Introduction

The pressing need to slow down climate change and switch to sustainable energy sources is changing the global energy scene. Although solar photovoltaic systems and other renewable energy technologies have advanced significantly, a steady power supply remains a concern due to their intermittent nature. Effective grid balancing and energy storage solutions are also necessary for incorporating renewable energy sources into the current grid. How to improve grid-connected PV-hydrogen energy systems' energy management to handle intermittency, grid stability, and energy storage concerns is the problem at hand.

Grid-connected photovoltaic and hydrogen energy system is a renewable energy system that combines solar photovoltaic and hydrogen technology to meet energy demand, reduce dependence on traditional fossil fuels, reduce greenhouse gas emissions, and promote sustainable energy development.

Then we introduce to how the system works. The first is a photovoltaic system that generates electricity from solar energy and delivers the electricity to a hydrogen production unit, usually an electrolytic device. Electricity is used to electrolysis water, splitting it into hydrogen and oxygen. The hydrogen can then be stored in hydrogen tanks for later use. Hydrogen can be converted into electricity again via fuel cells when electricity is needed to supply the grid or other applications. This system enables the storage and flexible use of energy, which can balance the load demand of the grid, especially when solar photovoltaic power generation is insufficient or the sun is not available.

There are several main advantages of grid-connected photovoltaic and hydrogen energy systems. The first is renewable energy integration, which can combine solar photovoltaic and hydrogen technology to make full use of renewable energy resources and improve the sustainability of the system. The second is energy storage, through the storage of hydrogen, can make up for solar discontinuity and seasonal fluctuations, to ensure the stability of energy supply. The third is environmental protection and clean energy production. The system reduces greenhouse gas emissions and helps mitigate the effects of climate change. The last is the flexibility of the energy system. The ability to provide power in different application areas, including grid support, transportation and industrial use.

Renewable energy based microgrids are increasingly receiving attention from the research community as they play a key role in the challenge of designing the next energy transition model. However, the randomness of the most major renewable energy sources, such as photovoltaic and wind energy, is a significant disadvantage for microgrid stability, security, and reliability. To address this issue, energy storage technologies that absorb the residual energy and assure power supply when renewable resources are insufficient must be combined.

Therefore, in microgrids, energy management systems are essential to optimize the use of these distributed energy sources in an intelligent, safe, reliable, and coordinated manner. Such grid operational planning based on renewable energy requires both efficient scheduling of available and required energy, as well as reliable short - and long-term forecasting tools. That said, the management of renewable energy based microgrids presents significant challenges that need to be addressed using advanced control technologies.

One of the most important tasks for raising the effectiveness and dependability of PV power generation systems is optimal management. First, in order to optimise the photovoltaic conversion efficiency, the system's design phase must fully account for aspects like light, temperature, and shading while choosing the right PV modules, inverters, and support structures. Second, by implementing routine inspections and maintenance, equipment problems are identified and promptly addressed throughout the operating phase, ensuring the system operates normally. Additionally, in order to increase the system's response time and lower the chance of failure, an intelligent monitoring system is employed to continuously check the PV modules' performance, current, voltage, and other parameters.

The development of a reasonable and scientific operation and management system, which specifies the responsibilities of accountable staff and guarantees the equipment's acceptable use and maintenance, is necessary for the management of photovoltaic power production systems in order to maximise their potential. By using data analysis technologies, the system's operating data is thoroughly examined to identify potential issues and areas for optimisation, thereby enhancing the system's overall performance. Simultaneously, enhance technical assistance and training to raise the professionalism of the operation and maintenance staff, enabling them to better handle a variety of crises and guarantee the system's steady functioning.

One crucial control method for solar photovoltaic power generation systems is maximum power point tracking(MPPT). The purpose of MPPT technology is to improve power generation efficiency by enabling the PV power generation system to constantly function at its maximum power point under various lighting situations. The voltage and current generated by solar panels fluctuate depending on the lighting conditions.

