PV Units Research Articles

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

A New Electric EEL Foraging Optimization Technique for Multi-Objective PV Unit Allocation in the Context of PHEV Charging Demand

A New Electric EEL Foraging Optimization Technique for Multi-Objective PV Unit Allocation in the Context of PHEV Charging Demand

An Adaptive Multi-Functional Control Strategy for Power Management and Voltage-Frequency Regulation of PV, BESS, and Hybrid Units in a Microgrid

An Adaptive Multi-Functional Control Strategy for Power Management and Voltage-Frequency Regulation of PV, BESS, and Hybrid Units in a Microgrid

Optimising power of PV module using modified MPPT for standalone load

The maximum power point tracking (MPPT) has been common terminology among the researchers for those who deal with solar energy. A lot of techniques involving MPPTs have been investigated, evolved and proposed by researchers. But those techniques have got some advantages and suffer from limitation to some extent. The gravity of limitations varies from case to case. In current work, it has been investigated the MPPT based Boost Converter without and with MPPT which is perturb and observe (P&O) type. After observing its limitation, this method of MPPT is modified so as to make it suitable for extracting the maximum power from PV unit at all different load conductance’s. In order to validate this concept, the mathematical model is developed using a MATLAB/Simulink environment and this Modified MPPT technique is implemented. The whole model is simulated and compared with conventional method. This concept is quite new and the proposed one exhibits better performance as compared to conventional methods.

Economic and technical analysis of an HRES (Hybrid Renewable Energy System) comprising wind, PV, and fuel cells using an improved subtraction-average-based optimizer

HRES (Hybrid Renewable Energy Systems) has been designed because of the increasing demand for environmentally friendly and sustainable energy. In this study, an Improved Subtraction-Average-Based Optimizer (ISABO) is presented for optimizing the HRES system by wind power, fuel cells, and solar energy. The suggested approach, by introducing adaptive mechanisms and enhancing processes, improves the performance of the traditional subtraction-average-based optimization. Optimization aims to provide reliable and efficient energy while lowering system expenses. The efficacy of ISABO is evaluated for this goal and compared with other optimization techniques. According to the findings, The ISABO algorithm, when equipped with adaptive mechanisms, surpasses conventional optimization techniques by achieving a 12 % decrease in Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) along with a 45 % cost reduction in electrolyzers. Through simulations, it has been shown that the ISABO algorithm ensures the lowest average NPC at $1,357,018.15 while also upholding system reliability with just a 0.8 % decline in Load Point Supply Probability (LPSP) in the event of a PV unit failure. This research validates that hybrid PV/wind/fuel cell systems present superior cost-effectiveness and reliability, thereby opening doors for more economical renewable energy solutions. The study reveals hybrid PV/wind/fuel cell systems are more cost-effective than purely wind, PV, or fuel cell systems. This advancement in HRES design and optimization techniques will enable more cost-effective renewable energy options.

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units

This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods.

Large Signal Stability Analysis of Hybrid AC/DC Microgrids When a Single-Phase-to-Ground Fault Occurs

Islanded hybrid AC/DC microgrids lack support for a large grid, and the negative incremental impedance of constant power loads (CPLs) aggravates the poor anti-disturbance capability of the system. When a single-phase ground fault (SPGF) occurs, the amount of fault impulse power that islanded AC/DC hybrid microgrids can stably withstand and when the protection equipment can work are both unknown. In this paper, the method of symmetrical components is utilized, and high-signal stability criteria for islanded hybrid AC/DC microgrids when a SPGF occurs are derived based on the mixed potential theory. The proposed criteria place quantitative constraints on the power of the PV unit, DC/AC converter current inner-loop proportional parameters, inductors, and inductor equivalent resistance, as well as energy storage unit power, CPL power, capacitors, DC bus voltage, line equivalent resistance, line equivalent inductance, equivalent inductance in the faulty branch, equivalent resistance in the faulty branch, positive-sequence equivalent impulse power of the SPGF, and zero-sequence equivalent impulse power. Furthermore, the maximum impulse power of a SPGF that islanded hybrid AC/DC microgrids could stably withstand is also presented, providing guidelines for protection equipment to decide when to work. In addition, the allowable maximum CPL power that islanded hybrid AC/DC microgrids could steadily support as a SPGF occurs is deduced, and the power is usually adopted to determine the states of an energy storage unit and load shedding in advance. Simulation and experimental validations prove the correctness of the derived high-signal stability criteria.

