Installation Of Photovoltaic Systems Research Articles

Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.

Modeling and techno-economic optimization of overhead panels and reflectors in near-wall mounted PV systems

Compact installations of solar photovoltaic (PV) systems to maximize the use of land are a necessity in urban regions, where the available installation areas exposed to solar irradiance are limited. Large-scale commercial buildings are typically surrounded by open areas and have busy roofs with mechanical and air conditioning equipment. This study assesses three candidate installation schemes of dual-row near-wall ground-mounted PV systems. The baseline configuration (BC) comprises the classic parallel PV rows. The overhead reflector (OR) configuration adds reflectors above the panels of the second row, to be supported by the wall. The overhead panel (OP) configuration moves the panels of the second row to be above the panels of the first row. Mathematical models are developed to evaluate and optimize the techno-economic performance (in terms of total annual energy production “EPV” and levelized cost of electricity “LCOE”, respectively) based on the tilt angles of the two rows, as well as the length and tilt angle of the reflector in the OR configuration. The results show maximum energy production of 12.54, 12.77, and 12.53 MW for the three configurations, respectively. When the land cost is excluded, the minimum LCOE is 0.0696, 0.0698, and 0.0696 USD/kWh, which correspond to 0.1318, 0.1332, and 0.1182 USD/kWh, respectively, when the land cost is included. The EPV-LCOE Pareto fronts are dominated by different designs of the OR and the OR/OP configurations when excluding and including land cost, respectively, which demonstrates the favorableness of the two proposed schemes, compared to the baseline one.

Experimental investigation and validation of solar PV cooling for enhanced energy conversion efficiency for Indian climatic conditions

Solar Photovoltaic (PV) cells convert an average of 10 to 15% of the incident solar radiation into electricity and remaining energy is wasted as unused heat energy. The p erformance of solar PV is largely dependent on its operating temperature, which is again dependent on solar irradiation. The efficiency of solar PV reduces the higher PV temperature due to charge carrier recombination. The solar PV efficiency drops considerably wit h increasing temperature. Dust deposition on the surface of solar PV cells reduce incident energy and no technology is commercially available to mitigate the problem. The objective of the present work is to enhance the energy conversion efficiency of solar PV by adopting Front Water (FW) cooling technique. The FW cooling technique maintains the cell temperature at Standard Test Conditions (STC) irrespective of ambient air conditions and also washes away dust deposits, thereby providing maximum energy conversion efficiency specified by the cell manufacturer during the operation with increased lifecycle of solar cells. The experiment was carried out on a 100 W solar panel for a period of 2 weeks and data acquisition system with Arduino controller was used to analyze and maintain STC of the panel to obtain maximum power. The mathematical model of the system was analyzed and obtained results were in good agreement with the experimental measurements. The solar PV panel with FW cooling yielded an efficiency improvement of 9% with 17 W of increased power output at Maximum Power Point (MPP). MATLAB Simulink software is used to model t he FW cooling technique. The model is able to predict the power generated by the solar PV cells for the given irradiance with and without cooling. The developed model can now be utilized to design cooling systems for larger installation of solar PV systems.

Economic and social utility of installing photovoltaic systems on affordable-housing rooftops: A model based on the game-theory approach

Due to the needed transition to a low-carbon economy, the cutback in dependency on fossil fuels will depend, among other things, on how much renewable electricity can be produced by photovoltaic (PV) systems, which require huge surfaces. This emphasizes the importance of using building rooftops. The current study examines the viability of installing PV systems on rooftops of affordable-housing buildings in Israel. To plan and optimize PV system installation projects, we analyze the determinants of process efficiency, detect planning difficulties, and consider the actions needed to enhance the welfare of various types of stakeholders (“players”). Using game-theory tools, these players are identified on the basis of fifteen semi-structured interviews that allow us to analyze their key and supporting roles and to define the parameters of their utility functions. The “game” is analyzed as both simultaneous and serial, and its outcomes show that the potentially crucial key player is the Ministry of Construction and Housing (MOCH), which may choose between leasing the rooftops or purchasing the systems. Due to the positive externalities of installing PV systems, the project serves as a social, energy, and economic engine for low-socioeconomic-status tenants. Analysis of the model in the case study shows that, on average, every transfer of 10% of PV system income to tenants will reduce household energy expenditure by 4.6% of gross household income, thus alleviating energy poverty while strengthening trust and cooperation among all players.

