Efficiency Of Photovoltaic Power Generation Research Articles

Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China

Solar photovoltaic (PV) plays a crucial role in China's energy transition. However, air pollution diminishes solar radiation resources, thereby reducing PV power generation efficiency. This study aims to quantify the impacts of air pollution on PV capacity factors in China while emphasizing the geographically specific potential benefits of improved air quality for the future PV sector. Using a PV power generation evaluation model, this study estimated solar PV capacity factors from 1961 to 2016 under both all-sky and clear-sky scenarios. Also, the spatiotemporal patterns of air pollution-induced capacity factor changes were analyzed. The results indicate that the all-sky and clear-sky PV capacity factors showed consistent annual anomalies, suggesting that air pollution due to aerosol emissions drove PV capacity factor temporal evolutions and trends in China. From 1961 to 1990, the national average clear-sky PV capacity factors for fixed modules with optimal tilt angle showed a significant decreasing trend of −0.0053 per decade, whereas from 1990 to 2016, an upward trend of 0.0030 per decade was observed. Compared with fixed modules, tracking systems suffered from greater productivity losses due to air pollution. In comparison to 1961–1965 averages, the provincial PV capacity factors for 2012–2016 decreased by 0.48–13.54%, 1.15–11.40%, 1.06–14.83%, and 1.31–16.51% for fixed modules with optimal tilt angle, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7–17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China's 14th Five-Year Plan, national solar PV power generation was expected to increase by 81.333 TWh to 1069.997 TWh in 2025 with average PV capacity factors for 1961–1965, compared to 2012–2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY.

Design and test of pneumatic dedusting equipment for photovoltaic panels

The multi-stage expansion cavity nozzle working with the vacuum dust suction cavity can suppress the re-entrainment of dust while removing the dust on the photovoltaic panels. But the dimension match and the form of the combination have not been studied adequately. In order to obtain the optimal cleaning performance and the energy consumption, an integrated pneumatic dust removal device is proposed. The internal flow field simulation and CFD-DEM simulation are carried out to optimize the size of the dust removal device and the combination form. Then the dust removal device was produced and the dust removal tests were carried out in dry and wet environments. The test results show that when the inlet pressure is 0.8 MPa, the integrated dust removal device can achieve the highest dust removal rate of 96.51% in dry environment and 85.22% in wet environment. Finally, a pneumatic dedusting equipment with the dust removal device was built and the cleaning performance tests were carried out. The test results show that the photovoltaic power generation efficiency of the entire photovoltaic module was increased by about 16.34%, which verified the feasibility of the pneumatic dedusting equipment.

Application analysis based on solar grid-connected photovoltaic power generation and intermittent energy storage system

Solar photovoltaic power generation as a building electrical professional in the energy use of important technical means, through the realisation of photovoltaic conversion, can be a large amount of light energy transmission to the building application, once the electrical energy surplus is too much, but also can be transmitted to the power grid use. Based on this, the article takes the solar photovoltaic power generation system as the basis, comprehensively analyses the operation principle and basic characteristics of solar photovoltaic power generation, summarizes the main influencing factors of grid-connected solar photovoltaic power generation, as well as the impact of grid-connected solar photovoltaic power generation on the grid, and researches on applying automation technology to solar photovoltaic power generation, such as photovoltaic buildings, DC inverters, reactive power compensation, etc., so as to comprehensively improve the grid-connected solar photovoltaic power generation efficiency and provide a constant source of energy for buildings. Solar thermal power generation technology is another kind of solar power generation technology besides photovoltaic power generation. It is a renewable energy generation method that integrates solar thermal conversion power generation, large-scale heat storage and the characteristics of power grid synchronmachine. According to the influence of different intermittent heat load fluctuation characteristics on the design of solar heat collection and heat storage system, the design method of heat collection and heat storage of intermittent solar heating system is established. The operation characteristics of solar energy system in three typical intermittent heating modes are compared and analyzed. It provides the design method and operation optimization strategy for the efficient utilization of solar heating system.

Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism

As one of the future's most promising clean energy sources, solar energy is the key to developing renewable energy. The randomness of solar irradiance can affect the efficiency of photovoltaic power generation, which makes photovoltaic power generation planning extremely difficult. The main goal of this study is to accurately predict solar irradiance and establish a prediction model with meteorological characteristics to improve prediction accuracy. This paper proposes a convolutional neural network (CNN) and attention mechanism-based long short-term memory network (A-LSTM) to predict solar irradiance the next day. In addition, the prediction accuracy is further improved by combining similar day analyses. A similar day prediction model is constructed by selecting solar energy data from Andhra Pradesh, India. The experimental results show that the method proposed in this paper can predict solar irradiance more accurately, providing a new idea for photovoltaic power generation planning.

Intelligent Optimization Control Method for Photovoltaic Power Generation Systems Under Shadow Occlusion Conditions

<p>In the process of photovoltaic power generation, maximum power point tracking is an important method to improve the efficiency of photovoltaic power generation. Under the actual local shadow condition, the maximum power point of Photovoltaic system fluctuates. For this reason, this paper establishes the mathematical model and output characteristic equation of photovoltaic cells according to the actual application, and then uses the adaptive inertia weight Particle Swarm Optimization algorithm to solve the problem of slow search speed and low accuracy in the process of maximum power point tracking. After optimization, the method proposed in this paper can significantly improve the tracking effect efficiency, and the optimization results in real operation scenarios can improve the photovoltaic cell power generation efficiency by 21.3%, which proves the effectiveness of the algorithm.</p> <p> </p>

Dynamic Reconfiguration Method of Photovoltaic Array Based on Improved HPSO Combined with Coefficient of Variation

In order to address the issue of power loss resulting from partial shadow and enhance the efficiency of photovoltaic power generation, the photovoltaic array reconfiguration technology is being increasingly utilized in photovoltaic power generation systems. This paper proposes a reconfiguration method based on improved hybrid particle swarm optimization (HPSO) for the photovoltaic array of TCT (total-cross-tied) structure. The motivation behind this method is to get the best reconfiguration scheme in a simple and efficient manner. The ultimate goal is to enhance the output power of the array, save energy, and improve its overall efficiency. The improved HPSO introduces the concept of hybridization in genetic algorithms and adopts a nonlinear decreasing weight method to balance the local search and global search ability of the algorithm and prevent it from falling into the local optimal solution. The objective function used is the variation coefficient of the row current without the weight factor. This approach saves time and balances the row current of the array by altering the electrical connection of the component. In the 4 × 3 array, the improved HPSO is compared with the Zig-Zag method. In the 9 × 9 array, the improved HPSO is compared with the CS (competence square) method and the improved SuDoKu method. The simulation results show that the power enhancement percentage of the improved HPSO is between 6.39% and 28.26%, and the power curve tends to single peak characteristics. The improved HPSO has a smaller mismatch loss and a higher fill factor in the five shadow modes, which can effectively improve the output power, and it is convenient to track the maximum power point later.

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.

Application of Photovoltaic Systems in Field Observation and Research Stations: Research on the Relationship between Power Generation Scale and Electricity Consumption to Improve Photovoltaic Application in Field Observation Stations

Most field scientific observation and research stations are located at the end of power grids which are usually not extended to such areas. Consequently, the power supply of equipment in field observation stations cannot be guaranteed. Meanwhile, regions with poor ecosystem stability are relatively sensitive to environmental changes and thus prone to degradation and succession due to external interference. In this paper, the photovoltaic (PV) power generation system of a grassland ecohydrological field scientific observation and research station was taken as the research object. Two kinds of distributed PV power generation systems were simulated and analyzed by use of PVsyst software. The total power of laboratory equipment, PV power generation efficiency, and system cost of the field observation station were calculated and analyzed. The design scheme and scale of PV power generation systems suitable for field observation stations were determined. Finally, a PV power generation test system was set up, and PV power generation data were sorted out. The feasibility of the design scheme of PV power generation systems was verified by analyzing the relationship between the simulated and actual power generation of systems and that between the daily energy use proportions of field observation stations. Besides, the environmental benefits of PV systems were analyzed, and their amount of energy saving and emission reduction was calculated. This study can solve the issue of the low power supply guarantee rate of field observation stations, provide a design basis and beneficial reference for the construction of environment-friendly field laboratory stations, and realize green energy saving and the sustainable use of energy while protecting the ecosystem from being destroyed.

