Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood

Abstract

: Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

“…Smart DC-MGs incorporate complex interactions among computational and physical procedures which make them as cyber-physical systems (CPSs), with vulnerability to cyberattacks [3][4][5]. Connecting MGs and sources of energy and loads to each other and exchanging information between them in order to improve the controller efficiency of converters and load sharing and coordination causes cybermalicious attack points.…”

Optimal Multi-Operation Energy Management in Smart Microgrids in the Presence of RESs Based on Multi-Objective Improved DE Algorithm: Cost-Emission Based Optimization

“…In addition to this, for consumers, the potential of lower costs, higher reliability of services and power quality, increasing in power efficiency and power independency can be the reasons for higher interests in DGs. Using RESs and green powers, including wind and photovoltaics, can offer notable environmental advantages [1][2][3][4]. In this way, MG can act as a concept that gives an effective solution for integrating renewable DGs into the major power and energy network and offers the requirements of next generation power systems [5][6][7][8].…”

Linear Distribution System State Estimation with Integration of DG

“…This paper also proposes techniques used to improve adaptive capability of the network to handle extreme events. Ref [10] explains optimal placement of DG in radial distribution network using evolutionary algorithms like Genetic Algorithm and Particle Swam Optimisation. A multi-objective performance index-based sizing and placement of DG in distribution systems is studied in [11].…”