pose an enormous challenge to the simulation efficiency. To alleviate the contradiction between accuracy and efficiency, this paper proposes a state-variable-preserving method to efficiently model inverter-based resources and a node tearing method to realize parallel simulation of the renewable energy power station consisting of inverter-based resources. The state-variable-preserving model uses discrete state space expression to eliminate the internal nodes on the basis of preserving the original variables of the generation unit and reduces the solving scale of the generation station. The node tearing method reduces the solving complexity of the associated variables, which is more consistent with the topology characteristic that different power generation clusters are interconnected by the same bus. In the case study, the results of numerical accuracy analysis and numerical stability analysis of a photovoltaic power plant verify the reliability of the proposed method, and its simulation efficiency is verified by changing the scale of the photovoltaic power plant.