Optimal System Efficiency Research Articles

Design and Optimization of an Integrated Power System of Solid Oxide Fuel Cell and Marine Low-Speed Dual-Fuel Engine

A combined system including a solid oxide fuel cell (SOFC) and an internal combustion engine (ICE) is proposed in this paper. First, a 0-D model of SOFC and a 1-D model of ICE are built as agent models. Second, parameter analysis of the system is conducted based on SOFC and ICE models. Results show that the number of cells, current density, and fuel utilization can influence SOFC and ICE. Moreover, a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently, as achieved by the particle swarm optimization algorithm in this paper. The results demonstrate that the optimal system efficiency of 51.8% can be achieved from a 22.4%/77.6% SOFC-ICE power split at 6 000 kW power output. Furthermore, promising improvements in efficiency of 5.1% are achieved compared to the original engine. Finally, a simple economic analysis model, which shows that the payback period of the optimal system is 8.41 years, is proposed in this paper.

Real-time and modelled performance assessment and validation studies of PV modules operating in varied climatic zones

With the increasing adoption of solar energy as a sustainable power source, it is crucial to evaluate the performance of photovoltaic (PV) modules under diverse environmental conditions to ensure optimal energy production and system efficiency. Present work gives a comprehensive overview of performance assessment and validation studies conducted on different PV module technologies such as multi-crystalline silicon (mc-Si), cadmium telluride (CdTe), copper indium gallium selenide (CIGS), amorphous silicon (a-Si), heterojunction with intrinsic thin layer (HIT) and interdigitated back contact (IBC) operating under varied climatic conditions at different locations in India. To ensure accuracy and reliability, rigorous data collection and analysis methodologies were employed. The performance assessment has been computed through simulation using PVSOL software. Moreover, the performance ratio (PR) of all the PV module technologies was found to vary from 0.76 to 1.04. The present work validates the efficacy of the software as we have compared the computed results with real-time data obtained for all the PV modules, which were installed at NISE (National Institute of Solar Energy), Gurugram India. The performance ratio of all the PV module technologies has been evaluated through both the PVSOL and using real time data. The results show that the mean percentage change in PR values has been estimated approximately to be 2% in CdTe, CIGS, and a-Si modules technologies and 3% in mc-Si, HIT and IBC module technologies.

Round-Robin Algorithm in Load Balancing for National Data Centers

The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial Government. The primary objective of this study is to address the significant gap in knowledge by conducting a comprehensive evaluation of the Round Robin algorithm's effectiveness in load-balancing on-premise servers inside the Bali Provincial Government. The primary objective of our study is to assess the appropriateness of the algorithm within the given context, with the ultimate goal of providing practical and implementable suggestions. The observations above can optimize system efficiency and minimize periods of inactivity, thereby enhancing the provision of vital public services across Bali. This study provides essential insights for enhancing server infrastructure and load-balancing strategies through empirical evaluation and comprehensive analysis. Its findings are valuable for the Bali Provincial Government and serve as a reference for other organizations facing challenges managing server loads. This study signifies a notable advancement in establishing reliable and practical public service applications within Bali.

Production system efficiency optimization through application of a hybrid artificial intelligence solution

ABSTRACT Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.

Conceptualizing State Re-Engagement With Public Higher Education

Three trends have characterized state policymaking for higher education in the 21st century: divestment, accountability, and race neutrality. These policy agendas are often justified as an attempt to optimize system efficiency and performance by making institutional actors (agents) responsive to the demands of state officials (principals). In this manuscript, we present quantitative evidence that these three policy initiatives have not achieved their stated purpose. We use contemporary social theory to explain why this is the case. Finally, we call for deeper re-engagement of states and their higher education systems in place of transactional and technocratic models.

Thermodynamic analysis of a gas turbine utilizing ternary CH4/H2/NH3 fuel blends

Ammonia and hydrogen have gained significant interest as fuels for power generation as part of a larger strategy to decarbonization the power generation sector. By mixing these carbon-neutral fuels with natural gas, lower carbon dioxide emissions can be achieved in gas turbine operations without any retrofitting. However, no studies have explored methane-hydrogen-ammonia ternary fuel blends on gas turbines performance and emissions. In this work, a 50 MW simple Brayton cycle model was developed, incorporating operating conditions found in commercial power plants and chemical equilibrium to represent realistic exhaust temperatures and species. Over 5150 fuel combinations of ammonia-hydrogen-methane were assessed and performance parameters (i.e., system efficiency and specific fuel consumption) are reported. The optimal system efficiency changes with fuel composition, with most ternary fuel blends achieving highest system efficiency at equivalence ratios between 0.37 and 0.39. Gas turbines operating with high ammonia concentrate fuel blends achieve high system efficiencies (up to ∼44.6%) but result in high specific fuel consumption (up to 0.44 kg.kWh−1). The fuel blend of 78%/22% H2/NH3 was found to be the optimal fuel choice, achieving higher system efficiency with similar fuel consumption as pure methane.

