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In the current scenario, the impact of the automobile sector on greenhouse gas emissions becomes inevitable. Considering the case, automobile manufacturers pay attention to commercializing Electric vehicles (EVs) in a wide range. The effective functioning of the electrified vehicle system is highly determined by its charging infrastructure. This paper presents a comparative analysis of wired and wireless charging methods, followed by an overview of the evolution of the Wireless Power Transfer (WPT) system. WPT system's performance is flexible, safe, reliable, and cost-effective operation retains its essentiality in the research area for further development in contactless charging methods. In recent years, EV battery charging is dominated by the attractive concept of wireless power transfer with the capability of automatic power transmission without human assistance. Planar pad structures such as polarized and non-polarized pads and compensation networks like SS, SP, PS, and PP are used in the inductive power transfer system to achieve high power transmission. This literature review briefly compares different charging pads and various compensation techniques used in WPT. The prominent role and essentiality of different stages of power converters in the WPT system are highlighted in detail. The effect of EV charging on the grid, the bidirectional capability of EVs, and the strategy of charge management are mentioned. The paper particularly brings out the essentiality of integrating Renewable Energy Sources (RES) with EVs in concern with environmental health. At last, the short notes on the status of the WPT system in the Indian scenario, and the future challenges like safety, health management, Foreign Object Detection (FOD), and economic criteria in the progress of wireless EV charging systems are also addressed. [ABSTRACT FROM AUTHOR]

• Energy consumption based Battery Energy Storage and rooftop Solar PV sizing. • Typical high-end units consumes 22% more than the medium-cost units and 56% more than low-cost units. • Community BESS and rooftop Solar PV has to be sized at maximum or 125% of maximum to supply for VPP. • More n R is needed if sizing is based on max E C while lesser n R if size is based on 125% of the max E C. Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE's in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), E N. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N -PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model. [ABSTRACT FROM AUTHOR]

Along with the popularity of the environment, social and governance (ESG) concerns, the circular economy concept has gained tremendous attraction from the industries, policy makers and academicians. On a firm level, this is implemented through the so-called Circular Business Model (CBM), which is seen through various initiatives. This includes firms in the energy industry, who are transitioning from conventional to renewable energy sources. Nevertheless, despite of the importance and the omnipresence of the practice, there is still lack of empirical research within the CBM topic. In particular, empirical research combining the CBM and technological innovation topic within energy industry (as technology-intensive and high risk industry) in developing country such as Indonesia, is still lacking. To answer this gap, using literature review as research method, this paper has arrived into the following propositions as main result; (1) there is a positive relationship between firm's level of CBM and technology innovation adoption to firm's performance, where (2) the two antecedents that are positively impacting firm's level of CBM and technology innovation adoption are leader's innovativeness and perceived level of technology. For academicians, this research is expected to provide contribution to the energy transition literatures within CBM and technology innovation adoption topic. Propositions provided in this paper is expected to serve as valuable basis in conducting further empirical research. For practitioners, this research is expected to provide valuable insights to make improvements and/or innovations in terms of business model, technology adoption, and strategy. At last, this research is expected to support energy transition. [ABSTRACT FROM AUTHOR]

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefit prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. Nonetheless, the deployment of P2P electricity trading in the electricity networks is challenging in terms of modelling the transactions in both the physical and virtual layers of the network. The physical layer is a physical network that enables the transfer of electricity from the sellers to the buyers once financial settlements between both parties are completed over the virtual layer platform. The virtual layer facilitates a secured connection for participants to decide on their electricity trading parameters. This paper systematically reviews the current developments in the P2P electricity trading literature. Importantly, the systematic literature review brings to light six essential components in P2P electricity trading processes, namely: P2P electricity trading platforms, P2P market schemes, P2P electricity trading market clearance algorithms, policy supporting P2P electricity trading, P2P electricity trading networks and P2P ICT infrastructure, which are discussed in detail in the paper. Accordingly, for each component, state-of-the-art technologies, notable findings, and best practice insights are comprehensively reviewed, based on which directions for future research are presented. The P2P electricity market is a rapidly emerging research domain with significant opportunities and prospects globally in the industry and academia. [ABSTRACT FROM AUTHOR]

[Display omitted] • Maize production due to its many climate friendly properties have been center of research and development in terms of yield, water per hectare requirement, and so forth. • Out of agricultural stubble waste, corncob showed highest potential for beneficiation amongst traditional cash crops. Corncob beneficiation products and their applications span from bio fuels to mattress, with over 3500 products. • With processes such as gasification, pyrolysis, combustion and so forth, we can use this agricultural waste product for energy generation. • With applications such as bio-oil, ethanol blending, poultry feed, bio char, methanol, and so on maize was found to be the answer to many global problems. Increased global energy consumption and energy generation constraints using non-renewable energy sources have stressed countries and major industries to practice and develop cleaner, sustainable, readily available and predictable source of energy. On the other hand, agricultural waste or stubble burning presents a major problem for developing countries like India as it alone produces 516 million tons of agricultural waste in the year 2017-2018, out of which 116 million tons waste was burnt producing 176.1 Mt Carbon dioxide, 10 Mt of Carbon monoxide, and other harmful greenhouse gases. Though 13% of India's total energy is produced by the biomass, a lot of scope and potential in waste biomass is still left unexplored. Research and development in biomass (agricultural waste) conversion to energy is the potential solution to both problems. Out of all agricultural waste biomass, corncob was the center of research for this review paper. This review paper covers global and Indian scenario in the generation of corn cob, its beneficiation and uses in multiple industries, characteristics of corn cob, technologies available and used for the biomass conversion, limitations faced by the industry in corn cob's beneficiation, future potential developments, pledged goals and projected targets. [ABSTRACT FROM AUTHOR]

