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In pursuit of the "Dual Carbon Goals" and to mitigate the adverse effects of "power supply restrictions," a microgrid scheme integrating wind and solar power with hydrogen energy storage is proposed. This paper introduces the principles of system capacity configuration and establishes a mathematical model. This research offers a novel method for configuring wind and solar hydrogen storage systems called quantum-enhanced multi-objective collaboration. This work intends to address the complicated issues of achieving effective energy storage, reducing prices, and maximising renewable energy utilisation by leveraging the capabilities of quantum computing. A multi-objective capacity optimization configuration model for wind–solar–hydrogen energy storage is developed using Homer Pro software and an enhanced BAS-GA algorithm. Under off-grid operating conditions, the wind–solar–hydrogen energy storage system's capacity optimization configuration model is validated through practical examples. The results indicate that, in comparison to wind storage, solar storage, wind–solar storage, and wind–solar–diesel storage systems, the net present value and levelized cost of electricity of the wind–solar–hydrogen energy storage system decrease to 14.25 million RMB and 1.529 RMB/(kW h), respectively. The renewable energy utilization rate increases to 98.7%, and the load shedding rate decreases to 5.50%. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ -synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers' superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts. [ABSTRACT FROM AUTHOR]