Life cycle assessment of photovoltaic electricity production by mono-crystalline solar systems: a case study in Canada.

Photovoltaic (PV) system is widely recognized as one of the cleanest technologies for electricity production, which transforms solar energy into electrical energy. However, there are considerable amounts of emissions during its life cycle. In this study, life cycle assessment (LCA) was used to evaluate the environmental and human health impacts of PV electricity production in Canada. The PV potential varies considerably among the provinces, with higher values in Manitoba (MB), Saskatchewan (SK), Alberta (AB), and southern Ontario (ON). A grid-connected slanted-roof mono-crystalline silicon (mono-Si) PV system with a capacity of 3 kWp (the peak power of the system in kilowatts) in Toronto, Ontario, was considered as the case study system. Ten impact categories were considered including (1) acidification, (2) carcinogenic, (3) ecotoxicity, (4) eutrophication, (5) fossil fuel depletion, (6) global warming, (7) non-carcinogenic, (8) ozone depletion, (9) respiratory effects, and (10) smog. Among the four components of the PV system, i.e., mono-Si panel, mounting system, inverter, and electric installation, the mono-Si panel production was the highest contributor in seven out of ten impact categories, including acidification (68%), eutrophication (60%), fossil fuel depletion (81%), global warming (77%), ozone depletion (88%), respiratory effects (74%), and smog (70%). For the other three processes, the electric installation contributed most to ecotoxicity at 58%, followed by the mounting system in the carcinogenic category (29%), and the inverter in the non-carcinogenic category (31%). By normalizing the impacts based on the reference scores in Canada, it was found that the ecotoxicity and carcinogenic categories had dominant contributions to the overall impact by 53% and 42%, respectively. The global warming potential impact was estimated as 79 gr CO2 eq /kWh, which is close to the mean value of 79.5 gr CO2 eq /kWh, reported in the literature. The sensitivity analysis indicated that a 10% increase in the panel and mounting system area will increase the ozone depletion and carcinogenic categories by 8.1% and 2.8%, respectively.

Performance Analysis of Thermal Image Processing-Based Photovoltaic Fault Detection and PV Array Reconfiguration—A Detailed Experimentation

Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology’s renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups.

The installation of residential photovoltaic systems: Impact of energy consumption behaviour

Numerous electricity consumers tend to start consuming energy excessively after installing a photovoltaic system. In Jordan, almost all residential PV systems are grid connected and operate by net-metering agreements. Therefore, an increase in consumption after the installation of PV systems can result in a huge overload on the national electric grid. The challenge this presents is to find a specific level of comfort that will also protect the environment by saving energy. This paper examines the effects of residential photovoltaic systems on energy consumption and determines whether an increase or decrease occurs after installing these systems. This research was conducted to tackle the excessive energy consumption that occasionally occurs after installing renewable energy systems, especially in the residential sector in Jordan, and ascertains whether a pattern exists in the increase or decrease. Local energy consumption data within the period between 2016 and 2020 for 150 households in the governorates: Irbid, Ajloun, Al-Mafraq, and Jerash, as well as the municipality of Ar-Ramtha, were sampled after installing residential photovoltaic systems. Several attempts to correlate the results with key factors are also presented, the purpose of which is to limit some of these to reduce energy consumption after installing residential photovoltaic systems. The results indicate that 88% of households experienced a uniform increase in energy consumption after installing residential photovoltaic systems across the four governorates and the municipality. An average 41% increase in energy consumption was found with results varying across all studied areas but all exhibiting an overall incremental increase. Furthermore, the average share of self-consumed energy out of the total energy generated by PV systems was found to be 27.7% for the studied population, indicating the need of regulatory interactions to alleviate the pressure on the national electric grid, and to ensure the maximum benefits for PV systems’ installers.

