Resolving Economic Dispatch with Uncertainty Effect in Microgrids Using Hybrid Incremental Particle Swarm Optimization and Deep Learning Method

Microgrids are one example of a low voltage distributed generation pattern that can cover a variety of energy, such as conventional generators and renewable energy. Economic dispatch (ED) is an important function and a key of a power system operation in microgrids. There are several procedures to find the optimum generation. The first step is to find every feasible state (FS) for thermal generator ED. The second step is to find optimum generation based on FS using incremental particle swarm optimization (IPSO), FS is assumed that all units are activated. The third step is to train the input and output of the IPSO into deep learning (DL). And the last step is to compare DL output with IPSO. The microgrids system in this paper considered 10 thermal units and a wind plant with power generation based on probabilistic data. IPSO shows good results by being capable to generate a total generation as the load requirement every hour for 24 h. However, IPSO has a weakness in execution times, from 10 experiments the average IPSO process takes 30 min. DL based on IPSO can make the execution time of its ED function faster with an 11 input and 10 output architecture. From the same experiments with IPSO, DL can produce the same output as IPSO but with a faster execution time. From the total cost side, wind energy is affecting to reduce total cost until USD 22.86 million from IPSO and USD 22.89 million from DL.

Thermal Behaviour of a Solar Dish Collector with Flat Mirrors Using CFD Analysis

In this work a Solar Thermal Generator is analysed from an energy point of view. It is a dish collector that is part of a concentrating solar grid system. The analysed plant is used for the generation of low enthalpy thermal energy as an integration to the hot water production plant for some student residences of the University of Calabria (Italy). A thermal model is developed that simulates the behaviour of the plant with the aim of analysing its performance and optimizing the design of its fundamental parts from an energy point of view. The optical model of the solar collector is carried out for a particular external condition. The emissive characteristics of the absorber is determined with the use of a thermal imaging camera. To identify the main parameters that affect the functioning and the thermal balance of the capturing system, a thermo-fluid dynamic analysis is performed. The results show which are the main causes of loss. It is possible to understand the weight of each factor in the energy balance.

Study of a solar air conditioning system with ejector

Air conditioning is one of the indispensable conditions of well-being in human life, so the face of this research to provide this basic necessity in remote areas and in desert places far from power grids. To achieve this goal, solar air conditioning has been adopted, where the compressor was replaced by an ejector, a parabolic trough solar collector and a small pump; this means that the solar air conditioner does not need a huge amount of electrical energy to operate. This paper is studding the thermodynamic cycles of this air conditioner as a function of changing the climatic conditions of Bouzaréah region in Algeria under several practical conditions of heat exchangers (Condenser, Evaporator and Generator). This study will allow the determination of the optical and thermal efficiency of the solar collector used as a solar thermal generator, refrigeration subsystem performance (COPEje) and system thermal ratio of the air conditioner, where the cooling load is estimated at 18 kW.