• Perform an inclusive assessment and comparative analysis of different APFs studied with the application of non-linear load. • Highlight the important shunt active filter (SAF) control techniques for real-microgrid system applications. • SHAF-based coordinated control literature study is conducted by emphasizing primary, secondary and tertiary methods. • The SHAF design and control with its significant merits and demerits makes this review different from other papers. • Comparative tables are structured by considering control architecture, power management, and installation complexity. Owing to avoid harmonic and power quality issues, the concept of eliminating their impacts on microgrid systems has gained a lot of interest. Incidentally, shunt active filters (SHAFs) is selected as the most reliable solutions against the concern problems and become the first choice of researchers. However, the performance of SHAF is strictly dependent upon the controller's action, design, and stability. Looking at the necessity, the detailed working of parallel/series filters for current and voltage source-based non-linear load application is discussed and compared. This paper reviews and collects information related to various SHAF control techniques for improving microgrid performances. The effectiveness of the controller is examined and justified by considering the non-linearity reduction, dc-link voltage balancing, current and voltage regulation, and improving the synchronization techniques. In this review, the most advanced control techniques are discussed and contrasted systematically to highlight their strengths and weaknesses. In addition to that, by considering different control architectures, the possible control outcomes and shortfalls are also summarized in different tabular forms. The survey can hypothetically serve as a standard and establishment of material for selecting the most significant methods for smoothening the SHAF operation for complex microgrid systems. [ABSTRACT FROM AUTHOR]

• I wish to submit an original research article entitled "Performance of a New Hybrid Approach for Detection of Islanding for Inverter-Based DGs" for consideration by Engineering Science and Technology, an, Elsevier. I certify that this work is unique and has never been published before, nor is it being considered for publication anywhere. • At present scenario renewable energy sources need to satisfy the load demand.In this Islanding identification is critical for inverter-based DGs to operate safely and reliably. Generally Islanding detection is possible by either active or passive method but in this paper, hybrid method follows the combination of both active and passive methods. For an inverter-based grid integrated DG system, this hybrid method allows for quick detection of islanding possible by minimizing the Non-Detection Zone (NDZ). The hybrid method combines the active Sandia Frequency Shift (SFS) with the passive reactive power variation technique. Adaptive tweaking of the gain parameter Ksfsutilising the Adaptive Particle Swarm Optimization (APSO) technique yields the improved active SFS method. The proposed hybrid method produces a significant reduction of NDZ and suitable for a more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy, the effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations. The simulation results presented for a three-phase grid connected 120 KVA DG, using MATLAB presents the capability of the proposed method. This paper proposes a new hybrid method for islanding detection and minimizes the Non-Detection Zone (NDZ), for an inverter-based grid-connected distributed generation (DG) system. The proposed hybrid method is an integration of the modified active Sandia Frequency Shift (SFS) method and the passive reactive power variation technique. The modified active SFS method is obtained by adaptive tuning of the gain parameter K sfs , using the Adaptive Particle Swarm Optimization (APSO) technique. The proposed hybrid method produces a significant reduction of NDZ and is suitable for more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy. The effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations. [ABSTRACT FROM AUTHOR]

This paper projects the risk associated with social benefit gained by Independent System Operator (ISO) due to unpredictability of load demand and intermittent nature of Renewable Energy Sources (RES). The hybrid method i.e. Wavelet Transform–Auto Regression Integrated Moving Average (WT-ARIMA) is used for forecasting the load in day-ahead market. The load demand for 24 hours is forecasted using the historical hourly data of 2 months. The mean absolute percentage error (MAPE) is calculated for both classical ARIMA and WT-ARIMA model. When compared to the ARIMA model, the WT-ARIMA model has a better fit to the time series data. This paper relates the load forecasting with energy market and shows the impact of forecasting error on market parameters. The system parameters like generation cost, social benefit and power loss are analyzed in day-ahead and real-time market. This work also elaborates the effect of intermittent nature of RES on the system parameters. The optimal location of RES based distributed generation is identified by higher Locational Marginal Price (LMP) based ranking method. The optimal energy management problem is formulated with the aim to minimize the total generation cost taking into account the variability in wind power and solar power. The uncertainty modeling of wind and solar is projected by the Rayleigh and Beta distribution respectively. The risk associated with social benefit of ISO due to uncertainty of wind and solar is calculated using indices, Value at Risk and Conditional Value at Risk. The proposed approach is implemented on IEEE 30 bus system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modeling of optimal operation in an unbalanced MG including CASE and EVs. • Reformulating the nonlinear and non-convex day-ahead optimal operation problem of unbalanced MG to a linear and convex problem via SDP and hence, guaranteeing the global optimum. • Considering shiftable load ToU as DRPs. • Applying the IGDT method with risk averse strategy in order to involve uncertainties in unbalanced MG. This paper proposes a mathematical optimization approach based on semidefinite programing (SDP) for optimal operation of an unbalanced microgrid (MG) equipped with renewable energy resources (RERs) as well as energy storage systems (ESSs) including compressed air energy storage (CAES) and electric vehicles (EVs). The SDP converts nonlinear and non-convex models proposed for day-ahead optimal operation into a convex approximation, which is easily implemented by commercial software. An optimization problem is formulated such that it minimizes the costs related to operation and environmental effects limited to several technical limitations. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a stochastic optimal operation model based on Information Gap Decision Theory (IGDT) with risk averse strategy to overcome this information gap and to help Microgrid Operator (MGO). As demand response programs (DRPs), the shiftable load and time of use (ToU) are considered for enhancing the flexibility of MG. Employing the presented model in the 21-bus test system shows that CAES and EVs, as novel ESSs, have good capability to reduce the costs pertaining to operation and emissions in the day-ahead optimal operation. [ABSTRACT FROM AUTHOR]