Study on the application of photovoltaic power generation systems in field observatories under the zero carbon goal

Emissions of greenhouse gases such as CO2 are rapidly increasing, causing the greenhouse effect, leading to global warming and serious impacts on the ecosystem, while photovoltaic power generation can reduce the large amount of pollutants and carbon emissions produced in the traditional power generation process. The article takes the photovoltaic power generation system of the national field observatory as the research object, and uses PVsyst software to design the system of two distributed photovoltaic power generation systems. After calculating and analysing the total power of the experimental equipment, the efficiency of photovoltaic power generation and the system cost of the field observatory, the scheme and scale of the photovoltaic power generation system are determined, and the construction of the photovoltaic power generation test system is completed. Finally, through the comparison and analysis of the one-year PV power generation data with the simulated data, the feasibility of the system design method is verified, which can meet the requirements of the field experimental station and create a zero-carbon field observatory to achieve green energy conservation and sustainable energy use, which is in line with the strategic development plan of carbon peaking and carbon neutrality promoted by the state and provides a design basis and useful reference for the construction of zero-carbon field experimental stations.

Intelligent Cleaning Strategy of Photovoltaic Solar Cell Modules

With the decrease of disposable energy and the increase of social demand for power resources, photovoltaic power generation technology has been rapidly developed. The photovoltaic modules exposed outdoors for a long time accumulate serious ash, and the photovoltaic power generation efficiency is affected, so the photovoltaic modules need to be cleaned. Since various factors affecting the power generation efficiency of photovoltaic modules are difficult to quantify and mostly rely on the experience judgment of operation and maintenance personnel, this paper uses the historical operation data of photovoltaic power stations, comprehensively considers various influencing factors, establishes an intelligent cleaning data model, and combines the cleaning cost analysis to provide a basis for intelligent control of photovoltaic module cleaning robots.

基于被动辐射制冷的光伏发电效率优化方案及其监测技术研究

基于被动辐射制冷的光伏发电效率优化方案及其监测技术研究

A Wind-Solar Hybrid Energy Harvesting Approach Based on Wind-Induced Vibration Structure Applied in Smart Agriculture.

Solar energy harvesting devices are widely used in smart agriculture nowadays. However, when lighting conditions are weak, such as through the night or on cloudy days, efficiency decays a lot. Additionally, as time goes by, more and more dust and bird droppings accumulate on the panel, which decreases the performance significantly. This paper aims to overcome the disadvantages mentioned above, and a novel wind-solar hybrid energy harvesting approach is proposed with an oscillation-induced dust-cleaning function. A wind-induced vibration device is specially designed in order to generate electrical energy and/or clean the photovoltaic panel. While in good lighting conditions, the device could keep the panel in a stable state and optimize the photovoltaic power generation efficiency. Such a hybrid energy harvesting approach is called a "suppress vibration and fill vacancy" algorithm. The experimental platform of the proposed device is introduced, and both experimental and simulation results are attained, which prove that using this device, we could realize multiple purposes at the same time.

