1. A system for optimizing the Hosting Capacity (HC) of wind turbines (WT) and reducing network losses in a distribution system, the system comprising: a Nonlinear Programming (NLP) formulation unit configured to determine the size and power dispatch of wind turbines and schedule Electric Vehicles (EVs); an uncertainty characterization module, operatively connected to the NLP formulation unit, designed to capture uncertainties in wind power generation, traditional system load demand, and EV driver behavior using Probability Density Functions (PDFs); a scenario generation mechanism linked to the uncertainty characterization module, facilitating the conversion of continuous PDFs into finite uncertainty realizations; and an Electric Vehicle (EV) aggregator modeling component for managing and optimizing the controllable features of clustered EVs in the distribution system.

2. The system of claim 1, wherein the NLP formulation unit utilizes a multi-objective problem framework to balance between the maximization of HC and the minimization of network losses.

3. The system of claim 1, wherein the uncertainty characterization module employs continuous PDFs to represent the variability and randomness inherent in wind power generation, system load demands, and EV driving patterns.

4. The system of claim 1, wherein the scenario generation mechanism is configured to synthesize a broad spectrum of possibilities into a limited set of representative scenarios, thereby streamlining computational processes.

5. The system of claim 1, wherein the EV aggregator modeling component utilizes a clustered approach to manage EVs, reducing the optimization problem's search space and computational requirements.

6. The system of claim 5, wherein the EVs are clustered at specific nodes in the distribution system, with each cluster supervised by an aggregator entity.

7. The system of claim 6, wherein the EV aggregator employs k-means clustering to group EVs based on similar arrival and departure times and daily mileage, thereby avoiding the challenges of individual EV driving parameters.

8. The system of claim 1, further comprising a Pareto front solution extraction mechanism that processes the results from the NLP formulation unit to determine optimal or near-optimal solutions for the given problem.

9. The system of claim 1, wherein the system is configured to dynamically adjust and adapt to changes in wind turbine generation, system load demand, and EV charging patterns.

10. A method for optimizing the Hosting Capacity (HC) of wind turbines in a distribution system using a system, comprising the steps of: determining the size and dispatch of wind turbines using the NLP formulation unit; capturing and characterizing uncertainties associated with wind power, system load, and EV driving behaviors; generating a set of representative scenarios based on continuous PDFs; utilizing the EV aggregator modeling component to manage and optimize the controllable features of clustered EVs; and extracting an optimal or near-optimal solution from the NLP formulation results using the Pareto front solution extraction mechanism. SYSTEM AND METHOD FOR OPTIMIZING THE HOSTING CAPACITY (HC) OF WIND TURBINES (WT) AND REDUCING NETWORK LOSSES IN A DISTRIBUTION SYSTEM Abstract The disclosure introduces a system and method to optimize the Hosting Capacity (HC) of wind turbines (WT) in a distribution system. The system employs a Nonlinear Programming (NLP) formulation to determine WT size, power dispatch, and Electric Vehicle (EV) scheduling. The captures uncertainties using Probability Density Functions (PDFs), which are then transformed into representative scenarios for computational efficiency. The system also implements a clustered approach for EVs, managed by an EV aggregator, to reduce optimization search space. The approach aims to maximize HC while minimizing network losses, catering to the dual challenges of renewable integration and the growing presence of EVs. , Claims:Claims :

1. A system for optimizing the Hosting Capacity (HC) of wind turbines (WT) and reducing network losses in a distribution system, the system comprising: a Nonlinear Programming (NLP) formulation unit configured to determine the size and power dispatch of wind turbines and schedule Electric Vehicles (EVs); an uncertainty characterization module, operatively connected to the NLP formulation unit, designed to capture uncertainties in wind power generation, traditional system load demand, and EV driver behavior using Probability Density Functions (PDFs); a scenario generation mechanism linked to the uncertainty characterization module, facilitating the conversion of continuous PDFs into finite uncertainty realizations; and an Electric Vehicle (EV) aggregator modeling component for managing and optimizing the controllable features of clustered EVs in the distribution system.