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

Are piezoelectric-electromagnetic hybrid energy harvesting systems beneficial?

The primary objective of this work is to investigate the performance of a hybrid energy harvesting system consisting of piezoelectric and electromagnetic transducers. We first show that a single–mechanism generator with negligible electrical losses, referred to as an electrically-lossless harvester, can reach the theoretical power bound regardless of the coupling strength between the mechanical and electrical domains, which renders the use of hybrid systems unnecessary. For a more realistic analysis, the electrically parasitic losses are then taken into account. We introduce effective figures of merit for the piezoelectric and electromagnetic generators that combine transducer coupling and resistive losses. The maximum output power of single-transducer and hybrid systems are determined analytically, expressed as functions of effective figures of merit. We find that there is no benefit to utilizing a hybrid system if one of the two, or both, effective figures of merit exceeds a threshold of . We also derive the narrow conditions under which a resonant hybrid harvester system with multiple transduction mechanisms can outperform its counterpart which uses a single energy conversion. In order to provide a comprehensive assessment of the configurations considered, we analyze the relationships between optimizing system efficiency and maximizing output power. We reveal that the two problems generally yield different solutions. However, for a hybrid structure, these objectives result in a unique solution when the effective figures of merit of the two transductions are equal. This is a distinctive property of a hybrid system compared to a single-mechanism device.

Sustainable management of water-economy-ecology nexus through coupling bi-level fractional optimization with effluent-trading mechanism: A case study of Dongjiang watershed

Ecosystem deterioration and freshwater depletion due to population growth and rapid urbanization have posed great challenges in managing water-economy-ecology nexus (WEEN) for watersheds. The objective of this study is to develop a bi-level fuzzy fractional programming (BFFP) method to identify sustainable watershed WEEN strategies with considering effluent-trading program. The BFFP method improves traditional methods for WEEN management over handling the bi-level decision process, ratio-objectives and fuzzy environmental policies simultaneously. Then, through coupling BFFP with effluent-trading mechanism, a BFFP-WEEN model is firstly formulated and employed to the Dongjiang watershed in China, where ninety-nine scenarios are examined to reflect various policy implications and decision-makers’ preferences. Major findings are: (1) compared with non-trading, effluent-trading can effectively reduce pollution discharges (e.g., reducing 140.1×106 ton of Chemical Oxygen Demand and 79.5×106 ton of Ammonia-Nitrogen over the planning horizon), revealing that effluent-trading mechanism is beneficial to the improvement of ecological environment; (2) under the trading mechanism, trading ratio has significant effects on marginal benefit and water allocation (e.g., improving marginal benefit by 15.4% and saving 363.7×106 m3 of total water allocation under 1.03 of trading ratio); (3) in order to alleviate the deterioration of ecosystem, industrial water use should be limited firstly and the area of forest land should be expanded; for ensuring the ecosystem requirements, it is recommended to promote the reuse of reclaimed water and improve wastewater treatment/recycling technology. The major improvements in this study include: (1) the development of the BFFP approach to deal with hierarchical decision conflicts and optimize system efficiency under uncertain environmental policies, and (2) the application of BFFP-WEEN model with coupling the effluent-trading mechanism to generate desired strategies for synergistic management of WEEN, and help balance the conflicts among water shortage, ecological degradation, and economic development in watersheds.

Thermodynamic performance evaluation and power/cooling energy trade-off estimation of an isolated system driven by nuclear energy

The wide temperature range requirement of components in an isolated system offers the possibility for power regulation for combined cooling and power system by adjusting the cooling energy supplement amount. To achieve the flexible operation and combined energy output for the independent nuclear power system, this paper proposes an isolated combined cooling and power system driven by nuclear energy to track power load by making an energy trade-off between net power output and cooling capacity by combing the steam Rankine cycle with the ammonia-water mixture cycle. Parametric analysis and single objective optimization are carried out to explore the effect of the key parameters on system performance. Multi-objective optimization is presented to obtain the optimal parameters group. The regulation capacity of power and cooling is analyzed by trade-off strategy for the proposed system based on the design condition. Results show that the optimal thermal efficiency of the proposed system is 31.60 % based on the single objective optimization. The rated thermal efficiency is 32.8 % with 2229.7 kW power energy and 1045.7 kW cooling energy output based on multi-objective optimization. In addition, the power load can be regulated from 78.1 % to 111.8 %, and the corresponding cooling load is adjusted from 176.1 % to 58.7 %. Furthermore, the improvement of the component performance should focus on the steam turbine and evaporator. The trade-off strategy offers an original conception of the regulation method for an isolated system.

Performance investigation and exergoeconomic optimization for a solar-driven direct heating and cooling system

Integrating solar energy with direct heating and cooling (DHC) systems can improve system efficiency and reduce polluting gas emissions. To assist buildings in better selecting a DHC system, an exergoeconomic analysis and optimization model combined with building loads and environmental parameters is proposed. The exergoeconomic optimization model is applied to a novel DHC system based on transcritical carbon dioxide heat pump (TCHP) and solar energy. The sold electricity ratio and operating time ratio are defined to transform the building loads and environmental parameters into specific operating conditions of the system. The compressor discharge pressure is optimized to obtain the lowest total unit exergy cost (UEC) of the DHC system. The exergy, energy, and exergoeconomic performance of the DHC system are analyzed under optimal conditions. The results indicate that when the compressor discharge pressure in the heating mode and cooling mode are 7.31 MPa and 8.93 MPa, respectively, the DHC system has the lowest UEC, 0.629 $/kWh. The total UEC is reduced by 43.56% compared to the reference system. Under optimal configuration, the exergy and energy efficiency of the DHC system are 1.81% and 40.30%, respectively. The sensitivity analysis shows that the exergy and energy efficiency, the total UEC, and the UEC in the heating and cooling modes are positively correlated with the sold electricity ratio. The above results demonstrate that the exergoeconomic optimization methods can provide better assistance in selecting the DHC system.

Novel hybrid power system and energy management strategy for locomotives

The traditional fuel locomotive is the primary type of locomotive currently in operation on non-electrified railways; however, it presents certain disadvantages including a low efficiency and high fuel consumption. Therefore, in this study, a multimode hybrid locomotive configuration scheme is designed to improve the system efficiency and reduce fuel consumption during locomotive operation; further, the power flow state under different modes is analyzed, the mathematical model of the hybrid locomotive is established, and a system optimal efficiency calculation method is developed. Moreover, an energy management strategy based on the optimal system efficiency and with a hierarchical architecture is proposed. In the upper layer of this proposed energy management strategy, the optimal efficiency of all operating points and operational state of each component are determined offline using the optimal efficiency calculation method. The lower layer selects and coordinates the mode online by identifying the condition of the wheel and distributes the torque of each power source and the state of each transmission system component. The simulation results indicate that the fuel economy of the proposed energy management strategy is improved by 27.22% compared with that of the traditional fuel locomotive, and the fuel economy is only 7.21% lower than the global optimization result obtained via dynamic programming. In addition, no frequent clutch switching is observed under this strategy, and the electric motor does not operate under non-rated conditions for prolonged periods; these advantages ensure the rationality of control and the reliability of component operation. Finally, the results of a hardware-in-the-loop simulation test confirm that the proposed energy management strategy demonstrates good real-time performance.

Strategies for sensor virtual in-situ calibration in building energy system: Sensor evaluation and data-driven based methods

Sensor errors greatly affect the optimal control, safe operation, and energy efficiency of the building energy system. The sensor virtual in-situ calibration (VIC) method based on Bayesian inference can calibrate the sensor errors and does not need to remove existing sensors or add redundant sensors. In the previous study, the calibration of all sensors (non-differential calibration) and the physical model-driven based methods are applied in the simulation system. However, in the practical building energy system, these reduce calibration accuracy and efficiency. To solve these two negative factors, two calibration strategies are proposed: (1) sensor evaluation, and (2) data-driven based methods. Four cases are designed to demonstrate calibration accuracy and efficiency improvement by applying the calibration strategies in the variable air volume system. The results show that non-differential calibration causes difficulties in practical applications because of its low accuracy (38.10 %) and long calibration time (5136.35 s). The calibration accuracy of the physical model-driven based methods is 9.06 %, which completely deviates from the true value. The two calibration strategies are applied to improve the sensor calibration accuracy (up to 91.88 %) and reduce the calibration time (28.99 %) although it will increase some replacement costs. Meantime, the calibration accuracy of the temperature sensor is further improved (exceeding 94 %) by dividing the operating conditions according to the compressor power. In summary, the application of the calibration strategies can effectively overcome the negative impacts on calibration accuracy and efficiency, and increase the robustness of the VIC method in the practical building energy system.

