The large-scale integration of distributed photovoltaic energy into traction substations can promote selfconsistency and low-carbon energy consumption of rail transit systems. However, the power fluctuations in distributed photovoltaic power generation (PV) restrict the efficient operation of rail transit systems. Thus, based on the rail transit system architecture with the “source-grid-storage” collaborative energy supply, a collaborative capacity planning method is proposed in this study for the photovoltaic power generation and hybrid energy storage system (PV-HESS) of rail transit self-consistent energy systems that consider the distributed photovoltaic power fluctuations and battery cycle life. First, based on the Peukert lifetime energy throughput theory, the process of battery life decay was quantitatively analyzed, and a quantitative calculation model for the service life of batteries was established. Next, the collaborative capacity planning and configuration model for PV-HESS was developed by considering the collaborative operation of the “source-gridstorage-vehicle”, with the economic efficiency of the rail transit self-consistent energy system as the goal and the safe operation constraint of the system as the boundary. A hybrid inner-and outer-layer optimization method was designed for the iterative solution of the proposed model. Finally, the effectiveness of the collaborative planning model for PV-HESS in rail transit systems was numerically verified.

Decarbonization of power systems has put Renewable Energy Sources (RES) at the forefront when it comes to electric power generation. The increasing shares of converter-connected renewable generation cause a decrease of the rotational inertia of the Electric Power System (EPS), and consequently deteriorate the system capability to withstand large load-generation imbalances. Low-inertia systems are subjected to fast and large frequency changes in case of in-feed loss, where the traditional primary frequency control is not sufficient to preserve the frequency stability and to maintain the frequency above the critical value. One possible solution to this rising problem is seen in Fast Frequency Response (FFR) provided by the High-Voltage Direct-Current (HVDC)-based systems. This paper presents the adaptive FFR control of HVDC-based systems for frequency stability enhancement in the low-inertia system. The EPS is considered as a “black box” and the HVDC response is determined only using the locally measured frequency change. Sliding Mode Control (SMC) of the Modular Multilevel Converter (MMC) was developed and demonstrated to provide faster and more appropriate frequency response compared to the PI controller. The described adaptive HVDC control considers the size of disturbance and the inertia of the power system, and it is verified by simulations on the IEEE 39 bus test system implemented in MATLAB/Simulink for different system configurations and different sizes of disturbance.

Limited financial resources significantly impact the ability of distribution system operators to prioritize maintenance, upgrade infrastructure, and integrate advanced technologies. To optimize resource allocation, a structured and detailed analysis is essential for maximizing reliability improvements within budget constraints. This paper presents a methodology for enhancing the reliability performance of semi-urban and rural distribution networks by identifying optimal investment strategies under financial limitations. Investment options are categorized into three groups: (1) installing new components where none previously existed, (2) replacing aging, higher-failure-rate equipment with new components, and (3) upgrading both network components and operational strategies. To determine the most effective investments, the selection process aims to minimize the System Average Interruption Duration Index (SAIDI) and Energy Not Supplied (ENS), while also considering an economic metric that quantifies reliability in monetary terms. The methodology was tested on networks with diverse consumer types, incorporating Monte Carlo simulation to account for uncertainties in component reliability indices and node loads. An optimization algorithm was developed, implemented, and validated using representative test networks, demonstrating its effectiveness in guiding investment decisions for improved distribution system reliability.

Transitioning towards carbon-free energy has brought severe difficulties related to reduced inertia in electric power systems. Regarding frequency stability, low-inertia systems are more sensitive to disturbance, and traditional frequency control is becoming insufficient to maintain frequency within acceptable limits. Consequently, there is a necessity for faster frequency support that can be activated before the primary frequency control and that can decelerate further frequency decay. This paper proposes a local control strategy for a multi-stage fast frequency response (FFR) provided as an ancillary service that considers the location of the disturbance and the distribution of system inertia. The novelty of the presented control strategy is the ranking of FFR resources by price, which takes the economic component into consideration. The proposed control is simple, based only on RoCoF measurements that trigger the activation of FFR resources. Its advantage over other methods is the ability to adapt the FFR resource response to the disturbance without complex calculations and the ability to ensure a bigger response closer to the disturbance, as well as in low-inertia parts of the system. In that way, there is a bigger activation of resources in the parts of the system that are more endangered by disturbances, which, as a result, minimizes the propagation of the disturbance’s impact on system stability. The applicability of the presented method is demonstrated in a simple 3-area power system and IEEE 68-bus system implemented in MATLAB/Simulink. The results show that the proposed control enables the largest response closer to the disturbance, thus mitigating the propagation of the disturbance. Furthermore, the results confirm that the proposed control enables lower provision costs and more support in low-inertia areas that are more vulnerable to disturbances.

Optimal charging of plug-in electric vehicles from the power grid requires the development of special algorithms. The presented system discloses one such algorithm used by server software for the optimal control of chargers for electric vehicles.