195 American Academic Scientific Research Journal for Engineering, Technology, and Sciences ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 https://asrjetsjournal.org/index.php/American_Scientific_Journal/index The Imperative of Holistic Optimization in Solar-Powered Systems Abiodun Bamidele Obisesan* Department of Chemistry, University of Jyväskylä, Finland Email: obisesan.abiodun@yahoo.com Abstract Solar energy is considered to be the safest technology as regard green energy generation. This study assessed several techniques adopted for distributed iterative algorithm and intelligent control of distributed solar-hybrid microgrids. The study reviews the hybrid renewable system and multi-objective optimization, it also analyses machine learning and AI-driven forecasting by examining the complexity in energy system optimization. This paper explains the integration into existing Infrastructure and dual-use approaches as it integrate solar energy into building envelopes like windows. Solar energy storage and integration were explained alongside with hybridization and System Compatibility. This study further narrates how smart technologies are used for optimization especially with AI-driven forecasting and demand response. Findings reveal that microgrids provide communities or industries with a reliable energy source by combining battery storage. The study concludes that artificial intelligence (AI) is highly efficient in advancing basic development in contemporary microgrid technology. Keywords: Solar Energy; Integrated Optimization; Hybrid; Microgrid; AI-Driven. ------------------------------------------------------------------------ Received: 7/30/2025 Accepted: 9/30/2025 Published: 10/11/2025 ------------------------------------------------------------------------ * Corresponding author. https://asrjetsjournal.org/index.php/American_Scientific_Journal/index American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 196 1.Introduction Solar energy has been considered as the most significant and fast-spreading technology in the green technology space, which is both “clean” and sustainable. It is also naturally volatile, which slows down large-scale deployment. The shift to renewable energy around the world requires good system integration and adequate optimization, especially when it comes to solar energy, which is considered to be naturally intermittent over a wide area. Hence, in attempt to improve dependability, cost-effectiveness, and sustainability, it is important to use integrated approaches that include solar generating, storage, control systems, and existing infrastructure.Renewable energy is already widely used; the interaction between different energy sources in a solar-based IES is much stronger than in a traditional energy system. So, it can be very helpful to simulate and optimize solar-based IESs. To develop "smarter" cities at the district level, we need new models that can look at how different energy vectors and functional units work together more closely [1]. Considering the submission of Wang and his colleagues [2], it was observed that the dynamic management of distributed energy resources (DER) inside a microgrid that includes energy storage, photovoltaic (PV) arrays and electric vehicle (EV) charging stations. Using a distributed iterative algorithm, each asset works on its own, which lets the microgrid change in real time to changing conditions while optimizing operation and taking part in wholesale markets. More so, create a control approach for PV-fuel-cell hybrid systems that entire stability and responsiveness. This therefore leads to a 99% improvement in system response time and a 96% decrease in overshoot, whether the system is linked to the grid or not. This study therefore aims to examine the combination solar energy with existing systems and using smart technologies to manage and optimize energy distribution. It also examines the design of smart microgrids with AI-powered load balancing to integrate solar power. Concept of blockchaain-enable peer-to-peer energy trading was also examined. 2. Literature Review Studies such as Diab and his colleagues, [3], El-Sayed and his colleagues, [4] examines optimal integration of renewable energy sources model complexity in the integration of weather-dependent renewable. They advise customizing model design and utilizing aggregation, decomposition, and linearisation. Energy systems are changing quickly all over the world because of changes in regulations, policy, technology, and consumer preferences. As cities, states, and municipalities continue to announce plans to cut carbon emissions, the world is moving towards renewable energy sources. Hao and his colleagues,[5] present a two-stage stochastic programming framework for microgrids that incorporates demand response and prediction error sensitivity to optimize both operation (day-ahead scheduling) and investment (e.g., solar, wind, and storage capacities). Al- Sokhna [1] introduces multi-objective optimization for solar-wind-battery configurations in off-grid and on-grid hybrid systems. This optimization balances Levelized Cost of Energy (LCOE) and Loss of Power Supply Probability (LPSP). For off-grid configurations, Particle Swarm Optimization (PSO) is found to minimize LCOE, whereas Transit Search Optimisation performs exceptionally well in on-grid settings. A stochastic hierarchical planning (SHP) framework that incorporates day-ahead, short-term and hour-ahead American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 197 decision horizons is put forth by Atakan and his colleagues, [6]. SHP dramatically enhances dependability, environmental results, and economics under high solar and wind penetration, according to simulation using the NREL-118 dataset. 3. Integration Strategies for Grid Compatibility and Control Financial, technical, and regulatory barriers frequently arise when integrating PV into current power networks. Coordination amongst stakeholders, financing options like PPAs, and compatibility with legacy systems are necessary for effective solutions. Technically speaking, distributed solar injection can be made possible without extensive transmission upgrades by using techniques like bi-directional transformers and dynamic line ratings. Grid stability is strengthened by the emergence of solar-plus-storage systems, which make solar dispatchable. With 288 operational solar-plus- storage facilities providing 7.8 GW of generation and 24.2 GWh of storage capacity, storage is now a feature of 61% of new hybrid power plants in the United States. 3.1 Machine Learning and AI-Driven Forecasting/Optimization Islam and his colleagues, [7] explore AI-driven algorithms, including ABC, PSO, ACO, PIO, and DIO—for optimizing solar PV systems. They report quantitative gains: PSO increased MPPT efficiency by 7.5%, PIO improved MPPT performance from 95.2% to 99.1%, and ABC produced a 6.4% increase in generation. In terms of solar forecasting, AI models like RNNs, GRUs, and LSTMs perform better than conventional techniques like SVM and CNN. Buildings can reduce grid reliance by up to 40%, improve energy dispatch, and proactively identify system inefficiencies by utilizing techniques like load balancing, battery management, and reinforcement learning (e.g., Markov Decision Processes, Deep Q-Networks). 3.2 Integration into Existing Infrastructure and Dual-Use Approaches Building-integrated photovoltaics, or BIPV, integrate solar energy into building envelopes like windows or façades, but they have drawbacks like higher module temperatures that lower efficiency and increase the chance of premature failure. Hence, in order to increase the coefficient of performance (COP) for building heating, solar-assisted heat pumps (SAHP) integrate heat pump technology with solar thermal or photovoltaic technology. Hybrid panels can support cost savings and decarbonisation objectives in a variety of geographical areas by offering both thermal and electrical advantages. Dual-use land for solar and agriculture, or agrivoltaics, maximizes land efficiency and promotes the production of both food and energy. The idea was first proposed in 1981 and is becoming more popular in arid regions. Shade from panels can help crops and enhance panel operating conditions. 3.3 Solar Energy Storage and Integration Energy storage connotes that electricity can be stored as a different kind of energy (such chemical, thermal, or mechanical). One example of this kind of technology is lithium-ion batteries. Energy storage is never 100% American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 198 efficient because some energy is always lost when it is converted and retrieved. However, storage may make systems work better and last longer, and it can also make power quality better by balancing supply and demand. With solar power emerging as a key component of the clean energy transition, the global energy space is rapidly changing. The amount of solar energy that can be made can change depending on the time of year, the time of day, clouds, dust, haze, or things that get in the way, including shadows, rain, snow, or dirt [5]. 3.4 Microgrids as Localized Solar Integration Hubs Microgrids have been considered to always give specific approaches to solar energy integration which allow better capacity for smart grid architecture. With the nature of a microgrid, being a self-contained network that can function independently (islanding) during outages. Therefore, microgrids provide communities or industries with a reliable energy source by combining battery storage, and other distributed energy resources (DERs). Distributed control algorithms in microgrids enable each energy component, such as a solar array, battery bank, or electric vehicle, to operate independently while preserving system-level balance [2]. 3.5 Hybridization and System Compatibility In recent times, the world has recently realized that switching to renewable energy (RE) is important in many areas, such as homes, businesses, and factories. This change is happening for a number of reasons, including the heavy usage of fossil fuels and the threat of running out, the problems caused by greenhouse gas emissions and global warming, and the unstable political situation and wars that affect energy pricing and supplies. A hybrid renewable energy system (HRES) is made up of more than one energy source.Power plants are very important for making the electricity that a grid needs and for storing thermal energy in different ways so that people can be comfortable. The relatively high cost of electricity generated from renewable energy sources is due to the need for government decisions and large investments to build power facilities that use these sources. The operators of the grid are still working on the rules for how PV+battery systems can be added to markets, and they are also thinking about how to change market structures to get the best capacity value from these systems. Bad rules may make energy more expensive and make the system less reliable. Good rules, on the other hand, could let markets get all the benefits that hybrid systems can give without paying them too much for the services they deliver. The unique elements stated above must be taken into account when designing rules for PV+battery systems, but the rules should also take advantage of the similarities with other types of resources. 