2. The system of claim 1, wherein the NLP formulation unit utilizes a multi-objective problem framework to balance between the maximization of HC and the minimization of network losses.

3. The system of claim 1, wherein the uncertainty characterization module employs continuous PDFs to represent the variability and randomness inherent in wind power generation, system load demands, and EV driving patterns.

4. The system of claim 1, wherein the scenario generation mechanism is configured to synthesize a broad spectrum of possibilities into a limited set of representative scenarios, thereby streamlining computational processes.

5. The system of claim 1, wherein the EV aggregator modeling component utilizes a clustered approach to manage EVs, reducing the optimization problem's search space and computational requirements.

6. The system of claim 5, wherein the EVs are clustered at specific nodes in the distribution system, with each cluster supervised by an aggregator entity.

7. The system of claim 6, wherein the EV aggregator employs k-means clustering to group EVs based on similar arrival and departure times and daily mileage, thereby avoiding the challenges of individual EV driving parameters.

8. The system of claim 1, further comprising a Pareto front solution extraction mechanism that processes the results from the NLP formulation unit to determine optimal or near-optimal solutions for the given problem.

9. The system of claim 1, wherein the system is configured to dynamically adjust and adapt to changes in wind turbine generation, system load demand, and EV charging patterns.

10. A method for optimizing the Hosting Capacity (HC) of wind turbines in a distribution system using a system, comprising the steps of: determining the size and dispatch of wind turbines using the NLP formulation unit; capturing and characterizing uncertainties associated with wind power, system load, and EV driving behaviors; generating a set of representative scenarios based on continuous PDFs; utilizing the EV aggregator modeling component to manage and optimize the controllable features of clustered EVs; and extracting an optimal or near-optimal solution from the NLP formulation results using the Pareto front solution extraction mechanism.

