Shading Conditions Research Articles

Effects of Power Optimizer Application in a Building-Integrated Photovoltaic System According to Shade Conditions

A building-integrated photovoltaic (BIPV) system produces power using photovoltaic (PV) modules as building exterior materials, whose architectural performance serves the same functions as those of existing building materials. Most relevant studies targeted general PV modules used in the building-mounted and -attached types. This study aims to integrate the building elevation-type BIPV system and exterior materials to secure both exterior material performance and PV electrical performance by embedding a power optimization device in an integrated system of BIPV modules and exterior materials. Thus, the advantages of economy, safety, aesthetics, and ease of maintenance can be achieved. In this study, experiments were conducted on elevation-type BIPV modules with and without a power optimizer, that is, a DC/DC converter, under various shade conditions, and the power loss rate of the BIPV system was analyzed. The power optimizer-equipped BIPV system was experimentally observed to have a PV power-loss rate approximately 2–3 times lower than that of the BIPV system without a power optimizer when the shade ratio of one module was approximately 10–75%. This exterior material-integrated BIPV-specific power optimization device reduces dependence on fossil fuels for power production and improves energy sustainability, contributing to the spread of zero-energy buildings and carbon neutrality.

Experimental results, integrated model validation, and economic aspects of agrivoltaic systems at northern latitudes

Agrivoltaic systems, which allow the coexistence of crop and electricity production on the same land, are an integrated water–energy–food nexus solution that allows the simultaneous attainment of conflicting Sustainable Development Goals. This study aims to analyse experimental results on the responses of ley grass yield and quality to shadings in the first agrivoltaic system in Sweden. It also aims to validate an integrated modelling platform for assessing agrivoltaic systems' performances before installation. An economic analysis is carried out to compare the profitability of agrivoltaic versus conventional ground-mounted photovoltaic systems and, using a Monte Carlo Analysis, to identify the parameters that most affect the profitability. Despite the agrivoltaic systems’ supporting structures and photovoltaic modules producing an average ∼25% reduction in photosynthetically active radiation at ground level, no statistically significant difference was observed between the yield of the samples under the agrivoltaic system compared to the yield of the samples in the reference area. The agrivoltaic system attained land equivalent ratios of 1.27 and 1.39 in 2021 and 2022, respectively. The validation results of the integrated modelling platform show that the sub-model concerning the crop yield response to shading conditions tends to underestimate ∼7% the actual average crop yield under the agrivoltaic system. The results of the economic analysis show that, from a net present value perspective, agrivoltaic systems have a profitability that is ∼30 times higher than a conventional crop rotation in Sweden.

Hybrid Metaheuristic Algorithms Mppt under Partial Shading Condition

Hybrid Metaheuristic Algorithms Mppt under Partial Shading Condition

Characteristics and variability of melon genotypes under shade conditions in greenhouse

The utilization of controlled greenhouses can be an alternative for melon (Cucumis melo L.) cultivation. Growing melon in greenhouses lowers the risk of pests and infections by diverse pathogens. Low solar irradiation during the rainy season and pollution in the greenhouse roof, may cause shade stress conditions for the plants inside. This study aimed to elucidate the plant and fruit characteristics of nine genotypes of melon grown under shade conditions in a greenhouse and the variability among them. The experiment was conducted in a greenhouse of Cikabayan Experimental Station, IPB University, Bogor, from November 2016 to January 2017. The genetic materials evaluated were nine melon genotypes from the Center for Tropical Fruit Studies at IPB University. A randomized complete block design with three replicates was followed. Shade intensity in the greenhouse was approximately 25%. The results showed that the genotype effect was significant for internode length, petiole length, plant height fruit, flesh thickness, fruit rind thickness, fruit weight, and total soluble solids. Genotype means for fruit weight were small in this experiment (< 300 g), whereas the total soluble solids were moderate to high (8.7-14.3 °Brix). Fruit diameter had a positive and significant correlation with leaf length, leaf width, and fruit length, whereas fruit weight had a positive and significant correlation with fruit diameter and fruit length.
 Keywords: correlation, honeydew, melon breeding, melon group, traits