Radiative sky cooling in low-medium concentration photovoltaic systems

Low-medium concentration photovoltaics (LM-CPV) can significantly reduce cost by using cheap optical lenses to reduce the area of the solar cell. Recently, radiative sky cooling (RSC) has been demonstrated to be promising for CPV systems. However, in the reported designs of heat dissipation for CPV systems combining the radiative cooler and a heat sink, a simple flat heat sink was used. Therefore, the effect of the radiative heat dissipation was magnified, but the contribution of convection, which accounts for a significant share in the natural working environment of CPV, was inhibited. To give full play to the role of convection, in this paper, a heat dissipation design combining a radiative cooling layer (RCL) and a finned heat sink was proposed. The RCL was laid on the upper surface of the heat sink to enhance heat dissipation for RSC and realize the effects of thermal radiation. The contributions of the RCL in reducing the temperature of the device, heat dissipation, and obtaining a tolerable concentration ratio under different external environments were examined, and the law whereby the cooling effect of the RCL was influenced by the wind speed and ambient temperature was investigated. The results of numerical simulations shown that the proposed passive cooling device could ensure that the temperature of the solar cell did not exceed 71.5 °C at a concentration ratio of 200, and the maximum difference in temperature inside the device was less than 3.8 °C even in extremely harsh environments (wind speed, 0 m/s; ambient temperature, 50 °C), where this satisfies the requirements of heat dissipation in low-medium concentration silicon solar cells. The radiative heat dissipation power per unit area of the RCL was 201 W/m2, far exceeding the convective heat dissipation. Although the area of the RCL only accounted for 7.9 % of the total area of heat dissipation, its radiative ratio of heat dissipation exceeded 15 % of the total heat dissipation and led to a drop in temperature of 1.76 °C. Moreover, the increase in ambient wind speed significantly improved the cooling effect of the device, which could ensure the rise in the temperature of the solar cell relative to the environment to smaller than 5 °C. However, this also diluted the effect of radiative heat dissipation by the RCL. The increase in the ambient temperature significantly improved the ratio of radiative heat dissipation of the RCL to the overall heat dissipation, reaching 20.2 % when the concentration ratio was 100. In all cases, the cooling power per unit area of the RCL was higher than that of convective and conventional radiative heat dissipation. An even better cooling effect could be achieved by further increasing the relative area of the RCL. Finally, we found that the maximum acceptable concentration ratio of the device was approximately linearly related to the size of the heat sink, and using the RCL could enable the device to withstand an additional 10 ∼ 15 multiples of concentration. The work here provides a new technical option for reducing the cost and improving the efficiency of photovoltaic power generation through the systematic exploration of the influence of the RSC on cooling LM-CPV systems.

Can conversion of CO2 into fuels via electrochemical or thermochemical reduction be energy efficient and reduce emissions?

Electrochemical and thermochemical conversion of CO2 into fuels are significant carbon conversion technologies to mitigate carbon emissions. Based on the captured CO2, many previous studies carried out tech-economic and environmental analyses for the CO2 conversion systems. However, it is unclear about the energy efficiency and emission reduction of the full-chain system after applying carbon conversion technologies to the carbon sources such as coal power plants and chemical industries. With a unified coal-fired power plant as the carbon source, this study conducted a full-chain systematic analysis of three representative CO2 conversion technologies (shown as routes 1, 2, and 3). Routes 1, 2, and 3 converted the emitted CO2 into fuels via CO2 electrolysis, MEA-CO2 electrolysis, and CO2 hydrogenation. The performance of three Routes in terms of energy efficiency and carbon emissions was comprehensively analyzed. And this work also investigated the energy efficiency improvement potential of three Routes. The results indicated that the energy efficiency of Route 1 was the greatest. And Route 2 exhibited optimal emission reduction. The core factors for enhancing the performance of full-chain systems were improving the efficiency of photovoltaic power generation and developing low-energy-consumption CO2 absorbents. The results also indicated that to date, from the perspective of converting CO2 into the fuel gas CO, no Routes had advantages over coal power plants with carbon capture and storage (CCS) or syngas production directly from coal gasification in terms of energy efficiency and carbon emissions. However, converting CO2 into high-value-added products might be more significant for integrating carbon emission reduction and storing renewable electricity.

