The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel optimum methodology for designing a combined solar/battery/diesel system in Yarkant, Xinjiang Uyghur Autonomous Region of China. In the desired system, the green energy combined system is designed to reduce the use of diesel generators. The diesel generator has been used in the photovoltaic, diesel, and battery to support green energy resources and batteries, as well as function as a backup generator for critical times whenever the production of green energy resources is low or the load demand is high. The amount of CO2 emitted, the probability of load shortage and the system cost on yearly basis are the major goals in the process of optimization. Here, the single‐objective problem is created by using the ε‐constraint technique to combine the many objectives. An improved Henry gas solubility optimizer handles the problem of optimization. To demonstrate the superiority of the strategy, a comparison is conducted between the simulation outcomes of the offered system, HOMER, and particle swarm optimizer ‐based optimum systems from the literature. The sensitivity of each parameter is also examined using sensitivity analysis.
Power systems based on centralized production are facing two limitations: the lack of fossil fuels and the need to reduce pollution; Therefore, the importance of distributed generation resources (DGs) has increased by connecting renewable energy systems to the network. With the increasing penetration of renewable energies in the network, power quality challenges in low voltage and medium voltage distribution systems have become one of the main fields of studies. Since most of the renewable energy systems are connected to the network with the help of electronic power converters devices and the main purpose of these converters is to connect DGs to the network in compliance with power quality standards; Therefore, if the switching frequency of inverters is not implemented properly, major power quality problems will be created. On the other hand, the reduction in power quality causes acute problems in power networks, which include lack of proper performance, reduction in useful life and efficiency of electrical and electronic devices in the network, creation of series and parallel resonance in some harmonics due to the presence of inductors and the capacitor, and as a result, more distortion in the voltage of the distribution network. This paper actually provides a road-map for researchers to investigate power quality issues. In other words, the mentioned cases show the importance of research and providing the solutions to improving power quality in distribution networks.
Large-scale grid integration of plug in ElectricVehicles (EVs) brings in many challenges and opportunities for electric power systems. Considering their relatively high power demand and flexibility in terms of charging time, EVs can offer opportunities for demand side management. In particular, the socalled EV Aggregators or Charge Optimization Systems (COS) can optimize the charging schedule of EVs with respect to a number of factors, such as available renewable generation and electricity market prices. However, the optimized schedules must be validated to assure that they do not lead to violation of any operational constraint of the power system. To this aim, major changes in the charging schedules to be applied by COS must be checked with the system operator. This paper describes an EV schedule verification system based on artificial neural networks. This system verifies if the COS schedules can be directly applied or if some modifications are necessary. The proposed approach is tested on two sample distribution systems.
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