Renewable energy (solar, wind, geothermal and hydrogen) are means by which corporations and government hope to mitigate the effect of climate change. However, literature is rife with examples of renewable energy projects not being able to deliver as promised, with difficulties like energy transportation, energy output and subpar financial outcomes. Yet, the sector has experienced exponential growth in recent years, with new projects being continually sanctioned. This paper aims to utilize the method of text mining and topic modeling to probe why this growth occurs. Additionally, we will use these data analytic methods to rank, understand, and discuss contributing factors to this growth, by analysing a collection of 100 peer reviewed scientific articles which specifically discuss "renewable energy project success". The papers selected encompass policy, economics, technology and engineering. Our results indicated that "softer" non-financial factors were more prevalent growth factors, but that financial considerations were not far behind. This paper aims to determine and rank success and growth factors in renewable energy projects worldwide. [ABSTRACT FROM AUTHOR]

• The work done in this paper provides the solutions for power access to rural and remote areas in developing nations. • The proposed affordable solution offers many advantages over the traditional central inverters. It provides more output power as compared to traditional central inverters under shading and dusting conditions. The results are validated through experimental investigation with regard to Indian weather conditions. • The proposed solution presents an optimal sizing strategy for Economically Weaker Section (EWS) for the low power rural household application. In literature, this kind of solution is yet not reported which makes this work novel. • This work presents the customized design of solar microinverter based PV System for different power provisioning levels for rural EWS houses. The proposed solution has an added advantage of addition of load in future without change in existing infrastructure. Reliable power supply is the key requirement for developing nations. Most of these nations are trying to modernize the rural lives by electrification in remote areas. Access to electricity is a bare requirement in future ages. This paper discusses the challenges faced by the utility companies in ensuring sustained electricity access to the new consumers followed by the various solutions. The techno – economic aspects of the proposed solution is validated by the experimental study. Based on the results, an optimal low-cost design is proposed in order for everyone to fulfil the minimum power needs. The paper presents an optimal size strategy for Economically Weaker Section (EWS) for the low power rural household application. The solar microinverter based PV System has been designed based on the daily load needs for one of the proposed layout plans of the (Gramin) Pradhan Mantri Awas Yojna adopted by Government of India. The proposed designs are also applicable for other developing nations. The proposed micro-inverter has been tested under different operating conditions and experimental validation proves its superiority over other inverter types, particularly for a low power application, as is the case for a typical low-income rural household. Economic considerations such as payback period has also been evaluated in this study. In addition, the proposed microinverter technology has also been tested in terms of power quality parameters under various operating conditions. [ABSTRACT FROM AUTHOR]

• The emerging dependency on Electric vehicles and the role of Machine Learning in bolstering it. • Hybrid techniques tend to outwit different approaches. • Comprehensive review of various battery life estimation techniques about Machine Learning and Deep Learning. • Challenges faced and future scope of Deep Learning and Machine Learning in the State of Charge, knee-point, and Remaining Useful Life prediction of Lithium-ion batteries. Rapid adoption in the usage of li-ion battery-fueled vehicles provides a promising approach to assuage the impact of climate change. The implementation and increasing sales trends in the Electric vehicle industry have created positive results in various regions. The reliability and consistency of the Battery management system in new-age Electric vehicles to predict the remaining battery cycles, State of charge, and knee point prove to be a complex task citing the non-linear behavior of lithium-ion batteries throughout their complete lifecycle. However, to make sure that the batteries work as intended consistently, there arises a need to monitor the battery's health and performance regularly. By examining recent literature, this paper carries out a comparative study of various published research regarding estimating the State of charge for Lithium-ion batteries using multiple methods, classified into three categories: adaptive, data-driven, and hybrid approaches. We also conducted a comparative study on Knee-point and the number of charge-discharge cycles left about electric vehicles. This review intends to furnish a comparative analysis of Machine learning & Artificial intelligence-related estimation techniques and analyze their superiority over traditional data-driven methods. It was observed that hybrid techniques consisting of various Machine learning algorithms yielded good results. This paper aims to perceive the most accurate algorithms and methodologies used, which can be further used in battery management systems to improve battery prognostics drastically. The future works and challenges encountered in various researches have also been mentioned at the end of the review that will hopefully pave the way for increasing efforts towards the development of the advanced SOC, knee point, and RUL methods for future EV uses. [ABSTRACT FROM AUTHOR]

• Regional integration presents a significant opportunity for African power systems. • Paper describes planning tool for regional power sector integration in Africa. • Model evaluates values of integration under different climate change scenarios. Regional power pools present a significant and potentially defining opportunity for African power systems to develop domestic energy resources, improve system reliability, and contribute to overall economic development. Hydropower is expected to play a significant role in many regional power pools in Africa. Feasible power transmission highways from Grand Inga in Democratic Republic of the Congo and the Grand Renaissance Dam in Ethiopia to other regions on the continent create the possibility of a pan-African electricity grid. However, in the medium and long-term, global climate change is expected to cause major variations in Africa's hydrological resources and it is not known how these changes may impact the value of regional power sector integration. This paper presents a model developed to study the value of different levels of regional integration in sub-Saharan Africa and how this value may change in the face of climate change. This work builds on previous studies by incorporating the ability to trade between different regional pools, co-optimization of generation and transmission, the ability to share reserves, and detailed simulation of the major hydro basins in Africa. Numerical results of the analysis will be presented in a parallel paper. [ABSTRACT FROM AUTHOR]

