Adaptive dynamic energy coordination device for integrated renewable and conventional energy networks

Abstract

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

Description

Technical field:

[0001] The invention relates to intelligent energy management and optimization systems, particularly in the field of smart grids and hybrid energy architectures that combine renewable and conventional energy sources. Specifically, it is a data-driven, adaptive coordination device that continuously analyzes generation, storage, and consumption parameters to dynamically adjust operating commands for distributed energy systems. Background of the invention:

[0002] Conventional energy management systems in hybrid power infrastructures are primarily based on fixed-threshold control and rule-based planning. However, such systems often exhibit inefficiencies under fluctuating renewable energy conditions, as electricity generation from solar and wind power is inherently variable and difficult to predict. Conventional monitoring controllers typically lack adaptive forecasting capabilities and cannot dynamically redistribute generation responsibilities between renewable and conventional sources. Furthermore, they are unable to perform real-time optimizations in response to evolving supply-demand imbalances or storage degradation effects.

[0003] Existing SCADA (Supervisory Control and Data Acquisition) systems offer only data visualization without automated computer learning. Their control measures are based on static setpoints, which often leads to energy constraints or suboptimal load distribution. Battery-based storage devices in such systems operate according to fixed charge and discharge schedules, which accelerates performance degradation and shortens their lifespan.

[0004] Furthermore, conventional systems lack real-time synchronization and anomaly tolerance in data exchange between monitoring units and central controllers, leading to inaccurate forecasts. Security vulnerabilities in communication channels can compromise operational integrity, particularly in distributed systems with remote renewable energy sources or microgrids. Additionally, integrated scenario analysis capabilities for simulating different operating strategies under changing climate or market conditions are lacking.

[0005] Therefore, there is an urgent need for an adaptive device that is capable of learning from operational feedback, performing probabilistic forecasts, dynamically optimizing generation and storage behavior, and securely integrating various energy systems into a unified, data-driven management framework.

[0006] The modern energy landscape is undergoing a paradigm shift driven by the rapid integration of renewable energy sources, digital control technologies, and distributed generation systems. However, the inherent intermittency and variability of renewable energy sources such as solar, wind, and small hydropower pose a significant challenge to maintaining the balance between supply and demand, grid stability, and the efficient use of available resources. Conventional energy management systems were historically designed for centralized grids dominated by predictable, controllable power plants such as coal, gas, and nuclear power stations. These legacy systems relied on static planning procedures, predefined thresholds, and operator-dependent decisions to control energy generation and distribution.With the increasing spread of renewable energies, these traditional systems are no longer sufficient for the complex temporal and spatial dynamics of modern power grids. The unpredictability of renewable sources, coupled with fluctuating load patterns and decentralized storage units, necessitated the development of intelligent, data-driven, and self-adaptive management systems.

[0007] Conventional energy management architectures such as SCADA (Supervisory Control and Data Acquisition) systems, EMS (Energy Management Systems), and DMS (Distribution Management Systems) have formed the backbone of grid operations for decades. These platforms offer visualization, data logging, and alarm functions, but lack the real-time computing power required for adaptive decision-making. Their forecasting capabilities are limited to simple linear regression models or heuristic trend extrapolations, which do not adequately capture nonlinear dependencies or stochastic variability in generation and consumption. Furthermore, SCADA-based frameworks typically operate hierarchically, meaning decision-making is centralized, leading to delayed response times and a lack of local autonomy.This centralized rigidity limits scalability, especially in distributed environments with microgrids or off-grid renewable energy clusters. Furthermore, data acquisition intervals in conventional systems are often on the order of minutes, while fluctuations in renewable energy can occur within seconds, leading to control delays and reduced operational efficiency.

[0008] From a decision-making perspective, current energy management solutions lack adaptive learning and self-correcting capabilities. The models used for control and optimization are typically trained once and remain static throughout operation. As environmental and consumption patterns evolve, these models gradually become outdated, leading to performance degradation. Machine learning and artificial intelligence have shown promise in this area, but most implementations are limited to offline analysis or research prototypes and are not integrated into real-time control systems. Without continuous feedback loops, systems cannot independently adjust forecasting or optimization parameters to new conditions. Therefore, the lack of self-learning capabilities limits adaptability and resilience in dynamic grid environments.

[0009] Furthermore, energy management systems are traditionally based on deterministic optimization, which requires complete knowledge of input parameters and system constraints. In reality, these parameters—such as renewable energy generation, consumer demand, and market prices—are uncertain and probabilistic. Deterministic methods therefore fail to capture the stochastic variability of renewable resources, leading to conservative control decisions that do not fully utilize available capacity. While stochastic optimization methods are theoretically robust, their computational complexity and lack of real-time scalability have prevented their widespread practical application. This gap underscores the need to develop an intelligent optimization system capable of real-time stochastic decision-making within acceptable computational limits.

[0010] Another significant drawback of existing energy systems is their limited capacity for scenario-based planning and simulation. Most conventional devices operate in real time based on current or short-term forecast data, without considering the impact of future conditions such as weather changes, equipment wear, or market fluctuations. This lack of foresight hinders proactive control and strategic planning. Advanced scenario analysis tools exist as standalone software packages, but they are not integrated into operational control devices and are therefore unsuitable for field-level decision-making. Consequently, there is a need for a unified framework that combines real-time forecasting, optimization, and scenario simulation in a single hardware device.

[0011] User interaction and visualization in current systems are also limited. Operators often rely on multiple dashboards across different subsystems, leading to a fragmented understanding of the situation. While many systems offer static displays of voltage, frequency, and power flow, they fail to provide actionable insights or performance indicators such as efficiency, reliability index, or cost metrics. This limitation hinders operator decision-making and increases reliance on manual analysis, which is time-consuming and prone to error. Furthermore, changing operating conditions necessitate manual parameter adjustments and configuration updates, resulting in inefficiency and potential downtime.

[0012] Given these limitations, there is a significant gap in the state of the art for a fully integrated, data-driven, self-learning, and adaptive device capable of orchestrating renewable and conventional energy sources, intelligently managing storage units, and dynamically optimizing energy flow across the grid. Such a device must unify forecasting, optimization, control, communication security, and user interaction within a single, scalable framework. Developing such an adaptive, dynamic energy coordination device would represent a major technological advancement, eliminating the inefficiencies, rigidity, and fragmentation of existing solutions while enabling a robust, autonomous, and efficient energy management paradigm for the future. Summary of the invention:

[0013] The main objective of the invention is to provide an adaptive dynamic energy coordination device that intelligently controls and balances renewable and conventional energy sources in real time through data-based forecasting and optimization. The invention aims to overcome the inefficiencies of conventional energy management systems by integrating predictive analytics, self-learning techniques, and coordinated control of generation, storage, and distribution units. Furthermore, it aims to ensure operational stability, maximize energy utilization, and extend the service life of storage components through adaptive charge / discharge control and predictive maintenance.