By continuously modifying the PV array's operating point, the MPPT system successfully solves the issue of power loss caused by variations in light intensity and maximises the array's output power. Dedicated electronic controllers or algorithms that track the PV array's voltage and current and instantly modify the operating point to maintain the system's maximum power output are commonly used to accomplish this technology.

Since MPPT technology lowers total system costs, increases energy conversion efficiency, and increases system adaptability to changing climatic circumstances, it is now a regular feature of solar PV systems. MPPT technology facilitates the broad use of renewable energy sources by precisely detecting the maximum power point, offering a dependable and effective solution for solar power generation.

To ensure the steady power supply of the power grid, the system must perform intelligent scheduling based on the load requirement of the grid and the volatility of solar PV power generation. Subsequently, the system can attain optimal energy utilisation and enhance overall energy efficiency through cooperative management of the PV power generation and hydrogen energy storage units. This calls for sophisticated energy management systems and clever control algorithms to guarantee the system's high dependability and efficiency throughout a range of operating modes. In order to guarantee a steady supply of power, the PV power generating system's output is dynamically changed in conjunction with the power grid's real-time demand. Simultaneously, excess photovoltaic energy is stored as hydrogen, which can be released during periods of peak grid demand, hence increasing dispatchability and flexibility of the system.

As a vital component of this system, hydrogen storage and management focuses on transforming the electrical energy supplied by PV power generation into hydrogen and releasing it as needed to fulfil electricity demand.

To begin, advanced water electrolysis technology is utilised in hydrogen storage, in which the power generated by the PV system is employed in the electrolysis of water to generate hydrogen. This storage technology is highly efficient and sustainable, and the storage system may be controlled in a flexible manner by modifying the electrolysis rate. When needed, the stored hydrogen can be transformed back into power using combustion or fuel cells, resulting in renewable energy recycling.

Second, the hydrogen energy management system intelligently manages the stored hydrogen in order to ensure flexible energy supply in response to grid demand and changes in the energy market. The system can dynamically regulate the rate of hydrogen release using advanced monitoring and control technologies to improve energy efficiency and assure system stability and reliability under a variety of operating circumstances.

By efficiently integrating and coordinating the various subsystems, the energy management system in a grid-connected PV hydrogen energy system plays a critical role in achieving intelligent monitoring, optimal scheduling, and efficient system management. The system continuously monitors the energy flow in each step of PV power generation, electrolysis hydrogen production, hydrogen storage, power generation, and energy storage, and performs intelligent scheduling based on time of day, meteorological conditions, and energy demand to maximise energy utilisation efficiency and overall system performance.

Through data analysis and clever algorithms, the energy management system's implementation in the PV-hydrogen energy system can optimise energy distribution and guarantee the coordinated operation between the production of hydrogen energy and PV power. Additionally, it can be used to flexibly modify hydrogen energy consumption in accordance with the grid's load requirement, provide balanced control over energy supply and demand, lower system operating costs, and achieve grid power regulation that is advantageous.

Taken as a whole, the integration of energy management systems in grid-connected photovoltaic-hydrogen energy systems is critical to encouraging the intelligent and efficient functioning of renewable energy sources, laying a solid foundation for achieving a clean and sustainable energy future.

Literature review

Introduction

An effective way to produce energy sustainably is through the grid integration of renewable energy sources, namely photovoltaic (PV) and hydrogen systems (de Oliveira-Assis et al., 2021). To handle the intricate interactions between hydrogen and solar power production, this dissertation investigates the best energy management practices in these grid-connected systems. The research aims to create ecologically friendly and efficient energy solutions by exploring this intersection. To optimize PV-hydrogen system benefits, energy management optimization is essential. Effective energy management reduces environmental effects, increases economic viability, and guarantees a steady and dependable power supply. To solve the issues raised by intermittent renewable energy sources and advance a sustainable energy paradigm, this research aims to identify approaches that strike a balance between these variables. This review aims to thoroughly survey the body of knowledge on the topic, covering mathematical modeling, artificial intelligence, and other relevant methodologies. It seeks to discover gaps, issues, and emerging trends by synthesizing various studies. The ultimate objective is to offer a solid basis for the ensuing stages of research, directing the creation of efficient plans for the best possible energy management in grid-connected photovoltaic-hydrogen systems.

