• Energy Research

The multi-infeed effective short-circuit ratio (MESCR) is widely used in indicating the strength of multi-infeed AC/DC power systems. However, when the widely used MESCR was adopted to evaluate the stability margin of the Eastern China Grid including three infeed ultra-high-voltage DC (UHVDC) and five high-voltage DC transmission lines in 2016, the MESCR result indicated the system was strong enough but in fact occasionally collapses after the N-1 contingency. To determine the reason for this conflict, this paper theoretically analyzes the limitations of the existing MESCR. The theoretical analysis reveals that when a large amount of capacitor compensations are concentratively installed in the system, the conventional MESCR will not be able to reflect the capacitor compensations’ influence on the system stability, and no matter how many capacitors are installed or where the capacitors are installed, the MESCR almost retains the same value; namely, the MESCR is saturated in such systems. To address the saturation problem of conventional MESCR, this paper proposes an improved multi-infeed effective short-circuit ratio (IMESCR) which considers the influences of all capacitor compensations by converting all capacitors installed throughout the system to virtual capacitors at the DC inverter station. Case studies are carried out based on the New England 39-bus system and the Eastern China Grid, respectively. The simulation results verify the theoretical analysis of the MESCR’s limitations in evaluating the stability of power systems with massive capacitors installed, and proves that the proposed IMESCR could accurately indicate the strength of AC/DC power systems. Therefore, the proposed IMESCR provides a new index for evaluating the stability margin of power systems with massive capacitor compensations installed.

The conventional voltage observer based distributed control can make a tradeoff between voltage restoration and reactive power sharing. However, it suffers from estimation error when there exist input disturbances, such as initial values and communication delays. To address this problem, this paper proposes a novel distributed secondary control based on PI consensus algorithm. A voltage controller and a frequency controller are designed separately to realize the restoration of voltage/frequency and also accurate reactive/active power allocation. Moreover, a detailed small-signal model with PI consensus algorithm is established to show the impact of the proposed control strategy on the system stability. Compared with the conventional distributed control, the proposed control scheme is simpler with higher control accuracy under input disturbances. Simulations are conducted to verify the effectiveness of the proposed control strategy in an isolated ac microgrid.

Abstract We investigate an adaptive neuro-control approach, namely goal representation heuristic dynamic programming (GrHDP), and study the nonlinear optimal control on the multi-machine power system. Compared with the conventional control approaches, the proposed controller conducts the adaptive learning control and assumes unknown of the power system mathematic model. Besides, the proposed design can provide an adaptive reward signal that guides the power system dynamic performance over time. In this paper, we integrate the novel neuro-controller into the multi-machine power system and provide adaptive supplementary control signals. For fair comparative studies, we include the control performance with the conventional heuristic dynamic programming (HDP) approach under the same conditions. The damping performances with and without the conventional power system stabilizer (PSS) are also presented for comparison. Simulation results verify that the investigated neuro-controller can achieve improved performance in terms of the transient stability and robustness under different fault conditions.

Abstract Chronological operation simulation (COS) is an essential tool for planning and analyzing power systems under high penetration of renewable energy. Conventional COS methods heavily depend on the availability of renewable power output data to obtain accurate results, and often require hours or even days of computational time while the sequential simulation could easily get infeasible for power systems with intensive flexibility. To cover the absence of output data for newly proposed wind and solar projects and accelerate the computation speed, this paper proposes a novel COS simulation framework for regional power systems with high penetration of renewable energies using meteorological data. The proposed simulation framework consists of the following three steps: data preparation, modeling and solving, and result output. In the data preparation step, wind, solar power output profiles and heat demands are converted from public accessible meteorological data. Then in the modeling and solving step, a unit commitment based COS model for simulating the hourly operation of power and heat sectors is proposed, and the proposed model is solved with a time domain partitioning (TDP) and a rollback mechanism to accelerate the computation speed as well as avoiding infeasible solutions. The accuracy of the wind and solar power output converted from meteorological data is verified through comparing with measured power output. Moreover, the feasibility and accuracy of utilizing the proposed COS framework to simulate the operation of a real regional power system is also verified through the 2015 annual operation statistics of the Northwest China Grid.

The virtual synchronous generator (VSG) provides a promising way to increase the inertia of inverter-based microgrids. However, when multiple VSGs are parallel in the grid, severe oscillations may be incurred. To address this problem, this article proposes a secondary frequency control for distributed VSGs with low bandwidth communication. The control can damp the oscillations and restore the frequency to the rated value without changing the virtual inertia provided by the VSGs. In addition, the proportional active power sharing is achieved by event-triggered communication mechanism, thereby reducing the communication burden considerably. The stability and Zeno-free behavior of the proposed control strategy are demonstrated by the ultimately uniformly bounded (UUB) theory. Simulations are conducted to verify the effectiveness of the proposed control strategy on the PSCAD/EMTDC platform.

Location of the static VAR compensator (SVC) and other types of shunt compensation devices is important for the enhancement of the voltage stability for practical power systems. With the theory of the normal forms of diffeomorphism, this paper proposes a new method to solve this problem. The proposed method makes use of the nonlinear participation factors, in which the nonlinearity of power systems can be taken into consideration. As a result, the most suitable location where the SVC should be used in power system can be determined, even for the cases in which the system is characterized with strong nonlinearity. In order to show the effectiveness of the proposed method, the New England 39-bus power system with SVC is used as an example. Calculation results show that with the SVC located at the place where the proposed method determined, the voltage stability is considerably enhanced. The steady-state voltage stability index and the time domain simulation results verify the effectiveness of the proposed method.

This paper proposes an approximate dynamic programming (ADP) based algorithm for the real-time operation of the microgrid under uncertainties. First, the optimal operation of the microgrid is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem, combining the ac power flow and the detailed operational character of the battery. For this NP-hard problem, the proposed ADP based energy management algorithm decomposes the original multitime periods MINLP problem into single-time period nonlinear programming problems. Thus, the sequential decisions can be made by solving Bellman's equation. Historical data is utilized offline to improve the optimality of the real-time decision, and the dependency on the forecast information is reduced. Comparative numerical simulations with several existing methods demonstrate the effectiveness and efficiency of the proposed algorithm.