[0014] Furthermore, the invention aims to enable secure and synchronized data communication between distributed IoT-based monitoring units to facilitate precise forecasting and decision-making. It also provides real-time visualization and operator interaction via an intuitive user interface, while supporting encrypted synchronization with remote monitoring servers for centralized oversight. The overall goal is to provide a unified, intelligent, and scalable energy management device that ensures optimal performance, resilience, and adaptability under dynamically changing environmental and operating conditions.

[0015] The present invention aims to overcome the inherent limitations of conventional energy management systems. To this end, a data-driven, intelligent, and adaptive device is provided that dynamically coordinates renewable and conventional energy sources in hybrid power grids. A key objective of the invention is the development of a unified energy coordination device that, through predictive analysis and continuous optimization, ensures real-time balancing between energy generation, storage, and consumption. The invention is intended to enable the seamless integration of intermittent renewable energy sources, such as solar and wind power, with conventional generators and to ensure that the total supply always meets the predicted demand under varying environmental and load conditions.

[0016] Another important objective of the invention is the integration of self-learning and adaptive forecasting functions into the energy coordination concept. This allows the system to continuously improve its forecast accuracy across successive operating cycles. By employing machine learning techniques and probabilistic forecasting models, the device can dynamically adapt its control strategies to observed deviations between expected and actual performance. This enables an intelligent, feedback-driven optimization process that significantly improves system reliability and operational efficiency compared to static, rule-based approaches in conventional energy management architectures.

[0017] A further objective of the invention is the coordinated optimization of energy generation plants, energy storage systems, and distributed loads to enable efficient use of available resources. The invention aims to intelligently charge and discharge storage elements such as batteries or capacitors based on predicted demand and generation forecasts, rather than according to fixed, time-based cycles. This adaptive charge / discharge management extends storage lifespan, minimizes energy waste, and reduces overall system losses. The invention also aims to detect a gradual deterioration in storage efficiency through continuous monitoring of charge / discharge profiles and to automatically trigger diagnostic alerts for predictive maintenance planning.

[0018] A further objective of the invention is the implementation of a secure and synchronized communication network that ensures precise and reliable data exchange between distributed monitoring units and the central processing unit. The aim is to eliminate data latency, timestamp errors, and packet inconsistencies through time-synchronized communication between all measurement nodes. Furthermore, simple, authenticated encryption techniques are used for all data transactions to prevent unauthorized access, the injection of false data, or the manipulation of control commands. Through this secure communication concept, the invention ensures the integrity and authenticity of the operational data, which is crucial for maintaining reliable network performance.

[0019] A further objective of the invention is to provide a real-time human-machine interface that enables intuitive visualization and interactive control of energy parameters. The user interface displays key operational indicators such as generation efficiency, consumption-generation ratio, energy cost index, and reliability rating, thus supporting operators in making informed decisions. It also allows the direct application of operator-defined input parameters—such as operating thresholds or forecast recalibration factors—to the optimization techniques, making the system both adaptive and user-friendly.

[0020] A further objective of the invention is to create a modular and scalable device architecture that can accommodate additional sensors, energy sources, or storage arrays without major reconfiguration. The modularity of the system enables its use in a wide range of applications—from local microgrids and industrial plants to large-scale hybrid power plants—thus increasing flexibility and interoperability. BRIEF DESCRIPTION OF THE FIGURE

[0021] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of an adaptive dynamic energy coordination device for integrated renewable and conventional energy networks.

[0022] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0023] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0024] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0025] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0026] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The systems, methods, and examples provided herein serve only for illustration and are not to be construed as limitations.

[0028] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0029] Fig.Figure 1 shows a block diagram of an adaptive dynamic energy coordination device for integrated renewable and conventional energy networks. The system 100 comprises: a processing unit (102) configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to balance available generation resources with forecasted consumption requirements; a storage unit (104) connected to the processing unit and configured to store historical records of energy generation and consumption, environmental data, operational thresholds, and learned model parameters, and to provide this data as input for the forecasting and optimization routines executed by the processing unit;a plurality of IoT-based monitoring units (106), each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate, and environmental conditions, each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit (108) implemented in the processing unit and configured to process historical and real-time data to generate forecast curves for demand and generation using statistical and probabilistic forecasting techniques and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual performance;an optimization control unit (110) implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operating data to determine a set of optimized control variables representing the target generation contribution of each energy source and outputting these targets to a lower-level controller for execution; a controller (112) communicatively connected to the processing unit and the multiple energy generation sources and configured to control the operation of each source by adjusting the activation state, output level, and operating priority based on the control signals received from the processing unit;an energy storage management unit (114) comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics back to the processing unit in real time for continuous recalibration; an alarm and notification control unit (116) connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit remote audible, visual, and digital alerts to designated operators;a user interface terminal (118) connected to the processing unit and configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecasting or optimization parameters; and a secure server interface (120) configured to synchronize operating logs, training data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

[0030] In one embodiment, the processing unit (102) continuously aggregates and normalizes the sensor data streams received from the monitoring units, detects anomalies or missing values, and performs adaptive filtering before using the data as input for forecasting and optimization.

[0031] In one embodiment, the forecasting unit (108) applies several forecasting models in parallel and compares their output accuracy over successive operating cycles. It dynamically increases the computational weight of models with lower prediction errors, thus achieving a self-learning improvement in forecast reliability.

[0032] In one embodiment, the optimization control unit (110) determines the generation priorities by correlating the forecasted demand, the available resource capacity, the storage levels and the environmental conditions, and calculates target generation levels that minimize the energy imbalance while keeping the reserve margins within predefined safety thresholds.

[0033] In one embodiment, the controller (112) executes the target generation levels output by the processing unit by transmitting digital control signals to actuators of individual energy generation sources to change their power output or operating state in real time.

[0034] In one embodiment, the processing unit (102) monitors the feedback data returned by the controller and recalibrates optimization variables in subsequent cycles if the deviation between expected and actual generation exceeds an adaptive tolerance value.

