Autonomous multi-factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration.

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Introduction
The global shift towards renewable energy sources has gained significant momentum in recent years, driven by the pressing need to address climate change, reduce greenhouse gas emissions, and minimize our ecological footprint [1] [2].Harnessing the power of renewable energy not only offers a cleaner and sustainable alternative to fossil fuel-based energy production but also contributes to the preservation of local ecosystems.As the deployment of renewable energy systems continues to expand [3], there arises a critical need for efficient and intelligent control systems that can optimize energy flows in various applications, to eliminate the uncertainties that arise from ''the integration of renewable energy sources'' [4].
This academic article aims to explore the role and significance of an autonomous multi-factor energy flows controller in the context of renewable energy micro-grid integration.By leveraging advanced technologies and intelligent algorithms, such a controller can monitor and manage the intricate dynamics of energy supply and demand in realtime, ensuring reliable and optimized operation based mostly like on off-grid (without being binding) renewable energy systems.
One of the key challenges in off-grid applications is the limited availability of energy resources.To address this, the autonomous multi-factor energy flows controller integrates multiple factors into its decision-making process.It considers parameters such as energy demand, weather conditions, and the available energy generation capacity.By continuously monitoring and analyzing these factors, the controller can dynamically balance the supply and demand of energy, thus managing energy shortages and optimizing the utilization of available resources, as it is analyzed in [5].Ιn exceptional cases, however, the ongrid network may be available.
Pumped Hydro Energy Storage (PHES) stands as a prominent solution for addressing the challenge of efficiently storing excess energy generated by renewable sources during daylight hours and utilizing it during periods of high demand, such as nighttime.This process involves harnessing gravitational potential energy to charge batteries or store energy in the form of elevated water.During off-peak hours when energy demand is low, surplus electricity generated from renewable sources is used to pump water from a lower reservoir to an upper reservoir.This elevation potential energy is then converted back into electricity during peak demand periods by allowing the water to flow downhill through turbines, thereby generating electricity.This unique approach aligns seamlessly with the need to balance energy supply and demand, especially when renewable energy generation is intermittent.Moreover, PHES systems have the capacity to store and discharge large amounts of energy quickly, making them an ideal solution for stabilizing grids, enhancing grid reliability, and providing essential backup power during emergencies.As the integration of renewable energy sources grows, PHES stands out as a robust and proven method for efficiently charging batteries at night, thus ensuring a continuous and reliable energy supply while maximizing the utilization of cleaner energy sources [54].
Intelligent control systems play a pivotal role in the effective management of renewable energy systems [6].They employ advanced algorithms and predictive models to anticipate energy demand patterns, forecast weather variations, and optimize the operation of energy generation sources, such as solar panels, wind turbines, and micro-hydropower systems.By leveraging real-time data and adaptive control strategies, the autonomous multi-factor energy flows controller ensures the efficient utilization of renewable energy sources, enabling the system to operate autonomously and reliably in remote and challenging environments.
The development of balanced city-scale grids is increasingly becoming a priority in the quest for a sustainable and resilient energy future.A promising approach lies in the integration of microgrids with intelligent power flow control systems.Microgrids, as localized energy distribution systems, possess the capacity to generate, store, and manage energy independently.When strategically interconnected within a city, these microgrids form a robust and decentralized network that can balance energy supply and demand effectively.The key lies in the integration of intelligent control systems that enable realtime monitoring, analysis, and adaptive management of power flows.These systems leverage advanced algorithms to optimize the utilization of various energy sources, including renewable resources, storage systems, and even demand response strategies [60].By dynamically rerouting power and adjusting energy generation and consumption across interconnected microgrids, these intelligent control systems ensure an optimized distribution of energy within the city-scale grid.This approach enhances grid stability, minimizes energy losses, and facilitates the integration of cleaner energy sources, consequently contributing to a more sustainable and reliable urban energy landscape.
Furthermore, the integration of remote monitoring and maintenance capabilities enhances the reliability and longevity of off-grid renewable energy systems.Through remote monitoring, operators can access real-time performance data, identify potential issues, and proactively address them, minimizing downtime and optimizing energy generation.This capability becomes particularly crucial in remote or inaccessible areas where regular on-site inspections may be challenging [7].
The scope of our work encompasses several key aspects vital for the successful integration of renewable energy systems through intelligent control systems: a.
Reliability and Resilience: Our focus on efficient and intelligent control systems addresses the foundational requirement of ensuring a dependable and resilient energy supply in off-grid scenarios.These systems are designed to meticulously manage energy flows, closely monitor battery storage levels, and employ real-time predictive models for the optimal utilization of available renewable energy resources.Through dynamic adjustments of power generation and consumption, these control systems significantly bolster the reliability and stability of off-grid energy systems, reinforcing their ability to weather uncertainties.
b. Energy Management and Optimization: As off-grid systems often operate with finite energy resources; effective energy management becomes paramount.Our research delves into the application of intelligent control systems that adeptly track energy demand patterns, assess prevailing weather conditions, and gauge the available energy generation capacity.By skillfully balancing the interplay between energy supply and demand, these systems proactively avert energy shortages while optimizing energy utilization.The integration of real-time data and advanced algorithms, as explored in [7], empowers these control systems to harness the full potential of renewable energy sources.
c. Scalability and Flexibility: Another critical facet of our study involves the examination of efficient control systems that facilitate the scalability and adaptability of off-grid energy solutions.These systems exhibit the capability to accommodate variations in energy demand, accommodate system expansions, and seamlessly integrate additional renewable energy sources or storage technologies.By providing this inherent scalability and flexibility, the off-grid systems under consideration can readily adjust to evolving energy requirements.Moreover, in scenarios involving on-grid systems, surplus energy generated can be channeled back into the grid, contributing to a sustainable energy ecosystem.This achievement holds a pivotal role in the development of balanced city-scale grids.It envisions a system where all the AmEFCs (Autonomous Multi-Factor Energy Flows Controllers) of micro-grids will communicate with each other, exchanging real-time information about their local energy demands.This interconnected approach not only optimizes energy distribution but also fosters a seamless exchange of resources, promoting a holistic and sustainable energy network.
We present the structure of this paper in the following diagram.

Literature review
The related work concerns various objects like optimal power flow, IoT, NN, Renewables sources, energy storage, microgrids and farther scale etc.We proccess the review in bibliography that concerns the current work.

Introduction to traditional and advanced solution methods to OPF (Optimal Power Flow).
The Optimal Power Flow (OPF) problem is a fundamental problem in power system operations and planning.It involves determining the optimal operating point of a power system that minimizes generation cost, power losses, or another objective while satisfying certain constraints.These constraints could be physical limits on equipment, power balance conditions (generated power must equal consumed power plus losses), and system stability requirements.
Typical objectives for the OPF problem include: 1.
Minimizing total power generation cost: This aims to determine the dispatch of generators to meet load demand at minimum cost while adhering to the operational limits of the generators and the network.

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Minimizing power losses: This involves adjusting the generator's outputs and control devices to reduce the total power losses in the transmission network.

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Maximizing social welfare: This involves the maximization of the difference between consumers' utility from consuming electricity and the cost of producing it.
The constraints of the OPF problem involve power balance requirements, voltage limits at each bus (node) in the network, and power flow limits on each line.Additionally, each generator's output power must be within its maximum and minimum limits.
It's important to note that the OPF problem is a non-linear and non-convex problem due to the power balance constraints, which involve non-linear equations describing the physics of power flows in the network.This complexity makes OPF a challenging optimization problem.
Optimal Power Flow (OPF) has been a topic of intense research in the power systems field for several decades, due to its fundamental role in power system operations and planning.Here's a brief survey of some key themes and publications: 1.
Initial Formulations: The OPF problem was first formulated by Carpentier in 1962.The objective is typically to minimize power generation costs while maintaining power system security and reliability. 2.
Traditional Solution Methods: Over the years, various solution methods for the OPF problem have been developed.These include Newton's method, linear programming, gradient methods, and others.For instance, "Optimal power flow solutions using linear programming" by Alsac and Stott.
3. Metaheuristic Approaches: Given the non-convex nature of the OPF problem, various metaheuristic approaches have been proposed including genetic algorithms, particle swarm optimization, simulated annealing, and others.

4.
Interior Point Methods: The emergence of interior point methods in the 1990s revolutionized the solution of the OPF problem.These methods are efficient and can solve large-scale problems.

5.
Convex Relaxation Techniques: More recently, researchers have turned to convex relaxation techniques, such as semidefinite programming (SDP) and second-order cone programming (SOCP), to approximate the OPF problem and solve it more efficiently.Notable papers include "Semidefinite Programming for Optimal Power Flow Problems" by Lavaei and Low, and "Convex Relaxation of Optimal Power Flow-Part I and Part II" by Gan et al. [9], [10] 6.
Stochastic and Robust OPF: With the rise of renewable energy, OPF has been extended to consider uncertainty in generation and load.Stochastic programming and robust optimization have been used to solve these problems.[11], [12], [13] 7.
Distributed and Decentralized OPF: In line with the trend towards more distributed power systems, researchers have developed distributed and decentralized algorithms for OPF.These are designed to operate in a setting where the decision-making is distributed among several agents, such as in a microgrid [14], [15], [16], [17], [18].
8. Applications of Machine Learning: Machine learning techniques are increasingly being applied to the OPF problem, either to predict the solution or to learn the underlying system dynamics for faster computations [19], [20], [21], [22], [23].
It is important to note that despite all these advances, the OPF problem, due to its non-convex nature, still presents significant challenges, and the search for efficient and effective solution methods is an ongoing area of research [24], [25].More specific to reinforcement learning (RL)-based solutions solving power management problems with relevant reviewed research works can be found in [6] that many agents cooperate in any level for OPF.
Meanwhile the specific research does involve research on the micro grid or scaling the problem to as large as a city of micro connected grids that can cause losses in large scale of power flows, and the maximization of the difference between consumers' utility from consuming electricity and the cost of producing it.

Integration of IoT and Neural Networks in Energy systems.
The convergence of the Internet of Things (IoT) and neural networks heralds a transformative era for the energy sector.IoT's pervasive connectivity and neural networks' predictive prowess hold the potential to revolutionize energy management [4][20].This synthesis opens avenues for optimizing energy consumption, enhancing efficiency, and facilitating the integration of renewable resources [26].Motlagh et al. (2020) emphasize the pivotal role of IoT in revolutionizing energy management [26].From optimizing energy supply to bolstering demand-side efficiency, IoT offers multifaceted applications across the energy spectrum [26].The fusion of IoT with cloud computing facilitates real-time data analysis, enabling informed decision-making [26].
In the domain of energy optimization, artificial neural networks, particularly Long Short-Term Memory (LSTM) models, exhibit remarkable potential [20].Bouktif et al. (2018) demonstrate LSTM's efficacy in electric load forecasting [27].Through synergistic amalgamation of machine learning and genetic algorithms, LSTM models optimize short to medium-term forecasting [27].This proficiency empowers utilities to make informed decisions for load scheduling and resource allocation.
A holistic energy management approach can be achieved by merging IoT's real-time data with neural networks' predictive analytics [4] [20].Al-Saadi et al. (2023) expound on the efficacy of reinforcement learning-based control strategies, further advancing optimal power management [6].This integration not only enhances energy efficiency but also fortifies the resilience of advanced power distribution systems [6].
In the broader context of energy systems optimization, the pioneering work of Farivar and Low (2013) deserves mention [16].Their development of the branch flow model demonstrates an innovative approach to solving the Optimal Power Flow (OPF) problem [16].By relaxing non-convex constraints, this model advances optimization techniques, offering enhanced solutions to complex energy management challenges [16].
In conclusion, the confluence of IoT and neural networks propels energy systems into an era of unprecedented efficiency and optimization [4] [20].As demonstrated by existing research, this synergy has the potential to reshape how energy is generated, distributed, and consumed [26].By harnessing the capabilities of IoT and neural networks, we can usher in a sustainable and resilient energy future.
In the next section, we will delve deeper into the specific technologies and advancements in renewable energy systems, such as PV systems, wind turbines, and energy storage solutions.

Renewable Energy Technologies -PV Systems, Wind Turbines, Hydro Pumps.
In the pursuit of sustainable energy solutions, the arena of renewable energy technologies has attracted significant attention for its potential to alleviate environmental concerns and ensure energy security.Photovoltaic (PV) systems, a pivotal technology in this domain, have undergone remarkable advancements recently, leading to enhanced efficiency and cost-effectiveness.These systems, harnessing semiconductor materials to directly convert solar irradiation into electricity, have exhibited progressive improvements in performance due to innovations in materials science and engineering.Noteworthy studies in this field include Green et al.'s work (2019) on perovskite solar cells, showcasing potential for boosting PV efficiency [28].
Wind turbines, another cornerstone of the renewable energy sector, have witnessed a surge in development and deployment.Modern wind turbine designs incorporate aerodynamic enhancements, material innovations, and sophisticated control strategies to optimize energy capture and overall efficiency.Pioneering research by Tchakoua et al. (2014) on wind turbine condition monitoring elucidates how condition-based maintenance strategies can enhance reliability and availability while curbing maintenance costs [29].Kusiak et al.'s comprehensive review (2020) of wind turbine design optimization approaches underscores the importance of efficient turbine design for maximizing energy output [30].Roy and Jadhav's work (2015) on optimal power flow solution incorporating stochastic wind power explores the integration of wind power into power systems optimization and highlights the challenges posed by wind power intermittency [31].
Vertical axis wind turbines (VAWTs) emerge as a distinctive alternative to the more conventional horizontal axis wind turbines (HAWTs).VAWTs boast advantages such as lower noise levels, simplified maintenance due to ground-level accessibility, and improved performance in turbulent wind conditions.These unique features stem from the VAWTs' omni-directional design, which eliminates the need for yaw mechanisms and enables efficient energy capture from variable wind angles.An insightful study by Kinzel et al. (2019) delves into the aerodynamic and mechanical aspects of VAWTs, shedding light on their potential to revolutionize urban and decentralized energy generation [32].
Hydro pumps, leveraging gravitational potential energy to store power, remain integral to the renewable energy landscape.They offer a dependable and controllable energy source, supporting grid stability and accommodating fluctuating energy demands.Lu et al.'s research (2018) on co-optimized operation of hydro-pumped storage with wind and solar resources exemplifies the potential synergy between hydro pump storage and other renewables, fostering a resilient and sustainable energy infrastructure [33].
In synthesis, the contemporary trends in renewable energy technologies, encompassing PV systems, wind turbines, and hydro pumps, delineate a dynamic realm of innovation and optimization.These technologies, steered by research endeavors spanning material science breakthroughs to advanced optimization strategies, promise to redefine the global energy landscape in a sustainable and ecologically-conscious direction.