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.

Revolutionizing photovoltaic excellence: Unveiling a cutting-edge approach to assessing module enhancers for optimal area and cost efficiency

A photovoltaic module (PV) functions as a device for producing electricity. Increasing the number of PV units leads to higher power generation, but this also requires a larger land's area. However, limitations in available space, especially on building rooftops, coupled with consumer demands for greater power output, pose a challenge. To tackle this issue, the PV enhancer technology has been introduced. It aims to boost power generation per unit area and has been evaluated in a previous study. While this existing approach effectively compares different PV enhancers, it becomes complex when considering both the PV and enhancer's area parameters. To simplify this, a novel method is introduced for the first time which incorporates a new correlation considering the area of both the PV and its enhancer, the combined net power generated, the PV's output power without an enhancer, and the maximum PV power under standard test conditions. It also includes the cost per watt of PV power and the manufacturing cost of the PV enhancer. The impact of each parameter on this new method is thoroughly explained. The proposed technique categorizes PV enhancers based on values derived from the new method. For instance, enhancers are classified as having maximum or minimum performance based on whether the value of the new method equals the minimum value of the area and cost-effectiveness factor or unity, respectively. The effectiveness of the new method is confirmed through testing on a real-world case from a previous experimental study, demonstrating its practicality. The new method has shown applicability in real world scenarios and can aid in categorizing types of PV enhancers, offering valuable insights for researchers, designers, and manufacturers for informed decision-making.

Energy management of a concentrated photovoltaic−thermal unit utilizing nanofluid jet impingement in existence of thermoelectric module

To generate a concentrated photovoltaic–thermal (CPVT) unit system, linear Fresnel concentrators have been attached to a PV unit and a thermoelectric generator (TEG) has been combined in this work to boost productivity. In the presence of concentrators, the temperature of the silicon layer increases. While electrical output is enhanced for the CPVT unit, the non-uniform isotherms can decrease the lifetime of the panel, so confined jets of alumina–water nanofluid have been applied for cooling. To validate the numerical code, previous papers were examined and good accommodations were reported. Various values of the inlet temperature (T in) and velocity (V in) of jets have been analysed. The impacts of installing concentrators on overall performance and CO2 mitigation have been investigated. An increase in V in leads to an incremental increase in thermal performance of about 4.2% and thermal uniformity is enhanced by about 13.91%. The thermal power of the system is enhanced by about 2.19 times as a result of adding concentrators. Also, the PV power is enhanced by 86.22% in the presence of the reflectors. With the increase in T in, the thermal and electrical performance decrease by about 19.95% and 5.24%, respectively. Installing concentrators enhances the overall performance by about 6.55%. After 10 years, the amount of CO2 mitigation for the CPVT-TEG system reaches 148.28 ton.

Power Regulation of Islanded PV-Battery DC Microgrid With Seamless Transition

In this paper, a power regulation for islanded PV-battery DC microgrid with seamless transition is proposed under sudden load changes. In the power regulation, there are two modes, mode 1 for normal operation and mode 2 for the case where the load power is greater than the PV output, using droop control of PV to maintain the bus voltage and constant voltage control of energy storage to maintain the bus voltage, respectively. Mode 1 ensures that the battery is in a stable charging state and avoids frequent charging/discharging of battery. Mode 2 can maximize the use of PV energy. A seamless switching strategy without communication between the two modes is also established to suppress the fluctuation of bus voltage through the cooperation of PV and energy storage units, and a suitable switching timing is selected to avoid the sudden change of power during the switching process. Finally, the effectiveness and feasibility of the proposed control strategy is verified by the OPAL-5700 hardware-in-loop simulation platform.

Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer

This paper examines and analyses a novel developed algorithm named Modified Artificial Rabbits Optimization (mARO), which is based on the modification of a bio-inspired meta-heuristic algorithm called Artificial Rabbits Optimization (ARO) combined with dimension learning-based hunting technique. In order to prove the efficiency and evaluate the constrained optimization of this modified algorithm mARO, it is applied to one of an engineering application. This engineering application is the study of the optimal sizing of a stand-alone hybrid system based on techno-economic criteria; this hybrid system consists of the PV, WT, Biomass system, and Battery units. The simulation of hybrid power systems must be carried out with the least amount of expense and harm to the environment so appropriate performance may be ensured using an efficient and optimal sizing strategy. In order to prove the superiority of this modified algorithm mARO, the results of this algorithm were compared with other new algorithms, and these algorithms are the original ARO, Dandelion Optimizer (DO), and Driving Training-Based Optimization (DTOB).

An experimental investigation of variable-step-size Affine Projection Sign based algorithm for power quality enhanced grid-interactive solar PV system

This article proposes a variable-step-size Affine Projection Sign algorithm that improves power quality ancillary services in a grid-interactive solar photovoltaic system. VSS-APS algorithm features decreased steady-state fluctuation, faster convergence, and robustness to disturbances. The VSS-APS algorithm efficiently extracts contaminated source current active and non-active fundamental weight components to estimate source current reference signals. The solar grid-integrated voltage source inverter manages solar PV unit active power and provides free multipurpose PQ-ancillary services. Harmonic current, active power, load, neutral current, reactive power, and power factor correction are the PQ indices that improved significantly with the proposed VSS-APS control. VSS-APS improves GISVSI's steady-state and dynamic performance. MATLAB®/SIMULINK and a scaled laboratory prototype developed to test the VSS-APS control approach. The DSP-dSPACE 1104 controller implements the suggested controller in real time. The proposed VSS-APS control algorithm is compared with the conventional algorithms, and test results from a laboratory experiment demonstrate its feasibility and benefits. The experimental results show that the proposed VSS-APS algorithm is effective in improving power quality while complying to IEEE 519-2014 and IEEE 1547-2003 standards for grid connections.

Towards a sustainable edge computing framework for condition monitoring in decentralized photovoltaic systems

In recent times, the rapid advancements in technology have led to a digital revolution in urban areas, and new computing frameworks are emerging to address the current issues in monitoring and fault detection, particularly in the context of the growing renewable decentralized energy systems. This research proposes a novel framework for monitoring the condition of decentralized photovoltaic systems within a smart city infrastructure. The approach uses edge computing to overcome the challenges associated with costly processing through remote cloud servers. By processing data at the edge of the network, this concept allows for significant gains in speed and bandwidth consumption, making it suitable for a sustainable city environment. In the proposed edge-learning scheme, several machine learning models are compared to find the best suitable model achieving both high accuracy and low latency in detecting photovoltaic faults. Four light and rapid machine learning models, namely, CBLOF, LOF, KNN, ANN, are selected as best performers and trained locally in decentralized edge nodes. The overall approach is deployed in a smart solar campus with multiple distributed PV units located in the R&D platform Green & Smart Building Park. Several experiments were conducted on different anomaly scenarios, and the models were evaluated based on their supervision method, f1-score, inference time, RAM usage, and model size. The paper also investigates the impact of the type of supervision and the class of the model on the anomaly detection performance. The findings indicated that the supervised artificial neural network (ANN) had superior performance compared to other models, obtaining an f1-score of 80 % even in the most unfavorable conditions. The findings also showed that KNN was the most suitable unsupervised model for the investigated experiments achieving good f1-scores (100 %, 95 % and 92 %) in 3 out of 4 scenarios making it a good candidate for similar anomaly detection tasks.

Analysis of PV/Wind systems by integer linear programming

This paper presents a mathematical formulation for integrating renewable energy sources in order to build up economical hybrid energetic systems in the case where each type of energy are only available as specific units. For instance, we may need to combine photovoltaic panels and wind turbines with specific capacities to meet an energetic demand in a specific site with a lowest cost. Therefore, determining the optimal energy to be installed leads of determining the number of units from each source. This problem is formulated as an integer linear program where the objective function to be minimized is the initial capital investment and where the decision variables are the numbers of units which should be pure integer numbers. The proposed method is generic since it is directly adaptive to various sites, demands, and different type of renewable energy sources. To illustrate our approach, we provide some examples and scenarios combining PV and wind turbines units.