The Effects of Shading on the Photosynthetic Performance of Endangered Plant Horsfieldia hainanensis Seedlings

Shading is one of the management practices for preventing the damage or injury of plant seedlings during extreme weather and climate events, such as very high temperatures and heat stress. In this study, we investigated the effects of different shading conditions on the photosynthetic characteristics of the endangered plant Horsfieldia hainanensis in Guangxi, China. The H. hainanensis seedlings in this study underwent five shading treatments, including 20% (L1), 40% (L2), 60% (L3), 80% (L4), and 100% (control) of full sunlight. The net growth of their diameter and height, and photosynthetic gas exchange parameters including their photosynthesis rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), and water use efficiency (WUE) were measured for the examined seedlings. The OJIP curve and 820 nm light absorption curve, and the osmotic substances and products of membrane lipid peroxidation were employed to assess photosynthetic capacity, identify the factors constraining photosynthetic carbon assimilation, and investigate the mechanisms influencing photosystem II (PSII) and photosystem I (PSI) in the seedlings under shade stress. The results showed that the seedlings in the L2 treatments had the highest net growth and Pn, the best photosynthetic performance, and the best coordination between PSII and PSI. The net photosynthesis (Pn) levels exhibited a declining trend in the following order: L2 > L3 > L4 > L1. In the L1 treatment, non-stomatal factors emerged as the primary determinant affecting the Pn of the seedlings. The performance index (potential) of PSII, representing the conservation of absorbed photon energy to intersystem electron acceptor reduction (PIABS and ΔI/I0) of the seedlings, decreased in the order of L2 > L3 > L4 > L1. The photosystem performance and the coordination between PSII and PSI (Φ(PSI/PSII)) of the seedlings decreased in the order of L2 > L1 > L3 > L4. Under the low and moderate shading stresses (L1–L3), more serious damages occurred in PSII than in PSI, including on the donor side of PSII and in the electron transfer from QB to the acceptor side of PSI. In contrast, more considerable injury occurred in PSI than in PSII under the stress of the heavy shading treatment (L4). Considering the alterations in their leaf osmotic regulatory substances and membrane lipid peroxidation products, our findings indicate that the L2 treatment was the most conducive to the growth of the H. hainanensis seedlings. In contrast, the L1 treatment subjected H. hainanensis seedlings to the most significant stress, resulting in substantial damage to their growth and photosynthetic mechanisms. Our research provides a scientific insight into and a practical guide for the selection of an appropriate light intensity for the conservation and cultivation of endangered plant species, such as H. hainanensis.

NnSnRK1-NnATG1-mediated autophagic cell death governs flower bud abortion in shaded lotus.

Many plants can terminate their flowering process in response to unfavourable environments, but the mechanisms underlying this response are poorly understood. In this study, we observed that the lotus flower buds were susceptible to abortion under shaded conditions. The primary cause of abortion was excessive autophagic cell death (ACD) in flower buds. Blockade of autophagic flux in lotus flower buds consistently resulted in low levels of ACD and improved flowering ability under shaded conditions. Further evidence highlights the importance of the NnSnRK1-NnATG1 signalling axis in inducing ACD in lotus flower buds and culminating in their timely abortion. Under shaded conditions, elevated levels of NnSnRK1 activated NnATG1, which subsequently led to the formation of numerous autophagosome structures in lotus flower bud cells. Excessive autophagy levels led to the bulk degradation of cellular material, which triggered ACD and the abortion of flower buds. NnSnRK1 does not act directly on NnATG1. Other components, including TOR (target of rapamycin), PI3K (phosphatidylinositol 3-kinase) and three previously unidentified genes, appeared to be pivotal for the interaction between NnSnRK1 and NnATG1. This study reveals the role of autophagy in regulating the abortion of lotus flower buds, which could improve reproductive success and act as an energy-efficient measure in plants.