[0035] In one embodiment, the energy storage management unit (114) performs bidirectional energy flow control by continuously monitoring voltage, current and state of charge and adjusting charge and discharge cycles according to the control signals generated by the processing unit to ensure balanced utilization and a longer storage lifetime.

[0036] In one embodiment, the alarm and notification control unit (116) classifies alarm states into several severity levels and dynamically changes the alarm repetition rate and the communication channel priority according to the urgency of the detected state.

[0037] In one embodiment, the processing unit (102) manages a continuously updated operating log in the storage unit, with each log entry containing sensor readings, control commands, system status and environmental context to support post-event diagnostics and the training of predictive models.

[0038] In one embodiment, the communication network includes a time-synchronized data aggregation controller configured to timestamp and align data packets received from geographically distributed monitoring units, thereby ensuring temporal coherence for forecast accuracy.

[0039] In one embodiment, the processing unit (102) also includes a machine learning subunit configured to analyze stored operational logs, detect seasonal and environmental dependencies, and automatically adjust forecasting or optimization parameters without manual reconfiguration.

[0040] In one embodiment, the energy storage management unit (114) is configured to detect a gradual loss of storage efficiency by analyzing charge / discharge profiles and to transmit diagnostic information to the processing unit for predictive maintenance planning.

[0041] In one embodiment, the processing unit (102) generates key performance indicators and displays them via the user interface terminal, including the energy generation efficiency, the energy cost index, the consumption-to-generation ratio, and the system reliability rating, which are updated in real time as the operational data changes.

[0042] In one embodiment, the secure server interface (120) uses authenticated encryption for all transmitted data records and performs regular integrity checks to ensure the authenticity of the system data synchronized with the remote server.

[0043] In one embodiment, the processing unit (102) performs a scenario analysis routine that simulates alternative operating strategies under predicted climate and demand conditions, evaluates the simulated results based on stored cost and reliability metrics, and updates the control priorities for subsequent operating cycles accordingly.

[0044] The activation of the components mentioned in the claimed data-driven dynamic energy management system is achieved through an integrated configuration of electronic, electromechanical, and communication-based hardware units that work in a coordinated manner to achieve the system's functional objectives. The processing unit is implemented as a microprocessor-based computing hardware module, for example, an embedded multicore controller or an industrial-grade computing board equipped with integrated arithmetic units and digital signal processing functions to perform real-time energy optimization calculations.The storage unit consists of non-volatile and volatile hardware storage components, including solid-state drives and dynamic random-access memory (DRAM), which are electrically connected to the processing unit via high-speed data buses to enable the rapid storage and retrieval of operational data sets and learned model parameters. The multitude of IoT-based monitoring units is implemented as hardware sensor nodes, comprising microcontrollers, converters, and wireless communication modules. These are physically deployed at energy generation, storage, and consumption points to acquire analog measurements of electrical and environmental parameters and to transmit digitized telemetry packets over a secure communication network such as WLAN, ZigBee, or LoRa.The forecasting unit and the optimization control unit are hardware-implemented computing subsystems instantiated within the processing unit using dedicated coprocessors or FPGA (field-programmable gate array) logic to execute numerical forecasting and optimization routines in real time. The controller is implemented as an electronic control hardware interface, including driver circuits, relay modules, and communication interfaces, to physically regulate and modulate the output of renewable and conventional power generators according to control signals received from the processing unit. The energy storage management unit consists of physical battery arrays, charge controllers, and bidirectional power conditioning circuits integrated into microcontroller-based monitoring hardware to implement storage, discharge, and feedback control operations.The alarm and notification control unit comprises electronic comparator circuits, signal amplifiers, and communication transceivers that generate hardware triggers for visual indicators, audible alarms, and network-based notifications when limit deviations are detected. The user interface terminal is implemented as a touchscreen or display-based hardware console and includes a graphical display controller, input peripherals, and a communication interface for exchanging user input and visualization data with the processing unit. The secure server interface is implemented as a hardware communication gateway with cryptographic modules, Ethernet or cellular modems, and a secure data transmission circuit that enables real-time synchronization of logs and analysis data with remote monitoring servers.Together, these physically instantiated hardware modules and interconnect architectures ensure that each claimed component is technically enabled, operationally feasible, and capable of performing the described computing, monitoring, and control functions within an integrated power management environment.

[0045] Designing a diversified energy matrix is ​​a guiding principle for risk management and optimizing systemic resilience. Diversification, understood as the strategic distribution of generation across different sources and technologies, not only reduces dependence on specific resources and mitigates the risk of outages or interruptions, but also enables the utilization of each region's comparative advantages and the adaptation of supply to geographical, climatic, and socioeconomic characteristics. The DEM model, conceived as a flexible and adaptable instrument, facilitates the coherent integration of renewable and conventional energy sources and promotes region-sensitive management in line with sustainable development goals.This holistic perspective strengthens the system's ability to absorb disturbances, accelerate recovery from crises, and ensure operational stability in highly complex and uncertain scenarios.

[0046] Below is a formalization of a number of conditions and restrictions that the system must meet.

[0047] Generation capacity by source G(t)=∑t=0T∑i=1nXi,t ∀t ∈ [t0,T] i ∈ [1,n] ∑t=0T∑i=1nXi,t ∀t ∈ [t0,T] i ∈ [1,n] 1. Xi: Energy generation capacity per source. 2. Xi,t: Energy generation capacity per source in a period T. 3. n: Number of energy sources. In the case of the Antarctic pilot project, n=4 was defined, since four energy sources are used: sun, moon, wind and H2V. 4. T: The period or time interval in which the generation of the various energy sources is measured.

[0048] Energy demand is understood as D(t) and is the total energy consumption, which corresponds to the sum of the consumption of all electrical appliances. =∑t=0T∑i=1nappliance consumptioni,t ∀t ∈[t0,T]i∈[1,n] (n: number of energy sources) In the case of the Antarctic pilot project, n=4 was defined because four energy sources are used: solar, lunar, wind and H2V energy. T: This is the period or time interval in which the generation of the various energy sources is measured. Ci (t) = Copi (t) + Cmi (t) (cost per source)

[0049] Total production costs during period T. C(t)=∑t=0T∑i=1nCi,t ∀t∈[t0,T]i∈[1,n] (n: number of energy sources) In the case of the Antarctic pilot project, n=4 was defined because four energy sources are used: solar, lunar, wind and H2V energy. T: This is the period or time interval in which the generation of the various energy sources is measured. C(t)≤Budget(PPTO) OPT G / C=Max{G(t)}−Min{C(t)} OPT G / C=Max{G(t)}−Min{Cop(t)}−Min{Cm(t)} OPT R=Max{G(t)}−Min{Dt}−Min{Ft} OPT R=Max{G(t)−Min(Dt+Ft)} Symbols G(t): Energy production over time. German: Energy demand. F(t): Energy loss or depletion over time. R: Energy reserve. C(t): Energy costs over time. OPT G / C: Optimization of the production / cost ratio. OPT R: Optimization of energy reserves.

