Optimal Setting of Thyristor Controlled Series Compensator with Brain Storm Optimization Algorithms for Available Transfer Capability Enhancement

Applications of Flexible AC Transmission Systems (FACTS) devices for enhancement of Available Transfer Capability (ATC) is gaining attention due to economic and technical limits of the conventional methods involving physical network expansions. FACTS allocation which is sine-qua-non to its performance is a major problem and it is being addressed in recent time with heuristic algorithms. Brain Storm Optimization Algorithms (BSOA) is a new heuristic and predicting optimization algorithms which revolutionizes human brainstorming process. BSOA is engaged for the optimum setting of FACTS devices for enhancement of ATC of a deregulated electrical power system network in this study. ATC enhancement, bus voltage deviation minimization and real power loss regulation are formulated into multi-objective problems for FACTS allocation purposes. Thyristor Controlled Series Capacitor (TCSC) is considered for simulation and analyses because of its fitness for active power control among other usefulness. ATC values are obtained for both normal and N-1-line outage contingency cases and these values are enhanced for different bilateral and multilateral power transactions. IEEE 30 Bus system is used for demonstration of the effectiveness of this approach in a Matlab software environment. Obtained enhanced ATC values for different transactions during normal evaluation cases are then compared with enhanced ATC values obtained with Particle Swarm Optimization (PSO) set TCSC technique under same trading. BSO behaved much like PSO throughout the achievements of other set objectives but performed better in ATC enhancement with 27.12 MW and 5.24 MW increase above enhanced ATC values achieved by the latter. The comparative of set objectives values relative to that obtained with PSO methods depict suitability and advantages of BSOA technique.

Operation of the Egyptian Power Grid with Maximum Penetration Level of Renewable Energies Using Corona Virus Optimization Algorithm

Countries around the world are looking forward to fully sustainable energy by the middle of the century to meet Paris climate agreement goals. This paper presents a novel algorithm to optimally operate the Egyptian grid with maximum renewable power generation, minimum voltage deviation and minimum power losses. The optimal operation is performed using Corona Virus Algorithm (CVO). The proposed CVO is compared to the Teaching and Learning-Based Optimization (TLBO) algorithm in terms of voltage deviation, power losses and share of renewable energies. The real demand, solar irradiance and wind speed in typical winter and summer days are considered. The 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software application. The results have proved the effectiveness of the proposed CVO, compared to the TLBO, to operate the grid with the highest share possible of renewables. The paper is a step forward to achieve Egyptian government targets to reach 20% and 42% penetration level of renewable energies by 2022 and 2035, respectively.

Optimal Capacity and Location Wind Turbine to Minimize Power Losses Using NSGA-II

Voltage deviations and power losses in the distribution network can be handled in various ways, such as adding diesel power plants and wind turbines. Adaut Village, Tanimbar Islands Regency, Maluku Province has installed a diesel power plant with a capacity of 1,200 kW, while the average hourly electricity load is 374.9 kW. Adaut Village has high wind potential that can be used for distributed generations namely wind turbine (WT). WT can be used to improve power quality in terms of power losses and voltage deviations. In adding WT, the capacity and location must be determined to get good power quality in terms of power loss and voltage deviation. The research applied an optimization technique for determining the capacity and location of WT using non-dominated sorting genetic algorithm II (NSGAII) with an objective function of power losses and voltage deviation. In addition, the economic aspects of the power plant were calculated using the levelized cost of energy (LCOE). The research used scenarios based on the number of WT installed. The best results were obtained in scenario IV or 4 WT with 1.38 kW on Bus 2, 422.43 kW on Bus 15, 834.33 kW on Bus 30, and 380.81 kW on Bus 31 which could reduce power losses by 80% with an LCOE value of Rp7,113.15/kWh. The addition of the WT could also increase the voltage profile to close to 1 pu, which means it can minimize the voltage deviation in the distribution network.