3.6 Smart Technologies for Optimization Although it is not controversial in any way that hardware also plays an important role, but smart technologies are increasingly driving the real connections in solar integration. Demand forecasting, solar output forecasting, and energy distribution optimization are all made possible by artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) systems. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 199 In practical analysis, optimization algorithms like Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) can enhance maximum power point tracking (MPPT), which will increase the amount of energy extracted from PV panels. Furthermore, larger-scale smart grids with sensors and automated control systems allow for real-time load distribution adjustments, which lower energy waste and guarantees dependability. In cities where demand varies greatly and exact coordination between solar input and grid stability is necessary, these technologies are especially useful [7]. 3.7 Microgrids and Decentralized Energy Management The management of decentralized energy is highly required to ensure effectiveness in solar integration. The creation of microgrids localized networks that can function separately or in conjunction with the main grid—is another creative approach to solar integration. Communities can attain greater energy self-sufficiency while lowering gearbox losses by implementing microgrids that include solar PV, storage devices, and even electric vehicle charging stations. Going by the submission of Wang et. al., [2], it was concluded that the distributed iterative algorithms enable each microgrid component to function independently while still advancing overall optimization. 4. AI-Driven Forecasting and Demand Response The rate at which AI is taking over tech- industries and delivering great prospects is highly recommendable. In order to deal with the unpredictable nature of solar power, forecasting is essential. Consultants in Energy management presently prefer AI prediction rather than the usual conventional methods of prediction. It was predicted that solar generation and weather patterns with far greater accuracy than they could with conventional methods thanks to advanced AI techniques like recurrent neural networks (RNNs) and long short-term memory (LSTM) models. A well-structured and optimized energy ecosystem can only be delivered by combining this with demand response programs, which in turn provide incentives for consumers to modify their consumption in response to grid conditions [6]. To minimize grid stress and maximize the use of available solar generation, smart appliances and IoT-enabled devices, for example, can automatically shift or reduce their energy use during peak demand. 4.1 Dual-Use Applications and Sustainability Benefits In addition to homes and grids, solar integration is discovering creative uses in industrial processes and agriculture. For instance, agri-voltaic systems optimize land use by combining crop production with solar panels to produce food and energy at the same time. Through natural cooling, shading from the panels can even increase crop yields in arid areas while increasing panel efficiency. This dual approach shows the prospects and sustainable future in solar energy especially in building various sectors. The necessity for infrastructural development has increased based on the integration of solar energy into current power systems. The bidirectional and variable inputs of solar photovoltaic (PV) systems are a challenge for conventional power grids, which were initially built for one-way electricity flow from centralized plants to consumers. The concept of smart grids promote digitally enhanced electrical networks that use automation, real- American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 200 time data analytics, and communication technologies to ensure energy distribution was mainly achieved 4.2 AI-Powered Load Balancing in Smart Microgrids Microgrids allow energy to be adequately generated, distributed, and consumed in smaller units based on programmed schedules; microgrids enhance solution to issues with contemporary power systems. The adoption of artificial intelligence (AI) for load balancing is highly efficient in order to advance basic development in contemporary microgrid technology [8]. The efficiency of AI algorithms can be further traced to an accurate prediction analysis by forecast solar output and energy demand, which allow proactive supply management as opposed to reactive management. With the methods such as machine learning, reinforcement learning, neural networks and Microgrids can easily distribute solar energy [18]. This therefore guarantees effective usage without stressing out the entire system. Predictive scheduling is also made possible by AI-driven load balancing. Through the use of historical consumption data and real-time weather forecasts, microgrids are able to either move non-essential loads (such as EV charging or water heating) to times when solar availability is abundant or preemptively store excess solar energy during peak sunlight hours for later use. Overall system stability is improved, curtailment is decreased, and the need for fossil fuel backup is reduced thanks to this predictive capability [6]. 