Energy Management in Renewable Energy Systems

Renewable energy systems have attracted much attention because they can reduce environmental impact and meet the world's growing energy demand (Atteya, Ali, and Hossain, 2022). Systems that combine photovoltaic (PV) and hydrogen technologies have received significant attention. The present literature review examines the development of energy management solutions over time, elucidates the unique obstacles associated with photovoltaic-hydrogen systems, and emphasizes the critical role that effective energy management plays in facilitating smooth grid integration. The past development of energy management techniques shows a flexible reaction to the changing environment of renewable energy sources. Earlier methods concentrated on simple load balancing to balance energy supply and demand (Rouholamini and Mohammadian, 2016). With the development of technology, more advanced techniques arose. More accurate management approaches became possible with the introduction of machine learning methods, like decision trees and neural networks, to predict energy production patterns. Furthermore, integrating optimization methods like genetic algorithms and linear programming represented a significant advancement in developing energy systems with increased efficiency.

Figure 1.1 PV-hydrogen system

García-Triviño et al. (2016) have conducted a thorough analysis of the development of energy management, highlighting the shift from rule-based strategies to intelligent, data-driven tactics. This background information serves as a basis for comprehending the development of cutting-edge methods in modern PV-hydrogen systems. PV-hydrogen systems present special energy management issues that require a detailed grasp of their complex dynamics. The intermittent nature of solar power generation offers several challenges, including the need for adaptive management systems due to the swings in energy production. The difficulty of producing hydrogen is exacerbated by electrolysis requiring steady power supplies. The technological challenges associated with hydrogen storage systems' storage and conversion efficiency also affect the system's overall performance.

These issues have been explored in recent studies by Fan et al. (2021), which have suggested creative solutions, including sophisticated control algorithms and hybrid energy storage systems. To provide stability and dependability in the energy supply, effective energy management strategies must address the intermittent nature of solar power and the intricate interaction between PV and hydrogen components. The integration of renewable energy systems into the grid is a significant achievement. This integration is made possible mainly by optimal energy management, which considers the inherent fluctuation of renewable energy sources. The predictability and controllability of PV-hydrogen systems become critical for preserving grid resilience and stability as these systems add electricity.

Song, Mu, Li, and Wang (2023) highlight that the implementation of effective energy management practices not only improves the economic feasibility of renewable energy systems but also mitigates environmental effects by optimizing the use of clean energy. Advanced forecasting algorithms combined with real-time monitoring and control systems allow for a smooth integration into the grid, guaranteeing a consistent and sustainable electricity supply.

Artificial Intelligence in Energy Management

Artificial intelligence (AI) has become a game-changer for optimizing energy management in renewable energy systems, especially in systems that combine hydrogen and photovoltaic (PV) technology. To effectively address the inherent difficulties of PV-hydrogen systems, artificial intelligence (AI) has several uses in renewable energy systems (Abadlia et al., 2020). AI makes decisions more accessible, improving a system's ability to adapt to changing external conditions in real time. Research has demonstrated how artificial intelligence (AI) may enhance overall system performance and dependability in defect detection, predictive maintenance, and system optimization. Artificial intelligence (AI) algorithms help to create a self-regulating, sustainable energy ecosystem by offering clever grid integration solutions.

Solar power forecasting has received significant attention in the field of machine learning methods for energy forecasting. Recurrent neural networks and support vector machines are examples of advanced models that have demonstrated promise in reducing the sporadic nature of solar energy. By using historical data, weather patterns, and other pertinent factors, these models can produce accurate forecasts that allow for proactive energy management. Simultaneously, predicting hydrogen production is essential to guaranteeing PV-hydrogen systems' efficiency. Machine learning models, such as extended short-term memory networks and ensemble approaches, have effectively predicted hydrogen generation based on input factors like sun irradiation and system characteristics. These models aid in creating flexible energy management plans that balance hydrogen production with the fluctuating availability of energy.