The impact of modeling assumptions on retrofit decision-making for low-carbon buildings

To reach decarbonization goals, effective and robust building retrofit decisions are critical. Environmental retrofit assessments rely on full or partial life cycle assessments, which require several modeling assumptions. However, the currently typical ‘status quo’ modeling assumptions for assessing building retrofits need to be questioned with an increasing state of climate emergency. We identify and study the impact of five relevant modeling assumptions that potentially influence the effectiveness of building retrofit: (i) the environmental allocation of photovoltaic electricity, (ii) the effect of global warming, (iii) decarbonization pathways, (iv) choice of the analysis period, and (v) the allocation of biogenic carbon stored in building materials. To investigate the impact of modeling assumptions on the environmental performance and the robustness of retrofit decisions, we establish a framework to compare calculated GHG emissions under different modeling assumptions. The framework is applied to a typical Swiss building. The results show that the choice of modeling assumptions can indeed affect retrofit decision-making based on GHG emissions. In particular, grid decarbonization assumptions strongly impact the choice of renovation strategies. Similarly, the allocation method of locally produced renewable electricity can be decisive when investigating the installation of photovoltaic systems. Aligning the analysis period with the urgency of global warming mitigation shifts the focus towards the embodied emissions, favoring low-emission materials. In contrast, assumptions on global warming do not majorly affect the retrofit decision for the example building. Overall, the introduced approach leads to a more comprehensive view on building retrofit robustness, allowing decision-makers to make better-informed retrofit decisions.

Decomposition and decoupling analyses of CO2 emissions and economic growth: A comparative study between Malaysia and Vietnam

During the COVID-19 pandemic, Malaysia and Vietnam has managed to show a strong positive growth domestic product (GDP) growth indicator – with both countries recording an average increase of more than 4% annually. However, there was a strong positive correlation between GDP and carbon dioxide (CO2) emissions worldwide. As the impact of climate change becomes more evident, especially in Southeast Asia, it is crucial to assess the relationship between these two indicators caused by gross electricity generation, especially in the post-pandemic period. In this study, we compared the decoupling performance of CO2 emissions and GDP growth between Malaysia and Vietnam. The data for Malaysia and Vietnam were quantified by the decoupling method based on the Tapio decoupling model. The data were then decomposed using the Kaya identity into the driving factors affecting decoupling. This was then used to form the decomposition index using the Logarithmic Mean Divisia Index-I (LMDI-I). Data were generated based on three scenarios: i) 2017-2019, i.e. pre-pandemic, ii) 2019-2020, peri-pandemic and iii) 2021-2025, to predict post-pandemic. In terms of the overall decoupling trend, the study shows that: (1) the change in CO2 emissions in both sovereign states decreased dramatically at the beginning of the COVID-19 pandemic. However, Malaysia experienced negative change in CO2 emissions, while Vietnam still experienced a positive change. This is due to Vietnam’s slightly higher reliance on coal for power generation than Malaysia. (2) A strong decoupling in the projected decoupling between the change in CO2 emissions and GDP in Vietnam, while the two indicators remain coupled for Malaysia. This is due to Vietnam’s more aggressive policy in the adoption of renewable energy - especially through the mass installation of rooftop photovoltaic systems. In contrast, Malaysian policy remains conservative, emphasising reliability at the cost of a huge year-on-year change in CO2 emissions.

Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result

S4 Framework for the Integration of Solar Energy Systems in Small and Medium-Sized Manufacturing Companies in Mexico

