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This study aims to conduct the domain mapping of consumer behaviour research in the context of solar energy. The study can help in understanding the intellectual structure, evolution of keywords and key research producers (at the author, institutional and source level) related to the domain of solar energy consumer research.

This study uses R-studios’ bibliometrix package for analysing the bibliographical data collected from the Scopus database. Analysis has been conducted at the descriptive level (summary, author, institution and source) and analytical level (co-citation analysis, co-occurrence analysis, thematic maps and historiography).

This study finds out the most relevant authors, institutions and sources using criteria such as production, citations and H-index. Relevant research clusters have been identified using the clustering of authors, co-citations and keywords. Thematic mapping has identified the basic and motor themes. Historical citation analysis shows the direct linkage of previous studies. Overall, this study reports the most relevant bibliometric indicators in the domain of solar energy consumer research.

Identified patterns can help policymakers, business experts, social marketers and energy conservation organisations to study consumer behaviour.

Thiis bibliometric study can effectively assess sustainable development goals and suggest improved action plans.

This study examined bibliometric analysis in solar energy products (SEPs), recognised varied domains of research work on consumers’ intention to purchase solar household products and mapped them into six groups. This study provides an overview of 40 years of research on consumer behaviour towards SEPs and discusses its findings to identify the research gap.

The purpose of this paper is to introduce a double-stator switched reluctance machine (DS-SRM) for electric vehicles (EVs) and to propose multi-mode operations for this machine.

Analysis of flux linkage distributions and torque characteristics using finite element method (FEM). Building a dynamic simulation model based on electromagnetic characteristics, mathematical equations and mechanical motion equations of the DS-SRM drive system. The paper proposes multi-mode operations (inner-stator excitation mode, outer-stator excitation mode and double-stator excitation mode) based on motor working regions. It also conducts simulation and experimental results to verify the effectiveness of the proposed multi-mode operations strategies and control schemes.

There is almost no electromagnetic coupling between the inner and outer stators due to the specially designed rotor structure and optimized windings polarity configuration. Analysis of flux linkage distributions and torque characteristics verified the independence of inner and outer stators. Proposal of multi-mode operations and corresponding control rules achieved the smooth switching between different modes.

The paper introduced the DS-SRM for EVs and proposed multi-mode operations, along with control rules, to optimize its performance. The specially designed rotor structure, optimized winding polarity configuration, and the proposed multi-mode operations contribute to the originality of the research.

This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations.

The framework is developed and applied in a case example of a TEA of four designs for achieving net zero-water and energy in an affordable home in Saint Lucie County, Florida.

Homes built and sold at current market prices, using combinations of well versus rainwater harvesting (RWH) systems and grid-tied versus hybrid solar photovoltaic (PV) systems, can meet affordable housing criteria for moderate-income families, when 30-year fixed-rate mortgages are at 2%–3%. As rates rise to 6%, unless battery costs drop by 40% and 60%, respectively, homes using hybrid solar PV systems combined with well versus RWH systems cease to meet affordable housing criteria. For studied water and electricity usage and 6% interest rates, only well and grid-tied solar PV systems provide water and electricity at costs below current public supply prices.

This article provides a highly adaptable framework for conducting TEAs in diverse locations for designs of individual net-zero water and energy affordable homes and whole subdivisions of such homes. The framework includes a new technique for sizing storage tanks for residential RWH systems and provides a foundation for future research at the intersection of affordable housing development and residential net-zero water and energy systems design.

This study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.

The method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.

Pumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.

Because the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

To generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time.

In this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence.

The effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques.

This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

This paper aims to introduce the quasi impedance source inverter (qZSI)-based static compensator (STATCOM), which is incorporated into the hybrid distributed power generation system for enhancement of power quality. The distributed power generation system includes the photovoltaic (PV), wind energy conversion system (WECS) and battery energy storage system.

The WECS is used by the self-excited induction generator (SEIG) and the flywheel energy storage system (FESS). To regulate its terminal voltage and frequency, the SEIG requires adjustable volt-ampere reactive (VAR). A combination of a STATCOM and a fixed condenser bank usually serves to satisfy the VAR demand. The maximum correntropy criterion-based adaptive filter technique (AFT) is proposed to control the qZSI-STATCOM and to guarantee that the voltage at the SEIG terminal is harmonic-free while providing non-linear three-phase and single-phase loads.

The coordinated operation of the suggested voltage control and flywheel control systems ensures that load voltage and frequency are retained in their respective values at very low harmonic distortions regardless of wind speed and load variation. The simulation and experimental studies are carried out under different load conditions to validate the efficiencies of the PV-assisted STATCOM.

To improve system stability and minimize total costs, extra load current sensors can also be avoided. This paper proposes to control the SEIG terminal voltage and harmonic elimination in the standalone WECS systems using maximum correntropy criterion-based AFT with a fuzzy logic controller.

The purpose of this study is optimization of existing PV system and by making the optimization to reach the heights energy performance from the system.

The methodology used in this work is analytical as well as software using PV*SOL premium software. Both methods are used to achieve a more realistic analysis of the results achieved at the end of the work.

After analyzing the optimization of the PV system in terms of certain atmospheric conditions, it is clear that the optimization of the system is necessary. Through the optimization of the systems, a better performance of the system is achieved, as well as in the case in question, it affects the increase of the energy generated annually up to 500 kWh.

This work is the original work of the author, which represents a part of the topic of the doctorate.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.