Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

Gardening is an activity that requires fully care and time which two main factors of nowadays people lack of due to fast moving daily lifestyle. Growing a plant requires a person to monitor several parameters such as water, oxygen, nutrient, light, temperature and support for the plant. In conjunction to overcome the lack of commitment for the people to enjoy their hobby in gardening, an automatic plant pot is introduced. This paper discusses the studies of plant growth which later will be used as the criteria to design and develop a prototype of automatic plant pot. From the studies three outstanding elements were used as the main criteria to design a prototype of automatic plan pot which are water, nitrogen and temperature. These three elements need to be fully automated control to meet the objective that this prototype can help people to do gardening with less care and attention.

Interconnected network of transmission and distribution lines lead to losses in the system and weakening the voltage stability in the system. Installing Static VAR Compensator (SVC) in power system has known to improve the system by minimizing the total loss and improve the voltage profile of the system. This paper presents the application of Multistage Artificial Immune System (MAIS) technique to determine optimal size of SVC. The performance of this technique is tested on the IEEE 14-Bus Reliability Test System (RTS). The optimization results show that the proposed Multistage Artificial Immune System (MAIS) technique gives better solution of SVC compensator planning problem compared to single stage Artificial Immune System (AIS) in terms of lower total system loss and improved minimum voltage magnitude.

Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.

Installing DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators.