Photovoltaic Wind Research Articles

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

An effective sizing study on PV-wind-battery hybrid renewable energy systems

Hybrid renewable energy system is the effective and efficient way of energy harvesting in remote or isolated areas having the advantage of harnessing of multiple renewable sources at a time. Solar, wind and battery-based hybrid system is one of the most promising alternatives in this field. Sizing of the system components depend on various technical and economical specifications. The research studied the effect of solar panel tilt angle and wind turbine hub height on sizing optimization of PV wind battery-based hybrid renewable energy system using HOMER software. Through empirical method, an optimal monthly, seasonal, yearly optimal tilt angle and wind turbine hub height have been computed. These values are used in addition with various other technical and economical specifications in order to determine the optimal sizing of hybrid system. At first, three different scenarios have been considered for the study of yearly, seasonal, and monthly optimal tilt angle with an optimized hub height for the wind turbine. Then, a comparative analysis of the sizing of hybrid system at unoptimized and optimized tilt angle and hub height have also been done. Furthermore, a detailed comparative and sensitivity analysis have also been performed across different scenarios and approaches. A detailed financial study for policy planning and implementation issues with other, traditional state of the art approaches significantly demonstrates the effectiveness of the proposed method for optimal sizing of the hybrid system.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

A Review on Privacy Protection Techniques in Smart Grid Applications

: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

Research on Optimal Scheduling Strategy of Differentiated Resource Microgrid with Carbon Trading Mechanism Considering Uncertainty of Wind Power and Photovoltaic

Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG.

Optimal design and three-level stochastic energy management for an interconnected microgrid with hydrogen production and storage for fuel cell electric vehicle refueling stations

A new trend in the transportation sector globally can be observed in a shift away from gasoline-powered vehicles to Hydrogen-based fuel-cell electric vehicles (FCEVs), aimed at reducing harmful emissions. However, there are challenges in producing hydrogen for vehicle stations related to microgrid design and energy management under uncertain conditions. This research sought to identify the optimum design of an electric microgrid to provide the required energy for electric loads, together with a hydrogen refueling station. The microgrid under study consists of various renewable energy resources (RERs), such as photovoltaic (PV) devices, wind power systems, and hydrogen storage systems. The energy management strategy (EMS) aims to reduce the total costs (investment, operation, replacement, procurement energy costs) considering four uncertain parameters associated with PV panels, FCEVs, wind turbines, and power demand. A three-level EMS is proposed based on testing various solutions: without RERs or a hydrogen energy storage system (Level 1); with RERs and a hydrogen energy storage system (Level 2), with RERs and hydrogen energy storage that includes demand side response (DSR) (Level 3). The results indicate annual cost savings of 1.946 E+06 $ for Level 2 and 2.001 E+06 $ for Level 3, compared to Level 1.

Understanding the Influence of Wind and Solar PV on Socioeconomic and Environmental Trends: A Non-causality Perspective

This paper investigates the non-causality perspective of economic growth, investment, social well-being, employment, GHG emissions on renewable energy sources (RES) (wind and solar PV), providing a comprehensive overview of renewable technologies that effect on socio-economic and environmental drivers. The analysis and enhancing linear regression techniques, addressing cross-country RES installed capacity heterogeneity, and applying time-lags techniques to better understand the temporal effect of RES´s investment. The study tested: I. economic growth in a country is related to the development of RESs, employment, investment. II. Increased RESs share positively impacts social well-being. III. RESs implementation reduces GHG emissions. Our findings suggest that RES positively influenced economic growth over time for countries with higher installed capacity. However, this statistically significant is not observed across countries with lower installed capacity nor across all indicators, like social well-being, employment, investment, which show modest growth trend over time. GHG emissions also showed an inverse trend, suggesting the needs the renewable electricity´s expansion accompanied by additional mitigation measures to reduce emissions. One limitation is the temporal restriction, which limits the assessment of long-run trends. Future research should focus on gathering a larger dataset for long-run trends analysis.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

A three-level model for integration of hydrogen refuelling stations in interconnected power-gas networks considering vehicle-to-infrastructure (V2I) technology

The coupling with natural gas networks creates an excellent opportunity for renewable-rich power systems to facilitate the utilisation of renewable energy resources; on the other hand, with the increase in employment of fuel cell vehicles (FCVs), the number of hydrogen refuelling stations (HRSs) is increasing. The integration of HRSs in electric distribution systems may impact the operation of electric distribution systems. Through vehicle-to-infrastructure (V2I) technology, vehicles may exchange information with HRSs and know hydrogen prices. The operation of the coupled power and natural gas networks with integration of green HRSs, considering the model of FCVs has not been investigated in the literature, so, it has been set as the goal of this paper. To avoid the challenges of nonlinear gas flow model, they are linearized through piecewise linearisation. The case study is a 33-bus electric distribution system, coupled with a 14-node gas distribution network; each HRS includes a battery, a hydrogen tank, an electrolyzer, a PV and a wind turbine. MILP models have been used for FCVs, HRSs and power-gas networks. CPLEX solver is used for solving the developed MILP models. The results show that the total cost of the coupled electricity-gas network is $760.37 and the refuelling cost of each FCV is $17.30. The results indicate that both electric and hydrogen storage systems enhance the flexibility of HRSs, enabling efficient utilisation of PV modules and wind turbines, so, only in few hours, HRSs need to purchase electricity from electric distribution system; that is why each HRS makes a considerable profit of $519 per day. The achieved results also indicate that the pressure of all gas nodes and gas flow of all pipelines fall within their prespecified range.

Simulation and Optimisation of Utility-Scale PV–Wind Systems with Pumped Hydro Storage

Simulation and Optimisation of Utility-Scale PV–Wind Systems with Pumped Hydro Storage

(Invited) Stack Test Facility for PEM Water Electrolysis up to 400 Kilowatt and Lessons Learned at Stack Scale-up

A new type of energy system based on renewable energy sources and hydrogen technologies is put into operation at Forschungszentrum Jülich, Germany. The aim of the "Living Lab Energy Campus (LLEC)" project is testing and optimizing the systems interactions. Central components are wind power and PV systems, PEM electrolysis system, conditioning and subsequent gas transport in H2 and O2 pipelines, H2 storage in LOHC reactors and hydrogen use in combined heat and power plants.The PEM electrolysis system in Fig. 1, left, is an R&D test platform which was jointly configured by Forschungszentrum Jülich (IEK-14) and Greenlight Innovation Corp. and built by Greenlight. It offers a range of different operating modes and system configurations that will be studied embedded into the LLEC: Equal pressure or differential pressure anode/cathode up to 50 bar(g).Water circulation on anode and/or cathode side.Stack cabin with flexible connections for fluid inlets and outlets as well as electrical current. This makes it easy to connect and operate different stacks (also from other manufacturers).Automated system operation, simulation of load profiles (e.g. wind/PV). The system is CE-certified and can run continuously under automatic control. Critical system conditions, such as exceeding the H2 concentration limit (currently set at 30 % LEL) in the O2 gas flow, immediately initiate an emergency shutdown program. In the emergency shutdown program, the power supply unit is first switched off and the pressure on the anode and cathode side is reduced to ambient pressure. The reason for the fault leading to the shutdown is stored in the system control unit and can be called up. In addition, all measured values and states of the installed sensors and actuators are recorded by the system control unit and stored with an adjustable time resolution. The measured values can be used to monitor the system behavior and detect problems at an early stage, but also to characterize and balance the overall system, system components and the stack.For generating large amounts of hydrogen within the LLEC project, IEK-14 initiated the development of a PEM electrolysis stack. The electric design data was taken from the performance data of a laboratory cell (1.75 V at 3 A cm-2, 20 cm² active area) and scaled up. The stack consists of 60 cells with an active cell area of 1056 cm².The bipolar plate was completely redeveloped. The main features are: Use of porous layer structures with different degrees of fineness as a flowfield structure.Welding of all components of the bipolar plate to form a material-locking unit. Preliminary tests in short stacks with bipolar plates on a smaller scale (100 cm² active cell area) showed promising results. The design and manufacturing process for the bipolar plates have now been patented (EP4128399) and published (Janßen H et al., A facile and economical approach to fabricate a single-piece bipolar plate for PEM electrolyzers, Int. J. of Hydrogen Energy, 2023).An interesting aspect that will be discussed further in the presentation is the scale-up of both the active cell area and the number of cells. Ultimately, the aim is to understand which parameters (and their fluctuations) are mainly influencing the stack performance during scale-up. A data analysis of component properties and a comparison with operating data should provide initial indications. Fig. 1, right, shows a 60-cell PEM stack with an active cell area of 1056 cm², of which short stacks with 4 cells were also constructed. Figure 1

A coordinated operation method of wind-PV-hydrogen- storage multi-agent energy system

Wind-photovoltaic (PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization. In this study, a coordinated operation method was proposed for a wind-PV- hydrogen-storage multi-agent energy system. First, a coordinated operation model was formulated for each agent considering peer-to-peer power trading. Second, a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers. Third, a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies. Finally, the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures, and the operational revenues of the wind power, PV, hydrogen, and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%, 233.28%, 16.75%, and 145.56%, respectively, compared with the independent operation model.

Improving capacity configuration and load scheduling for an integrated multi-sourced cogeneration system

The integration of renewable energy sources and fossil fuel e.g. coal in a heat and power cogeneration system has been proven to be an effective way to reduce CO2 emissions. An integrated multi-sourced cogeneration (IMC) system consisting of wind, solar photovoltaic (PV), and coal-fired combined heat and power (CHP) plant on the energy supply side, is studied in this paper. A bi-level collaborative optimization model for capacity configuration and load scheduling of the wind-PV-coal IMC system is developed. In the upper level, the capacity of wind farm and PV farm are optimized with the enumeration method, while the load scheduling under typical annual conditions is optimized with adaptive particle swarm optimization algorithm in the lower level. The results show that the optimal capacities of wind and PV are 120 MW and 280 MW for the IMC system, respectively. The annual and typical day load scheduling with optimal wind and PV capacities are analyzed. The result of the load scheduling shows that heating and power load distribution unevenly between the two CHP units contributes to reducing total standard coal consumption, which saves 34.96 t in a typical day during the heating season. This study can provide a reference for the capacity configuration and operation scheduling of the IMC system.

Exponential slime mould algorithm based spatial arrays optimization of hybrid wind-wave-PV systems for power enhancement

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases.

Feasible synergy between hybrid solar PV and wind system for energy supply of a green building in Kota (India): A case study using iHOGA

This research article analyses the optimal feasible synergy options of renewable energy sources combined into a hybrid PV panel and wind energy power producing system to deliver green energy for the Renewable Energy Department building at Rajasthan Technical University Kota, India. The configuration of the modeled system is optimized to be estimated with improved hybrid optimization by genetic algorithm (iHOGA). This study generates operational conditions for this hybrid solar-wind energy system which simulates the energy usage process over one year. The optimal synergy configured between solar and wind hybrid systems provides peak-demand energy to the green office building with little surplus energy. The whole year’s production of energy was 6988 kWh and the energy used by the building was 6759 kWh. The energy loss estimated is 276 kWh/year. The CO2 mitigated by using the integrated renewable energy option is evaluated and observed at 5273.14 kg/year. The other pollutants like SO2 and NOx are also being mitigated by 9601.512 kg/year and NOx 32424.32 kg/year respectively.