[0050] The creation of prospective scenarios and the use of dynamic simulation models are fundamental tools for anticipating and addressing the challenges and opportunities of the energy future. The DEM model utilizes trend analyses, socioeconomic projections, and technological simulations to examine various development horizons, taking into account variables such as the evolution of electromobility, the emergence of new industries, and policy changes. This approach enables the development of adaptive and flexible strategies that can proactively respond to disruptive changes and capitalize on new opportunities. It thus strengthens the ability of decision-makers to align energy development with goals such as sustainability, equity, and resilience in an increasingly dynamic and complex global environment.

[0051] The goal of optimization (OPT) is to efficiently manage a power generation system to meet consumption demand while minimizing operating and maintenance costs and maximizing generation efficiency.

[0052] The objective function aims to maximize power generation, minimize consumption and losses, and minimize the total costs associated with operating the model over a given period.

[0053] The model weighs the relative generation capacity of each energy source and adheres to the specified constraints. It must ensure that the generation capacity always covers consumption and losses and stays within operational limits such as the rate of change of generation to guarantee the stability and operational continuity of the system, which in turn ensures reliability. Mathematical optimization methods such as linear and nonlinear programming can be used to solve this problem and find the best operating strategy that satisfies all constraints and optimizes the objective function. • Functional objective. Min Z=∑t=0T∑i=1nXi,t⋅Ci ∀t ∈ [t0,T] i∈[1,n] Xi,t: Energy generation capacity of source i over a specific period. Ci: Unit cost of energy production using source i (Fi). Egg: Greenhouse gas emissions per unit of energy produced by source i (Fi), (kg CO2 / MWh). Dt: Total energy demand during period T. CapMaxi: Maximum generation capacity of source i during period T. • Restrictions 5. Meeting demand. ∑t=0T∑i=1nXi,t≥Dt+Ft ∀t∈[T0,T] 6. Capacity limits. 0≤Xi,t≤CapMaxi ∀i,t 7. Minimum provision. ∑i=1nXi,t>α⋅Dt ∀t

[0054] α: This is the additional energy supply compared to the actual demand over a period T.

[0055] Efficient management of operational constraints and the creation of strategic reserves are key elements for the stability and reliability of modern energy systems. The increasing prevalence of renewable energies, characterized by their variability and unpredictability, necessitates the integration of advanced storage technologies and the implementation of rapid response mechanisms to fluctuations in supply and demand. The DEM model integrates solutions such as high-performance batteries, thermal storage systems, and emerging technologies like green hydrogen, complemented by active demand management strategies. These instruments not only mitigate the effects of temporary imbalances but also optimize resource utilization, reduce losses, and strengthen the system's operational resilience to various contingencies.

[0056] A reserve constraint in an energy model ensures that an additional amount of energy is available that exceeds the expected demand. This serves as a safety or resilience measure for the system. The reserve enables a response to all eventualities related to unplanned additional demand. This is expressed as follows: R=G(t)−D_t−F t ∀ t∈[t0,T]where R>0 • Rt: Energy reserve. • Dt: Total energy demand during period T. • Ft: Energy loss or leakage over time T. • CapMaxi: Maximum generation capacity of the source during period T.

[0057] Where: Current demand = current energy consumption (in MWh, GWh, etc.) Growth rate = expected percentage growth (expressed as a decimal, e.g. 3% = 0.03) n = number of years into the future you want to project. Future demand = Current demand x(1 + growth rate) n

[0058] In this context, energy system planning is based on a number of key inputs that define the energy generation strategy: Available financial resources for the development, operation, maintenance, and improvement of energy generation infrastructure. This input determines the prioritization of financial resources with regard to investments in renewable energy infrastructure and the implementation of automation and storage technologies.

[0059] The allocation of funds is based on projected expenditures for the next 12 months, using rolling annual data. This amount should be allocated based on historical and projected consumption during a calendar year (January to December).

[0060] This refers to the financial resources, fixed assets, inventories, and human resources provided for the operation and maintenance of the system. The more resources are invested, the higher the generation capacity will be.

[0061] This refers to the physical, geographical, and technological availability of energy resources. These include: Natural resources (sun, wind, H2V, moon). Existing infrastructure (solar modules, turbines, thermal power plants, etc.). Human resources and technical capabilities.

[0062] Energy demand is the total amount of energy that a system, community, facility or area needs within a specific time period and within a specific geographical area to meet its total operating needs.

[0063] This demand encompasses the consumption of all forms of energy such as electricity, fossil fuels (natural gas, oil, coal), renewable energies (sun, wind, water, biomass) and other sources, and is usually expressed in units such as kilowatt hours (kWh), megawatt hours (MWh) or gigawatt hours (GWh), depending on the scale.

[0064] Demand can be classified according to consumer sectors, for example: - Housing (houses) - Commercial (offices, services) - Industry (production processes) - Transport (vehicles, public networks) - Among other things, agricultural use.

[0065] Since energy demand fluctuates over time due to factors such as time of day, season, or weather conditions, its analysis requires the observation of temporal and spatial patterns for appropriate planning, infrastructure management, optimization of energy generation, maintenance, and formulation of sustainable energy policies.

[0066] Inefficiency detection is a systematic process that aims to identify and analyze areas, processes, systems, or activities where available resources such as time, money, energy, and materials are not used optimally, resulting in waste, cost overruns, or poor performance.