Maximum power point tracking of photovoltaic array based on improved Particle Swarm Optimization Algorithm

With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason affecting the efficiency. The efficiency of photovoltaic power generation can be effectively improved by adopting the maximum power point tracking method (MPPT), but the traditional MPPT method is not ideal in the partial shadow occlusion of the photovoltaic array. To solve this problem, this paper proposes an improved particle swarm optimization method to effectively improve the tracking efficiency of MPPT when multiple peaks appear in the photovoltaic arrays power curve (P-V) under the partial shadow. The proposed method improves the learning factor of the traditional particle swarm optimization algorithm and designs the initial position of the particles according to the characteristics of the photovoltaic array. By adding the particle elimination mechanism, the number of particles changes dynamically, and the tracking speed of the algorithm for the maximum power of the photovoltaic array is improved. Through the result of the simulation, it is not difficult to get the conclusion that the improved particle swarm optimization algorithm can effectively improve the performance of the photovoltaic system under partial shadows.

Research on a novel and improved incremental conductance method

Aiming at the problem that the steady-state error and response speed cannot be taken into account in the traditional fixed-step incremental conductance method, combined with the current–voltage (I–U) characteristic curve of photovoltaic (PV) panel, this paper proposes a novel and improved variable-step incremental conductance method. The proposed tracking strategy of maximum power point (MPP) analyzes the I–U characteristic curve, and divides the I–U characteristic curve into four sections, each section has a different variable step size, thus realizing the variable step size control of global four-section. The traditional incremental conductance method and the improved incremental conductance method are simulated under the environment of Matlab/Simulink. The simulation results show that when the light intensity changes rapidly, the novel improved incremental conductance method not only has the advantages of no oscillation in steady state and fast response speed, but also improve the efficiency of photovoltaic power generation, which better realizes the fast and accurate tracking of MPP.

Spatial and temporal characteristics of surface solar radiation in China and its influencing factors

The total-sky direct solar radiation at Earth’s surface (SRS) not only has an important impact on the earth’s climate and ecology, but also is a crucial parameter for solar photovoltaic power. SRS determines whether photovoltaic power plants can be built in the region and directly affects the efficiency of photovoltaic power generation. Therefore, the spatial and temporal distribution characteristics of SRS have a very important guiding significance for the construction of photovoltaic power stations. This study discusses the temporal and spatial characteristics of SRS and its influencing factors in China during 1961–2020 using ERA5 data and the empirical orthogonal function (EOF), rotated empirical orthogonal function (REOF), and ensemble empirical model decomposition (EEMD) methods. Our investigation reveals that the high-value SRS center is located on the southwestern Tibetan Plateau, while the low-value center occurs on the northeastern Yunnan–Guizhou Plateau and in the Sichuan Basin. Seasonal variability in SRS means that maximum values occur uniformly in summer, followed sequentially by spring, autumn, and winter. The spatial distribution of the leading SRS EOF mode exhibits a dipole pattern between the southern Tibetan Plateau and other regions. Combined with the time series, SRS in China underwent an interdecadal transition around the year 2000. The regression analysis shows that this pattern is mainly affected by surface air temperature, total precipitation, relative humidity and cloud cover. The time series evolution of SRS primarily reflects the interannual variability in annual-mean and four seasons; the variance contributions of decadal variability and secular trend are minor through the EEMD. The REOF separates Chinese SRS into 11 central regions, the top 5 being the western Tibetan Plateau, western Northwest China, the eastern Tibetan Plateau, northern Xinjiang, and North China. The relationship between SRS and meteorological parameters shows that SRS is positively correlated with surface air temperature and wind speed but negatively correlated with total precipitation, relative humidity, low and total cloud cover, and aerosol concentrations.

Electronic structure and optical properties of B-, N-, and BN-doped black phosphorene using the first-principles.