• Risk assessment scheme evaluation and improvement via Systems Theoretic Process Analysis-Hybrid (STPA-H). • Case study on grid connected PV system with Li-ion battery storage for large scale/utility services. • Hazards and mitigation measures of STPA-H are compared with existing approaches. • Comparative studies of risk assessment schemes including FTA, ETA, FMEA, HAZID, HAZOP, STPA. • STPA-H risk assessment scheme as future framework reference model. Renewables recorded 26.2% of global electricity generation in 2018 and expected to rise to 45% by 2040 attributed to nations commitment to reduce greenhouse gases since the signing of Paris agreement 2015. Policies friendly towards the development of renewable energy sources are developed, but the risk and safety assessment are still based on the traditional analytic and probabilistic risk-based assessment practices such as Event Tree Analysis (ETA), Fault Tree Analysis (FTA), Failure Mode Effect Analysis (FMEA), Hazards Identification (HAZID), Hazards and Operability (HAZOP). These practices are viewed as own envelop of methodology whilst sharing a lot of common parameters and working principles. Analytic approach is becoming ineffective with increasingly complex energy system integrating renewables. There are on-going research proposing systemic based risk assessment approach but without incorporating advantages from analytic approach. The aim of this paper is to provide a comprehensive analysis of risk and safety assessment methodology for large scale energy storage currently practices in safety engineering today and comparing Causal Analysis based on System-Theoretic Accident Model and Process (STAMP) and Systems-Theoretic Process Analysis (STPA) with fault tree analysis, FMEA, HAZID, HAZOP. This paper demonstrated that systemic based risk assessment such Systems Theoretic Process Analysis (STPA) is suitable for complicated energy storage system but argues that element of probabilistic risk-based assessment needs to be incorporated. This is to ensure holistic risk assessment is performed to energy storage system and provide a new viewpoint for underlying safety model in integrated manner based on performance requirements, human factor, environmental, management, safety, and legislation. The results describe the improvement of risk assessment technique via Systems Theoretic Process Analysis-Hybrid (STPA-H) using a case study of grid connected Photovoltaic (PV) system with Li-ion battery storage. The hazards and mitigation measures identified from STPA-H are presented and compared with existing approach. STPA-H technique proposed is applicable for different types of energy storage for large scale and utility safety and risk assessment. This paper is expected to benefit Malaysian government with the progression of Large-Scale Solar 3 (LSS3) and serve as reference to future energy system risk assessment. [ABSTRACT FROM AUTHOR]

One key driver of sustainable goal development is the transition to sustainable electricity resources. The sustainability impact assessment of power production evaluates all potential impacts on society, the environment, and the economy, as well as finds a reasonable solution to shift towards renewable. Given the multitude of quantitative and qualitative methodologies for sustainability analysis, alongside the absence of a unified procedure, selecting an appropriate method poses a significant challenge in conducting this task. Therefore, this paper conducts an extensive review of the methodologies employed for assessing the sustainability of power generation within the future energy mix, as reflected in scientific publications over the past decade (2013–2024). The main objective of this paper is to compare the methodologies and assess their efficiency as suitable tools for analyzing the sustainability of technologies in different geographical regions. The research methodology, following the screening process, selects 102 papers within the study's scope to undergo a critical examination based on sustainability evaluation approaches. It also provides an overview of novel dynamic methods and the application of artificial intelligence in sustainability assessment. The primary findings indicate a deficiency in a standardized approach for sustainability evaluation within electrical technology. In addressing uncertainties in impact assessment due to various parameters, dynamic methods with multiple temporal accuracies are recommended over a static life cycle. The paper includes a case study comparing methods—multi-criteria decision-making, and ranking, scoring—in the Indonesian context. Considering 15 environmental, social, and economic indicators to evaluate the sustainability in Lombok, results indicate that hydropower, gas, and solar technologies exhibit the highest sustainability scores, respectively. [ABSTRACT FROM AUTHOR]

• Hybrid microgrid is an emerging and exciting research field in power engineering. • Presents systematic review on various control strategies for hybrid microgrid. • Comparison between control strategies satisfying various control objectives. • Discussion on research challenges in use of effective and robust control scheme. • Discussion on emerging trends in hybrid microgrid's operation and control. Presently, there is huge development in conventional power systems due to the evolution of modern smart grids, wherein interconnected microgrids with a high level of energy storage and renewable energy penetration are gaining popularity in the modern distribution network. Future electrical distribution systems, or even transmission structures that combine AC and DC sources and loads are thought to be most promising when it comes to hybrid AC-DC systems. Using a combined operation of both AC and DC microgrids through an interfacing converter, hybrid AC-DC microgrids are advanced and benefitted with the use of both AC and DC topologies. Power management techniques for these microgrids are among the most important operational aspects. This paper provides a systematic review on numerous schemes to control hybrid AC-DC microgrids. Basically, microgrid control strategies are categorized as local control and coordinated control. Coordinated control is further divided in three control strategies as, distributed, centralized, and decentralized control. All these control strategies are employed to satisfy many control objectives, like bus voltage and frequency regulation, power sharing, and management, power quality, stability and reliability improvement, energy management, and economic dispatch. In this paper, various aspects associated with these control objectives and limitations in their control strategies are discussed. Also, future prospects on control of hybrid AC-DC microgrids and an effective and robust control strategy for power management possibly to be employed, are presented in the concluding section of this paper. [ABSTRACT FROM AUTHOR]

A successful and just energy transition is subject to policies that adhere to a multi-dimensional approach to renewable energy development. To achieve success in renewable energy development, well-thought-out and efficient policies must be implemented. This paper aims to address a major shortcoming in renewable energy development in terms of understanding and recognising the policy space that promotes or hampers renewable energy growth. The paper examines the relevant literature to identify major themes and factors in renewable energy development. A thematic analysis was conducted using a hybrid inductive and deductive approach and resulted in the identification of 5 major themes namely Institutional, Environmental, Financial, Socio-cultural, Technical and 144 factors. The identified themes and factors will assist to make a policy action decisive, just, and sustainable. Recommendations including comprehensive incentivisation, regulatory support for renewable energy technologies in developing nations, environment impact assessment programs, promotion of financial transparency and opting for a just transition to benefit local and vulnerable communities. [ABSTRACT FROM AUTHOR]

• A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. • Optimal network reconfiguration allow a better penetration of renewable energy in the system. • Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. • DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement. This study is an extension of the earlier published conference paper. [ABSTRACT FROM AUTHOR]