Figure 1.2 Artificial intelligence method

Reinforcement learning is unique in control and optimization algorithms because it allows systems to interact with their surroundings and learn the best course of action. Reinforcement learning has been used in PV-hydrogen systems to optimize energy distribution and storage while considering grid demand and energy pricing. Because of its versatility, reinforcement learning is especially well-suited to handling the dynamic and uncertain character of renewable energy sources. Natural selection-inspired genetic algorithms have shown promise in solving nonlinear energy system challenges. Using evolutionary algorithms to fine-tune parameters in PV-hydrogen systems improves overall performance and efficiency. The efficient exploration of solution spaces by the genetic algorithm aligns with the complex optimization demands of renewable energy management.

Moreover, neural networks are excellent at identifying complex patterns and relationships within large, complicated datasets because they closely resemble the structure and operation of the human brain. Neural networks have been used in energy management for control optimization, predictive maintenance, and load forecasting. Their versatility in assimilating past data and refining energy management tactics renders them indispensable instruments inside the PV-hydrogen domain.

This review of the literature highlights the complex role AI plays in PV-hydrogen systems energy management. Artificial intelligence (AI) offers a complete toolkit for tackling the difficulties of integrating renewable energy sources, from projecting solar power and hydrogen production to implementing sophisticated control and optimization algorithms. The amalgamation of artificial intelligence and machine learning methodologies amplifies the precision of energy projections. It advances the creation of intelligent and adaptive systems that can react promptly to energy supply and demand fluctuations. To achieve a sustainable and efficient energy future, improving further and developing optimal energy management techniques as the dissertation explores the synergies between AI and mathematical models will be essential.

Mathematical Models for Energy Optimization

Understanding the quantitative facets of energy management is made possible by investigating mathematical models for energy optimization, especially when merging photovoltaic (PV) and hydrogen systems. An introduction to mathematical optimization in energy systems shows how vital accuracy and effectiveness are when managing complicated variables. Mathematical optimization is a potent technique for conceptualizing and resolving issues with PV-hydrogen systems, particularly those about intermittency and inefficient storage.

The general review of mathematical optimization in energy systems includes a wide variety of methods that are used to find the best solutions. This covers mixed-integer linear programming, linear programming, and non-linear programming. Energy systems have seen widespread use of linear programming, a basic optimization approach, to maximize efficiency while respecting restrictions (Saraidarian, 2015). In contrast, non-linear programming allows for non-linear connections between variables, which helps account for real-world systems' complexity. This adaptability is essential for understanding the complex dynamics of PV-hydrogen systems, which show non-linear behaviors in both energy production and storage. By adding discrete choice variables, mixed-integer linear programming expands these possibilities and improves the optimization framework's ability to replicate actual decision-making processes.

Creating optimization issues for PV-hydrogen systems is an essential first step toward creating models that reflect the complexities of integrating renewable energy sources. Robust optimization formulas are required to address challenges like the storage limits of hydrogen systems and the intermittent nature of solar power generation. Scholars have examined this topic in depth, developing questions that balance energy management's technical, environmental, and economic aspects (Mubaarak et al., 2021). In the optimization process, constraints imposed by system limitations, ecological issues, and operational requirements are considered while creating objective functions that quantify the intended outcomes, such as maximizing energy efficiency or minimizing costs. Algorithms and solver approaches are essential for converting optimization formulae into workable solutions. Solvers of linear programming problems having linear relationships between variables, like the simplex method, handle them effectively. The sequential quadratic programming approach is one example of a non-linear programming solution that works well at optimizing systems with non-linear objective functions and restrictions. Branch-and-bound and other mixed-integer linear programming solvers expand these capabilities to situations where discrete choice factors affect the best solution.

