Electrical modelling of rail tracks with multiple running trains is complex due to the difficulties in solving the power flow. The train positions, speed and acceleration are constantly varying resulting in a nonlinear system. In this work, a method is proposed for modelling DC electric railways to support power flow analysis of a simulated metro train service. The method exploits the MathWorks simulation tool Simscape, using it to model the mechanical and electrical characteristics of the rail track system. The model can be simulated to provide voltages at any position in the track and additionally, the voltages seen by any train. The model includes regenerative braking on trains, this is demonstrated to cause overvoltage in the feeding line if it is higher than the power demand of the other trains at that time. Braking resistors are used to protect the network from overvoltage by burning the excess energy. Through the implementation of Energy Storage Systems (ESSs), it will be possible to improve the energy efficiency and remove timetabling restrictions of electric railways by effectively controlling the rail track voltage. The paper proposes several methods to validate the model.

Nowadays, the trend of countries and their electrical sectors moves towards the inclusion of renewable distributed generators (RDGs) to diminish the use of the fossil fuel based DGs. The solar photovoltaic-based DG (PV-DG) is widely used as a clean and sustainable energy resource. Determining the best placements and ratings of the PV-DG is a significant task for the electrical systems to assess the PV-DG potentials. With the capability of the PV-DG inverters to inject the required reactive power in to the system during the night period or during cloudy weather adds the static compensation (STATCOM) functionality to the PV unit, which is being known as distributed static compensator (DSTATCOM). In the literature, there is a research gap relating the optimal allocation of the PV-DGs along with the seasonal variation of the solar irradiance. Therefore, the aim of this paper is to determine the optimal allocation and sizing of the PV-DGs along with the optimal injected reactive power by their inverters. An efficient optimization technique called Gorilla troop’s optimizer (GTO) is used to solve the optimal allocation problem of the PV-DGs with DSTATCOM functionality on a 94 bus distribution network. Three objective functions are used as a multi-objective function, including the total annual cost, the system voltage deviations, and the system stability. The simulation results show that integration of PV-DGs with the DSTATCOM functionality show the superiorities of reducing the total system cost and considerably enhancing system performance in voltages deviations and system stability compared to inclusion of the PV-DGs without the DSTATCOM functionality. The optimal integration of the PV-DGs with DSTATCOM functionality can reduce the total cost and the voltage deviations by 15.05% and 77.05%, respectively. While the total voltage stability is enhanced by 25.43% compared to the base case.

Incorporating energy storage systems (ESSs) into electric railways has been shown to be advantageous for energy saving and power quality enhancement. For DC railways, the connection method of the ESS to the track may impose restrictions on charging and discharging the ESS to control the state of charge (SOC). Without management of the SOC, the ESS is shown in this study to reach minimum or maximum limits, reducing its effectiveness due to unavailability. Whilst it is possible to oversize the capacity of ESS, this incurs increased costs and requires more physical space. The main objective of this study is to propose and validate a control algorithm that prevents the ESS from reaching the maximum or minimum SOC limits whilst maintaining the benefits of the system. The main concept of the proposed control method is to dynamically update the voltage and current setpoints of the ESS to manage its SOC. The control algorithm is implemented in the MATLAB software and the simulation results are validated against experimental results, using a track emulator and supercapacitor. The findings demonstrate that, with appropriate dynamic charge/discharge control, the SOC levels can be adequately managed and no external load or source is required.

Reducing the emissions of greenhouse gases has directed energy sectors toward using renewable energy sources (RESs) and decreasing the dependency on conventional energy sources. Recently, developing efficient load frequency control (LFC) schemes has become essential to face the reduced inertia due to RESs installations. This paper presents a modified tilt fractional order (FO) integral–tilt FO derivative with a fractional filter (TFOI-TFODFF or namely TIλ-TDμFF) LFC method. Although the proposed controller uses the same elements of standard controllers, it adopts FO control capabilities and flexibilities, including the tilt, FO integral, FO derivative, and FO filter. Thence, a new control structure is obtained, merging the advantages of both controllers. Moreover, the proposed TFOI-TFODFF controller employs two control loops to be able to mitigate low-frequency as well as high-frequency disturbances in power grids. Additionally, a new modified marine predator algorithm (MMPA) is proposed for optimally tuning the parameters of the proposed TFOI-TFODFF LFC method. The performance of the MMPA is enhanced in terms of initialization and exploitation phases using the chaotic maps and weighting factor. A two-area interconnected power system case study is implemented with wind and photovoltaic RESs and electric vehicles (EVs) contribution. The proposed TFOI-TFODFF LFC is compared with the FOPID, TID, TI-DF, and FOTPID controllers, wherein the proposed TFOI-TFODFF has offered superior performance of the proposed controller. Moreover, the proposed modified MPA is compared with the original MPA and other competitive optimization algorithms, and statistical analyses are carried out through parametric and nonparametric tests.

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