Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

Energy Management Method for Fast-Charging Stations with the Energy Storage System to Alleviate the Voltage Problem of the Observation Node

Large-scale fast charging of electric vehicles (EVs) probably causes voltage deviation problems in the distribution network. Installing energy storage systems (ESSs) in the fast-charging stations (FCSs) and formulating appropriate active power plans for ESSs is an effective way to reduce the local voltage deviation problem. Some deterministic centralized strategies used for ESSs at FCSs are proposed to solve the voltage deviation problem mentioned above. However, the randomness of the EV load is very large, which can probably reduce the effects of deterministic centralized strategies. A fast and reliable centralized strategy considering the randomness of the EV load for ESSs is a key requirement. Therefore, we propose in this paper a day-ahead scheduling strategy with the aim of maximizing the probability of the nodal voltage change being smaller than a preset limit at the observation node. In the proposed strategy, the uncertainty of EV load is taken into account and the probability of the voltage change of an observation node is quantified by a proposed analytic assessment model (AMM). Furthermore, a voltage change optimization model (VCOM) based on a novel control parameter β is proposed, where β can be used as a constraint to suppress the nodal voltage change at the observation node. Finally, the IEEE 33-bus test system is used to verify the effectiveness of the proposed day-ahead ESS strategy.

A Method of Demarcating Critical Failure Impedance Boundary of Multi-Infeed HVDC Systems Based on Minimum Extinction Angle

A method of rapidly demarcating the critical commutation failure (CF) region of a multi-infeed high-voltage direct-current (HVDC) system is proposed. Based on the nodal impedance matrix and nodal voltage interaction factor, for different AC fault conditions—both balanced and unbalanced—a method of calculating the extinction angles of converters in multi-infeed HVDC systems is deduced in detail. First, the extinction angles of convertor stations under single-phase, double-phase, and three-phase ground faults and line-to-line faults occurring at any bus in an AC system are calculated. The minimum extinction angle serves as a CF criterion. If the calculated extinction angle for a certain bus is smaller than the minimum extinction angle, then a fault at that bus will cause CF of the HVDC system and put that bus into a failed bus set. The critical failure impedance boundaries of the topology diagram can therefore be demarcated by examining every bus in the AC system. The validity and accuracy of the proposed index and the method were verified by calculation results based on the three-infeed HVDC system model of the IEEE 39-bus system. Finally, the critical failure impedance boundary was demarcated in the IEEE 118-bus system to demonstrate the application in a wider range of systems.

Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.

Influence of voltage fluctuations on core vibration of a UHV shunt reactor

A new core vibration calculation method of UHV shunt reactors is proposed to deal with the problem that vibration parameters of the core of an ultra-high voltage (UHV) shunt reactor cannot be measured on site. A series of tests have validated that the method can calculate the vibration parameters of a UHV shunt reactor core with different voltage ratios. The method is adopted to study the influence of nodal voltage fluctuations of the UHV AC tie line on the core vibration characteristics of a UHV shunt reactor under normal and abnormal operating conditions. The conclusion is drawn as follows: in the three operating states assessed herein, no matter whether the power grid is in a normal state or not, the core of the UHV shunt reactor will not reach magnetic saturation, and the vibration parameters of the reactor always maintain a linear relationship with the operating voltage. The changes of power grid operation parameters are introduced to the research into reactor vibration, which is conducive to a more comprehensive understanding of the actual operating state of a UHV shunt reactor. It can also provide help for the design and operation of UHV shunt reactors.

Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.