A Frequency and Voltage Coordinated Control Strategy of Island Microgrid including Electric Vehicles

Frequency and voltage deviation are important standards for measuring energy indicators. It is important for microgrids to maintain the stability of voltage and frequency (VF). Aiming at the VF regulation of microgrid caused by wind disturbance and load fluctuation, a comprehensive VF control strategy for an islanded microgrid with electric vehicles (EVs) based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First of all, the SOC constraints of EVs are added to construct a cluster-EV charging model, by considering the randomness of users’ travel demand and charging behavior. In addition, a four-quadrant two-way charger capacity model is introduced to build a microgrid VF control model including load, micro gas turbine (MT), EVs, and their random power increment constraints. Secondly, according to the two control goals of microgrid frequency and voltage, the structure of DDPG controller is designed. Then, the definition of space, the design of global and local reward functions, and the selection of optimal hyperparameters are completed. Finally, different scenarios are set up in an islanded microgrid with EVs, and the simulation results are compared with traditional PI control and R(λ) control. The simulation results show that the proposed DDPG controller can quickly and efficiently suppress the VF fluctuations caused by wind disturbance and load fluctuations at the same time.

Improvement of the Voltage Profile and Loss Reduction in Distribution Network Using Moth Flame Algorithm: Wolaita Sodo, Ethiopia

Voltage stability and line losses are inevitable issues even in modern power systems. There are several techniques that emerged to solve problems in the power system to provide quality and uninterrupted supply to customers. The algorithms used in this paper to determine the appropriate location and size of the Static Var Compensator (SVC) in the Distribution Network (DN) are Moth Flame Optimization (MFO) and Particle Swarm Optimization (PSO). The objective function is defined to minimize voltage deviation and power loss. The burning problem of voltage stability improvement current scenario is because of a rise in electricity demands in all sectors. Paramount duties of power engineers are to keep the system stable and maintain voltage magnitude constant even during peak hours. The results were checked with the aid of MATLAB on Wolaita Sodo radial distribution of 34 bus data networks. The potential use of SVC is key to solve distribution system power quality issues and estimating the advantage of the installation. The results obtained from the test system were compared with PSO results. This comparison was done to know the computational time of proposed techniques. The performance of the MFA based SVC was superior in distribution system and highlighted the importance of device.

Expected electromagnetic environment at the energy infrastructure facilities of remote ports (berths)

The article deals with the issues of electromagnetic compatibility of coastal and floating objects in the waters of ports (berths). The subject of the study is the processes occurring in the electrical network when powering ships of the technical fleet and floating objects from the shore, which determine the need for automated calculation of conductive low-frequency electromagnetic disturbance for their subsequent suppression. An algorithm for calculating the parameters of the electromagnetic environment is presented, which is based on the analysis of the distribution parameters of slow voltage changes. The mechanism of EMD occurrence in voltage deviation is described and a mathematical model explaining the probability of its occurrence is presented. An algorithm for calculating the parameters of the electromagnetic environment based on slow voltage changes in the network is compiled. This algorithm is implemented in the “Program for the study of slow voltage changes by the load changingof the electrical network ". For the object under study, the parameters of the distribution of conductive low-frequency electromagnetic disturbance over a slow voltage change are determined. A block diagram of an automatic voltage regulator implementing the proposed voltage stabilization law has been developed.

A novel approach of a dynamic multi objective optimization of a power distribution system

A new dynamic multi objective optimization approach is covered in this paper. The technique for optimizing the power distribution system is dynamic reconfiguration. The goal is to propose an optimal dynamic reconfiguration which minimizes the active power losses and the voltage deviation of the nodes of the power distribution system according to the energy available at the source, while constantly guaranteeing the supply of the electrical energy to priority consumers. The reliability indices considered in this paper are the system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI) and are used to check the reliability of the optimal configurations obtained. This study subdivides a day into periods. The variations in the available power of the source and the power requested by the load, cause a new optimal configuration of the network at each period. In this work, the load adapts to the source and the optimal network topology evolves according to the maximum available power of the source. A mathematical formulation of the dynamic optimization problem by period or piece is proposed. The dynamic approach consists in acquiring the power of the load and of the source by period or piece and to compare them. When the available energy is sufficient, an optimal configuration that minimizes the power losses and voltage deviation while ensuring the supply of electrical energy to all consumers in the network is proposed. On the other hand, when the available energy is insufficient, an optimal topology of the power system minimizing the power losses and voltage deviation while guaranteeing the supply of electrical energy to priority consumers of the network is proposed. The optimal solutions per period are obtained using the MIP and MINLP methods. The approach is implemented on standard IEEE 15, 33 and 69 node power distribution system. The results obtained are satisfactory and prove the effectiveness of this new vision for the conduct of the power distribution system.

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.