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In a DC Microgrid, accurate power sharing and voltage restoration are two primary control goals to guarantee power quality and reliable operation. Inaccurate power sharing is a significant concern due to discrepancies in feeder line resistance, faulty equipment, lack of monitoring and control, and nonlinear load. Moreover, inaccurate power sharing may lead to overloading of converters and cause a cascade of failures throughout the entire system. This study proposes a new adaptive droop control strategy to address these challenges. To enhance accurate power sharing, error current sharing is formulated by considering bus current and total rated current. This is regulated by the adaptive controller to adjust droop resistance. Additionally, the impact of droop voltage because of feeder line resistance is considered in the proposed strategy. The primary control loop regulates the output voltage of converter utilizing the proposed observer-based optimum sliding mode control (OOSMC). The controller gain of the OOSMC is optimized using a gradient-based method to enhance transient and steady state response of the converter output. In the secondary loop, the twin-delayed deep deterministic policy gradient (TD3) for voltage restoration is employed with a new reward function to optimally tune the proportional-integral (PI) controller. Finally, the superiority of the proposed strategy is evaluated through rigorous testing scenarios, including the use of constant power load (CPL) and sudden failure in the converters. Moreover, the proposed strategy is validated by simulations and laboratory-based experiments. The results show that the OOSMC outperforms GA and PSO while the TD3 has better performance than traditional PI. Furthermore, when the equivalent power-rated converters are implemented in the system, the proposed strategy can transfer identical power sharing of 4 W to the load and provide accurate current sharing of 0.167 A compared to conventional droop control while the DC bus voltage is maintained at the 24 V reference value. In the case of different power-rated converters, the proposed strategy can achieve power sharing accuracy. The first, second and third converters supply 2 W, 4 W, and 6 W, respectively, to the load and provide accurate current sharing. Meanwhile the DC bus voltage can be kept at the reference voltage of 24 V. In the scenario of various kinds of disturbances, the proposed strategy can achieve accurate power and current sharing when the system is tested under load and input voltage variations. Meanwhile the DC bus voltage always returns to the voltage reference. Moreover, the proposed strategy can share power and current accurately when the converter occurs a sudden failure. [ABSTRACT FROM AUTHOR]

Energy mix optimization is considered an essential tool for assessing the efficacy of renewable energy system (RES). Countless approaches have been employed by grid planning engineers and scientists to optimally size grid-connected photovoltaic (PV) systems. Optimizing grid-connected PV systems is beneficial since it causes a reduction in the energy produced by the conventional power plants, and as a result, a reduction in the harmful emissions that are released as by-products. In this study, a large commercial load in the city of Makkah in Saudi Arabia is connected to an optimally designed grid-connected PV systems with the support of a battery storage system (BSS). First, using HOMER software, the system components are chosen by considering the electrical and economic variables. A comparative investigation is carried out for three configuration systems along with the detailed technical study for the proposed systems. Simulation results recommend utilizing a battery system and RES with a minimum cost of energy (COE) of $0.11. Using both HOMER and MATLAB, the PV, converter, and battery system optimal size are estimated by considering the net present cost (NPC), COE, excess electricity and energy savings, emissions, and renewable penetration ratio to find the most economical and environmental-friendly combination. The results show that 1.60 MW is the optimal capacity of each PV, converter, and battery BSS elements to achieve the minimum targeted emission and COE, which resulted in a reduction in the overall NPC of the scheme. When the optimal design is configurated, it is found that the unmet load is zero. [ABSTRACT FROM AUTHOR]

Remote areas electrification is usually achieved through costly and eco-unfriendly diesel generator (DG) systems with additional cost-obstacles arising through diesel–fuel transportation. Hybridization of renewable energy systems (RESs) with DG units offers a dynamic approach of electrification for the remote areas. A techno-economic study is required to assess the viability of RES in such locations. This research work examines the case of Al-Shumaisi cluster, located in the west of Makkah Province, Kingdom of Saudi Arabia (KSA). This is where the Makkah transport company (MTC) is located. A techno-economic investigation is performed for a commercial load profile of MTC with a hybrid wind/DG/fuel cell/battery/electrolyzer system. In this study, two hybrid configurations are proposed: 1) wind/DG/FC/battery system; 2) wind/FC/battery system. Moreover, another configuration that uses only DG units is also investigated. A comparative study is achieved for the most optimal combination, depending on the gross present cost (GPC) and levelized cost of energy (LCOE) as the essential parameters. Furthermore, other financial parameters like operation and maintenance (O&M) costs, capital costs, and replacement costs are investigated. The well-recognized simulation tool, HOMER (Hybrid Optimization of Multiple Energy Resources) developed by the National Renewable Energy Laboratory, is used to fulfill this study. The sensitivity investigation is carried out by optimally scaling the configuration capacities as well as by monitoring the future changes like weather, load demand, and fuel price uncertainties. The results of the sensitivity investigation and HOMER simulation confirm that the wind/DG/FC/battery hybrid off-grid configuration is the most economic and eco-friendly solution with a total GPC of 7.045 million USD, COE of 0.271 USD/kWh, and CO2 emissions of 566 tons. This signifies the 13.84% reduction of net present cost compared to DG-based units, with a 64.2% decrease of pollutants and zero unmet load. [ABSTRACT FROM AUTHOR]

Distributed generation is a collective term that covers the generation of energy at micro level, distributed in a location near the end user by using renewable and nonrenewable distributed energy generation (DEG) resources including among others, solar, wind, hydro, geothermal and diesel generators. This paper presents a review on the optimization approaches for hybrid DEG systems, considering both stand-alone and grid-connected systems. There are several optimization techniques used on DEG systems, comprising of analytical and artificial intelligent (AI) and hybrid techniques. This work encompasses the selected journal papers published especially in the last five years. A brief background of the optimization approaches been highlighted, particularly identifying the most common techniques to give the basis for analysis of the approaches currently applied on hybrid DEG systems. The analysis shows that AI techniques are still dominating the techniques used for optimization of DEG systems, with particle swarm optimization (PSO) recognized as the most used AI method. The objective functions in the optimization of hybrid DEG systems are currently defined to maximize the reliability, to minimize the expected interruption cost, and to optimize operation schedule of DEG resources. [ABSTRACT FROM AUTHOR]