A detailed comprehension of the system's dynamics is necessary for using these solver techniques in the context of PV-hydrogen systems. Solutions must be flexible and adaptable due to the sporadic nature of solar power output and the dynamic storage needs of hydrogen synthesis. To improve the flexibility of optimization tactics in response to shifting demand patterns and environmental variables, researchers have investigated the integration of solver techniques with real-time data. Conclusively, the literature survey on mathematical models for energy optimization highlights the critical importance of mathematical optimization methods in managing the intricacies of photovoltaic-hydrogen systems. Combining the review of optimization techniques, formulation of issues unique to the integration of renewable energy, and application of solver techniques facilitates a thorough grasp of the quantitative facets of energy management. As the dissertation develops, a comprehensive framework for determining the best energy management tactics for grid-connected PV-hydrogen systems will be made available by synthesizing mathematical models with knowledge from artificial intelligence and other approaches.

Multi-Objective Optimization

Investigating multi-objective optimization in the context of renewable energy systems—particularly in combining photovoltaic (PV) and hydrogen technologies—offers a sophisticated viewpoint considering the concurrent optimization of competing goals. This review of the literature explores the origins of multi-objective optimization in the context of renewable energy, clarifies the trade-offs that are present in energy management concerning economic, environmental, and technological aspects, and examines evolutionary algorithms that are used to solve multi-objective optimization problems (Mokhtara et al. 2021). The introduction of multi-objective optimization in renewable energy highlights the transition from single-objective optimization to a more comprehensive strategy that considers numerous competing goals at once. Multi-objective optimization is essential for PV-hydrogen systems because of the interdependence of technological efficiency, environmental sustainability, and economic viability. To find solutions that reconcile competing aims, researchers have understood the necessity of developing approaches to handle the inherent trade-offs between these goals.

Figure 1.3 Multi-objective optimization

Energy management trade-offs result from a complicated technological, environmental, and economic interactions. Cost-minimization techniques are frequently used to achieve economic efficiency, but they may conflict with the ecological objective of lowering carbon emissions. Similarly, improving technological features like hydrogen generation efficiency could affect economic feasibility. Scholars (Author et al., Year) have conducted a thorough analysis of these trade-offs, highlighting the necessity of adopting a comprehensive strategy that considers the interdependence of technological, environmental, and economic goals. By effectively classifying solutions into non-dominated fronts, NSGA-II solves problems with many conflicting objectives and provides decision-makers with a wide range of Pareto-optimal options.

In contrast, MOEA/D breaks down the multi-objective problem into several more minor issues and optimizes each separately. This method makes it possible to solve complex optimization problems in a more scalable and adaptable way. The efficiency of NSGA-II and MOEA/D in managing trade-offs related to technological, environmental, and economic goals has been shown by their application to PV-hydrogen systems. Researchers have used these evolutionary algorithms to find Pareto-optimal solutions, which provide a variety of trade-offs between opposing objectives. This makes decision-making more accessible by offering a range of options to suit different needs and interests.

Finally, the literature review on multi-objective optimization in renewable energy systems illuminates the critical transition from single-objective optimization to a more comprehensive approach, particularly in integrating PV and hydrogen technologies. Analyzing trade-offs between technological, environmental, and economic goals highlights how intertwined these aspects are. Evolutionary algorithms become essential tools for effectively navigating challenging optimization environments; specifically, NSGA-II and MOEA/D stand out. As the dissertation progresses, solid solutions for optimizing energy management in grid-connected PV-hydrogen systems will be developed by synthesizing insights from multi-objective optimization with other methodologies, such as artificial intelligence and mathematical models.

Case Studies and Applications

Understanding the real-world applications and practical ramifications of optimized photovoltaic-hydrogen systems largely depends on investigating case studies. The present literature review undertakes an extensive examination of practical applications, scrutinizes the methodology utilized in significant case studies, clarifies the takeaways, and pinpoints deficiencies in the current body of research (Song, Mu, Li, and Wang, 2023). Examining real-world applications offers insightful information about the many settings in which optimized PV-hydrogen systems have been used. The project in the United Arab Emirates that won the Zayed Future Energy Prize is one prime example. This project combines cutting-edge photovoltaic panels with effective hydrogen generation to provide a desert area with a sustainable energy source. To optimize energy management in this project, the erratic solar power must be balanced with steady hydrogen generation for on-site consumption and grid export. The case study's success highlights how feasible optimized PV-hydrogen systems are in dry areas.