System level performance analysis and parameter optimization of hydrogen production based on solid oxide electrolytic cell

The high-temperature solid oxide electrolytic cell (SOEC) is expected to be widely used in large-scale energy storage, the chemical industry, distributed energy, and other fields due to its high efficiency and cleanliness. Moreover, selecting the operation point of the SOEC system with variable load under high-temperature conditions and striking a balance between safety and efficiency are difficult. Therefore, a 3 kW SOEC system model is built following the electrochemical reaction principle and the considered system layout. Based on this, the influence of operating parameters on stack voltage and power, stack outlet temperature, system H2 production, specific energy consumption, system efficiency, and the power consumption distribution of auxiliary equipment are analyzed. Finally, the operating current, temperature, air flow, and H2O utilization rate were taken as optimization variables, and the safe range of operating parameters was taken as the constraint condition to optimize system efficiency. The results show that under the given operating conditions and assumptions, the energy efficiency of the SOEC system can be adjusted from 60 % to 100 %, and the temperature difference can be controlled within 100 ℃. The durability of the system is improved by maximally reducing the temperature difference while ensuring the high efficiency of reactor operation.

Performance investigation of a solar/biomass based multi-generation system in pig farm

Intensive pig farm usually needs a large amount of energy, renewable energy supply system contributes to reducing the reliance of fossil fuel. In this study, load prediction of piggery considering different breeding characteristics and climate condition are carried out. An innovative solar/biomass hybrid multi-generation system combined with biomass combustor, ice thermal energy storage, absorption chiller, evacuated flat plate collector, photovoltaic panel and thermal energy storage is proposed. The operation strategy of following cooling demand is designed for proposed system, which considers the feature of high cooling load of piggery. The influences of key parameters on thermodynamic, economy and environment performances are investigated, thermodynamic operation performances of multi-generation system on different typical days are analyzed. Moreover, non-dominated sorted genetic algorithm is used for tri-objective optimization to obtain the optimal operating parameters and performances. Under the optimal condition, exergy and energy efficiency of integrated system are 19.05% and 53.59%, respectively. Compared with separation system, CO2 emission reduction ratio and dynamic investment payback period of multi-generation system are 70.39% and 5.12 years, respectively. In summary, this study offers a novel way for multiple energy coupling poly-generation system in intensive pig farm so as to obtain optimal performance in operation and scheduling.

Theoretical and experimental study on the heat collection of solar dish system based on adjustable receiver

Based on the cascade utilization of solar energy at different DNI levels, the establishment of a solar dish Stirling cogeneration system is one of the effective solutions both from the current Stirling engine technology level and from the perspective of overall system efficiency optimization. In this paper, a model of solar dish concentrating and receiving based on the position adjustable receiver is proposed, which provides a theoretical basis for the application of the solar dish system in cogeneration. The accuracy and reliability of the model are verified by the multi working condition heat collection experiment, and the change rule and value range of the key parameters in the model are determined. The results show that by adjusting the value of the excess heat removal factor in the model according to a certain rule within a certain range, the calculated results of the theoretical model can be well matched with the experimental results, and the average relative error can be controlled within 10% or even 5%. However, compared with the experimental results, the theoretical model results are more volatile, especially on a shorter time scale. In addition, with the increase of heat transfer oil temperature, the excess heat removal factor shows an increasing trend, and the overall value range is 0.45–0.95. The cases below 0.5 and above 0.9 usually occur at the beginning and end of the experiment. As the experiment goes on, when the oil temperature reaches about 80–100 ℃, the value of the excess heat removal factor begins to fluctuate, which intuitively shows that the overall trend is still subject to increase with the increase of the oil temperature, but the corresponding oil temperature range starts to overlap.

Computational social science in smart power systems: Reliability, resilience, and restoration

AbstractSmart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.