Bark and crown morphology drive differences in rainwater distribution in an upland oak forest

Throughout the central and eastern US, the composition and structure of upland oak (Quercus) forests are changing. These changes are in part due to fire exclusion, whereby shade-tolerant, often fire-sensitive tree species are gaining a competitive edge over relatively shade-intolerant, fire-adapted oaks. This shift has been hypothesized to occur via a positive feedback termed mesophication, whereby encroaching tree species (‘mesophytes’) create dense, shaded understory conditions that limit oak regeneration and create cooler and wetter microclimates that decrease forest flammability. The change in canopy composition and structure may also impact hydrologic inputs by redistributing rainfall via throughfall and stemflow. To assess the extent of this redistribution by mesophytes compared to oaks and the net impact to forest floor moisture availability, we monitored throughfall, stemflow, and surface soil moisture at depths of 7.6 cm, 12.0 cm, and 20.0 cm near the bole, mid-crown, and crown edge monthly over a 14-month period. Measurements were made across seasons in both the midstory and overstory canopy strata, including oak (Q. alba, Q. falcata) and non-oak species that are increasing in relative abundance in midstory canopy strata in northern Mississippi (Carya tomentosa, Acer rubrum, Ulmus alata). Non-oak species in the midstory (A. rubrum, U. alata) had smoother bark that led to more than three-times larger stemflow generation and more than two-times larger throughfall generation per basal area compared to oak species. However, there was mixed response to soil moisture, with both non-oak (A. rubrum) and oak (Q. falcata) species having wetter than average volumetric water content compared to all other midstory species. Overstory C. tomentosa generated 2.7-times larger stemflow volume than Q. alba while Q. alba generated the largest throughfall volume. Although overstory A. rubrum generated intermediate volumes of throughfall and stemflow, the cumulative effect led to wetter than average soils compared to all other overstory species. These findings suggest that the hydrologic partitioning by mesophyte crowns may serve as an additional mechanism by which encroaching non-oak species act to reduce forest flammability and promote conditions favorable for their own regeneration. This differential rainwater redistribution mechanism may ultimately compromise long-term oak regeneration and our ability to restore fire to fire-dependent forest ecosystems.

Hybrid global search with enhanced INC MPPT under partial shading condition

The photovoltaic system is quickly emerging as a highly favored option among renewable energy sources. However, it faces several significant challenges, including variable solar irradiance, temperature, and partial shading. Unfortunately, conventional Maximum Power Point Trackers (MPPTs) cannot accurately track partial shading. Artificial intelligence and optimization techniques have been proposed as alternatives, but they require extensive training and can take a long time to achieve maximum power point (MPP) under partial shading circumstances. In this paper, a dynamic and fast-moving method of MPP tracking is proposed for use under both uniform solar irradiance and partial shade. This method combines an enhanced incremental conductance (INC) algorithm with a global search algorithm that looks at how well solar cells work when partly shaded. Simulation investigations are performed to validate the method's applicability and ensure that it reaches the most accurate value of MPP with a short-tracking time of less than 0.2 s and a steady-state error of less than 0.3% of the PV power. The results confirm the efficacy of the suggested tracking method under uniform solar irradiance and partial shade.

Design, implementation and empirical analysis of a cascaded hybrid MPPT controller for grid tied solar photovoltaic systems under partial shaded conditions

Solar Photovoltaic (PV) system based power generation is going to become the major power source in the following few decades. Because of the advantages such as reliability, cleanliness, feasibility in small scale to medium scale power generation etc., Solar Photovoltaic (PV) based power generation is developing very rapidly. Hybrid MPPT control methods are essential to track and achieve maximum power generation from solar PV systems under normal and Partially Shaded Conditions (PSC). The proposed Hybrid Grey Wolf Optimization (GWO) algorithm with Adaptive Neuro-Fuzzy Inference System (ANFIS) will effectively tracks the Maximum Power Point when compared to the conventional hybrid MPPT methods. Luo converter is employed as DC-DC converter to convert the variable DC output from solar panel into a nominal DC level. The Luo converter output is given to a single phase Voltage Source Inverter (VSI) and the inverter is connected to grid. The Arithmetic Optimization Algorithm (AOA) tuned Fractional order Proportional Integral controller (FOPI) is adopted to maintain the stability of the grid connected inverter. The tracking efficiency of the proposed system is maintained around 98 % even under different irradiance conditions with very less convergence time of 0.016 s.