The paper proposes the optimal placement of distributed generators (DGs) in radial distribution system along with reconfiguration. The DGs which are considered in this paper are designed with photo voltaic (PV) cells and wind power generation. The paper studies on the PV and wind generation penetration in radial distribution system as per the irradiation and wind velocity throughout the day. The irradiation levels are considered throughout the day. Unlike in former hybrid optimization techniques, the placement of DGs and reconfiguration is shared between two optimization techniques. The paper also details the reliability improvement of radial distribution system with reconfiguration and DGs placement. Some part of reconfiguration and DG locations are selected with one optimization technique and rest of reconfiguration and DG size are selected with another optimization technique. The combination of two optimization techniques properties leads towards the novel hybrid optimization. The methodology is formulated with the black widow algorithm and crow search algorithm. The performance of the method is tested on two test systems which are 16 bus RDS and 33 bus RDS. [ABSTRACT FROM AUTHOR]

In this paper, a new system was designed and built to generate electrical power using solar energy. A parabolic solar dish was designed with a diameter of 3 m and aperture area reach of 7.1 m2. It was equipped with a sun tracking system which allowed movement on two axes. Its primary function was to generate water vapor as an alternative to the steam boiler used in steam power stations. Many modifications were made in order to allow the new system to work according to the Rankine cycle system for power generation. Generated steam has to pass through a steam turbine to rotate it, this connected to an electric power generator. The steam then exits through the condenser and is recycled using the pump. The implementation of the new station included experimental work to ensure its suitability for different climatic conditions which impact the systems' components, including changes in the amount of solar radiation, wind velocity, air temperature and other climatic conditions. The results showed how successful the new system was in generating electricity within a range of 800–1000 W. Steam temperatures reached to 400.1 °C with a total cycle efficiency ranging between 32 and 42%. A statistical analysis of the system was also carried out using a SPSS software, application of Artificial Neural Network (ANN) showed for the effective role of useful energy in solar heater on power output. Thus, the outcomes acquired during this study, provided an accurate view of the system. Finally, the paper strategy can be applied to different processes of solar system. Also, the results showed a high accuracy as the maximum value of coefficient of determination (R2) of 91.3%. [ABSTRACT FROM AUTHOR]

The study explored the impact of strategic photovoltaic (PV) deployment on regional electricity self-sufficiency in Iraq, offering key insights into the advantages and challenges of transitioning towards an energy-independent system by 2050. Findings indicate a noteworthy contribution of rising PV supply towards improved self-sufficiency, with a PV supply of 83.1 TWh catering to a total demand of 66.2 TWh in 2022, achieving a 76% self-sufficiency level. This suggests PV generation covers 76% of the districts electricity needs, thus bolstering energy security by reducing external energy source reliance. Furthermore, the study emphasizes the significance of expanded rooftop PV capacity to augment self-sufficiency and decrease dependency on conventional energy. Predictions suggest a potential rise in rooftop utilization for PV installations from 66% in 2022 to 88% by 2050, accentuating the need for policy initiatives to endorse and incentivize rooftop PV system adoption. The paper offers several recommendations, including the creation of supportive regulatory frameworks for PV integration, the introduction of financial incentives, capacity building initiatives, public awareness campaigns, grid modernization efforts, and regional energy planning which incorporates PV deployment considerations and sets self-sufficiency targets.. [ABSTRACT FROM AUTHOR]

• We employed MRA to specify the authentic empirical effect based on 328 estimation results from 33 primary studies. • Financial development leads to higher renewable energy consumption / use / capacity / production according to the MRA. • There is no publication bias in the existing literature. • The authentic empirical effect may change depending on financial development or renewable energy indicators used, time period covered, the unit analyzed, and the estimation technique employed. • There is an omitted variable bias in the existing literature. The current paper tries to determine and quantify the authentic empirical effect of financial development on renewable energy beyond publication selection bias, if any, based on 328 estimation results from 33 primary studies that were published or presented over the period of 2009-2022. Bivariate and multivariate FAT-PET analyses were used as tools of meta-regression analysis. A positive significant authentic empirical effect of financial development on renewable energy is found as a first contribution. Hence the results support Hypothesis 1; by offering more reliable, efficient, and less costly funds to investors; whether private or public, financial development boosts renewable energy. An absence of publication bias is resulted regarding the overall findings of the primary studies or design characteristics (moderating variables) as a second contribution. Based on the results for moderating variables, the authentic empirical effect may change in direction and magnitude due to the financial development or renewable energy indicators used, the time period covered, the unit analyzed, and/or the estimation technique employed as a third contribution. Omitted variable bias is found in the existing literature analyzing this subject. [ABSTRACT FROM AUTHOR]

Electrical Distribution Systems (EDS) are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time-series data of active power monitored at nodes of the electrical network. For these correlated networks, compute the network parameters such as clustering coefficient, assortative coefficient, average degree, and power law exponent for the anticipation; and percolation threshold for the determination of the network's withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavorable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using the simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system. [ABSTRACT FROM AUTHOR]

In the last couple of decades, the integration of photovoltaic (PV) systems with the utility grid has gained significant attention due to its potential to harness renewable energy and contribute to sustainable power generation. However, the integration of the PV system into the grid leads to issues related to power quality and system stability under varying solar irradiation, dc offset, and distorted grid conditions. Therefore, this paper focus on the robust and efficient control of a single-phase, two-stage PV grid-tied system to improve power quality and stable system operation under abnormal grid conditions. These objectives can be achieved using the synchronization method, dc link voltage control, and grid current control. The hybrid PLL is proposed in this paper for synchronization and to mitigate the effect of dc offset and lower-order harmonics in the estimated phase and frequency. Further, the sliding mode voltage controller and integral sliding mode current controller are proposed for dc link voltage regulation and grid current tracking. The stability of voltage and current control loops is analyzed using the Lyapunov stability theory. Furthermore, the proposed schemes are validated using MATLAB simulation and experimental analysis. The proposed approach outperforms conventional methods and exhibits better disturbance rejection capability under both dynamic and steady-state conditions, making it a robust and efficient control strategy for grid-tied systems. [ABSTRACT FROM AUTHOR]