[0067] Essentially, the goal is to uncover leaks or losses that prevent the full utilization of the generated energy. This leads to higher costs and puts a strain on the system's ability to achieve its planning objectives. Energy loss of the system (overall inefficiency). Ft=F(t)−Dt−R ∀T∈[t 0,T]where R>0 Ft=∑i=1nIEAi,t ∀ i∈[1,n] ; t∈[t0,T] IEA = Usable Energy (UE) / Provided Energy (ES) ⋅ 100% FtAi = supplied energy (ES) − usable energy (UE) Ft: Energy loss or leakage over time T. G(t): Energy production during period T. Dt: Total energy demand during period T. Rt: Energy reserve. IEA: Energy inefficiency artifact n. EU): Energy consumption. Income tax: Energy supplied. Rt: Energy reserve. 2.2 Seasonal weather conditions and weather forecasts

[0068] Climate information that directly influences the availability and efficiency of renewable energies. This data is crucial for the functioning of the machine learning technology, which is trained on this data to make precise recommendations.

[0069] The model features permanent technology-based monitoring, enabling real-time control of energy consumption and thus facilitating decision-making, anomaly detection, and resource utilization optimization.

[0070] This involves the permanent monitoring of system operation by IoT devices. - IoT devices: Hardware equipment such as sensors that measure energy storage (kW) and energy flow to determine real-time demand, smart cameras, weather stations, etc. - The model generates a series of warnings that alert to factors triggered under certain conditions defined in the system configuration / parameterization, with the aim of taking timely action to ensure the proper operation of the system. - Logs: These are records of relevant system activities. For example: - Energy storage level: Four energy levels are defined.

[0071] Critical energy level: Upon request, a warning is recorded in the log, triggering emails and SMS messages to indicate that the energy level has reached a level that jeopardizes the operational continuity of the energy supply.

[0072] Minimum energy level: A demand-based alarm is recorded in the log and triggers emails and SMS messages at a specific frequency. Additionally, an audible alarm and / or lights are continuously activated until the energy level rises to a different level. At a specific frequency, an indication is given that the energy level is below normal and could reach a critical level if it continues to drop.

[0073] Normal storage status: Alarm that is only recorded in the system log; consequently, no emails (max. 1 per day) or SMS messages are sent indicating that the energy level has reached a level that jeopardizes the operational continuity of the energy supply.

[0074] Maximum storage capacity: A demand-driven alarm, recorded in the log, triggers emails and SMS messages at a specific frequency. Additionally, an audible alarm and lights are continuously activated until power generation is reduced.

[0075] This prevents battery overload, damage to the energy storage infrastructure, leaks and energy losses, which could lead to significant cost overruns in the system. Development strategy.

[0076] To ensure rapid prototype development, it is recommended to begin with a simple forecasting method such as moving average or exponential smoothing. After developing the functional prototype, the methods of simple moving average, exponential smoothing, linear regression, multiple regression, ARIMA SARIMA, and Monte Carlo simulation are developed. Finally, the combined weighted method is implemented, which is undoubtedly the most complex, as it measures daily how the forecasts of each method align with the actual results. A weighted estimate is then assigned based on how closely the individual forecasting methods matched each other. The gaps between the individual methods are identified, and a weight or weighting factor (W) is assigned based on these differences to develop the next forecast.

[0077] The aim is to forecast energy demand and the associated costs over a period of at least 12 months. - Estimated monthly consumption in kW / MWh. - Energy generation costs C(t) per kW / MWh. - Determine the seasonality pattern of energy consumption. - Variables to be fed into the model, such as: - Consumer trend (growth / stabilization / decline) - Population, equipment, etc. Assignment of energy sources mde.

[0078] One of the fundamental parts of the technique is to iteratively evaluate the overall efficiency of the MDE system and the particular efficiency of each source using the following expressions: EFFn=GFn(t)Cfn(t) ; EFTotal=∑i=1nGFi(t)CFi(t)

[0079] Subsequently, a generation quota must be assigned according to the efficiency of each source in relation to the overall efficiency of the MDE system, which is expressed as follows: MDE=(100%−EF1EFTotal,100%−EF2EFTotal,100%−EF3EFTotal,100%−EF4EFTotal) Technology for allocating production quotas

[0080] The following describes a technique that dynamically allocates the generation quota (contribution) for each energy source within a period T with regard to the overall efficiency of the system. Quote F1=0 Quote F2=0 Quota F3=0 Quota F4=0 EF_1=0; EF_2=0; EF_3=0; EF_4=0; EF_Total=0; EF_1=G1 / C1 EF_2=G2 / C2 EF_3=G3 / C3 EF_4=G4 / C4 EF_Total=SUMA(EF_1,EF_2,EF_3,EF_4); Quota F1=(100%−EF_1 / EF_Total); Quota F1=(100%−EF_2 / EF_Total); Quota F1=(100%−EF_3 / EF_Total); Quota F1=(100%−EF_4 / EF_Total); Signal MDE(quote F1;quote F2;quote F3;quote F4). System target

[0081] The main objective is to prioritize and activate the most efficient energy sources according to weather conditions, while deactivating those sources whose operation is not justified due to their generation capacity and maintenance costs. Phased development

[0082] The initial functional version proposes the application of machine learning techniques to achieve the defined goals. Future versions could integrate additional artificial intelligence techniques. Furthermore, the use of professional online services with advanced analysis and optimization capabilities could be considered. Technical specifications

[0083] The technology must be able to: 1. Measuring the historical and trend accuracy of each forecasting method used. 2. Identify whether these methods are subject to seasonal fluctuations and learn from these fluctuations to optimize the recommendations over time. 3. Determine the correlation between the generation levels of each energy source and the seasons to improve decision-making regarding the activation or deactivation of sources. • Reports and KPIs (Key Performance Indicators) These are reports on the operation of the system. A set of metrics is defined. KPI 1: Cost efficiency of energy generation Source x-th To the x-th ECGE Fn=100%−(CT FnCTS×100)% ECGE Fn=100%−(CO Fn+CM FnCTS×100)% CTS=∑i=1nCT Fi n: Number of energy sources (solar, green hydrogen, wind and moon) ECGEFn: Cost efficiency of energy generation of the nth source. CT Fn: Total cost of the nth source. CO Fn: Operating costs of the nth source. CM Fn: Maintenance costs of the nth source. CTS: Total system costs. KPI 2: Efficiency (V) of energy generation source n-th EVGE Fn=100%−(VG FnVGS×100)% EVGE Fn=100%−(VG FnVGS×100)% VGS=∑i=1nVG Fi n: Number of energy sources (solar, green hydrogen, wind, liquefied natural gas and moon) EEFn: Energy efficiency of the nth source. VG Fn: Energy generation rate of the nth source VGS: System generation speed. KPI 3: Energy generation capacity CGE = Ultra-hours of energy generation (kWMWh) Total energy generation (kWMWh) HH OPERATION criteria CGE>1 Electricity generation above historical capacity. CGE=1 Electricity generation according to historical capacity. CGE<1 Electricity generation below historical capacity. KPI 3: Energy consumption (EC). E = Usable energy consumption period (kWMWh) / Total energy generation (kWMWh) / Operating period criteria CE>1 Energy consumption higher than previous consumption. Interpretation: Indicates that current consumption is higher than historical consumption and therefore the risk of energy shortages is increasing. CE=1 Interpretation: Indicates that current consumption is higher than historical consumption, and therefore the risk of energy shortages is increasing. Energy consumption equal to historical consumption: EC<1 Energy consumption lower than previous consumption: KPI 4: Consumption vs. Production CvsG=(Consumption period) / (Production period) criteria CvsG<1 Energy consumption is lower than the generation capacity. CvsG=1 Energy consumption equals generation capacity. CvsG>1 [Energy consumption greater than generation capacity] Energy storage.