An efficient power extraction using artificial intelligence based machine learning model for SPV array reconfiguration in solar industries

In the field of solar industries, extracting maximum power from solar photovoltaic systems under partial shading conditions has gained significant attention in recent years. One of the most efficient concepts brought out to extract the power output of the existing solar photovoltaic system is the reconfigurable solar photovoltaic system. Numerous static and dynamic reconfiguration techniques are mentioned in the literature. This work proposed a Modified Sudoku reconfiguration based on static techniques and compared to the most common existing configuration i.e., Total-Cross-Tied and Sudoku photovoltaic array configurations. In addition, an Artificial Intelligence-based machine learning model (Fuzzy Expert System) is implemented for the prediction of suitable configurations for solar photovoltaic arrays under partial shading conditions. The performance of the FES model is evaluated by comparing the predicted results and the results obtained from the simulations on 5 × 4, 6 × 4, 6 × 6 and 9 × 9 PV arrays. Results demonstrated that the implemented FES model generated accurate results and 0% MAPE in all 33 sample cases for predicting the best suitable configuration. This realistic, simple, and cost-effective fuzzy model can be utilized to replace existing estimation systems that employ the use of complex technological analysis and simulation models. Thus, this approach can be very effectively used in the solar industry for selecting the configuration for installing solar panels.

Assessment of Ginger Genotypes under Coconut Ecosystem for Quantitative and Qualitative Traits

Ginger is one of the major spice grown in subtropical hilly regions and ultivation of ginger under coconut ecosystem is gaining popular among the farmers. Coimbatore district, Pollachi region is one of the major coconut growing belt in Tamilandu. With this view an experiment was conducted to evaluate the performance of ginger genotypes under coconut ecosystem at Coconut Research Station, Aliyar Nagar, Tamil Nadu Agricultural University, Coimbatore during 2018- 2020. . Fourteen ginger genotypes/ varieties were collected for this study. The ginger rhizomes were sown in the prepared land all cultural operations as per the TNAU recommendations were followed uniformly. Growth and yield parameters of ginger genotypes were recorded for three seasons. The date collected were pooled and analysed for statistical significance. Among the fourteen varieties of ginger, var. Athira excelled with respect to number of primary fingers (4.8), length of primary fingers (4.4 cm), diameter of primary finger (2.5 cm), number of secondary finger (5.7), days to maturity (248), TSS (10.40 Brix), acidity (0.41%), Dry matter content (18.9%), fibre content (9.6%), Essential oil content (11.2 mg/g of dry ginger), Gingerol content (19.6 mg/g of dry ginger ) and lowest incidence of soft rot (11.4 %). On the basis of good performance Athira is adjudged as the suitable ginger genotype under coconut shade condition of this region.

Output performance optimization and peak-load shifting based on row current compensation and extraction in PV system

In this paper, the row current compensation and extraction (RCCE) method is presented to avoid power loss of the photovoltaic (PV) array caused by bypass diode conduction of PV modules under partial shading conditions (PSCs). In the PV system employing the RCCE method, PV arrays are directly connected to the battery through bidirectional DC/DC converters. These converters can be controlled to compensate or extract the current of each row of the PV array under PSCs. As a result, the power-voltage (P–V) curve only exhibits a single peak, reducing the complexity of the maximum power point tracking (MPPT). Additionally, the peak-load shifting can be achieved by the RCCE method under different working conditions. The feasibility and effectiveness of the RCCE method have been verified through prototype experiments. The results demonstrate that the PV modules in shaded areas can operate normally instead of being bypassed, and MPPT control will not lead to misjudgment of peak point through the RCCE algorithm.