The structural stability, electronic structure, and optical properties of BN-doped black phosphorene systems at different concentrations were investigated using a density generalized theory approach based on the first principles. BN-doped black phosphorene was found to be more stable than B and N atom doping. With the increase of doping concentration, the stability of the structure gradually decreases, and the structure of the system with 25% doping concentration is the most stable. The intrinsic and N-doped black phosphorenes are direct bandgap semiconductors, and B and BN doping make the black phosphorene change from direct bandgap to the indirect bandgap. The total density of states is mainly contributed by the p-state electrons of the B and P atoms, and the N atoms have a role in the local density of states with little contribution to the overall one. The black phosphorene undergoes charge transfer between the B and N atoms. The amount of charge transfer increases with the increase of doping concentration. The BN-doped black phosphorene system is blue-shifted at the absorption and reflection peaks compared to the intrinsic black phosphorene system. From the dielectric constant, it is found that the doped system is shifted towards higher energy at the highest peak, leading to an increase in the intensity of the electric field generated by light, which is beneficial to increase the efficiency of photovoltaic power generation. The photoconductivity decreases and shifts toward higher energy after doping, with the most pronounced performance at BN doping concentrations of 12.5% and 25%.

Development of a Mathematical Model to Size the Photovoltaic and Storage Battery Based on the Energy Demand Pattern of the House

Solar energy is used in buildings worldwide. However, because the efficiency of photovoltaic power generation varies with environmental fluctuations, it is difficult to control. Therefore, electricity generation from photovoltaics is often poorly matched to the electricity load of the house. The use of storage batteries and photovoltaic panels can effectively improve the stability of the energy supply; however, it also introduces the problem of higher initial costs. Generally, a larger photovoltaic area and battery capacity can lead to higher costs and more renewable energy; therefore, to determine a suitable size of photovoltaic and storage battery for a house, the energy demand of the house must also be considered. The traditional method for sizing photovoltaics and storage batteries mainly considers the daily average electricity demand and the useful area for installing photovoltaics. To size the photovoltaic in a more precise manner, we propose a mathematical model (nonlinear programming) for selecting a relatively optimal solution for the photovoltaic area, battery capacity, and photovoltaic installation angle by considering the hourly and annual demands for electricity from the grid. To validate this mathematical method, a detached house in Zhouzhi county, Shaanxi province, China, was selected to size the devices by the proposed method. Results show that the device sizes determined using the proposed mathematical model are significantly smaller than those determined using the traditional method, without suffering a significant increase in the demand for electricity from the grid. According to our economic analysis, although the proposed method for sizing devices reduces the device cost payback period by half compared to that of the traditional method, the payback period for the devices sized using the proposed method is still 10.6 years. The extremely low electricity price in China may contribute to the extended payback period and thus discourage residents from installing renewable energy devices.

Application and evaluation of phase change materials for improving photovoltaic power generation efficiency and roof overheating reduction

With the development of countries, concerns about global warming have increased. Many countries are taking steps to implement green building-related policies to reduce carbon dioxide emissions; however, these policies are generally applied to new buildings. Therefore, this study focused on the applicability of phase change materials (PCMs) and the optimal application method for educational buildings that have a long service period and are difficult to dismantle among existing buildings. The best application is focused on reducing the building energy demand. phase change materials were applied to address concerns regarding surface temperatures of the building. The passive element, phase change material integrated brick, was applied to the roof, while the active element was a phase change material packed into the back of photovoltaic (PV) panels. Five scenarios were considered in assessing these technologies. photovoltaic installation on the roof surface was assumed for all scenarios, except scenario 1. In scenario 3, the phase change material was applied to the back of the photovoltaic panel, and in scenario 4, a brick layer impregnated with the phase change material was added to the roof surface. In scenario 5, the technologies of scenarios 3 and 4 were applied in combination. As a result of the analysis across the five scenarios, a maximum annual cooling load reduction of 14 521.6 kWh was derived. The overall heating demand increased, with the maximum annual heating demand increasing by 7134.2 kWh. Electrical energy was produced by installing photovoltaic panels, and energy savings were achieved; the highest overall load reduction rate was 22.55%. The calculated shortest payback period was seven years, which is reasonable. Based on cost and energy aspects, scenario 4 is considered the optimal retrofit plan. These results suggest that significant cost and energy savings can be achieved through building renovation with phase change materials.