Energy Storage Systems -Battery Storage (chemical) -Water tower (physical dynamics).
Energy storage systems play a pivotal role in ensuring the stability, reliability, and efficiency of power grids by bridging the gap between intermittent renewable energy generation and variable energy demand.Two prominent energy storage technologies, battery storage and water towers, offer distinct advantages and mechanisms for storing energy.

Battery Storage (chemical)
Battery storage, particularly chemically-based systems, has garnered immense attention due to its high energy density and versatility.Lithium-ion batteries, for instance, have emerged as a dominant player in various applications, from portable electronics to gridscale energy storage.These batteries rely on reversible chemical reactions to store and release electrical energy [34][35] [36].The mechanism involves the movement of ions between the cathode and anode materials during charging and discharging cycles.The energy storage capacity of a battery is determined by its capacity to hold and release these ions.Recent advancements have led to improved battery performance, enhanced cycle life, and reduced costs, making them an integral part of modern energy systems [34][35] [36].
However, challenges such as limited lifespan, environmental concerns over materials sourcing and disposal, and the need for further efficiency improvements persist.Research efforts are aimed at developing new materials, such as solid-state electrolytes, as well as refining existing chemistries to address these limitations [37][38] [39].

Water Tower (physical dynamics)
In contrast to chemically-based storage, the concept of gravitational potential energy is harnessed in physical energy storage systems like water towers.This approach capitalizes on the elevation difference between two reservoirs to store energy [40][41] [42].During periods of excess energy generation, surplus electricity is used to pump water from a lower reservoir to a higher one.This process converts electrical energy into potential energy.When electricity demand surges or renewable energy generation dips, the stored water is released from the upper reservoir to the lower one, passing through turbines that convert the potential energy back into electricity [40][41] [42].The advantage of this system lies in its inherent scalability and long cycle life.Moreover, it doesn't rely on chemical reactions or rare materials, making it an environmentally-friendly option [43][44][45] [46].The use of water towers for energy storage holds several advantages, especially in the context of transitioning to cleaner energy sources: Renewable Energy Integration: Water tower energy storage is exceptionally well-suited for complementing intermittent renewable energy sources such as solar and wind.Excess energy generated during sunny or windy periods can be efficiently stored in the elevated reservoir, and the electricity generated when renewable sources are not producing power can be released from the upper reservoir to meet demand [47].
Grid Stability: One of the challenges with renewable energy sources is their variability, which can lead to grid instability.Water tower energy storage acts as a stabilizing force by providing a buffer to balance supply and demand fluctuations.This enhances grid reliability and reduces the need for conventional fossil-fuel backup power plants [48].
Energy Time-Shift: Water tower systems enable energy to be time-shifted.Excess electricity produced during off-peak hours can be stored and then released during peak demand, optimizing energy utilization and reducing stress on the grid [49].
Environmental Benefits: Unlike some other energy storage methods that rely on potentially harmful materials, water tower systems have minimal environmental impact.They don't involve hazardous chemicals or emissions during operation, making them a cleaner alternative [50].
Local Resource Utilization: Water tower systems can take advantage of local topography and water bodies.This promotes decentralized energy storage solutions, reducing the need for extensive transmission infrastructure and minimizing energy losses during transportation [51].
Longevity and Reliability: Water tower systems are known for their long service life with minimal degradation over time.Properly maintained systems can function effectively for several decades, contributing to sustainable and reliable energy storage [52].
Emergency Power: Water tower systems can be designed to provide emergency power during grid failures or blackouts, enhancing grid resilience and ensuring essential services [53].
The potential of water tower energy storage in fostering the adoption of cleaner energy sources is substantial.By addressing the intermittency and reliability challenges associated with renewable energy, water tower systems contribute to a more sustainable and efficient energy landscape [54].

Microgrids and City-Scale Grids.
The emergence of microgrids and city-scale grids has sparked a paradigm shift in energy distribution and management, offering innovative solutions for enhancing sustainability, resilience, and energy efficiency.A microgrid is a localized energy system that can function autonomously or in coordination with the main grid, integrating diverse distributed energy resources (DERs) such as solar photovoltaics, wind turbines, and energy storage systems [55].Scaling up this concept, city-scale grids encompass interconnected microgrids within urban areas, enabling sophisticated energy sharing and management strategies [56].

Microgrids: Pioneering Energy Autonomy
Microgrids stand as a hallmark of decentralized energy systems, exemplifying the principles of energy autonomy and resilience.In the event of main grid failures, microgrids can seamlessly disconnect and continue to power critical loads, a feature vital for hospitals, emergency services, and other essential facilities [57].This autonomy is especially pertinent in regions prone to natural disasters or those with unreliable grid infrastructure.
Moreover, microgrids facilitate optimal utilization of local energy resources.For instance, in a solar-rich region, a microgrid can substantially reduce carbon footprint by relying predominantly on solar energy [58].Advanced control algorithms are pivotal in microgrids, orchestrating the dispatch of energy resources in real-time, considering factors such as demand fluctuations, availability of renewable sources, and energy storage capacity [59].

City-Scale Grids: Urban Energy Renaissance
City-scale grids expand the microgrid concept into a collaborative urban framework, fostering intricate energy exchange and load optimization strategies.By interconnecting diverse microgrids, cities can facilitate efficient sharing of surplus energy, curbing wastage and bolstering overall energy efficiency [60].The implementation of city-scale grids can be viewed as a strategic step toward achieving the sustainable energy goals outlined in various international agreements, including the Paris Agreement.
Critical to the success of city-scale grids are advanced control and communication systems, which enable real-time monitoring, data analytics, and predictive modeling.These systems empower energy managers to make informed decisions concerning energy allocation, storage management, and demand response [61].Furthermore, city-scale grids provide a platform for the integration of electric vehicles (EVs) as mobile energy storage units, contributing to grid stability and optimizing EV charging infrastructure [62].

Benefits and Challenges
The integration of microgrids and city-scale grids offers multifaceted advantages for both energy systems and urban development: • Enhanced Resilience: Microgrids ensure a continuous power supply during grid outages, thus bolstering energy resilience in critical facilities [63].• Renewable Integration: These systems promote the integration of renewable energy sources, thereby reducing greenhouse gas emissions and dependence on conventional fossil fuels [64].• Efficiency Augmentation: Microgrids mitigate transmission losses through localized energy generation, leading to heightened energy utilization efficiency [65].• Grid Stability: City-scale grids contribute to grid stability through intelligent load distribution, demand response mechanisms, and surplus energy sharing [66].• Energy Security: By diversifying energy sources, these systems enhance energy security, minimizing vulnerability to supply disruptions [67].• Despite their potential, challenges persist in the practical implementation of these concepts: • Regulatory Complexities: Integrating microgrids and city-scale grids into existing regulatory frameworks presents complexities, necessitating the establishment of standards and compensation mechanisms [68].• Economic Viability: Initial setup costs can be substantial, encompassing infrastructure development and integration of advanced control systems [69].• Technical Integration: Seamless integration of disparate energy resources and technologies requires sophisticated control systems and optimal design [70].• Data Privacy and Security: The real-time data exchange integral to these grids raises concerns regarding data privacy and cybersecurity [71].
• Community Engagement: Successful implementation hinges on effective community engagement and awareness campaigns, as behavioral changes in energy consumption are often imperative [72].

