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Abstract 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.

A novel approach is presented in this research to improve the design of a combined cooling, heating, and power (CCHP) system. The focus of the study is on a gas turbine system that can provide efficient power, cooling, and heating. The efficacy of the proposed technique is evaluated based on four parameters, namely energetic, exergetic, economic, and environmental features. To achieve superior and optimal results, an Improved Mother Optimization Algorithm (IMOA), a recently developed metaheuristic algorithm, is employed. This study provides a comprehensive guide for enhancing the performance, efficiency, and sustainability of CCHP systems. A practical case study was conducted to evaluate the proposed methodology in rural areas of Xinjiang Uygur Autonomous Region, China, over a period of one year. The simulation results reveal that the Gas Engine (GE) and the boiler components play a significant role in utilizing fuel energy, contributing 65 % and 35 %, respectively to the overall utilization. The analysis of the destruction rate of profile highlights that heat dissipation, mechanical losses, and electrical losses are the primary sources of energy losses within the system. When comparing the proposed IMOA with existing techniques, it is evident that the IMOA achieves a noteworthy 10 % reduction in energy consumption and a remarkable 15 % increase in overall efficiency of system. Furthermore, in terms of environmental impact, the IMOA leads to a substantial 20 % reduction in carbon dioxide (CO2) emissions compared to traditional optimization methods. The economic analysis results demonstrate that the IMOA-based approach not only improves cost-effectiveness by 25 %, but also yields an estimated return on investment (ROI) that is 18 % higher than alternative optimization techniques. Moreover, the proposed method with minimum fuel usage of 0.200 L/h, compared with Improved Butterfly Optimizer (IBO) with 0.201 L/h, Modified Mayfly (MM) algorithm with 0.205 L/h, Developed Owl Search (DOS) algorithm with 0.211 L/h, and Improved Owl Search (IOS) algorithm methods with 0.207 L/h, provides better results.

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.

Abstract The proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi‐level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)‐based forecasting method. To enhance the flexibility of the system, a price‐based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.

With the introduction of various loads and dispersed production units to the system in recent years, the significance of precise forecasting for short, long, and medium loads have already been recognized. It is important to analyze the power system’s performance in real-time and the appropriate response to changes in the electric load to make the best use of energy systems. Electric load forecasting for a long period in the time domain enables energy producers to increase grid stability, reduce equipment failures and production unit outages, and guarantee the dependability of electricity output. In this study, SqueezeNet is first used to obtain the required power demand forecast at the user end. The structure of the SqueezeNet is then enhanced using a customized version of the Sewing Training-Based Optimizer. A comparison between the results of the suggested method and those of some other published techniques is then implemented after the method has been applied to a typical case study with three different types of demands-short, long, and medium-term. A time window has been set up to collect the objective and input data from the customer at intervals of 20 min, allowing for highly effective neural network training. The results showed that the proposed method with 0.48, 0.49, and 0.53 MSE for Forecasting the short-term, medium-term, and long-term electricity provided the best results with the highest accuracy. The outcomes show that employing the suggested technique is a viable option for energy consumption forecasting.

Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.