Currently, the industrial sector consumes more than 60% of the energy produced in Mexico, mainly from fossil fuels, causing negative impacts on the environment and human beings. Solar energy helps companies diversify their energy sources, generate savings, and reduce dependence on fossil fuels. Moreover, the environmental impact can be reduced when CO2 emissions are reduced. Nevertheless, in Mexico, less than 3.5% of the electricity comes from solar energy, and along with a lack of information about the technical and social aspects involved in photovoltaic (PV) systems, it is difficult for companies to analyze and evaluate relevant data, and thus make effective decisions based on their needs. As such, companies cannot understand the complete lifecycle of PV systems, and, usually, the economic, environmental, and technical decisions are made only using the installation analysis, which is only one stage in the lifespan of PV systems. This paper proposes an S4 framework with the sensing, smart, sustainable, and social features that small and medium-sized companies must consider to install, operate, and dispose of PV systems, considering the Mexican context. The current literature does not show a complete classification to cover the essential S4 features to describe PV systems, so companies only have partial information when deciding about the installation of PV systems. This framework considers all the needs that may exist during the PV systems’ lifecycle, making a detailed evaluation of each of its elements in each lifecycle stage. Consequently, this S4 framework gives a complete guideline allowing companies to decide on PV systems. Finally, this paper presents a case study about a Mexican company that uses the proposed S4 framework to analyze the PV’s lifespan.

A general algorithm for the optimization of photovoltaic modules layout on irregular rooftop shapes

Although the installation of photovoltaic systems on roofs is a successful investment, rooftop space availability has been identified as a significant limiting factor in achieving zero-energy buildings, especially by building components such as chimneys, elevator machine rooms, fans, plumbing vents, etc. This study presents a general algorithm for the optimal deployment of photovoltaic module rows installed on irregular flat roof shapes. The presented algorithm takes into account the irregular rooftop shape, the self-shading of photovoltaic modules, the inclusion of building components, commercial photovoltaic modules with different sizes, mounting systems with different configurations, distances required for maintenance, and the technical reports to minimize shading effects. The proposed algorithm allowed to increase in the amount of solar energy received by the photovoltaic modules. The optimization process takes into account the weather conditions at the specific location. The optimization algorithm was implemented using a specific Mathematica™ code in which the commands used in the development of the code were introduced to facilitate its replication. The optimization algorithm output provides the essential parameters for the optimal photovoltaic system design such as: the optimum number of mounting systems and their configuration, the optimum tilt angle of the mounting system and its dimensions, the photovoltaic module model, the maximum total area of the photovoltaic field and the maximum annual energy captured by the photovoltaic modules. We compared the mounting system layout obtained with the proposed algorithm with the tilt angle photovoltaic module layout recommended by three technical papers ( IDAE Technical Report, Lorenzo’s equation and Jacobson’s equation) with respect to photovoltaic field area gain, energy gain and levelized cost of energy .The optimal photovoltaic module layout obtains the maximum photovoltaic field area gain of 35.52% with respect to the Jacobson’s equation and the minimum of 32.29% with respect to the IDAE Technical Report. The optimal photovoltaic module layout obtains the maximum energy gain of 27.83% with respect to the Jacobson’s equation and the minimum of 24.84% with respect to the IDAE Technical Report. The levelized cost of energy of the optimal P V module layout is lower than that of the other arrangements studied. The algorithm presented may be useful for decision-makers or policymakers in determining the optimal distribution of photovoltaic modules on irregular rooftop shapes. • Algorithm for the optimal PV modules layout on irregular rooftop is presented. • Building components and irregular roof shapes are considered in the research. • The influence of the mounting system configuration is investigated. • The algorithm obtains 28% more energy than the obtained by a classical method. • The LCOE efficiency of optimal PV module layout is the best.

Applicability of solar systems with various technologies and sun-tracking: A case study of Baghdad, Iraq

The installation of a photovoltaic system is becoming increasingly attractive to consumers in residential areas due to higher electricity tariff rates and less dependence on local power generators in Iraq. Consequently, the paper's main objective is to determine the applicability of solar photovoltaic (PV) systems in the capital city (Baghdad) of Iraq. Additionally, this study aims to find suitable PV technology and sun-tracking system for building applications in Iraq. To obtain this aim, the PVGIS simulation tool is used. It has been found that the selected city is suitable for installing PV systems in the future because of the high value of solar radiation. Furthermore, the results indicated that CdTe technology has the highest performance compared to the crystalline and CIS PV technologies. Moreover, it is observed the two-axis system has the maximum energy generation compared to vertical and include systems. This study provides useful details regarding installing a PV system in Iraq to solve the electricity crisis in the country and reduce fossil fuel consumption.