4.3 Integration of IoT and Edge Computing in Smart Microgrids When paired with edge computing and Internet of Things (IoT) devices, AI-powered load balancing becomes even more effective. Granular, real-time data on energy production and consumption is provided by smart meters, sensors, and networked appliances. By processing this data near its source at the network's edge, latency is decreased and prompt decision-making is made possible. In milliseconds, for example, IoT-enabled microgrids can automatically redistribute loads, activate stored energy reserves, or request additional power from the main grid in the event of a sudden drop in solar generation. Despite solar variability, end users are guaranteed a smooth energy supply thanks to this quick response [9]. Beyond their technical capabilities, AI-powered microgrids have significant positive effects on the economy and the environment. This is feasible by maximizing the use of renewable energy sources and minimizing energy losses, they lower operating costs. Additionally, they promote peer-to-peer energy trading, which creates local energy markets by allowing homes or businesses to sell excess solar electricity to neighbors within the microgrid [5]. 4.4 Blockchain-Enabled Peer-to-Peer Energy Trading A blockchain model facilitates P2P energy trading and illustrates the impediments to trading, known as the trilemma of scalability, security, and decentralization. Then, a scalable, strong, and safe model is suggested to facilitate quick and frequent trading using the second layer solution that sits on top of a strong and safe blockchain substrate.P2P energy trading has garnered heightened interest from both the industry and academia. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 201 Mengelkamp and his colleagues, present a thorough assessment of P2P energy systems utilized throughout three distinct domains: building, storage, and renewable generation. This model has been made possible in large part by blockchain technology, which offers the security, trust, and transparency required for decentralized energy transactions [10]. Blockchain is popularly known for distributed ledger system that safely and permanently logs transactions. Blockchain makes it possible to record energy production and consumption data in real time, allowing P2P energy traders to conduct direct transactions with one another. In practical perspectives, using a blockchain-based platform, a home with rooftop solar panels can automatically sell its excess electricity to a neighbor. Without the need for a utility company to act as a mediator, the system instantly executes the financial settlement, validates the transaction, and logs the quantity of energy exchanged [11]. More so, smart contracts being self-executing agreements written on the blockchain verify every energy trade and automatically settle payments when certain criteria are satisfied [12]. This guarantees that trades are carried out fairly and transparently, does away with the need for middlemen, and lowers transaction costs. 4.5 Smart Contracts and Automation in Energy Trading As renewable energy sources like solar, wind, and bioenergy gain prominence inside electrical grids, the necessity for localized resource management intensifies. It suggests, however, taking into account issues such as grid condition, resource availability, and the ownership of various resources by distinct entities (typically prosumers). Local conditions, including voltage profiles and loading capacities, must be taken into account to guarantee efficient management [13]. The proactive management of local resources, encompassing the use of locally generated electricity, alleviates renewable variability and consumption unpredictability, hence enhancing the integration of renewable sources. Energy exchanges can enhance and refine the management of local resources. The inquiry pertains to the facilitation of local energy transactions among prosumers in a decentralized and dispersed way, hence empowering local communities. This is where blockchain technology excels. The capacity to enhance decentralized energy management presents a promising future for empowering consumers and prosumers, as well as local communities, to engage actively in the energy revolution. This research emphasizes a crucial aspect and represents a notable progression in energy management, motivating us to investigate the complete capabilities of blockchain technology. Blockchain is a decentralized system for transactions among peers organized into blocks. The blocks are linked, following validation, via a consensus procedure, so becoming the blockchain. All information is documented utilizing encryption, encompassing date and time, and all blockchain participants retain a copy of the documented data. These attributes render the blockchain virtually immutable, transparent, and auditable. The advancement of blockchains has facilitated the storage and execution of smart contracts. A smart contract is a collection of directives recorded and executed on the blockchain, facilitating decentralized operations. The smart contract documents conditions, events, intended values, dates, and transaction details. A smart contract's principal feature is its automatic execution upon the fulfillment of a certain condition or the occurrence of an event [14]. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 202 Technologies like blockchain and smart contracts are exceptionally suitable for situations involving several micro transactions over brief periods, rendering them highly appealing for local energy trade contexts. The majority of these transactions occur between two counterparts. In practice, numerous pairs may choose to engage in transactions, leading to multiple pairs transacting with one or more counterparts to fulfill their energy requirements. Consequently, it is essential to examine the progress achieved in recent years, specifically regarding the capabilities of smart contracts. The review articles examined in this study demonstrate diverse analytical focusses, encompassing blockchain applications in smart grids, energy management, investment platforms, and security concerns. Furthermore, concerns regarding transparency, the examination of decentralization, scalability, and reliability. Certain assessments examine the application of smart contracts in transactions, encompassing its merits and drawbacks, along with their advantages and constraints. Notwithstanding the swift proliferation of evaluations addressing blockchain in the energy sector, to the author's knowledge, a comprehensive review of smart contract features has yet to be conducted [15]. 4.6. Integration with Smart Grids and IoT The term "grid" refers to the electrical network that supplies power to all residents, businesses, and infrastructural services within a city. The "smart grid" represents the next evolution of energy systems, enhanced with communication technologies and connection to promote more intelligent resource utilization, energy efficiency, and a diminished carbon footprint. The technologies that render the contemporary IoT-enabled electricity grid "smart" encompass wireless devices, including sensors, radio modules, gateways, and routers. These gadgets facilitate advanced connectivity and communication, enabling consumers to optimise energy usage, allowing municipalities to conserve electricity and reduce costs, and assisting power authorities in expediting power restoration following a blackout [16]. Interest in the domain of smart grids emerged at the onset of this century. The progression of information and communication infrastructure has underscored its relevance in electrical networks and its essential role in the establishment of renewable-based sustainable energy systems for monitoring and efficient energy decarbonisation. 4.7. Challenges and Future Prospects One of the main challenges of renewable energy is that sources are naturally unpredictable, because wind and solar power are the most common renewable energy sources, they are hard to control, can't be used all the time, and don't always work as expected. To make sure reliability and operational needs are met, dedicated ancillary services like spinning reserves and other regulatory operations. The figure makes it easier to see the extra services that are needed for a power network to run properly and to keep the quality of the power throughout all the planning horizons that are related to power systems[12].One of the key reasons for RE integration is that the output power can change quickly. This, along with the fact that loads can change, makes the whole power network more random. So, both the intricacy and the support systems need to be improved and made better. However, new pilot projects in countries like Germany, Australia, and the American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 203 US show that P2P trading is feasible, and advancements in blockchain scalability, such as proof-of-stake consensus mechanisms, are addressing energy efficiency issues [17]. 6. Conclusion The study concludes that the combination of solar energy with traditional power systems is highly efficient, reliable and durable especially with the help of smart technologies like AI-driven load balancing and blockchain-enabled trading. Findings from this study confirm that these new innovations have been able to ensure cost minimization and prevent carbon emissions, but they also provide customers more leverage to take part in energy markets. The study further reveals that smart grids and decentralized energy exchange has greatly help energy systems to be adequate ready for the future by integration and optimization. Reference [1] Al-Sokhna, M. (2024). Multi-objective Optimization of Hybrid Solar-Wind-Battery systems: A comparative study of PSO and Transit Search Optimization. Journal of Engineering and Applied Science, 6(1), 1–15. https://jesit.springeropen.com/articles/10.1186/s43067-024-00186-6. [2] Wang, J., O’Neill, R., & Kamath, H. (2014). Dynamic Control of Distributed Energy Resources in a Microgrid. arXiv. https://arxiv.org/abs/1410.0054 [3] Diab, A. A. Z., Tolba, M. A., & Rezk, H. (2024). Optimal integration of renewable energy sources in unbalanced distribution systems using quasi-oppositional political optimizer. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 44. https://doi.org/10.1007/s40430-023-04437- x [4] El-Sayed, M. M., &Rezk, H. (2024). Optimal Energy Management for Smart Cities Integrating Hybrid Renewable Energy with Storage Systems using Radial basis Neural Network and Hunger Game Optimizer. Journal of Engineering Sciences and Sustainable Industrial Technologies, 2(1), 1–14. https://doi.org/10.21608/jessit.2024.224349.1002 [5] Wang, H., & Huang, J. (2015). Microgrids with Renewable Energy: Joint investment and operation optimization. arXiv. https://arxiv.org/abs/1511.01984 [6] Atakan, S., Gangammanavar, H., & Sen, S. (2021). Stochastic Hierarchical Planning for Power Systems with high Renewable Integration. arXiv. https://arxiv.org/abs/2107.14558 [7] Islam, M. S., Zainuri, M. A. A. M., & Noor, F. (2025). AI-driven optimization techniques for solar PV energy systems: A comparative analysis. Journal of Innovation in Science, Engineering and Management, 5(2), 1–10. https://www.jisem-journal.com/index.php/journal/article/view/5818 [8] Alhasnawi, B. N., Zanker, M., & Bureš, V. (2024). A Smart Electricity Markets for a Decarbonized https://jesit.springeropen.com/articles/10.1186/s43067-024-00186-6?utm_source=chatgpt.com https://arxiv.org/abs/1410.0054?utm_source=chatgpt.com https://doi.org/10.1007/s40430-023-04437-x https://doi.org/10.1007/s40430-023-04437-x https://doi.org/10.21608/jessit.2024.224349.1002 https://arxiv.org/abs/1511.01984?utm_source=chatgpt.com https://arxiv.org/abs/2107.14558?utm_source=chatgpt.com https://www.jisem-journal.com/index.php/journal/article/view/5818?utm_source=chatgpt.com American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 204 Microgrid System. Electrical Engineering, 106(4), 3913–3929. https://doi.org/10.1007/s00202-024- 02699-9 [9] Mahadeva, M., Bhavya, V., Gowda, N. M., Angadi, S. V., & Farooq, A. (2025). A Review on Integrating Smart Aquifer Recharge Systems into Urban Road Networks: A Sustainable approach to Water Management and Urban Resilience. Journal of Water Resources and Pollution Studies. https://doi.org/10.46610/jowrps.2025.v010i01.001 [10] Benedikt Kirpes*, Esther Mengelkamp, Georg Schaal, and Christof Weinhardt (2019). Design of a Microgrid Local Energy Market on a Blockchain-based Information System. https://doi.org/10.1515/itit-2019-0012 [11] Kouhizadeh, M., & Sarkis, J. (2018). Blockchain Practices, Potentials, and Perspectives in Greening Supply Chains. Sustainability, 10(10), 3652. [12] Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of chal-lenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143–174. https://doi.org/10.1016/j.rser.2018.10.014. [13] Naji Alhasnawi, B., et al. (2024). A novel efficient energy optimization in smart urban buildings based on optimal demand side management. Energy Strategy Reviews, 54, 101461. https://doi.org/10.1016/j.esr.2024.101461 [14] Alhussan, A. A., El-Sayed, M. M., & Rezk, H. (2024). Intelligent energy management system for smart cities integrating renewable and storage systems optimized by radial basis neural network and heap-based optimizer. Energy Strategy Reviews, 53, 101351. https://doi.org/10.1016/j.esr.2024.101351. [15] Janusz J. Sikorski1, Joy Haughton1, Markus Kraft1 (2016). Blockchain technology in the chemical industry: machine-to-machine electricity market. Cambridge Centre for Computational Chemical Engineering. [16] Tolba, M. A., Rezk, H., & Diab, A. A. Z. (2024). Optimal integration of renewable energy sources in distribution systems using salp swarm optimization. In 2024 25th International Middle East Power Systems Conference (MEPCON) (pp. 1–6). IEEE. https://doi.org/10.1109/MEPCON63025.2024.10850320 [17] Tolba, M. A., Diab, A. A. Z., & Rezk, H. (2024). Optimal allocation of renewable distributed generation in radial distribution systems using heap-based optimizer. In 2024 25th International Middle East Power Systems Conference (MEPCON) (pp. 1–6). IEEE. https://doi.org/10.1109/MEPCON63025.2024.10850154. https://doi.org/10.1007/s00202-024-02699-9 https://doi.org/10.1007/s00202-024-02699-9 https://doi.org/10.46610/jowrps.2025.v010i01.001 https://doi.org/10.1515/itit-2019-0012 https://doi.org/10.1016/j.esr.2024.101461 https://doi.org/10.1016/j.esr.2024.101351 https://doi.org/10.1109/MEPCON63025.2024.10850320 https://doi.org/10.1109/MEPCON63025.2024.10850154 American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 103, No 1, pp 195-205 205 [18] Mohammad Shariful Islam, Muhammad Ammirrul Atiq Bin Mohd Zainuri, S.Z. Mohammad Noor (2025). AI-Driven Optimization for Solar Energy Systems: Theory and Applications. Journal of Information Systems Engineering and Management. www.jisem-journal.com. [19] Kotzur, L., Markewitz, P., Robinius, M., & Stolten, D. (2020). Time Series Aggregation for Energy System Design: Modeling Seasonal Storage. Applied Energy, 213, 123–135. https://arxiv.org/abs/2009.07216 [20] Nenpower. (2024). The challenges of integrating PV into existing infrastructure. Nenpower Energy Blog. https://nenpower.com/blog/the-challenges-of-integrating-pv-into-existing-infrastructure [21] Wikipedia contributors. (2024a, August 12). Agrivoltaics. In Wikipedia. https://en.wikipedia.org/wiki/Agrivoltaics [22] Wikipedia contributors. (2024b, July 21). Building-integrated photovoltaics. In Wikipedia. https://en.wikipedia.org/wiki/Building-integrated_photovoltaics http://www.jisem-journal.com/ https://arxiv.org/abs/2009.07216?utm_source=chatgpt.com https://nenpower.com/blog/the-challenges-of-integrating-pv-into-existing-infrastructure?utm_source=chatgpt.com https://en.wikipedia.org/wiki/Agrivoltaics?utm_source=chatgpt.com https://en.wikipedia.org/wiki/Building-integrated_photovoltaics?utm_source=chatgpt.com