Optimal design of wind energy generation in electricity distribution network based on technical-economic parameters

In order to satisfy electricity customers and avoid some environmental constraints and problems, the transition to renewable energy sources has become increasingly important given their advantages and benefits, such as reducing pollution and improving the reliability of the targeted distribution system. In this paper, several state-of-the-art metaheuristic optimisation algorithms are used to investigate the optimal setting and sizing of wind turbines (WTs) when connected to the electricity distribution network (EDN). The selected algorithms were implemented to optimise and minimise a multi-objective function (MOF) considered as the sum of the techno-economic parameters of total active power loss (TAPL), total voltage deviation (TVD) and investment cost of the WTG (ICWTG) when the daily uncertainties and variations of the load-source powers are taken into account. The effectiveness of the selected algorithms was validated on the two standard test systems IEEE 33-bus and 69-bus. The simulation results in this paper showed the superiority of the Gorilla Troops Optimizer (GTO) algorithm compared to other new metaheuristic optimisation algorithms in terms of providing the best optimised results. Accordingly, the GTO algorithm showed excellent effectiveness and robustness in determining the optimal setting and sizing of the WTG units in EDN. Thus, the daily active power losses were reduced to 1,415 MWh for the first test system and 1,072 MWh for the second test system, while also improving the bus voltage profiles and favouring the investment costs of the installed WTG units, all with daily uncertainties in terms of load demand and WTG power variations

Optimal distributed generation and load shedding scheme using artificial bee colony- hill climbing algorithm considering voltage stability and losses indices

Around the world, the demand is increasing due to industrial activity and advances in both developing and developed countries. This situation has pushed many power system operators to operate their system closer to the voltage stability limits. Increase in power consumption can cause serious problems in electric power systems, such as voltage instability, frequency instability, line overloading, and power system blackouts.Voltage stability index (VSI) is a tool for detecting voltage stability related problems. This work proposes an index of the line voltage stability limits based on Thevenin’s Theorem, which is referred to as the Maximum Line Stability Index (MLSI). The function of MLSI is to estimate the voltage stability condition and determine sensitive lines in power system. To increase voltage stability and improve other aspects of power quality, many power system operators are considering the idea of integrating distributed energy resources into the existing power system. Another part of this work focuses on enhancing the stability of the power system using distributed generator (DG). The proposed solution is based on the optimization method developed from a combination of the Artificial Bee Colony and Hill Climbing algorithms (ABC-HC) to give the optimal placement and sizing of DG units to be deployed in the system. Under severe contingency conditions, such as increase in demand and loss of transmission lines, frequently the problem cannot be solved by just using the DG, the possible solution is to consider load shedding as to reduce the congestion in order to maintain voltage stability in the system. To solve this problem, an optimal load shedding approach, integrated with optimal DG sizing is proposed using the ABC-HC algorithm. This technique can find the load location to be shed, as well as the size of DG. The performance and effectiveness of each proposed solution was tested on IEEE test systems. The simulation results showed that the MLSI index has strong sensitivity to detect the overloaded line in the system and as reliable as other voltage stability indices. Meanwhile, the proposed ABC-HC optimization technique shows its ability to identify the bus location and the optimal active energy injection from the DG with a substantial power loss reduction. Finally, under severe contingency condition, the optimization of DGs and load shedding shows the system able to maintain its voltage stability