Solar energy development in the GCC region – a review on recent progress and opportunities

ABSTRACT Exponential increase in global energy demand has led to the extensive integration of renewable energy resources. Solar energy has particularly attracted major interest, due to its scalability and ease of installation. A rise in solar insolation levels in the gulf-region has led to the rise of solar integration within the energy mix to provide strategies for clean energy production. Furthermore, GCC countries implemented various strategies to increase the installed capacity of photovoltaic system installations to cope with the rapid consumption of energy by consumers. In this review, the highlighted progress in solar energy development is discussed with respect to the gulf-situated countries. Present diversification of implemented and future projects paves the way to successful green energy integration. Furthermore, present challenges with photovoltaic (PV) implementation within the region such as oil dependence and arid climatic conditions affect its performance dramatically. However, the potential impact and benefits are numerous, which in hand encourage photovoltaic implementation within the region. In addition, measures are discussed for the steady improvement of solar energy integration within the electrical network, to be considered by the countries situated within the region.

The status of implementation of photovoltaic systems and its influencing factors in European countries

AbstractThis paper gives an overview of the status of implementation of photovoltaic (PV) systems among 34 member countries involved in the COST Action PEARL PV in the year 2018. Besides this, influencing factors that have an impact on the implementation of PV systems in each country have been assessed. The implementation of PV systems varies in European countries due to differences in solar irradiation and weather conditions, electricity consumption, national economic situation, and governmental approaches to promote PV. For this paper, data sets covering, among others, numbers installed PV systems, and the variables mentioned above, were collected, processed, and synchronized. Subsequently, relations between data were analyzed by geographic mapping and mutual correlations. The 34 countries evaluated produced together 120 TWh of PV electricity in 2018. It was notable that Germany produced the highest amount (45 TWh) and has the highest share (8.4%) of PV in its electricity generation. Weak correlations were found between the different variables and the gross domestic product, annual global irradiation, number of subsidy schemes, cumulative installed capacity, annual electricity generated by PV, the share of RE, and share of PV. Logically, as expected, strong correlations (R2 = 0.9) were found between the number of inhabitants and the annual electricity production and consumption. From our analysis, we conclude that four different groups of countries can be identified to categorize the usability of PV. One type covers countries with a significant unused PV potential while local–global irradiation is redundantly available, such as Turkey, Spain, Cyprus, Portugal, and Israel.

Climate Change Mitigation Approach for School Buildings in Palestine: A Combination of Energy Efficiency and Energy Production

The rising energy demand for school's buildings in Palestine is one of the problems facing the energy sector. This paper aims to provide estimation for the potential production of electricity from the installation of PV systems on the roof of schools' buildings, to produce its energy needs and to provide electricity to its surrounding buildings. The most used school building types were selected for the installation of the photovoltaic (PV) system. The produced electricity from the installation of PV systems was estimated using PVSOL software. The energy consumption for the selected type of the schools was simulated using design-builder thermal simulation software. A comparison between energy production and consumption was done for two climatic zones, with different tilt angles and different scenarios for the school's building envelope and indoor systems. The results show that the PV systems on schools' buildings can supply their estimated consumption, and provide a surplus in electricity production. This surplus can be a base for the transition to renewable energy in the residential areas surrounding school buildings. The study concluded that the installation of PV systems should be combined with building envelope thermal improvements or a combination of envelope improvements and heating and cooling systems.

Solar Panel Identification Via Deep Semi-Supervised Learning and Deep One-Class Classification

As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But, historical records are unreliable and constant re-assessment of active residential PV locations is resource-intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning setup based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available data set but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids.

Contrasting effects of electricity prices on retrofit and new-build installations of solar PV: Fukushima as a natural experiment

This study examines the effects of financial incentives, particularly electricity prices, on residential solar photovoltaic (PV) system installations. We highlight the importance of a factor in the adoption of low-carbon building technologies that the literature has largely overlooked: the distinction between retrofit and new-build installations. To address the endogeneity of electricity prices, we use the 2011 Fukushima nuclear accident and the subsequent shutdown of nuclear power plants in Japan as a natural experiment that caused substantial, exogenous, and regionally varying increases in electricity generation costs and prices. Electricity prices have a statistically significant, positive effect on PV system installations for existing homes (with a mean elasticity of 1.6), but a statistically insignificant, much smaller effect for new-build homes. A policy implication of these contrasting responses is that the cost-effectiveness of financial incentive schemes for low-carbon building technologies can be improved if they are targeted more at retrofits. We also find a large downward bias (40%–60%) in the estimated effect of electricity prices if they are not instrumented with exogenous cost-shifters.