Future Perspectives
The concept of microgrids [73] and city-scale grids aligns seamlessly with the overarching trend of decentralizing energy systems and amplifying the share of renewables in the energy mix.Technological advancements are expected to drive down the costs of renewable energy components, rendering these solutions economically competitive [74].Simultaneously, innovations in energy storage, demand-side management, and smart grid technologies will catalyze the evolution of these systems, bolstering their capabilities and efficiency.
To successfully realize the potential of microgrids and city-scale grids, interdisciplinary collaboration among policymakers, energy experts, technology developers, and local communities is paramount [75].By harnessing the benefits offered by these pioneering energy distribution models, societies can reimagine and transform their energy landscapes, ushering in a sustainable, resilient, and environmentally conscious energy future.

Photovoltaic and Wind Turbine Energy Production Forecast Using Weather Data.
A critical aspect of optimizing renewable energy systems' performance lies in accurate energy production forecasting.Photovoltaic (PV) and wind turbine systems, being reliant on weather conditions, necessitate sophisticated forecasting methods for efficient operation and integration into the grid.Numerous studies emphasize the significance of weather data-driven forecasting models for enhancing the reliability and grid integration of renewable energy sources [76][77] [78].The integration of internet-provided weather data has emerged as a key approach in this realm.
Weather data forecasting utilizes meteorological information such as solar irradiance, wind speed, and temperature to predict energy generation patterns.Advanced machine learning techniques, particularly artificial neural networks, have demonstrated their efficacy in developing precise forecasting models [79] [80].These models leverage historical weather data to predict short-term and long-term energy production with high accuracy.
The proliferation of internet-based weather data sources from providers like the National Oceanic and Atmospheric Administration (NOAA), European Centre for Medium-Range Weather Forecasts (ECMWF), and local meteorological agencies has revolutionized energy forecasting [81] [82].Real-time weather data accessed through these sources enables dynamic adjustments in energy management strategies.For instance, as the cloud cover changes or wind speeds fluctuate, energy systems equipped with accurate forecasts can adapt their energy generation and distribution patterns in response.
Incorporating real-time weather forecasts into energy management systems enhances overall grid stability, enables efficient scheduling of energy storage and demand response, and supports the integration of renewables into the broader energy landscape [83] [84].Moreover, such forecasting systems contribute to cost savings by optimizing the utilization of available resources and minimizing the operational uncertainties associated with renewable energy sources.
In conclusion, the accurate forecasting of energy production [85] from photovoltaic and wind turbine systems using internet-provided weather data stands as a pivotal factor in achieving efficient grid integration and optimal energy management.As research in this area continues to advance, the collaboration between meteorology and energy systems engineering is poised to bring about innovative solutions that underpin the transition to a sustainable energy future.

Conclusion of Literature Review.
We conclude the literature review of the factors that combines the technologies of the AmEFC and renewable energy topics in the following table.Within the expansive tapestry of these advancements and potential, the comprehensive review exposes notable lacunae and opportunities that beckon further exploration.The juxtaposition of renewable energy technologies with advanced control systems necessitates incessant inquiry into optimization algorithms, integration modalities, and realtime decision-making frameworks.Furthermore, the pragmatic realization of microgrids and city-scale grids demands the nuanced negotiation of regulatory intricacies, economic viability conundrums, technical integration complexities, data privacy quandaries, and robust community involvement.

Transition to Research's Focus
As we transition from this sweeping literature review, the forthcoming sections of this scholarly discourse will delve into the specifics of our focal research: the formulation and realization of the Autonomous Multi-Factor Energy Flows Controller (AmEFC), aimed at optimizing the assimilation of renewable energy and streamlining energy management within the milieu of microgrids and city-scale grids.Envisioned as an extension of the bedrock established by the corpus of reviewed literature, our research seeks to contribute substantively to the cultivation of intelligent and resource-efficient energy systems poised to circumvent the challenges intrinsic to contemporary energy landscapes.

AmEFC Design and Architecture Framework
The prototype microgrid, designed for the application of AmEFC, comprises a synergistic assembly of cutting-edge renewable energy technologies, each offering its unique benefits to bolster efficiency, resilience, and sustainability.
Photovoltaic Solar (PV) Systems: At the forefront are the advanced PV systems, which have been beneficiaries of rapid strides in materials science and engineering.A pivotal contribution to this domain is the exploration by Green et al. (2019) into perovskite solar cells [28].Their research unveiled an avenue for potential enhancements in PV efficiency, underscoring the transformative potential of innovative materials in solar technology.The design philosophy emphasizes modularity, allowing for the system's scalability, catering to varying energy requirements.
Vertical Axis Wind Turbine (VAWT): The microgrid incorporates VAWTs, prized for their multifaceted advantages.Their design ensures lower auditory disturbances, while ground-level accessibility streamlines maintenance tasks.Furthermore, these turbines demonstrate heightened efficacy under turbulent wind conditions, harnessing energy even when conventional turbines might falter.The VAWT configuration is also envisioned to be modular, facilitating future expansions as necessary.
Hydro Pumps: Augmenting the energy storage capabilities of the microgrid are hydro pumps.By exploiting gravitational potential energy, they present a reliable avenue to squirrel away power for later use.Their inclusion ensures a steady energy source, adept at buttressing grid stability and addressing sporadic energy demands.Significantly, Lu et al.'s study in 2018 elucidates the complementary relationship between hydro-pumped storage and other renewables like wind and solar [33].This symbiosis underscores the viability of a microgrid that seamlessly weaves various energy sources into a cohesive unit.Much like its peers, the hydro pump system is designed for scalability, anticipating future growth and diversification.

MPPT Integration with the Prototype Microgrid for AmEFC Application.
Building upon the innovative framework of the prototype microgrid, an integral component warrants special mention -the Maximum Power Point Trackers (MPPTs).Designed to optimize the extraction of energy from each Renew-able Energy Source (RES), every RES within the microgrid, be it the PV system, VAWT, or hydro pumps, will be individually interfaced with its dedicated MPPT.This ensures that each energy source operates at its peak performance, maximizing efficiency and energy harvest.
The interconnection of these MPPTs culminates at the DC Bus, serving as a unified hub for energy distribution and management.This architecture not only ensures streamlined power flow but also provides a modular approach, allowing for easy scalability and potential integration of additional RES in the future.
Beyond the realm of energy optimization, the MPPTs play a pivotal role in data management and communication.They are intricately networked to facil-itate real-time data sharing among themselves.This data-centric approach allows for instantaneous upload of energy production metrics to the cloud infrastructure of the MPPT manufacturer.This real-time data integration offers a multitude of benefits, from remote monitoring to predictive analytics, ensur-ing that the AmEFC application is not just about harnessing energy but also about harnessing the power of data to drive informed decisions and optimizations.

