Strategy for Optimal Grid Planning and System Evaluation of Networked Distribution Systems

The meshed network may become a standard for future distribution systems owing to its various benefits regarding voltage profile, reliability, losses, and the distributed generation (DG). Therefore, in Korea, there is a plan to introduce an advanced form of meshed network called a networked distribution system (NDS). This refers to a system with permanent linkages between four distribution lines (DLs) and N×N communication-based protection. To properly introduce NDS to an actual grid, this study proposes a strategy for optimal grid planning and system evaluation. Four different topologies and four practical indicators are explained. First, load imbalance is used to find the optimal grid that maximizes the load capacity. Second, line overload, fault current, and temporary overvoltage (TOV) were used to evaluate the necessity of load transfer, availability of circuit breakers, relay settings, and system stability. PSCAD/EMTDC were employed for the simulation. This study establishes the construction and evaluation guidelines of NDS for distribution system operators (DSOs).

Congestion management using grey wolf optimization in a deregulated power market

Transmission congestion issues became more severe and difficult to control as the power sector became more deregulated. The grey wolf optimization algorithm is proposed to relieve congestion by rescheduling generation effectively, resulting in the least congestion cost. The selection of participating generators is based on sensitivity, and the proposed technique is used to determine the best-rescheduled output active power generation to minimize line overload. The IEEE-30 bus system is used to test the proposed optimization technique. It has been demonstrated that when compared to other algorithms like the real coded genetic algorithm, particle swarm optimization, and differential evolution algorithm, the proposed approach produces excellent results in terms of congestion cost.

EV Charging in Case of Limited Power Resource

In the case of the widespread adoption of electric vehicles (EV), it is well known that their use and charging could affect the network distribution system, with possible repercussions including line overload and transformer saturation. In consequence, during periods of peak energy demand, the number of EVs that can be simultaneously charged, or their individual power consumption, should be controlled, particularly if the production of energy relies solely on renewable sources. This requires the adoption of adaptive and/or intelligent charging strategies. This paper focuses on public charging stations and proposes methods of attribution of charging priority based on the level of charge required and premiums. The proposed solution is based on model predictive control (MPC), which maintains total current/power within limits (which can change with time) and imparts real-time priority charge scheduling of multiple charging bays. The priority is defined in the diagonal entry of the quadratic form matrix of the cost function. In all simulations, the order of EV charging operation matched the attributed priorities for the cases of ten cars within the available power. If two or more EVs possess similar or equal diagonal entry values, then the car with the smallest battery capacitance starts to charge its battery first. The method is also shown to readily allow participation in Demand Side Response (DSR) schemes by reducing the current temporarily during the charging operation.

Optimization Method for Multiple Measures to Mitigate Line Overloads in Power Systems

Line overload is one of the important causal factors of cascading failures and blackouts in power systems. An optimization method for protection and control measures to mitigate line overloads is proposed in this study. The method consists of two main parts, i.e., the modeling process and the solving process. In the modeling process, an optimization model including overload protection and emergency control measures is developed using PFT (Power Flow Tracing). In the solving process, a multi-stage optimization method using IBSO (Improved Brain Storm Optimization algorithm) is proposed to obtain the final result. The aim of this study is to form a coordinated protection and control strategy that reduces the power on the overloaded line within the safety limits and minimizes the load loss of the power system. The simulation results show the effectiveness of the proposed method.

Overload Control Strategy Based on Triangular Fuzzy Analytic Hierarchy Process

After the high-voltage transmission line is cut off due to a fault, the bearing transmission power will be transferred, which may cause overload. If overload cannot be eliminated quickly and scientifically, it is very likely that the line backup protection will be activated, which will trigger chain trip or even blackout accidents. In view of this, this paper proposed a cross-voltage level optimization load shedding control strategy based on fuzzy analytic hierarchy process. Through step-by-step progression in the three indexes of line overload degree, load importance degree, and unit load shedding cost, the lower-level lines of the overload line are selected for optimal line removal, thus achieving the goal of optimizing load shedding across voltage levels.