• The MG along with its benefits also causes various problems and challenges due to the DG interconnection. • Out of the reviewed papers the Adaptive protection is emerging as the new and reliable solution for the MG protection. • Therefore, designing a new MG adaptive protection scheme is key to proper operation and control of DS with DGs. The main technical challenges in the emerging concept of the micro-grid (MG) and integration of sustainable energy resources in the Distribution System (DS) is the low fault current phenomena in the islanded mode of the MG operation and change in the network topologies with frequent DG connection and disconnection. The existing protection schemes require modification as they may fail to operate because of their inability to adapt to the changes due to the integration of Distributed Generator (DG). Various new protection schemes have been introduced by many researchers for this new concept of Microgrid (MG) that is not only adaptive to the changes in the levels of fault current and network topologies in both grid-connected and islanded modes but also intuitive enough to determine and clear the fault with minimum disturbances in the supply. This paper gives a brief review of the challenges associated with Microgrid protection and presents a critical review of the existing Microgrid protection schemes. The limitations of the existing adaptive protection schemes that need to be overcome to make adaptive protection scheme a viable solution are also presented. [ABSTRACT FROM AUTHOR]

• This is fast-developing area, so review should be done often. • Microgrid control is reviewed through different points of view. • Hierarchical organization review compares different number of levels in references. • Control tasks done on each level based on large number of references were defined. • Future research possibilities given on various aspects of research. Microgrids are the building blocks for the future smart grid, the means of integrating more renewable sources into the power grid. The main challenges are keeping the microgrid safe, reliable, economical and under full control. A lot of references regarding control and energy management of microgrids are published, and there is a constant need to stop, and review what has been suggested so far in this area. This paper offers an extensive literature review of microgrid control through several points of view. Control system architecture can be organized as centralized or decentralized structure, the main features of both of them are different and there are positive and negative sides of both architectures. In decentralized architecture, there are different levels of decentralization, mostly regarding the level of communication between system elements. Hierarchical structure is widely used im microgrids, where control functions are mapped to hierarchical levels. Islanding detection as a part of primary control level, microgrid clusters, a relatively new concept in organizing microgrid control, differences between the control of grid connected microgrid and islanded microgrid, as well as standalone microgrids are also reviewed in this paper stating research trends and gaps. This article offers microgrid control related topics in need of more research and possible future research interest in the area. [ABSTRACT FROM AUTHOR]

• The rooftop PV electricity potential has been evaluated for the city of Ibadan. • Optimum fixed tilt angle for rooftop PV deployment in the city of Ibadan is 11°. • The total roof area of the city was estimated as 49,540,000 m2 (i.e. 49.54 km2). • The available roof area for rooftop PV deployment was evaluated as 7,542,500 m2. • The annual solar energy harvestable on rooftop was estimated to be 6.6714 TW h/year. In this paper, the possibility of meeting the growing electrical energy demands of the city of Ibadan (one of the Nigeria cosmopolitan city) using the rooftop solar PV systems is investigated with the aim of determining the available rooftop for possible PV deployment. First, the optimal tilt angle as well as solar irradiation on tilted surfaces are determined for the city, Thereafter, the available roof area for possible solar PV- rooftops placement are analysed using reduction factor technique with the aid of ArcGIS, Google earth Imagery and population sampling techniques. Some of the key results reveal that the total roof area for the city of Ibadan is about 49.54 sq km while the available roof area for rooftop PV deployment is about 7.54 sq km with a maximum installation capacity of 1734.8 MWp, at an optimum fixed tilt of 11°. This could generate PV electricity of about 6.67 TW h per annum. This paper is important as it could serve as first-hand scientific information for government agencies, NGOs, investors, researchers and policy makers at maximizing the potential of solar PV rooftop potentials in Nigeria. [ABSTRACT FROM AUTHOR]

• The city of Burdur has been selected as the target city. • Solar power target of Turkey has been analyzed at urban level for Burdur. • The results show that the solar share of the total installed capacity will reach 49%. The energy profile of a city, with all of the relevant details about production and consumption phases, is a significant part of its sustainability rating. Therefore, municipalities have initiated the development of Sustainable Energy Action Plans as a key document to introduce the respective course of actions in order to define the technology adaptation process, identify the principal sources of CO 2 emissions and their respective reduction potentials for the designated target years. A complete energy-strategy plan is required to handle the financial, technical, and environmental dimensions, not just for the country, but also for cities. As a powerful energy model generator among the energy planning and optimization tools, TIMES utilizes the energy, economy, and environmental parameters simultaneously. This paper aims to oversee and indicate the possible implications of "2023 energy targets" of Turkey in a city-level energy modeling. In this perspective, the city of Burdur is selected as the target to implement the designated solar energy policy targets. With this paper, nationwide official targets are evaluated for a city level by calculating long-term effects and costs to the system. A total of 5000 MW of solar energy utilization target of Turkey has been reduced to the urban level for Burdur, depending on its share of solar energy, and then applied as an alternative scenario on the energy model developed for this analysis. The results show that the solar share of the total installed capacity of Burdur city will reach 49% while tripling the solar-based electricity generation in its energy mix. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using LSTM and GSA-LSTM methods to forecast PV power in different horizons. • Proposes more precise hyperparameters for LSTM to improve performance prediction. • Couples LSTM with GSA to optimize hyperparameters, enhances prediction accuracy. • GSA-LSTM is superior by improving MSE at peak points compared to LSTM. This research paper investigates the use of Long Short-Term Memory (LSTM) and Grid Search Algorithm (GSA)-LSTM methods to forecast PV power output in different horizons. The study proposes precise hyperparameters for the LSTM network for improved performance prediction. Initially, LSTM was evaluated solely, and three different scenarios were investigated to obtain the best LSTM network hyperparameter settings for various horizons using a sensitivity study. Afterwards, LSTM was coupled with GSA to optimize LSTM network hyperparameters, which enhanced prediction accuracy and minimized error. The proposed methodology identified the most effective values for each hyperparameter based on different forecasting horizons. Additionally, Spearman Correlation Coefficient (SCC) and Pearson Correlation Coefficient (PCC) were deployed to determine the relationship between input data and target as well as identifying optimum lag value which has substantial effect on LSTM network. Optimal lag values of 150 and 280 were determined following the strong correlation between the data and target. LSTM and GSA-LSTM methods were compared with each other at various horizons and it was demonstrated that GSA-LSTM is superior by improving MSE at peak points by 10%, 30%, and 34% for 12 hours, 3 days, and 2 weeks horizons respectively. Comparing the proposed methodology with other studies in the literature revealed that the current study is capable of predicting PV power output for the 1 hour ahead horizon with significantly higher accuracy and with maximum improvement of about 28%. [ABSTRACT FROM AUTHOR]