Incorporation of Battery Strings in the Prototype Microgrid for AmEFC Application.
Further bolstering the robustness of the prototype microgrid is the strategic in-tegration of battery strings.Recognizing the variable nature of energy pro-duction from renewable sources, the inclusion of batteries is pivotal to ensure consistent power availability and optimized energy utilization.The microgrid facilitates flexibility in battery technology choice, accommodating either Lead-Acid or advanced Lithium-based battery systems, catering to specific requirements and considerations of the deployment scenario.
Each battery string is equipped with a state-of-the-art Battery Management System (BMS).The BMS plays a quintessential role in safeguarding battery health and longevity.It meticulously monitors and manages critical parame-ters like voltage, current, and temperature.Its algorithms are designed to pre-cisely control the charging and discharging phases, ensuring that the batter-ies are neither overcharged nor excessively depleted, thereby preserving their lifespan and operational efficiency.
Beyond its primary role of battery health and charge management, the BMS serves as a nexus for data acquisition and communication.Similar to the MPPTs, the BMS systems are endowed with the capability to relay real-time data on battery performance and health metrics to the cloud platform of the battery manufacturer.This data-centric integration paves the way for remote monitoring, health diagnostics, and prognostic analytics, granting stakeholders invaluable insights into the performance and expected lifespan of the battery assets.
In summation, the inclusion of battery strings, whether Lead-Acid or Lithium-tech, extends the functional horizon of the AmEFC prototype microgrid.Not only do they act as energy reservoirs, buffering against the intermittencies of renewable sources, but with the integrated BMS, they also usher in an era of smart energy storage, where decisions are not just based on real-time needs but are informed by a wealth of data, ensuring optimal operation and sus-tainability of the energy infrastructure.

Integration of Single-phase Inverters to the Prototype Microgrid for AmEFC Application.
In the pursuit of flexibility and adaptability within the prototype microgrid, a novel approach in inverter technology has been employed.Specifically, in-stead of a traditional three-phase inverter, the design leverages three single-phase inverters, seamlessly interlinked to serve as a unified three-phase sys-tem.This arrangement provides not only a pathway for energy conversion from DC sources like PVs, wind turbines, and battery strings to AC loads but also guarantees an inherent redundancy and modularity to the system.
The strategic advantage of utilizing three single-phase inverters becomes ap-parent when considering the scalability of the prototype.Should the load consumption demands vary or expand in the future, the microgrid's architec-ture easily allows for the integration of additional single-phase inverters.This modularity ensures that the microgrid can be dynamically tailored to meet evolving energy needs without necessitating a complete overhaul of the core infrastructure.
Each of the inverters is imbued with smart connectivity features, echoing the overarching theme of data-centric operation observed in the other compo-nents of the microgrid.These inverters are not just passive components; they are equipped with sensors and communication modules that continuously monitor energy flows.This data is uploaded in real-time to the cloud platform maintained by the inverter's manufacturer.Such cloud integration facilitates remote monitoring, analytics, and potential predictive maintenance, minimiz-ing downtime and ensuring consistent energy delivery.
Moreover, the inverters are interconnected, establishing an intra-grid com-munication network.This inter-inverter communication is instrumental in coor-dinating their operations, maintaining phase synchronization, and ensuring a balanced load distribution across the three-phase system.

Load Classification and Management in the Prototype Microgrid for AmEFC Application.
Within the sophisticated architecture of the AmEFC prototype microgrid, energy consumption is intelligently stratified according to the significance and priority of the connected loads.This hierarchical structure is particularly crucial to ensure a seamless balance between energy production, storage, and consumption, thereby providing consistent and uninterrupted power supply to essential services.
Critical Loads: These represent the cornerstone of the system's commitment to reliability.Critical loads encompass essential equipment and infrastructure that demand an unwavering 24/7 energy supply.Such loads might include, but are not limited to, lifesupport systems in healthcare settings, emergency lighting, communication systems, and other indispensable services.The microgrid's energy forecasting algorithms are geared towards ensuring that the combination of renewable energy production and battery storage will always suffice to meet the energy requirements of these critical loads, come what may.
Normal Loads: Constituting the bulk of daily energy consumption, normal loads represent everyday electrical appliances and systems, ranging from lighting and HVAC to household appliances.While these loads also benefit from the 24/7 power supply, they are subject to a hierarchy of service.In situations where energy production lags or unforeseen consumption spikes occur, the microgrid prioritizes the critical loads, potentially drawing from the city-scale grid to ensure that normal loads remain operational without affecting the critical ones.
Surplus Energy Hydro Pump Load: Serving as a tangible manifestation of energy surplus management, the hydro pump load introduces a unique dimension to the microgrid.In scenarios where the energy production significantly overshadows consumption, instead of letting this surplus dissipate, the system funnels it towards powering hydro pumps.These pumps transfer drilled water to elevated storage towers, effectively converting surplus electrical energy into gravitational potential energy.However, this operation isn't executed blindly.The microgrid, equipped with weather forecasting data and real-time battery charge levels, intelligently decides if it's optimal to divert this surplus to the hydro pump.

Grid-Connected Support and Auxiliary Power Sources for AmEFC Microgrid.
To further bolster the resilience and reliability of the AmEFC microgrid, two additional layers of energy support are seamlessly integrated: the grid-connected support and an auxiliary diesel/electric generator.Both of these are introduced as contingencies, ensuring that the microgrid remains impervious to energy lags and unforeseen consumption spikes, making the system not only versatile but also exceptionally energy-secure.
On Grid-Connected Support: The microgrid is designed to seamlessly interface with the city-scale grid, serving as an external reservoir of energy when needed.This grid-connected support comes into play especially during prolonged periods of low renewable energy production or during unexpected surges in demand.By drawing power from the larger grid, the system ensures that its internal energy balance remains undisturbed, preserving the sanctity of its critical and normal loads.Moreover, this connection also offers the microgrid an opportunity to feed surplus energy back into the grid during periods of excess production, fostering a symbiotic relationship that benefits both the local and broader energy ecosystems.
Diesel/Electric Generator: While the grid-connected support offers a robust backup, there might be situations or locations where the city-scale grid itself is compromised, such as during widespread power outages or in remote areas.For such scenarios, the AmEFC microgrid is equipped with the option to integrate a diesel/electric generator.This auxiliary power source can be swiftly activated to bridge any energy gaps, ensuring that the microgrid remains operational and continues to power its loads without interruption.Designed for efficiency and minimal environmental impact, these generators serve as the last line of defense, underlining the system's commitment to uninterrupted power supply no matter the external circumstances.

New Possible Discharging Capabilities in Prototype AmEFC Micro-grid.
Within the paradigm of integrated energy systems, the adept management of superfluous generation is paramount.In configurations inclusive of wind turbines, it's conceivable to encounter scenarios where wind-driven power generation surpasses the system's consumption and storage capacity.This phenomenon is especially accentuated when the affiliated battery storage units are saturated.Historically, conventional methods would either disconnect the wind turbine or redirect the surplus energy to a dump load, serving as a preventive measure against potential system overloads.Contrastingly, the prototype AmEFC micro-grid presents a pioneering resolution to this conundrum.During intervals of energy overproduction, the excess power actuates hydro pumps, thereby facilitating the transfer of water into a designated tower.This strategic operation not only capitalizes on the superabundant energy but also primes the system for impending energy demands, transforming the stored water into a potential energy reservoir.In the eventuality of the water tower reaching its volumetric threshold, the AmEFC micro-grid is ingeniously devised to channel the overflow into an adjacent stream.This ensures a consistent equilibrium within the energy infrastructure, while concurrently optimizing resource utilization and averting wastage.This avant-garde mechanism accentuates the prototype AmEFC micro-grid's commitment to ensuring resourceful and sustainable energy management.