[0084] To store the generated energy, an area with a state-of-the-art battery bank is being set up. These batteries must prevent energy losses. The technology adapts the storage capacities of the various energy sources of the MDE (Mobile Data Exchange) and recommends them according to demand and generation capacity.

[0085] Reports and KPIs: These are reports on the operation of the system. A number of metrics are defined. - Background information on the development of the model. - Find out about the location, origin(s), destination(s), costs, and transport times for oil. (Historical costs must be determined.)

[0086] This includes transported oil and potential losses of input. - Production costs for each renewable energy source. - Hours of sunshine per season. - Wind speed per season. - Transport times. - Historical energy production levels.

[0087] The MDE promotes efficient energy consumption by ensuring responsible use of energy resources, integrating technologies and thus meeting the same or higher demand with lower energy consumption.

[0088] Efficient consumption promotes optimal energy use through: - Devices or technologies with high energy efficiency. - Control and automation systems: timers, presence sensors, intelligent climate control, etc. - Reduction of power consumption in "standby" mode (connected devices that are not in use). - Switching off lights and appliances when they are not in use. - Adjust thermostats (instead of extreme temperatures). - Promoting rational behavior and rational habits. Utilizing natural light. - Avoidance of waste and energy losses in the conversion, distribution and consumption process. - Timely preventive and corrective maintenance.

[0089] Minimize consumption without compromising comfort, quality of life, or productivity. For all these reasons, the Dynamic Energy Model (DEM) represents a significant advancement in energy resource management. It combines innovative technologies, renewable sources, and continuous monitoring systems to improve production and distribution in highly complex contexts with variable demand. Its adaptive design, based on predictive methods and machine learning, enables the anticipation of system changes and adjustment of the energy matrix, optimization of storage, and reduction of operating costs, thereby strengthening the system's sustainability and resilience.Furthermore, the MED integrates IT security protocols that protect the integrity of information and infrastructure, facilitate communication and collaboration between various stakeholders and platforms in the sector, and promote the active involvement of end users through decentralized energy management models. Therefore, this model is a key instrument for strategic decision-making and makes a significant contribution to the development of more efficient, robust, and environmentally conscious energy systems that meet current and future energy challenges.

[0090] The processing unit serves as a data center for executing techniques for demand and generation forecasting, evaluating optimization targets, and generating control measures. It continuously collects data streams from distributed monitoring units installed across generation facilities, storage units, and consumption nodes. Each monitoring unit is equipped with sensors that measure voltage, current, power output, state of charge, frequency, and environmental parameters such as temperature, irradiance, and wind speed. These data packets are time-stamped, encrypted, and transmitted via a synchronized communication network to ensure temporal consistency. The processing unit includes a data normalization routine that aggregates inputs from multiple sources, detects anomalies, filters noise, and compensates for missing data points through interpolation and adaptive smoothing.This ensures that all subsequent calculation routines receive accurate, consistent, and synchronized input data.

[0091] The forecasting unit, implemented as part of the processing framework, uses a hybrid forecasting technique that integrates statistical, probabilistic, and machine learning methods. The forecasting process begins with the collection of historical and real-time datasets, including records of past energy consumption, generation patterns, weather data, and operational logs. These datasets are processed using ARIMA (Autoregressive Integrated Moving Average) models to capture linear temporal dependencies. To account for nonlinear and stochastic fluctuations, the system also employs a recurrent neural network (RNN) structure with gated recurrent units (GRUs) trained on past operational cycles. The hybrid model calculates multiple short- and long-term forecasts for both energy demand and renewable energy generation.

[0092] A model evaluation layer continuously monitors the discrepancy between predicted and actual performance, known as the forecast residual. These residuals are analyzed using a dynamic weighting technique that adjusts the computational significance of each forecasting submodel. For example, if the neural model exhibits a lower mean absolute percentage error (MAPE) than the ARIMA model across successive cycles, the neural prediction is assigned a higher confidence weight in subsequent forecast iterations. This adaptive model weighting technique enables the forecasting system to learn and improve over time without manual reconfiguration.

[0093] The forecasted demand and generation capacities are then forwarded to the optimization control unit, which applies a multi-criteria optimization technique to determine the ideal allocation of energy generation and storage resources. The optimization problem is formulated to minimize the energy disequilibrium function (F = |G - D| + λ₁ C₉ + λ₂ Rₛ), where (G) represents total generation, (D) represents forecasted demand, (C₉) denotes the energy cost function, and (Rₛ) is a reliability or reserve safety margin term. Instead of solving this equation deterministically, the invention uses a stochastic optimization framework that includes probability distributions for the uncertainty of renewable energy generation and load variability.Monte Carlo simulation techniques are used to evaluate several possible future scenarios of the balance between production and demand, and the optimization routine identifies the control strategy that minimizes the expected imbalance under all simulated conditions.

[0094] The optimization control unit uses a real-time adapted variant of Particle Swarm Optimization (PSO) technology called Adaptive Dynamic Particle Swarm Optimization (ADPSO). In this framework, each "particle" represents a possible control vector that defines the target share of each energy source in power generation, the charging / discharging rate of the storage system, and the load matching parameters. The particles iteratively update their positions based on the locally best (p_best) and globally best (g_best) solutions derived from the evaluation of the fitness function, which is defined by energy costs, system stability, and the efficiency of renewable energy use. Unlike conventional PSO, the ADPSO technology implemented in the invention includes dynamic inertial adjustment and feedback-dependent reinitialization.If the deviation between actual and optimized performance exceeds a tolerance threshold, the technology triggers a partial reinitialization of the particle positions to accelerate convergence. This adaptive mechanism ensures that the optimization control unit remains responsive even under rapidly changing operating conditions.