Optimal concentration and efficiency of the photo fenton system for the treatment of a synthetic textile effluent

The Photo Fenton process is an alternative process for the treatment of effluents from the textile industry, being a reagent-based chemical on Fe2+ and H2O2 + UV light, whose mechanism consists of oxidizing the pollutants until they are suitable for discharge into bodies of water. This research seeks to determine the optimum concentration of Fe2+ - H2O2 in the treatment of a synthetic textile effluent and, subsequently, to evaluate the efficiency of the system. The synthetic textile effluent was prepared under laboratory conditions, FeSO4 and H2O2 were added at different concentrations, with exposure to UV-A light throughout the process, and it was determined that the optimum concentration of the Photo Fenton reagent was 400 mg L-1 of FeSO4 and 12 ml L-1 of H2O2, a dosage that reduced initial turbidity, color, and COD by 100%, 99% and 83%, respectively, and increased the DO by 11%. Turbidity removal was obtained by carrying out the treatment in an acid medium (pH 3). The presence of Fe2+ promoted the significant removal of COD. The increase in DO was obtained by the presence of H2O2 and UV-A light, and this in turn, had a considerable influence on the removal of effluent color. Regarding the efficiency of the system, it was determined that the Photo Fenton reagent is an excellent alternative for the treatment of textile effluents with high and low organic loads, reducing contaminants derived from industry processes.
 Keywords: clean technology, Fe2+-H2O2, organic, oxidation, pollution, water.

Integration Optimization of Integrated Solar Combined Cycle (ISCC) System Based on System/Solar Photoelectric Efficiency

Integrated solar combined cycle (ISCC) systems play a pivotal role in the utilization of non-fossil energy; however, the efficient application of solar energy has emerged as a primary issue in the study of ISCC systems. Therefore, it is extremely urgent to propose the best optimization scheme for ISCC under different operating conditions. In this paper, according to the idea of temperature matching and cascade utilization, the optimization of the ISCC system is carried out with the genetic algorithm for the whole working conditions, and the optimization schemes with the highest photoelectric efficiency and system efficiency under different working conditions are derived. In comparison with two optimization schemes with different objective functions, the conclusion can be drawn that: At 100% gas turbine load—30% DNI and 100% gas turbine load—100% DNI working conditions, respectively, the maximum system efficiency of 56.32% and the maximum solar photoelectric efficiency of 35.5% are attained. With the decreasing of gas turbine load, the solar energy integration position will gradually change from the topping cycle to the bottom cycle; with the gas turbine load variation from 100% to 75%, the optimal photoelectric efficiency model prefers two-stage integration, and up to 141.3 MW of solar energy could be integrated, which is greater than the maximum value of 127.1 MW for the optimal system efficiency model. Regarding the heat collection choice of bottom cycle, the optimal photoelectric efficiency model prefers the high-pressure boiler (HPB), while the optimal system efficiency model prefers the high-pressure superheater (HPS). The comparison between the optimal solution and the actual cases confirms the correctness of the optimization results and provides guidance for the subsequent ISCC study.

Comparative review and evaluation of state-of-the-art photovoltaic cooling technologies

Photovoltaic systems have a reliable method to generate electrical power from direct solar irradiance. Their efficiency is only up to 20% in real operating circumstances and is affected adversely by the inevitable temperature rise in their surface. Passive, active or combined cooling methods are applied to reduce operating temperature to a suitable level, depending on specific application. A Great body of literature has been investigating different PV cooling methods and techniques to reach optimized system efficiency and cooling effect. It is critical for both researchers and practitioners to have a thorough understanding and awareness of the most recent advances and existing gaps. Although there are certain review papers in this field, it is necessary to have up-to-date reviews of the most recent progress and research since the previous ones. In the present work, the main focus was to provide a review of the most recent studies and to compare their outcomes to provide a reference for practitioners and researchers. The holistic classification and evaluation of all the possible cooling technologies were provided in order to draw a comparison between various cooling approaches. Based on the analysis obtained, cooling techniques can ensure improvement in electrical efficiency in a range from 1% to 53% with expected decrease in the photovoltaic panel's surface operating temperature from 1.8 °C to 46 °C. It was also found that spectrum filtering and jet impingement indicated highly remarkable enhancements. The other factors, such as the size of the examined photovoltaic panel, photovoltaic technology, as well as climate circumstances have a strong impact on the cooling performance besides the selection of the specific coolant. The necessity for integral evaluation (performance, economic and environmental) of cooling techniques was also identified as key issue.