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p>

Metaheuristics-based maximum power point tracking for PV systems: a review

<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

Machine learning based cascaded ANN MPPT controller for erratic PV shading circumstances

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

Experimental riparian forest gaps and increased sediment loads modify stream metabolic patterns and biofilm composition

AbstractForest management operations greatly influence stream habitats. Canopy clearing and subsequent canopy development during succession, site preparation, and ditching alter the light environment, and increase sediment inputs and nutrient exports from upland and riparian soils to streams. These physicochemical changes affect aquatic biofilms and metabolic rates, and in this study, we tested their individual and combined effects. We used 12 artificial streamside channels, together with a field survey of nine streams in and around clear‐cuts, to assess the effects of shading, substrate composition, and nutrient addition on biofilm biomass and composition, as well as metabolic rates. We found that biofilm biomass and gross primary production (GPP) were light limited in channels under 70% canopy shading. Nitrate additions at this shading level only marginally increased autotrophic biomass, while the rates of respiration increased 10‐fold when carbon was added. Open (unshaded) channels had three times higher rates of GPP compared with channels with 70% shading, and autotrophic biomass was twice as high, largely caused by the colonization of filamentous green algae. These changes to biofilm biomass, composition, and GPP were caused by differences in light alone, as temperature was not affected by the shading treatment. Notably, higher rates of GPP led to no positive effect on net ecosystem production. Further, fine‐grained substrates negatively affected GPP as compared with stone substrates in the experimental channels. In the surveyed streams, the negative effects of fine‐grained substrates exceeded the positive influence of light on biofilm biomass. Altogether, our results highlight the need for riparian management that protects headwaters from unwanted stressors by focusing on preventing sediment erosion and carbon transport in clear‐cuts, while providing variable shade conditions in second‐growth forests.

Determining Nutritive Value of Dry Ash Leaves (Fraxinus Angustifolia) Harvested in Fall via the Regression Method for Growing Rabbits

Shortage acute and high price of animal proteins can be mitigated by sustainable farming of small, very prolific animal species with a short production cycle and valuing non-competitive foods on the resources and space reserved for human food. The rabbit (Oryctolagus cuniculus) combines all these assets. Ash leaves are little or not known nutritionally, nutritive value information relating them is very fragmentary and relates specifically to small ruminant herbivores.Therefore, the present work aim is to determine the nutritional value of oxyphylle dry ash leaves(Fraxinus angustifolia), distributed with increasing rates incorporation in pelleted food for growing rabbits. The nutritive value of Ash leaves collected in fall, dried under shade conditions and distributed for growing rabbits. We compared diets containing an increasing incorporation of Ash (Fraxinus angustifolia) leaves (0 to 40%) in substitution to a basal mixture. The crude protein (CP) concentration of Ash leaves was 10.9 % dry matter (DM), while neutral detergent fibre (NDF) and acid detergent fibre were 30.5 and 19.9%, respectively. A basal diet was formulated (32.51% NDF and 18.2% CP, on DM basis) and pelleted. Two others diets were obtained through substitution of 20 and 40% of basal diet by Ash leaves. Faecal digestibility was measured between 45 and 49 d of age on 12 young rabbits per diet, fed ad libitum since weaning (35 d, 802±197 g). The substitution of 40% of basal diet by Ash leaves didn't reduce the digestibility of organic matter, however digestibility of crude proteins, energy and NDF were reduced from 76 to 70%, 71 to 67% and 34 to 32, (P <0.01), respectively. The digestible energy obtained by regression for shade-dried Ash leaves was 8.67±0.47MJ/kg DM, and the digestible protein content of Ash leaves was 71.55±7.3 g/kg DM.

