Aiming at the problem of zero sequence voltage generated by unbalance parameters of line to ground, which affects arc suppression effect of grounding fault of controllable voltage source. By analyzing the influence of ground unbalance parameters on the arc suppression effect of controllable voltage source under different grounding modes, the mechanism of full compensation arc suppression based on zero sequence voltage of neutral point is revealed, and on this basis, a fully compensated arc suppression model of controllable voltage source controlled by double closed loop PI is established, and the deviation control is carried out by using the neutral voltage of distribution network and the voltage of fault phase supply. The residual voltage ring adopts the ground fault phase residual voltage for closed loop control. The simulation results show that the dual-closed-loop PI control algorithm can continuously stabilize the output waveform of the controllable voltage source. When the transition resistance is 0.1 ~ 10 kΩ, the residual voltage stabilization time of the independent controllable voltage source grounding method is 43 ms ~ 2.4 s, and the parallel arc suppression coil grounding method is 43 ms ~ 4.7 s. The proposed dual closed-loop PI control method for neutral point voltage deviation and fault residual voltage can stabilize the residual voltage of the grounded fault phase to below 10 V, forcing reliable arc extinction at the grounded fault point, exhibiting good stability. Low-voltage simulation tests have also proved the feasibility of the algorithm.

Economic development relies on access to electrical energy, which is crucial for society\'s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia\'s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95-1.025 p.u.). After reviewing the current distribution network\'s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system\'s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers\' charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs\' random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.