Description:SYSTEM AND METHOD FOR OPTIMIZING THE HOSTING CAPACITY (HC) OF WIND TURBINES (WT) AND REDUCING NETWORK LOSSES IN A DISTRIBUTION SYSTEM
Field of the Invention
[0001] The present invention relates explicitly to electrical power distribution systems, particularly involving the optimization of Hosting Capacity (HC) in wind turbines (WT) through the utilization of Nonlinear Programming (NLP).
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The rapid growth of renewable energy sources, particularly wind turbines (WT), poses significant challenges to power distribution systems. While WTs offer a sustainable and clean energy source, their integration into existing distribution systems can lead to several operational challenges. The primary concern stems from the variable nature of wind power, which can lead to fluctuations in the generated energy, affecting the power quality and causing network losses.
[0004] Hosting Capacity (HC) represents the maximum level of renewable energy sources a distribution system can integrate without compromising its operation and power quality. As wind turbines' penetration in the grid increases, the challenge of maximizing HC becomes paramount. Ensuring that a distribution system can support the maximum number of WTs without negative repercussions is essential for the widespread adoption of wind energy.
[0005] Electric Vehicles (EVs) have seen exponential growth in recent years due to their environmental benefits and advancements in battery technology. The integration of EVs into power distribution systems, coupled with their controllable features, offers a promising solution to some of the challenges associated with wind power integration. However, managing the charging and discharging schedules of a large number of EVs in a way that benefits the grid can be computationally intensive.
[0006] Traditionally, optimization problems in power distribution have been tackled using linear or mixed-integer linear programming methods. However, the nonlinear nature of power system equations and the inclusion of renewable sources and EVs necessitate the use of Nonlinear Programming (NLP) techniques.
[0007] While NLP offers a rigorous approach to such optimization problems, the inclusion of uncertainties associated with wind power generation, system load demand, and EV driver behavior poses another layer of complexity. Capturing these uncertainties requires advanced probabilistic methods. Although Probability Density Functions (PDFs) can describe these uncertainties reliably, their continuous nature complicates the computational feasibility of optimization formulations.
[0008] Lastly, considering each EV individually in the optimization problem can significantly increase computational requirements. This challenge accentuates the need for a clustered approach, wherein EVs are grouped based on certain common parameters, reducing the search space of the optimization problem.All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[0009] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00010] In an embodiment, a system is introduced that leverages Nonlinear Programming (NLP) to optimize the size, power dispatch, and scheduling of wind turbines in a distribution network. This system addresses the challenges posed by the intermittent nature of wind energy and its integration into power grids.
[00011] In an embodiment, the system comprises an uncertainty characterization module. This module captures the uncertainties inherent in wind power generation, traditional system load demand, and EV driver behavior. To describe these uncertainties, continuous Probability Density Functions (PDFs) are employed, which offer a reliable representation of the variability and randomness associated with these parameters.
[00012] In an embodiment, to tackle the computational challenges of using continuous PDFs in the optimization process, a scenario generation mechanism is implemented. This mechanism condenses a broad spectrum of possibilities presented by the PDFs into a finite set of representative scenarios, streamlining computational requirements and ensuring the optimization process's feasibility.
[00013] In an embodiment, an Electric Vehicle (EV) aggregator modeling component is introduced. This component uses a clustered approach to manage the charging and discharging of EVs, considering their controllable features. By clustering EVs based on specific parameters like arrival and departure times, the search space of the optimization problem is significantly reduced. The system, thus, avoids the computational challenges of dealing with the driving parameters of individual EVs.
[00014] In an embodiment, the NLP formulation unit of the system utilizes a multi-objective problem framework. This ensures a balance between the dual goals of maximizing the Hosting Capacity (HC) of wind turbines in the distribution system and minimizing network losses. The multi-objective nature of the problem underscores the complexity of integrating renewable energy sources into traditional power grids.
[00015] In an embodiment, the system integrates a Pareto front solution extraction mechanism. After obtaining the results from the NLP formulation, the mechanism processes the data to derive an optimal or near-optimal solution for the given problem. Such approach ensures that the solution is not only mathematically optimal but also practically implementable in real-world power distribution scenarios.
[00016] In an embodiment, the system is designed with adaptability in mind. Recognizing the dynamic nature of wind turbine generation, system load demand, and EV charging patterns, the system can adjust in real-time. Such adaptability ensures that as conditions change, the optimization process remains relevant and applicable, always striving for maximum Hosting Capacity and minimal network losses.
Brief Description of the Drawings
[00017] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00018] FIG. 1 illustrates a system for managing power dispatch and Electric Vehicle (EV) scheduling in a distribution network.
[00019] FIG. 2 illustrates a method 200 for power management in a distribution network, in accordance with an embodiment of the present disclosure.
[00020] FIG. 3 illustrates an exemplary electric vehicle (EV) aggregator model, in accordance with an embodiment of the present disclosure.
[00021] FIG. 4 illustrates a multi-objective optimization methodology, in accordance with an embodiment of the present disclosure.