AmEFC Software: The Nerve Center of the Microgrid System.
The sophistication of the AmEFC microgrid doesn't solely lie in its physical components.At the heart of this complex network of energy generators, storage systems, and diverse load categories is the AmEFC software, a cutting-edge computational and control framework designed to seamlessly harmonize the intricate dance of energy flows throughout the system.Cloud-Connected Equipment Data Streams: Virtually every component of the microgrid -from the MPPTs, inverters, charging controllers, to the BMS -not only performs its designated function but also continually communicates vital real-time operational data to their respective producer clouds.This constant stream of information provides an instantaneous snapshot of the microgrid's health, performance, and potential areas of optimization.
Data Aggregation and Storage: The AmEFC software goes a step beyond by collating this ocean of data into its centralized cloud database.Here, the information is systematically organized and stored, encompassing everything from water tower level heights, consumption histories of critical and normal loads, energy surplus records, to intricate weather forecasting data.This centralized repository ensures that any decision made by the system is rooted in comprehensive, up-to-the-minute information.Advanced Computational Strategies: Armed with this holistic view, the AmEFC software employs state-of-the-art computational methodologies to drive its decision-making processes.Using Optimal Power Flow (OPF) strategies, it determines the most efficient distribution of energy across the grid, ensuring that each component operates at its peak potential while meeting the demands of the connected loads.
Reinforcement Learning (RL)-based Control: Going beyond traditional algorithmic solutions, the AmEFC software incorporates RL-based solutions into its arsenal.By learning from historical data, recognizing patterns, and adapting to new situations, the RL mechanisms can make predictive and proactive decisions.For instance, based on weather forecasting data and past consumption trends, the system might optimize the energy stored in batteries in anticipation of a cloudy day, ensuring uninterrupted power supply to critical loads.
Real-time Decisions and Automated Control: The combination of OPF and RL ensures that the AmEFC software isn't just reactive but also proactive.Whether it's diverting surplus energy to the hydro pump, drawing power from the grid during peak demand, or optimizing the battery charging cycles based on weather forecasts, the system continually makes real-time decisions to maintain optimal energy flow and system efficiency.The true brilliance of the AmEFC system becomes apparent when examining the execution mechanism underpinning the entire microgrid's operation -the AmEFC Controller.The synergy between its software computations and hardware interfacing guarantees seamless energy flow, ensuring optimal system performance and reliability.
Hardware Interfacing: At the core of the AmEFC Controller's execution is its comprehensive electrical connectivity to the microgrid's vast array of devices and load power inputs.This connectivity is facilitated through a meticulously organized array of relays.These relays serve as the tactile interface, allowing the Controller to physically orchestrate the energy distribution across the microgrid by establishing or severing connections as needed.
Client-side Software Operations: While the AmEFC Cloud Center serves as the data repository and computational nerve center for all interconnected microgrids, the on-site (client-side) software plays a crucial role in real-time operations.Tailored specifically for the individual nuances and requirements of its designated microgrid, this software continuously fetches pertinent data from the AmEFC Cloud Center.With access to both real-time and historical data, the software is equipped to make informed, timely decisions about energy allocation and flow.Dynamic Energy Flow Control: Based on the computations derived from the cloud data, the client-side software dynamically manages energy flows throughout the microgrid.By manipulating the connected relays, it can swiftly connect or disconnect various components, enabling a fluid response to changing energy demands or supply fluctuations.This adaptive approach ensures that the microgrid consistently operates at peak efficiency, regardless of external variables.AmEFC Controller: A Holistic Solution: The AmEFC Controller's true prowess is its harmonious integration of software and hardware components.While the software, with its sophisticated computational capabilities, dictates the energy management strategy, the hardware acts on these directives in real-time through the relay system.Together, they form a cohesive unit, ensuring the microgrid is not just smart but also responsive and adaptable.

Architectural Framework summarized of AmEFC (Autonomous multi-factor Energy Flow Controller).
To ensure that the AmEFC embodies principles of scalability, adaptability, and resilience, the architectural framework is structured across different levels.These hierarchical levels form tiered approach, providing a systematic layout to the energy control ecosystem.DC/AC Inverters: Three single-phase inverters can be merged to emulate a three-phase system, ensuring adaptability to different load requirements.

Level 2: Dynamic Load Management
Critical Loads: Imperative appliances and services demanding uninterrupted power.
Normal Loads: Regular devices and systems which can be powered continuously, adjusting energy sourcing strategies as per availability.
Surplus Energy Hydro Pump Load: A dynamic energy storage unit, adaptable to energy surplus situations, and subject to factors like weather forecasts and battery charge conditions.

Level 3: Supplemental Energy & Backup Mechanisms
City-Scale Grid Connectivity: Facilitates adaptability by drawing power from the city grid during low energy generation intervals.
Diesel/Electric Generator: Ensures resilience by standing as a backup power source during prolonged energy shortages.

Level 4: The Centralized AmEFC Controller & Decision Systems
Data Integration & Cloud Center: The AmEFC Cloud Center acts as a data reservoir, collecting insights from various components, setting the stage for data-driven decision making.
Execution Backbone: The relay network, controlled by the AmEFC software, transforms data insights into actionable energy flow decisions, thereby demonstrating the system's adaptability and resilience.
3.9.5.Level 5: New Possible Discharging Capabilities in Prototype AmEFC Micro-grid Dynamic Discharge Management: In situations where there's excessive wind energy, and batteries are at peak capacity, the system disconnects the wind turbine, rerouting the excess power to dump loads.In the hybrid model, the hydro pump directs water to the tower for storage.If the tower reaches its full capacity, the excess water can be safely discharged into a stream, offering an innovative mechanism to manage surges in energy production.
Through this multi-level architecture, the AmEFC showcases a balance between foundational energy components and advanced decision-making systems.The layered approach ensures that while the base remains robust and scalable, the top layers are nimble and adaptable, ensuring a resilient energy framework for the future.

Interconnectivity Challenges and Opportunities of AmEFC in City-Scale Microgrids
In the evolving energy landscape, the challenge of integrating multiple AmEFCs across diverse microgrids via the AmEFC Cloud Center presents both complexities and unprecedented opportunities.At the heart of this ecosystem, the AmEFC Cloud Center becomes the nexus, mediating energy exchanges, predictive analytics, and real-time adaptability, transcending traditional grid constraints.
One core challenge lies in synchronizing myriad microgrids with varying generation capacities, consumption patterns, and operational contingencies.Diverse geographic locations introduce variability in energy generation, especially with renewable sources that are highly dependent on environmental factors.This discrepancy can lead to instances where one microgrid experiences an energy surplus while another faces a deficit.
However, this challenge births an opportunity through the capabilities of the AmEFC local controllers.By being seamlessly interconnected and pooling their data into the central Cloud Center, these controllers enable real-time energy flow decisions not just at a micro-level but also at the macro, city-scale level.For instance, should a particular microgrid register an energy surplus, and forecasting data indicates a period of full generation ahead due to weather conditions, the AmEFC local controller can dynamically decide the energy flow strategy.Instead of conventionally feeding its surplus to local energy storage like the water tower, it can channel this excess to the city grid.This energy injection comes with a contractual understanding, facilitated by the Cloud Center, where the supplying microgrid can retrieve equivalent energy from the city grid in times of need, essentially acting as a short-term energy loan.Such a give-and-take mechanism, governed by the AmEFC controllers, not only optimizes local energy distribution but also fortifies the broader city grid, enhancing resilience, adaptability, and sustainability.