• An overview of solar photovoltaic (PV) power generation in respect of all the other renewable energy sources (RES) have been presented on cumulative basis. • The different solar PV configurations, international/ national standards and grid codes for grid connected solar PV systems have been highlighted. • The state-of-the-art features of multi-functional grid-connected solar PV inverters for increased penetration of solar PV power are examined. • The various control techniques of multi-functional grid-connected solar PV inverters are reviewed comprehensively. The installed capacity of solar photovoltaic (PV) based generating power plants has increased significantly in the last couple of decades compared to the various renewable energy sources (VRES). As a result, the increased penetration of solar PV-based generating units leads to several issues related to power quality, system stability, and reliability. In view of these concerns, various international standards and grid codes have been amended to address such critical issues. Therefore, most solar PV-based generating units have undergone several changes in operational and control structure. Subsequently, varieties of solar PV configurations and control structures have been proposed by several researchers to augment the desired operational features. This paper presents a comprehensive review of various solar PV configurations, control strategies, and ancillary services with multifunctional features within this context. The utilization and benefits of various configurations and their practical aspects have been discussed thoroughly for all the configurations and architectures. Apart from this, the control aspects of grid-connected solar PV systems are categorized into two important segments, namely, a) DC-side control and b) AC-side control. This article covers the important features, utilization, and significant challenges of this controller and summarizes the advanced control techniques available in the literature. Further, the various control techniques have been compared concerning different operational parameters, such as controller gains, duties, and selector quantities in tabular form with ready references. Furthermore, the concerns related to power quality, stability, and reliability of various techniques have been included for understanding the applicability of grid-connected PV systems. In the end, a detailed comparison of some available industrial inverters concerning their power ratings, voltage ratings, configurations, and advanced functions incorporated are also presented, which will help the researchers, academicians, and practicing engineers. [ABSTRACT FROM AUTHOR]

Nanofluids are reported in the literature as advanced heat transfer fluids that improve significantly the thermo-physical properties of base fluids and, thus, enhance the performance of the solar systems. However, despite their great potential to improve the thermal and rheological properties when combined with a base fluid, it has been proven in this paper that they have a major limit that makes their integration in commercial scale CSP plants questionable. Indeed, based on various numerical simulations of three different nanofluids (MgO-VP1, Al 2 O 3 -VP1, Fe 2 O 3 -VP1), the results have revealed that when the base fluid (Therminol VP1) is doped with a certain volume fraction of nanoparticles, its circulation through the CSP's canalization becomes more energy-intensive. Accordingly, the pumps' electrical energy consumption increased proportionally with the nanoparticles' concentration ratio. Consequently, when the nanofluids are used, the net energy production of the studied CSP plant (Noor I, Morocco) decreases compared to the reference case when the base fluid is used. From an environmental point of view, the use of nanofluids in CSP plants aggravates the negative environmental impact by increasing the GHG emissions related to the pumps' operating process during the life cycle of the CSP plant. Thus, this article has a dual interest; on the one hand, it confirms the benefits of nanofluids on a thermo-physical level and, on the other hand, it highlights their major limitation from an energetic and environmental point of view, when used in CSP plants. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system. • A modified hybrid PSO-GWO algorithm is proposed for optimal power allocation among dispatchable DGs. • Fuzzy based approach is adopted for solving multi-objective optimization problem. • PHEV charging/discharging load and voltage dependent load models are used. • Hong's 2 m + 1 PEM with Nataf Transformation is employed to model the correlated uncertainties. [ABSTRACT FROM AUTHOR]

• The basic reasoning model used by permit officers is condition-based aggregation. • It is hard to add disadvantages when performing value difference-based aggregation. • It is hard for permit officers to assess the value of renewable energy. • Multi-criteria reasoning models could be practical tools for application assessment. Assessment of an application for wind power establishment is a multi-criteria problem including the core problem: whether to grant permission or not. In Sweden, County Administrative Boards decide the outcomes of these kinds of applications. Five permit officers were interviewed to investigate the difficulties and the type of value aggregation in this work, and to test reasoning models as possible decision support tools. The commonly used type of aggregation was condition-based aggregation. Aggregation based on value differences, which means weighing together aspects for and against the wind power establishment, was considered difficult to apply by the respondents. Most of them agreed that some of the aspects that speak against granting permission could be aggregated but that aggregation of all aspects would be hard due to differences between aspects. In addition, the value of the main aspect that speaks for permission, climate friendly energy supply, is very difficult to estimate. Thus, aggregation based on value differences is a difficult question and how it could be performed is discussed in the paper. If policymakers wish to make it possible to take both positive and negative aspects into consideration and to discuss the trade-offs transparently, the investigated method can be a way forward. [ABSTRACT FROM AUTHOR]