Social life cycle hotspot analysis of future hydrogen use in the EU

Abstract Purpose The widespread use of hydrogen in the EU aimed at reducing greenhouse gas emissions may involve complex value chains (e.g. importation from third countries) with potential effects (positive or negative) on the different sectors of society. Achieving sustainable hydrogen deployment must be motivated not only by environmental and economic aspects but also by social responsibility and the search for human well-being. Given this, and the scarcity of studies currently available on prospective social impacts of hydrogen production, the present purpose of this article is to unveil and assess the main social impacts linked to the future hydrogen value chains. Methods The methodological approach adopted in this article encompasses the following steps: (i) analysis of two potential value chains for hydrogen use in EU: an on-site option, where hydrogen is produced and used in the same European country, and an off-site option, where hydrogen is produced in a European country different from its usage involving more unit processes, in terms of storage and transport activities, and working time to deliver the same quantity of hydrogen. This framework will include (i) scenario analysis and a forward-looking perspective taking into account the critical raw materials employed across the entire value chain, (ii) identification of a list of relevant social impact categories and indicators through a systematic procedure, (iii) social hotspot analysis using Product Social Impact Life Cycle Assessment (PSILCA) to assess the selected representative value chains, and (iv) conducting scenario analysis and subsequently interpreting of results. Results and discussion The off-site value chain shows a relatively worse social performance (6 to 72 times) than the on-site value chain across most selected indicators due to the more complex value chain. Although the identification of social hotspots depends on the specific social indicator under evaluation, the power source components (wind and solar PV) manufacturing processes and the relatively increased complexity of the off-site option highly conditioned the social performance of the hydrogen value chains in most of the indicators considered. A scenario analysis was carried out comparing both value chains with two additional locations for hydrogen production: Northern Africa and Western Asia. The findings indicate that the on-site value chain presents the lowest impact scores. For the off-site option, the production of hydrogen in a European country is the most preferable scenario in terms of the social indicators evaluated. Conclusions According to findings, producing hydrogen in a different location than where it is consumed increases the social impacts of its deployment. Measures at mid and long term should be considered for improving the social impact of hydrogen deployment in Europe. This includes increasing reuse and recycling, responsibly sourcing raw materials, and creating regulatory frameworks ensuring safe working conditions across global value chains. Furthermore, this article highlights the crucial role of the S-LCA methodology in evaluating social aspects as a support for targeted policy interventions, and the need to adapt this to the specific case study. At the same time, it acknowledges that other relevant social aspects that can influence the social sustainability of the hydrogen technology are not captured with this methodology (in particular social acceptance, affordability and energy security). Improvements in selecting indicators and refined geographical and temporal representations of the value chains to better represent hydrogen technologies and future size market are research gaps filled in the present scientific work.

Optimal power flow method with consideration of uncertainty sources of renewable energy and demand response

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

Influence of high-resolution data on accurate curtailment loss estimation and optimal design of hybrid PV–wind power plants

Hybrid photovoltaic (PV) - wind power plants (HyPPs), i.e., where the PV and wind systems are co-located and share the Point of Interconnection (POI) with the grid, have recently attracted more attention. This trend is driven by the expected reduced capital and operational expenditures achieved through, e.g., shared land and POI infrastructure for HyPPs compared to two individual PV and wind installations. However, if the POI is underdimensioned relative to the PV and wind capacities, the generation from either or both of the assets must at times be curtailed, unless mitigated by solutions like energy storage. This curtailment might lead to income losses. Moreover, as HyPPs typically are designed using wind and PV generation data on hourly resolution, the actual curtailment losses can be underestimated. This might in turn lead to HyPP designs which are techno-economically sub-optimal.In this study, a comparative analysis is conducted to analyze how the curtailment and income loss estimations for HyPPs, as well as the techno-economic optimal HyPP topologies, are impacted by varying the input data time resolution. One year of site measured PV and wind power generation data on 5 s resolution from an existing HyPP located in Eastern Germany are used as basis for the study. For a HyPP topology with an undersized POI, where the installed capacities of the POI, PV, and wind systems are all equal, the curtailment losses are estimated to be 1.45%, 1.43% and 1.09% using the 5 s, 1 min and 1 h resolution datasets, respectively. Moreover, using the 1 h instead of the 1 min dataset leads to a 1.86% overestimation of the total Net Present Value (NPV) for this HyPP topology. As the shares of the PV and wind systems increase relative to the POI capacity, the differences in the estimation of the curtailment losses and NPVs between the high- and low-resolution datasets become more significant.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.