AmEFC controller & micro-grid Simulation
The Simulink model represents the comprehensive functionality of the on-grid AmEFC microgrid prototype.Designed to holistically integrate Renewable Energy Sources(RES), such as PV systems, Wind Turbines, and Hydro Pumps, with a Battery Storage System equipped with a BMS (Battery Management System), its main objective is to manage and streamline energy distribution across different load categories.A pivotal emphasis of the model is to consistently maintain power to critical loads, while also ensuring seamless interaction with the main grid.Critical and Normal Loads: Representing primary and secondary electrical consumers, the model dynamically orchestrates the energy supply to these sectors based on availability and controller directives.
Grid Interface: Allows for two-way communication and power transfer be-tween the microgrid and the main electrical grid.Central to the model, the AmEFC controller is meticulously designed to ensure the unbroken power supply to critical loads across varied scenarios.It assesses real-time data inputs from interconnected systems, synergizing this information with sophisticated algorithms to make instantaneous energy distribution decisions.These decisions span across energy storage, utilization, prioritization of certain RES, momentary disconnection of non-essential loads, and grid interactions.

Interconnections:
The DC bus is the keystone of the prototype, linking all RES, the BMS-equipped battery storage system, and the inverters.The inverters subsequently connect to the AC loads, AC drilling pump, and the main electrical grid.
Simulation Settings: Depending on the simulation's objectives and the data's granularity, the time step can be calibrated accordingly.Solvers like 'ode15s' are generally efficient for power system simulations.Typically, the simulation spans between a single day to multiple days, encapsulating the microgrid dynamics through diverse scenarios.3.9.5.Level 5: New Possible Discharging Capabilities in Prototype AmEFC Micro-grid.Dynamic Discharge Management: In situations where there's excessive wind energy, and batteries are at peak capacity, the system disconnects the wind turbine, rerouting the excess.

Running the Simulink model
Scenario Analysis: Reliability and Surplus Energy Management in the AmEFC Microgrid System.
The Objective is to validate the system's reliability in consistently powering critical loads and efficiently utilizing surplus energy through hydraulic energy storage, ultimately gauging its responsiveness during battery discharge phases.
Components Under Test: Critical Loads: These are the primary focus of this scenario, representing essential appliances and services that cannot afford a power interruption.
Drilling Pump and Water Tower: The drilling pump transfers water to the storage tank, converting surplus electrical energy into potential energy.
The hydro pump recovers this energy, converting the potential energy back to electrical energy when the battery is in a discharge state.

Battery and BMS:
The battery's state of charge, as monitored by the BMS, plays a pivotal role in the decisionmaking process.It aids in determining when to initiate energy storage in the water tower and when to draw from the hydro pump.
The simulation starts with the AmEFC system in an operational state, generating power from available renewable sources.

Surplus Energy Detection:
As the renewable energy sources generate electricity, the system continually satisfies the critical load requirements.Any excess energy, after catering to the critical loads and charging the battery to its optimum level, is identified as surplus.
Energy Storage through Drilling Pump: The AmEFC controller activates the drilling pump upon detecting surplus energy.The pump transfers water into the storage tower, converting this surplus electrical energy into gravitational potential energy.
Battery Discharge Monitoring: When the renewable sources are insufficient, and the battery starts discharging to cater to the critical loads, the BMS provides real-time data about the battery's state of charge.
Hydro Pump Activation: Once the battery reaches a predetermined discharge level, the AmEFC controller prompts the hydro pump into action.This hydro pump "releases water from the storage tank", converting the stored potential energy back into electricity, thus supplementing the energy requirements of the critical loads.
Evaluation Metrics: • Continuity: The uninterrupted operation of critical loads throughout the simulation period.

•
Energy Conversion Efficiency: The efficiency in converting surplus electrical energy to potential energy and vice versa.

•
Response Time: The system's agility in detecting the need and transitioning between different energy sources and storage mechanisms.
Through this scenario, the system's ability to seamlessly transition between different energy storage and generation methods, all while ensuring uninterrupted power to critical loads, will be established.

Analyzing results and Discussion
The pioneering design of the AmEFC micro-grid system is anchored around three primary energy producers: the Photovoltaic (PV) System, the Vertical Axis Wind Turbine (VAWT), and the Hydro Pump-based Water Tower.An essential aspect to understand before diving into the system's performance analysis is its initialization stage, characterized by zero energy production or storage.This stage serves as the reference point, laying the foundation for evaluating how the system responds to various external environmental stimuli.The system's adaptability and efficiency can be best observed when subjected to a range of weather conditions.To delineate this, we categorize the weather scenarios based on two pivotal factors: solar irradiance and wind speed.These factors are further segmented into three distinct categories for comprehensive analysis: The subsequent sections will delve into the detailed interplay between these environmental conditions and the corresponding energy production, unveil-ing the robustness and responsiveness of the AmEFC micro-grid system.

Week-long Simulation Sequence for AmEFC Micro-grid Systems Testing
To rigorously test the AmEFC micro-grid system and, more crucially, the operational efficiency of the controller, a comprehensive week-long simulation was devised.The simulation was structured to cover a myriad of environmental conditions by intertwining the aforementioned solar and wind categories.The sequence provides a holistic view, encompassing both optimal and challenging conditions to gauge the system's adaptability, resilience, and efficiency.
Here's the introduced sequence for the week: This sequence, featuring an amalgamation of varying solar irradiance and wind speeds, forms the backbone of the simulation.The resulting data, arising from this blend of conditions, will furnish insights into the AmEFC system's capability to adapt and optimize energy flows, thereby underlining its real-world applicability and robustness.
We present a series of graphs that vividly illustrate the interplay between the primary environmental factors-solar irradiation and wind speed-and the resultant energy production in the AmEFC micro-grid system.These visuals stem from a week-long observation, where days were categorized based on their solar and wind profiles.This classification not only simulates the system's response under varying real-world conditions but also underscores the versatility and efficiency of the AmEFC controller.Each graph is designed to offer a granular view of the daily trends, peaks, troughs, and anomalies, providing a comprehensive understanding of how the system navigates through these varying energy input scenarios to maintain optimal energy distribution running various scenarios.