[0095] Once the optimization control unit has determined the optimal control parameters, it generates digital control signals that are transmitted to the control unit. The control unit acts as an interface between the computer system and the physical energy hardware. It modulates the operation of renewable energy generators by adjusting inverter reference points, wind turbine pitch angles, or the fuel injection quantity of conventional generators. It also instructs the energy storage management unit to execute specific charge or discharge cycles according to the optimization results. The control unit continuously monitors the execution feedback and transmits the operating status to the processing unit, which evaluates deviations such as the difference between the generated power and the target value.These deviations are incorporated into the next optimization cycle, forming a closed adaptive feedback loop that continuously improves performance.

[0096] The energy storage management unit features a bidirectional control structure that balances the energy flow between generation and consumption nodes. It utilizes a predictive control method that combines model predictive control (MPC) and rule-based adaptive tuning. The MPC subroutine forecasts short-term energy imbalances based on the current storage level, forecasted demand, and expected generation. It calculates a control curve that minimizes the cost function, represents storage load, and accounts for efficiency losses. The adaptive, rule-based layer adjusts the MPC parameters using historical charge / discharge efficiency data, thus ensuring the long-term maintenance of the storage elements. This hybrid approach enables dynamic, real-time charge / discharge planning, extends storage lifetime, and maintains energy balance stability.

[0097] To improve reliability and safety, the alarm and notification control unit continuously compares measured operating conditions with stored limit values. It classifies alarm events into severity levels—warning, critical, or emergency—based on the magnitude and duration of the deviation. The system dynamically adjusts the alarm repetition rate and priority according to the severity level. For example, critical overvoltage or overtemperature conditions trigger immediate local alarms and remote notifications via secure network protocols, while less severe deviations generate logged alarms for later review.

[0098] The secure communication protocol implemented in the device uses authenticated encryption and hash-based integrity checks for all transmitted data packets. The encryption keys are regularly rotated using an Elliptic Curve Cryptography (ECC)-based key exchange. This ensures that even distributed, low-power IoT sensors can communicate securely without excessive computational overhead. Communication synchronization is based on a Network Time Protocol (NTP) correction mechanism that aligns the timestamps of all connected monitoring units to the microsecond range, thus ensuring accurate data correlation for forecasting.

[0099] A machine learning unit embedded in the processing architecture is responsible for long-term performance optimization. It regularly analyzes historical operating logs stored in memory to identify seasonal or environmental dependencies in generation and demand. Using clustering techniques such as K-means and Gaussian mixing models, it classifies operating patterns and adjusts the forecast parameters accordingly. If a recurring anomaly pattern is detected—for example, a gradual decline in storage efficiency—the system automatically updates its operating parameters or schedules maintenance.

[0100] The user terminal serves as the central interface for human access. It visualizes real-time data, including supply and demand graphs, storage status, and key performance indicators such as generation efficiency, energy cost index, and reliability rating. The display subsystem is connected to the control unit, allowing operators to input adjustment parameters, recalibration commands, or queries for scenario simulation. The interface software includes an embedded analytics dashboard that enables the simulation of hypothetical scenarios, such as changes in solar irradiance or peak demand, using the same forecasting and optimization techniques as in live operation.

[0101] The entire process of the invention takes place in a continuous feedback cycle: data acquisition by monitoring units → data normalization and filtering → forecasting of production and demand → multi-objective optimization for control decisions → triggering of control commands → feedback monitoring → recalibration and learning. This cyclical process enables adaptive control in real time with predictive intelligence and ensures that the system dynamically balances supply and demand even under uncertain and fluctuating conditions.

[0102] Through its integrated technical architecture, the device enables autonomous and efficient energy coordination across distributed hybrid systems. It combines machine learning-based forecasting, probabilistic optimization, predictive energy storage control, secure communication synchronization, and interactive visualization within a unified operating framework. The invention thus offers a comprehensive, intelligent, and self-adaptive energy management solution that overcomes the limitations of conventional systems and establishes a new paradigm for dynamic energy coordination and optimization in complex energy infrastructures.

[0103] The adaptive dynamic energy coordination device of the present invention is a composite intelligent structure with interconnected sensor, processing, control, and communication subsystems that together perform predictive optimization of hybrid energy grids. The device is physically implemented as an integrated hardware assembly and consists of a processing housing, an energy storage interface panel, an IoT data acquisition rack, and a secure communication gateway.

[0104] The core of the device is a processing unit configured to perform real-time computing tasks such as energy forecasting, control optimization, and data normalization. This processing unit continuously analyzes data streams from multiple sources received by distributed monitoring units. It uses data filtering routines to detect and compensate for missing or erroneous values, thus ensuring robust forecast accuracy.

[0105] A storage unit is coupled to the processing unit, storing operational data such as production and consumption logs, historical weather and environmental parameters, learned model coefficients, and adaptive thresholds. The storage unit enables structured data retrieval for forecasting and optimization techniques and also stores operational event logs for diagnostic purposes or retraining through machine learning.

[0106] The device comprises several IoT-based monitoring units, each equipped with multimodal sensors to capture current parameters such as voltage, current, frequency, battery charge level, generator fuel level, and ambient temperature and radiation. These monitoring units are configured to transmit encrypted data packets over a synchronized communication network to ensure time-synchronized data aggregation in the processing unit.

[0107] The processing unit incorporates a forecasting subunit that combines statistical and machine learning techniques—such as autoregressive integrated moving average (ARIMA), random forest regression, and recurrent neural networks—to generate forecast curves for demand and generation. This subunit continuously compares forecasted and actual values ​​and adaptively adjusts model weights using gradient-based feedback, thereby improving accuracy across successive operating cycles.

[0108] Adjacent to the forecasting unit is an optimization control unit that calculates generation priorities by evaluating available generation capacity, forecasted demand, storage levels, and environmental conditions. It formulates an optimization function that minimizes the overall energy imbalance while ensuring reliability and reserve margins. The subunit then generates a control vector representing the target generation outputs for each source. These control vectors are transmitted to the control system via a secure local bus.

