Effect of optimal Multi-DG siting and sizing in transmission system using hybrid optimization technique for voltage control

Abstract

The advancement of Distributed Generation (DG) technologies have caused great impact to power system operation. Inappropriate installation of DGs may lead to over-compensation or under-compensation situation. Thus, a reliable optimization is urgent to avoid any unwanted effect. This paper analyses the installation impact of different types-multi-DGs determined using a pre-developed hybrid optimization technique termed as Immunized-Brainstorm-Evolutionary Programming (IBSEP). It is imperative to study the effect of multi-DGs installation such that a relevant utility can make a correct decision, whether its installation is worth or vice versa. Rigorous study has been conducted in terms of identifying the optimal location and sizing, installed on transmission system for voltage control involving different DG types. Comprehensive results are embedded in this paper to demonstrate the effect of multi-DGs installation in transmission system which in turns beneficial to the utility.  Effect of optimal multi-DG siting and sizing in transmission system… (Sharifah A. Shaaya) 647 ability to deliver or absorb reactive power [13,14]. These studies found that different type of DG will affect power network performance differently.Inventing robust and reliable optimization technique to determine optimal DG location and size is currently trending in the researchers community, aiming at alleviating setbacks in the existing optimization techniques [15][16][17][18][19][20]. Apart from finding higher probability of optimum solution towards meeting the objective functions, new optimization algorithms are developed to reduce computational burden in the classical optimization techniques [21,22] Brainstorm Optimization (BSO) is a newly developed algorithm that made its appearance in 2011. An algorithm that mimics the collective behavior of human has caught attention of many researcherst. Though it is proven reliable in solving science and engineering problems, it does has high computational burden due to its K-means clustering technique as well as easily trapped in the local maxima [23][24][25][26]. In this paper, an optimization technique that embed Artificial Immune System (AIS) optimization technique and BSO into the frame of Evolutionary Programming (EP) algorithm is used to determine optimal locations and sizes of multiple DGs, of different power delivering capabilities, in power transmission system for voltage control. The hybrid technique is termed as Immunized-Brainstorm-Evolutionary Programming (IBSEP).

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

“…The problem formulations can be stated by eqn. (1), which represents the minimization of active power transmission loss [19].…”

“…One of the compensation techniques such as installation of distributed generation (DG) can be an alternative to optimize the current demand. Distributed generation (DG) in a power system refers to the generation of electricity from many small, decentralized sources located close to the end-users of the electricity [1] or also known as dispersed generation and embedded generation [2]. These sources can include renewable energy technologies like solar panels, wind turbines, small-scale hydroelectric generators, and combined heat and power (CHP) systems, as well as conventional generators such as diesel engines and natural gas turbines [3]- [4].…”

Wireless electric energy transmission system and its recording system using PZEM004T and NRF24L01 module

“…Electricity is an example of this form of energy. Electricity is the most flexible form of energy because it can be obtained from all forms of energy [5,6]. Because of its flexibility that is why electrical energy is the energy most widely used by humanity.…”

Multiverse optimisation based technique for solving economic dispatch in power system

“…Previously, many engineers have been utilising the calculative methods to solve ED problems, such as Lambda Iteration and Lagrange Multipliers techniques [1]. In the modern era, engineers and researchers have taken interest in Artificial Intelligence (AI), including that of Genetic Algorithm (GA) technique [2], Particle Swarm Optimisation (PSO) [3], and Firefly Algorithm [4], all of which can be used in many different conditions to obtain better and more appropriate results. However, the issues with these methods are that they are not very accurate and can behave speculatively while taking a longer time to compute, as well as producing random population that will generate the local minimum or maximum value.…”