Testing Scenarios for the AmEFC Controller
To validate the efficiency and adaptability of the AmEFC controller within diverse operating environments, using math and numbers from Appendix B, we have curated for distinct scenarios.Each of these scenarios progressively introduces added complexities and functionalities, simulating both typical and atypical conditions for the micro-grid system.In this initial and "naked" setup, the micro-grid operates entirely in isolation, without any external data inputs like weather forecasts.The primary objective of the AmEFC controller in this scenario is straightforward -manage the energy production from the RES efficiently and channel losing all surplus energy.This scenario represents a basic, test of the system's autonomous energy management capabilities.In this rudimentary setup, the micro-grid operates entirely in isolation, without any external data inputs like weather forecasts.The primary objective of the AmEFC controller in this scenario is straightforward -manage the energy pro-duction from the RES efficiently and channel any surplus energy directly to the water tower for storage.This scenario represents a basic, yet essential, test of the system's autonomous energy management capabilities.Without weather forecast the system fails to keep critical loads on line, even if it stores energy on Water tower.Building upon the previous setup, this scenario integrates real-time weather forecasting data into the system.With this addition, the AmEFC controller is not only managing the energy production and storage but also preemptively adjusting energy distribution strategies.By predicting potential energy pro-duction shortfalls or surpluses based on weather data, the controller can en-sure uninterrupted power supply to critical loads.Simultaneously, it may choose to temporarily disconnect normal loads as a strategic move to conserve energy during forecasted low-production periods.The weather forecast capability keeps the system safe for the critical loads, using energy on Water tower for fine tuning turning off the normal loads when necessary The final and most sophisticated testing scenario introduces the capability to connect the micro-grid with the larger city-scale grid.In this setup, the AmEFC controller dynamically manages energy surplus by making decisions between storing energy in the water tower or selling it back to the grid.The primary ob-jective is to ensure all loads remain online continuously, reaping the dual benefits of energy storage and potential revenue generation through energy sales.This scenario demonstrates the AmEFC controller's potential in a more integrated energy landscape, where micro-grids can symbiotically interact with larger grid infrastructures.The on-grid system, keeps critical and normal loads online (100%) with the same priorities using surplus storage in Water tower first and selling the addi-tional surplus (instead of spend it as useless) to PPC.

Conclusions
The evolution of energy systems to become more sustainable, resilient, and adaptable has been vividly exemplified by the development and validation of the AmEFC (Autonomous multi-factor Energy Flow Controller).This research has systematically broken down the multi-level architectural framework of AmEFC, elucidating its capability to balance foundational energy components with advanced decision-making systems.Central to the system's efficacy is the incorporation of diverse Renewable Energy Sources, battery storage mechanisms, dynamic load management, and supplemental energy backup systems, all coordinated by the sophisticated AmEFC controller.
Our Simulink model simulation of the on-grid AmEFC microgrid prototype distinctly exhibited the system's reliability in consistently catering to critical loads while effectively managing surplus energy through hydraulic storage.Moreover, the intricate interactions within city-scale microgrids highlighted both the challenges and opportunities, emphasizing the system's adaptability and potential to transform traditional energy grid dynamics.The innovative potential to integrate weather forecasting and real-time decision-making capabilities of the AmEFC controller stands out, potentially revolutionizing the way microgrids operate and interact on a broader scale.

Future Works
Enhanced Interconnectivity: With the foundation of the AmEFC laid out, a logical progression would be to explore more intricate inter-microgrid communications.This would mean establishing a more robust protocol for AmEFC controllers across multiple microgrids to communicate, share, and even trade surplus energy, enhancing overall grid resilience and efficiency.
Incorporation of AI and Machine Learning: The introduction of artificial intelligence and machine learning algorithms can further optimize the decision-making process of the AmEFC controller.Such enhancements could lead to more accurate predictive analyses based on historical data and real-time inputs, optimizing energy flow strategies.
Extended Simulink Model Scenarios: Future research can expand upon the presented Simulink model by introducing varied scenarios, such as extreme weather conditions or unexpected equipment malfunctions, to test the system's adaptability and resilience further.
Cost-Benefit Analyses: As with all advancements, it's pivotal to evaluate the economic feasibility of widespread AmEFC implementation.Comprehensive cost-benefit analyses, considering both short-term implementation and long-term operational costs against potential savings and benefits, would be instrumental.
Real-World Implementation and Pilot Testing: While simulations provide valuable insights, real-world testing in diverse geographic and climatic regions would offer invaluable practical knowledge.Such pilots can be instrumental in refining the system for broader applications.
In summation, while the presented research has made significant strides in advancing the capabilities and potential of energy systems, the horizon still holds numerous avenues to explore, refine, and innovate.The AmEFC, with its promising architectural framework, sets the stage for a future where energy systems are not only sustainable but also smart, interconnected, and truly autonomous.

Figure 1 Figure 2
Figure 1 AmEFC & prototype micro-grid Block diagram full design

Figure 4
Figure 4 Controller's flowchart (*Refer to appendix A for pseudo code and variables definition).

Figure 7
Figure 7The model in Simulink

Figure 8
Figure 8 PV System Diagram on Simulink Wind Turbine (VAWT): Designed to model the conversion of wind energy based on given wind speeds and specific turbine attributes.A AC/DC con-verting process is stabilizing finally the DC Bus to 48V (PMSG -Universal Bridge)-DC/DC.

Figure 9 Figure 10 "
Figure 9 Wind Turbine AC/DC/DC Circuit Hydro Pump: This module signifies the mechanism wherein energy is stored as gravitational potential energy.The hydro pump is activated to lift water, which, when released, is converted back to generate electricity.The load is the Drilling pump load and the breaker controls the on/off operation.The function is as simple as

Figure 11
Figure 11 BMS Battery System

Figure 12
Figure 12 DC Bus

Figure 14 Grid
Figure 14 Grid

Figure 20
Figure 20 Sun irradiations and Wind speeds on various days (blue very sunny and windy, blue moderate and orange a bit)

Day 1 :
High Sunshine Day (Category A) paired with High Wind Day (Category A).Day 2: High Sunshine Day (Category A) combined with Moderately Windy Day (Category B).Day 3: Moderately Sunny Day (Category B) combined with High Wind Day (Category A).Day 4: Moderately Sunny Day (Category B) paired with Moderately Windy Day (Category B).Day 5: A Little Sunny Day (Category C) paired with High Wind Day (Category A).Day 6: A Little Sunny Day (Category C) combined with Moderately Windy Day (Category B).Day 7: Moderately Sunny Day (Category B) paired with a bit Windy Day (Category C).

Figure 21
Figure21 Sun irradiation for a week-long period, daily.

Figure 22 PVFigure 23 Figure 24
Figure 22 PV System production according to sun irradiation

Figure 25
Figure 25 Off-Grid Operation without Weather Forecasting & Water tank (Battery orange, Water blue, Critical red, Normal green)Without weather forecast the system fails to keep critical loads on line.Espe-cially without any surplus storage on Water tower there are many cut offs.

Figure 26
Figure 26 Critical loads often failure without weather forecasting

Figure 27
Figure 27 Off-Grid Operation without Weather Forecasting (Battery orange, Water blue, Critical red, Normal green)

Figure 28
Figure 28 Water tower surplus storage and critical loads failure

Figure 29
Figure 29 Off-Grid Operation with Weather Forecasting (Battery orange, Water blue, Critical red, Normal green)

Figure 30
Figure 30 Water tower surplus storage, critical loads on-line 100%, normal loads on & off

Figure 31
Figure 31 On-Grid Operation with Weather Forecasting (Battery orange, Water blue, Critical red, Normal green, PPC purlpe)

Figure 32
Figure 32 Water tower surplus storage, critical & normal loads on-line 100%, surplus to PPC.

Table 1
Literature review conclusion table.
Essential for optimal energy extraction from RES. Interconnected to upload real-time data to the producer's centralized cloud.Battery Storage System: Available in Lead-Acid or Lithium technology variants, they are crucial for energy storage and management, with the BMS controlling and monitoring their operations.

Table 2
Domain Various Values for Sun irradiation and Wind speed