A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults

As photovoltaic (PV) arrays are exposed to the outdoors year-round, they are susceptible to various faults. The shading condition, degradation or dust coverage can make fault signals more complex, forming compound faults. These faults can lead to a large loss of power generation or irreversible damage to the PV modules, and even fires in severe cases. Moreover, unknown fault types that have never been seen in the training set may occur at actual working conditions. Therefore, accurate diagnosis of various types of single and compound faults (closed-set faults) by considering the identification of unknown faults, namely open-set faults diagnosis, is crucial to improve the efficiency of operation and maintenance. A 1D VoVNet-SVDD based open-set fault diagnosis model for PV arrays is proposed. The model is a two-stage network model consisting of a 1D VoVNet network and a multi-classification Support Vector Data Description (SVDD) in series. The 1D VoVNet network automatically extracts fault features from the input original I-V curve data. These extracted fault features are then combined with environmental parameters to construct the SVDD model. The SVDD identifies known fault types by constructing a hypersphere for each fault type. Fault types that are not classified into any of the hyperspheres are considered as unknown faults, enabling open-set diagnosis. The experimental results show that the proposed model can accurately classify the closed-set faults among the three designed testing tasks while identify unknown type faults. The comparison demonstrates that the proposed algorithm is superior to the compared models.

Dual‐Objective Optimization for Maximizing Photovoltaic Output Power in a Two‐Stage Single‐Phase Grid‐Tied System

The effective utilization of photovoltaic systems (PVS) is significantly affected by partial shading (PS). Although total cross‐tied topology minimizes the power loss, the partial shading condition (PSC) decreases the output power generation and exhibits multiple power peaks in the Power Voltage characteristics. To address this issue, a dual‐objective optimization technique based on reduced search space equilibrium optimization (RSS‐EO) for a single‐phase, two‐stage grid‐connected PVS, is presented. The proposed technique accomplishes two objectives 1) maximizing power generation by reconfiguring the PV modules using a switching matrix based on irradiance equalization and 2) extracting the global maximum power (GMP) with faster convergence, negligible steady‐state oscillations, and reduced steady‐state error. The simulation and experimental results validate the effectiveness of the proposed RSS‐EO technique in efficiently distributing shade over the entire PV array and tracking GMP. The power enhancements achieved by RSS‐EO, particle swarm optimization, and Munkres are 23.03%, 20.02%, and 19.01%, respectively. Additionally, the proposed method exhibits an average mismatch power loss of 738 W, a fill factor of 58.1%, and a percentage power loss of 26.83%. Furthermore, the proposed algorithm is evaluated based on energy calculations and shows better performance by achieving the shortest payback period of 1.6 years.

Efficiency optimisation and converterless PV integration by applying a dynamic voltage on an LVDC backbone

A low-voltage DC (LVDC) backbone with a battery energy storage system (BESS) and distributed photovoltaics (PV) is proven to be a more efficient alternative compared to the traditional AC architecture. However, previous research has also proven the fact that the benefit strongly depends on the operating voltage level. In this study, a dynamic backbone voltage on architectures with and without distributed maximum power point trackers (MPPT) is investigated. The dynamic voltage is driven by a multi-objective optimisation algorithm that will minimise the cable and converter loss. Applying the dynamic voltage with MPPT leads to a massive decrease of the loss compared to a static voltage with MPPT. Although, if the optimisation objective is extended with the minimisation of the PV curtailment loss, the MPPT can be eliminated without causing a considerable curtailment loss while maintaining a high efficiency. The sensitivity analysis showed that even when the cable is longer, the orientations of the PV distributed systems are different or the PV system is exposed to dynamic shading, dynamic voltage without maximum power point is still competitive compared to the alternatives. However, heavy static shading conditions could cause a notable shift of the MPP making it very hard to track this operating point by the algorithm. The elimination of the MPPT leads to a simpler design and installation, lower investment costs and a reduction of the raw material use. The proposed strategies and architectures are hence favourable to be applied in LVDC backbones massively fed by roof-mounted or building-integrated PV systems. This study provides the general approach which could be extended with distributed electric vehicle chargers.