[0109] The controller acts as an interface between the processing unit and the physical power generation equipment. It converts control signals into executable operating commands to change the operating state of individual generators, photovoltaic inverters, or wind turbines. The controller also continuously provides feedback data on actual performance and equipment behavior, enabling the processing unit to recalibrate optimization parameters in real time.

[0110] An integral part of the device is the energy storage management unit, which consists of modular battery arrays, bidirectional converters, and power conditioning circuits. This unit receives commands from the processing unit to adaptively execute charge and discharge cycles. It continuously monitors voltage, current, and temperature to maintain battery health and optimize lifespan. If demand exceeds current generation, the storage unit releases energy in a controlled manner, while overproduction triggers charging of the storage system. The energy storage management unit also reports degradation trends to the processing unit to enable predictive maintenance planning.

[0111] The alarm and notification control system continuously compares current operating conditions with stored threshold profiles. Upon detecting abnormal conditions—such as over-discharge, insufficient power generation, or communication loss—the unit triggers local audible and visual alarms as well as remote notifications via the network interface. The system categorizes the severity of the alarms and dynamically adjusts the notification frequency according to urgency.

[0112] An integrated user interface terminal visualizes generation statistics, consumption rates, battery status, and forecast accuracy in real time. Operators can enter recalibration parameters such as tolerance values ​​or optimization constraints, which are immediately processed by the main controller for adjustment. The interface also displays dynamic performance indicators such as energy efficiency index, cost ratio, and reliability metrics.

[0113] For large-scale deployments, the device features a secure server interface that synchronizes operational logs and analysis models with a remote monitoring server. This interface uses authenticated encryption for all transmissions and performs checksum-based integrity checks. The remote server enables centralized fleet management, cross-site analytics, and distributed decision support.

[0114] An advanced feature of the invention is the integration of a scenario analysis routine into the processing unit. This enables the virtual simulation of various energy distribution strategies under predicted climate or market conditions. The simulated results are compared using stored cost and reliability indicators, and optimal strategies for subsequent operating cycles are automatically selected.

[0115] Structurally, the device is housed in an industrial enclosure containing separate compartments for computing electronics, power supply, and communication interfaces. The internal bus architecture ensures electromagnetic isolation between high-voltage and low-voltage data lines. The modular design facilitates maintenance and scalability, and allows for the connection of additional monitoring units or battery clusters during network expansion.

[0116] During operation, the device continuously receives streaming data from all sensors, normalizes it into synchronized data frames, performs probabilistic forecasts, calculates optimal control signals, and executes a control mechanism to balance supply and demand. Its adaptive learning capability ensures consistent operational accuracy even under dynamic environmental fluctuations.

[0117] The present invention relates to intelligent energy management and control systems, in particular those for coordinating, optimizing, and balancing renewable and conventional energy sources in hybrid power grids in real time. The invention relates to a data-driven device for dynamic energy coordination that integrates advanced computing techniques, machine learning-based forecasting, probabilistic optimization, and secure communication for the adaptive management of distributed energy systems. The device can be used in smart grids, microgrids, industrial energy management systems, and autonomous renewable energy generation plants, and enables predictive balancing of demand and generation, intelligent storage utilization, and efficient operational decisions under variable environmental and load conditions.

[0118] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0119] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 An adaptive dynamic energy coordination device for integrated renewable and conventional energy networks. 102 processing units 104 storage units 106 Variety of IoT-based monitoring units 108 Forecast Unit 110 Optimization control unit 112 Controller 114 Energy Storage Management Unit 116 Notification control unit 118 User interface terminal 120 Secure Server Interface

Claims

[1] A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support. [2] System according to claim 1, wherein the processing unit continuously aggregates and normalizes the sensor data streams received from the monitoring units, detects anomalies or missing values, and performs adaptive filtering before the data are used as input for forecasts and optimizations; wherein the forecasting unit applies several forecast models in parallel and compares their output accuracy over successive operating cycles, dynamically increasing the computational weighting of models with lower prediction error, thereby achieving a self-learning improvement in forecast reliability. [3] System according to claim 1, wherein the optimization control unit determines the generation priorities by correlating forecasted demand, available resource capacity, storage levels and environmental conditions, and calculates target generation levels that minimize energy imbalance while keeping reserve margins within predefined safety thresholds; wherein the controller executes the target generation levels output by the processing unit by sending digital control signals to actuators of individual energy generation sources to change their power output or operating state in real time. [4] System according to claim 1, wherein the processing unit monitors the feedback data returned by the controller and recalibrates optimization variables in subsequent cycles when the deviation between expected and actual generation exceeds an adaptive tolerance value; wherein the energy storage management unit performs bidirectional energy flow control by continuously monitoring voltage, current and state of charge and adjusting charge and discharge cycles according to the control signals generated by the processing unit to ensure balanced utilization and extended storage lifetime. [5] System according to claim 1, wherein the alarm and notification control unit classifies alarm states into multiple severity levels and dynamically changes the alarm repetition rate and the communication channel priority according to the urgency of the detected condition; wherein the processing unit maintains a continuously updated operating log in the storage unit, each log entry containing sensor readings, control commands, system status and environmental context to support post-event diagnostics and predictive model training. [6] System according to claim 1, wherein the communication network comprises a time-synchronized data aggregation controller configured to timestamp and align data packets received from geographically distributed monitoring units, thereby ensuring temporal coherence for accurate forecasting. [7] System according to claim 1, wherein the processing unit further comprises a machine learning subunit configured to analyze stored operational logs, detect seasonal and environmental dependencies, and automatically adjust forecasting or optimization parameters without manual reconfiguration. [8] System according to claim 1, wherein the energy storage management unit is configured to detect a gradual loss of storage efficiency by analyzing charge / discharge profiles and transmits diagnostic information to the processing unit for predictive maintenance planning. [9] System according to claim 1, wherein the processing unit generates and displays key performance indicators via the user interface terminal, including the energy generation efficiency, the energy cost index, the consumption-to-generation ratio and the system reliability rating, which are updated in real time as the operational data changes. [10] System according to claim 1, wherein the secure server interface uses authenticated encryption for all transmitted data records and performs regular integrity checks to ensure the authenticity of the system data synchronized with the remote server; wherein the processing unit executes a scenario analysis routine that simulates alternative operating strategies under predicted climate and demand conditions, evaluates the simulated results based on stored cost and reliability metrics, and updates the control priorities accordingly for subsequent operating cycles.

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