• Pilot study of solar/hybrid/storage system for vertical farming under tropical climate condition with a case study at Malaysia. • Energy utilization and management via optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. • Comprehensive analysis and assessments including technological, economic and environmental aspects of the proposed work. • The proposed work is evaluated under different design configuration (grid connected, stand alone, hybrid, storage etc) considering various optimization and design requirements and real-life constraints. • Comparative studies with hypothetical model and verification with additional energy modeling tool. Human population is projected to reach 9.7 billion in 2050 and strike 11.2 billion by 2100. Residence shift from countryside to cities will occur and the human population resides in the cities is envisaged to rise from 55% to 68%. Vertical farming was proposed to address food insecurity caused by overcrowded population and farmland shortages. The compound annual growth rate (CAGR) of vertical farming in the Asia Pacific is forecasted to grow at 22.1% with a market size reaching USD 2,101.0 million by 2024. Malaysian government encourages the implementation of vertical farming as the production of major agricultural commodities has contracted by 3.4% from 2015 to 2017. However, high energy consumption is one of the shortcomings of a vertical farm as the estimated annual energy consumption for each square meter of growing area is 3500 kW h. Thus, this paper proposes an optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. This research examines the load demand in the vertical farming systems and develops solar/hybrid/storage for vertical farming system with energy yield, performance ratio, economics and environmental assessments. Preliminary resources assessment by analyzing the solar radiation of the sites was carried out at stage 1. At stage 2, load consumption was calculated, and specifications of the system were justified. Stage 3 included the development of solar PV system for the vertical farms, the economic evaluation in the context of net present cost (NPC), levelized cost of energy (LCOE) and investigation of the environmental impact. Grid-connected and standalone solar PV systems were developed for comparative studies. The estimated daily energy consumption for vertical farms in Selangor and Kuching was 430.116 kW h and 1002.024 kW h. Energy generated by grid-connected solar PV systems supported 11.6% and 8.35% of the load consumption in Selangor and Sarawak. The performance ratios of grid-connected solar PV systems in both selected sites were 82.22% and 82.56%. The finding proved that proposed work has significantly reduced the dependency of the utility grid. Additionally, the LCOE with solar PV integration vertical farming system appeared to be lower than the LCOE of the grid and lower CO 2 emission. This paper serves as a reference guide to the vertical farming stakeholders, national policy-decision makers and non-profit organizations who planned for similar initiatives especially those within the tropical climate regions. [ABSTRACT FROM AUTHOR]

This paper is an attempt to examine the long-run and short-run relationship between inflation, exchange rate, and renewable energy use in Mexico during the period 1990–2019 by employing ARDL bounds test approach. The results of the study show that in the long-run there is a bidirectional causality between inflation and exchange rate. Renewable energy use in Mexico also impacts both inflation and exchange rate but exchange rate and inflation do not impact renewable energy in the long-run. The short-run ARDL model results provides that an increase in renewable energy use in Mexico has the impact of encouraging exchange rate appreciation. The results of this paper shows that renewable energy does not only reduce carbon-dioxide emissions which causes global warming, rather it encourages exchange rate appreciation in Mexico. Therefore, renewable energy use should be encouraged since it will cause currency appreciation of nations as well as leading to zero carbon in the future, a goal by the United Nations. High inflation in Mexico is also found to cause currency depreciation and vice versa. [ABSTRACT FROM AUTHOR]

• This paper introduces an explicit I–V relation formulated by three coefficients (known as an exponent model) to calculate accurate VMP using analytical expression with a single step. • A simple procedure for the determination of the exponent model coefficients in the standard test conditions (STC) and in the different environmental test conditions is described. Successively, the adjusted coefficients are used to calculate VMP. • The exponent model compares its accuracy with the universally adopted practical model (single-diode-model) in terms of the relative error in VMP and PMP tested in different environmental conditions. • The performance of the proposed model in different test conditions is simulated in detail using a PV system, and is also compared with, the P&O, the series-diode model based MPP (SDMPP) [24] and the Lambert W function based MPP (LWMPP). The efficiency comparison is conducted in the steady state and the dynamic conditions with proving the superiority of the proposed model. • The model has an additional feature of the estimation of the irradiance with an additional cost of PV temperature measurement. • The model successfully tracks the accurate MPP if 10% tolerance is considered in the output power of the PV modules manufactured by the same manufacturer. Moreover, the model performance is found marginally deviated in case of aging of a PV module. • In the laboratory, a prototype PV system with a boost converter, a resistive load and a controller (to regulate the boost converter voltage at the MPP) is designed and tested when the PV modules are placed in an open atmosphere under the clear sky. The experimental results are also compared with the P&O algorithm to establish the superiority of the proposed model. • The only limitation of the proposed model is a frequent adjustment of the coefficients with an additional cost of a PV temperature measurement in order to estimate accurate MPP. In this paper, a new model based MPP tracking algorithm in different environmental conditions is presented. A PV power converter driven by a maximum power point (MPP) controller adjusts the PV source voltage at the MPP (V MP) to harvest maximum power from the PV source. The proposed EXMPP algorithm calculates V MP using an analytical expression with a single step; derived from an explicit I–V relation. This algorithm overcomes voltage fluctuations in steady state condition which occurs in iterative based algorithms such as perturb-and-observe, incremental conductance and their improved versions. The algorithm also enhances the dynamic performance when it was compared to iterative and explicit model based algorithms. The algorithm is tested and validated in different climatic conditions using simulation study and experimental results. The proposed algorithm is also verified for a larger manufacturer tolerance and aging effect in the PV module. The proposed explicit I–V relation based EXMPP algorithm with module integrated converters provides an attractive performance in rapid environmental conditions changes. [ABSTRACT FROM AUTHOR]