Multiple distribution networks hosting capacity assessment using a stochastic approach

The stochastic approach is applied to an entire concession area with 1264-LV distribution networks to estimate the hosting capacity. About 15 000-customers are connected to the individual secondary distribution networks supplied through 48-medium voltage 10 kV radial feeders.The hosting capacity assessment uses the end-customer voltage magnitude rise and transformer thermal overload. The hosting capacity is estimated by applying the “stochastic mixed aleatory-epistemic method” to determine the voltage magnitude rise and load flow with solar PV. The minimum power consumption is compared with the solar PV power infeed through the individual transformers.The hosting capacity estimation is done for three-phase connected solar PV sizes from 3 to 18 kW. At moderate PV penetration (25%–50%), the results showed that overvoltage would limit the hosting capacity more often than overload, but it becomes an issue only for LV networks studied with more than 8-customers. Considering all LV networks, most of the customers could install 6 kWp. Even when installing PV systems of 18 kWp (about twice the average size today and about the maximum area of a typical residential roof), two-thirds of houses would not need an upgrade to withstand SS-EN 50160 voltage limits. The latter customers can connect solar PV units with 18 kWp size without overvoltage or overload issues.

Off-grid Operation Research Energy Router Strategy on the Base of Hierarchical Control

To coordinate off-grid control of the Energy Router, the Energy Router topology is first analyzed using isolated bidirectional full-bridge DC/DC inverters for DC ports, boost converters for PV units, and three-phase voltage-based bridge inverters for AC ports. Then the system is layered, with the lower layer developing different control strategies for different ports, the upper layer layering the DC bus voltage and dividing different operating modes in combination with the energy storage SOC, and then developing strategies for each mode. Finally, simulations are built in the MATLAB platform according to the topology to verify the possibility of off-grid operation of the energy router.

The effect of the properties of porous fillers on the performance of a PCM-based photovoltaic thermal system: A CFD study

In thermal systems, combination of PCMs with porous fillers with high thermal conductivity is a proper method to improve their performance. In photovoltaic thermal (PVT) systems, as porous fillers are introduced to the collector tube and PCM bulk, the thermal efficiency increases. Moreover, since the temperature of the PV unit has dropped, the electrical efficiency has improved too. However, the properties of the porous insertion, i.e., permeability and porosity, might affect the pumping work, and consequently, electrical efficiency. In the present study, the effect of metal foam insertions on thermal and electrical performances is analyzed using a computational fluid dynamics (CFD) method. The results show that installing metal foams with porosities of 0.70–0.94 inside the collector and PCM container of a PVT system improves the thermal efficiency without any considerable decrement in electrical efficiency. When copper foam with a porosity of 0.705 is installed within the collector and PCM tank, the highest thermal efficiency of 66.70% is achieved; while, when copper foam with a porosity of 0.94 is used, the maximum electrical efficiency of 13.24% is resulted.

Thermal and electrical performances of PV/T system based on reversible thermochromic coating

In this study, a new PV/T system structure is proposed based on the upper surface cooling PV/T system of PV cells. Conventional PV/T system PV unit and solar thermal unit are mutually constrained. The PV/T system proposed in this study solves this problem by separating the collector unit and the photovoltaic unit. A reversible thermochromic coating that can be applied to PV/T system collectors was prepared to further improve the system efficiency. The function of regulating the ratio of PV/T system photothermal and photovoltaic output is achieved by utilizing the property of thermochromic coating to change color when reaching the target temperature. An experimental platform was built for the experimental study and analysis of the PV/T system based on reversible thermochromic coating. The results show that: Adding the right amount of high thermal conductivity powder to the reversible thermochromic coating can improve the thermal efficiency of PV/T systems. The thermal efficiency of the PV/T system reached 46.09% with the addition of 10,000 mesh graphite at a mass fraction of 2‰ in the thermochromic coating, which was 16.57% higher than that of the PV/T system without the coating. When the mass fraction of 36,000 mesh graphite added in the thermochromic coating was 3‰ the PV cell output power regulation effect was the best, and the average output power of PV cell was increased by 10.19% after coating color change. The primary energy saving efficiency of PV/T system reached 53.26% when the mass fraction of 10,000 mesh graphite added to the thermochromic coating was 2‰, which was 10.29% higher than the primary energy saving efficiency of PV/T system without coating. The PV/T system in this study is applicable to the application scenario where the thermal energy demand is dominant, broadening the scope of application of the PV/T system.