Marmara Bölgesindeki Bir Konteyner Limanı İçin Fotovoltaik Enerji Sistem Kurulumunun Tekno-Ekonomik ve Çevresel Analizi

Dünyada 1960’lı yıllardan itibaren gelişen konteyner gemileri ve konteyner limanlarının, ülkemizin dış ticaret, kabotaj ve transit yüklerinin taşınmasında önemli bir rolü bulunmaktadır. Özellikle Marmara Bölgesinde yer alan konteyner limanlarının ülkemizin toplam konteyner elleçleme kapasitesinde önemli bir payı vardır. Diğer taraftan, güneş enerjisinden faydalanarak elektrik üretimi, dünya genelinde olduğu gibi ülkemizde de yaygınlaşmakta olup konteyner limanlarında da bu teknolojiden azami ölçüde faydalanılması gerekmektedir. Bu çalışmanın amacı; Marmara (Kocaeli) Bölgesinde bulunan büyük ölçekli bir konteyner limanında fotovoltaik yenilenebilir enerji sistemi kurulumunun tekno-ekonomik ve çevresel analizini gerçekleştirmektir. Bu amaçla, büyük ölçekli bir konteyner limanı incelenmiş, yıllık elektrik ihtiyacı tespit edilmiş ve Fotovoltaik Coğrafi Bilgi Sistemi yazılımı yardımıyla uygun PV sistem gücü belirlenmiştir. Ayrıca, İndirgenmiş Geri Ödeme Süresi Yöntemi kullanılarak PV sistemin ilk yatırım maliyeti ve geri ödeme süresi hesaplanmış ve RETScreen yazılımı yardımıyla PV sisteminin zararlı sera gazı salımını azaltmadaki rolü ve çevresel etkileri analiz edilmiştir. Çalışmada, konteyner limanındaki kapalı binaların çatılarına 962 k Wp gücünde PV sistem kurulabileceği ve PV sistemin bu güç ile yılda 1.061.034 kWh elektrik üretimi gerçekleşebileceği sonucuna ulaşılmıştır. Limana yapılacak PV sistem yatırımının geri ödeme süresi 8,33 yıl olarak belirlenmiştir. Söz konusu PV sistemin yılda 37,7 tCO2/yıl sera gazı emisyonuna engel olacağı da tespit edilmiştir. PV sistemden üretilecek elektrik ile limanının yıllık elektrik ihtiyacının %9,47’lik bir kısmı sağlanabilmektedir. Bu çalışmada, bir limana kurulacak PV sistemden en yüksek seviyede yararlanılmasına katkı sağlamak üzere bazı öneriler de sunulmuştur.

Operation Scheduling of Household Appliances Integrating Solar Photovoltaic and Battery Energy Storage Systems

Demand Side Management (DSM) is one of the most interesting techniques in smart grids that can reduce consumer electricity costs. Many recent works studied on DSM with smart homes discussing how to control optimally electricity consumption in a residential household. However, there is no work studying the optimal control of electricity consumption that simultaneously considers multiple residential households with solar Photovoltaic (PV) and Battery Energy Storage Systems (BESS) installations. Consequently, this study proposes an energy management system model to minimize electricity costs for multiple households with solar PV and BESS installations. The constraints consist of consumer preferences, energy requirements of each appliance, BESS operation and solar PV generation. The proposed model can be described as a linear optimization problem. Mixed Integer Linear Programming (MILP) is used to solve this proposed model. The performance of the proposed model is tested by two different scenarios composed of single household management and multiple households management. From the simulation results, the proposed model can reduce the electricity cost by 59.04% for single household management and 68.81% for multiple households management compared with electricity cost without management.