This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0% unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution. 

Techno-Economic Modeling and Optimal Sizing of Autonomous Hybrid Microgrid Renewable Energy System for Rural Electrification Sustainability using HOMER and Grasshopper Optimization Algorithm.

Turbo-refrigerator based on reverse Brayton cycle is an attractive solution for providing refrigeration at ultra-low temperatures below −60 °C for deep freezing. Employing a motor-driven turboexpander compressor (MTEC) not only allows the turbo-refrigerator to achieve high compactness, reliability and flexibility, but also leads to new operating characteristics especially in a closed cycle. This paper aims to investigate the operating characteristics of a closed-cycle single-stage turbo-refrigerator with a MTEC. A high-speed turbo-refrigerator system with the design refrigeration temperature of −86 °C was manufactured and then tested under variable rotating speeds and refrigeration conditions. Test results show that a refrigeration temperature below −96.4 °C was obtained in a pull-down progress. Compared with a fixed-speed operation, variable-speed operation enables the independent control of the refrigeration temperature and the cooling capacity. With the decrease in the refrigeration temperature (or in the cooling capacity), the rotating speed has a greater effect on the COP than that on the cooling capacity (or on the refrigeration temperature). The mass flow rate along with the compression and expansion work shows a decreasing trend as the refrigeration temperature decreases at a fixed speed of the MTEC. The advanced exergy analysis shows a total exergy efficiency of 0.42 (with a COP of 0.62 at refrigeration temperature of −86 °C) could be expected with component improvement in the near future, and it is of the priority to increase the efficiency of the turboexpander. 

Experimental investigation on a closed-cycle single-stage turbo-refrigerator for deep freezing{fr}Étude expérimentale sur un réfrigérateur turbo à cycle fermé monophasé pour la congélation profonde.

Analytical and experimental study on operating characteristics of a closed-cycle turbo-refrigerator for deep freezing

A design method of turboexpander-compressors based on multi-objective optimization applied to reverse brayton cycle

An efficient measure to eliminate voltage oscillation of a multi-terminal DC distribution system (MTDCDS) is locating oscillation source timely and adjusting its control parameters. Combined with oscillation source judgment indexes (OSJIs), a control parameters optimization method based on adaptive model predictive control (A-MPC) is proposed. Specifically, based on the static model, a new adaptive updating mechanism of weighting coefficients is proposed. The mechanism adjusts the weighting coefficients according to the real-time states and OSJIs adaptively. Furthermore, taking the minimum weighted square sum of the voltage tracking error and control parameters increment as objective. Compared with the traditional model predictive control (MPC), the proposed method directly adjusts the control parameters of the oscillation source adaptively. which can significantly improve the system response performance and reduce the control cost. The performance has been evaluated using the MATLAB/Simulink and RT Box experimental platform and the results prove the superiority of the proposed method. 

A real-time optimization method in multi-terminal DC distribution system based on adaptive model predictive control

Performance optimization of turboexpander-compressors for energy recovery in small air-separation plants

Modular multilevel converter with supercapacitor (SC) packs-based energy storage system (MMC-SESS) can play a role in energy transition and renewable energy consumption. However, SESS's imbalance problem is caused by the inconsistency of its packs, which has a negative effect on the utilization of the MMC-SESS and has become a hot research topic. Confronting this problem, this paper proposes a state of charge (SOC) balancing control strategy amongst SC packs based on a discrete time-domain predictive power model in MMC-SESS based medium-voltage direct-current (MVDC). It is a double-loop control to accurately control the charging and discharging power. The outer power control establishes a discrete time-domain predictive power model for the SOC differences. The inner current loop designs a hybrid controller based on the continuous control set-model prediction control (CCS-MPC) and the feedback control strategy to accurately track the dynamic current reference. Simulations in PSCAD/EMTDC have shown that the proposed control can achieve SOC balancing amongst SC packs and improve the dynamic response as well as mitigate the SC pack current ripples. 

SOC Balancing Control Based on Predictive Power Model Amongst Supercapacitor Packs in MMC with Embedded Energy Storage System

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Performance optimization of turboexpander-compressors for energy recovery in small air-separation plants

SOC Balancing Control Based on Predictive Power Model Amongst Supercapacitor Packs in MMC with Embedded Energy Storage System