Understanding the strategies used to solve energy management challenges is emphasized when examining the methodology used in notable case studies. Examine the European HyResponder project, which aims to equip emergency response vehicles with optimized photovoltaic-hydrogen systems (Huangfu et al., 2023). The process uses artificial intelligence to forecast and optimize energy in real-time, guaranteeing emergency vehicles have a dependable and sustainable power source. This case study emphasizes the necessity for flexible and responsive energy management systems in essential applications by showcasing the integration of cutting-edge technology and approaches.

The knowledge gained from these case studies' lessons offers essential insights into the difficulties and achievements faced when implementing optimized photovoltaic-hydrogen systems. A critical task from the California Solar-to-Hydrogen project, for example, is the significance of scalability. This project sought to show that solar-powered electrolysis could produce hydrogen on a big scale. The effective execution demonstrated the possibility of scalable approaches for improving energy management. Nonetheless, storage and infrastructure-related issues were also noted, highlighting the necessity of comprehensive strategies that consider both logistical and technical factors (Kong, Yu, and Cai, 2019). Even with advancements in practical applications, there still needs to be more in the knowledge. The scant attention paid to community-level implementations is one apparent area for improvement. Large-scale initiatives demonstrate the potential of optimized PV-hydrogen systems, but smaller, community-based applications are less well studied. The potential benefits at the community level are illustrated by case studies such as the HyGreen initiative in a rural village in India, where local agriculture and home energy demands are supported by solar-powered hydrogen production. Closing this research gap is essential to creating sustainable and inclusive energy solutions that work in various socioeconomic settings.

Conclusion

The thorough literature research has revealed critical insights into the complex challenges of optimizing energy management in grid-connected photovoltaic-hydrogen systems. The progress of energy management techniques, the function of mathematical models and artificial intelligence, the importance of multi-objective optimization, and practical applicability in many settings were among the main conclusions. Three key issues emerged: the application of evolutionary algorithms in optimization, the trade-offs in energy management, and the integration of AI and mathematical models. In addition, case studies emphasized real-world achievements, difficulties encountered, and lessons discovered while implementing optimized systems, offering a solid basis for subsequent research projects. The conclusions from the literature evaluation provide a guide for upcoming studies in this area. The gaps found, like the requirement for community-level implementations and the incorporation of developing technologies, offer rich opportunities for additional research. Subsequent investigations should focus on enhancing and broadening approaches, investigating the interaction among financial, ecological, and scientific goals, and investigating the expandability and flexibility of optimized systems. More thorough studies of these systems' socioeconomic effects are also needed to ensure that research advances technology and the more enormous societal implications of sustainable energy management.

Because the research topic is multidisciplinary and complicated, the chosen research approach—which incorporates insights from artificial intelligence, mathematical models, and real-world applications—is warranted. The literature assessment showed that to handle the dynamic difficulties presented by integrating renewable energy, a comprehensive understanding necessitates the convergence of approaches. To provide a comprehensive and helpful framework for efficient energy management in grid-connected photovoltaic-hydrogen systems, the research strategy incorporates artificial intelligence (AI) for forecasting, mathematical models for optimization, and real-world case studies for practical validation. This methodology not only conforms to the latest advancements in the field but also places the research in a position to significantly contribute to the progress of sustainable energy solutions.

To sum up, the literature evaluation has not only compiled the body of information already in existence but also established the framework for further research projects. Together, the conclusions, ramifications, and study methodology pave the way for a thorough and forward-thinking investigation of the best energy management practices in the dynamic field of renewable energy systems.

Integration of cited literature

Table 1.1

References

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