[00022] FIG. 5 illustrates a validation of multi-objective optimization on modified 33-bus radial distribution system, in accordance with an embodiment of the present disclosure.
Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00026] The present invention relates explicitly to electrical power distribution systems, particularly involving the optimization of Hosting Capacity (HC) in wind turbines (WT) through the utilization of Nonlinear Programming (NLP).
[00027] FIG. 1 illustrates a system 100 for managing power dispatch and Electric Vehicle (EV) scheduling in a distribution network. The system 100 comprises a wind turbine (WT) sizing module 102, a Nonlinear Programming (NLP) formulation module 104, a scenario generation module 106 and an EV aggregator model 108.
[00028] In an embodiment, the disclosed system aims to address challenges inherent in managing power dispatch and Electric Vehicle (EV) scheduling in a distribution network. Central to the system is the integration of various modules and models that harmonize for seamless and efficient operation. The pivotal challenge in electrical power distribution networks, especially with the integration of renewable energy sources and the burgeoning adoption of EVs, is managing power dispatch and consumption in a way that assures system stability, maximizes efficiency, and meets user demands. As the global push towards cleaner energy sources intensifies, the integration of wind turbines (WT) into power grids becomes increasingly prevalent. The system seeks to alleviate the complexities introduced by these dynamics.
[00029] In an embodiment, the system incorporates a wind turbine (WT) sizing module. Such module is meticulously designed to determine the most appropriate sizes for wind turbines within a distribution network. By considering parameters like expected wind speed, direction, duration, and frequency in the specific area, the module makes data-driven decisions on turbine size. The optimal size ensures that the turbines harvest the maximum amount of wind energy, converting the wind energy into electrical power without undue stress on the machinery or creating inefficiencies. For instance, in regions with persistent strong winds, larger turbines might be optimal, whereas smaller turbines may be preferable in areas with milder wind patterns. The WT sizing module makes these distinctions clear, enabling energy producers to make informed choices, maximizing energy output and ensuring a return on investment.
[00030] In an embodiment, further bolstering the system's proficiency is the Nonlinear Programming (NLP) formulation module. Such module particularly shines in its capacity to handle the uncertainties associated with generated wind power, traditional system load demand, and the behavior of EV drivers. The inherent unpredictability of wind, combined with fluctuating energy demands and the sporadic nature of EV charging, presents a challenge that conventional linear programming approaches might struggle with. By employing NLP, the system can find an optimal solution even amidst these complexities. For instance, during a scenario where multiple EVs require charging simultaneously on a day with minimal wind, the system, through the NLP module, can predict such scenarios and adapt in real-time, ensuring uninterrupted power supply and distribution.
[00031] In an embodiment, the system introduces a scenario generation module. Traditional approaches that rely on Probability Density Functions (PDFs) to describe uncertainties, while effective, can become computationally cumbersome, especially when they are applied continuously. Recognizing this, the system leverages the scenario generation methodology. Such technique effectively generates a spectrum of possible scenarios that capture the range of potential outcomes. These scenarios are then distilled into a finite number of uncertainty realizations. For example, using the scenario generation module, the system might ascertain that there's a 70% likelihood of low wind and high EV charging demand between 5 PM and 7 PM on weekdays. Such insight enables the system to optimize turbine operations and manage EV scheduling during these peak times.
[00032] In an embodiment, the intricate challenges of dealing with individual EV parameters, especially in networks with a large number of vehicles, are elegantly circumvented by the system's EV aggregator model. Rather than grappling with the specifics of every single EV, the model groups EVs into clusters at designated nodes. These clusters are managed and overseen by entities referred to as aggregators. A practical application of the clusters could be a parking lot or charging station where multiple EVs converge. Instead of each EV being a separate entity, the aggregator sees them as a collective unit. For instance, if ten EVs at a node have similar arrival and departure times and daily mileage, they're grouped together. Such streamlined approach, enabled by k-means clustering, significantly reduces the search space and computational efforts required.
[00033] In an embodiment, to bring these modules into a real-world context, imagine a bustling urban center with a sizable EV population and a distribution network powered significantly by wind energy. As the sun sets and winds taper off, office workers head home, plugging their EVs in for an overnight charge. The system, foreseeing the scenario through the NLP module and the scenario generation module, has already adjusted the wind turbine operations and readied the grid. The EV aggregator model, recognizing the clustering of EVs in residential areas, efficiently manages the power distribution. The lights stay on, EVs charge without hitches, and the distribution network operates at peak efficiency.
[00034] In an embodiment, the modules of system are designed to learn and adapt. As wind patterns change due to climatic shifts or as EV adoption rates surge, the system can recalibrate its operations. Such dynamic adaptability ensures that it remains efficient, effective, and relevant regardless of external changes.
[00035] In an embodiment, the system's holistic approach brings together the WT sizing module, the NLP formulation module, the scenario generation module, and the EV aggregator model. Such integration ensures that while individual modules handle their specific tasks, they operate in tandem, ensuring the smooth and efficient running of the distribution network. The resulting synergy ensures that power producers can maximize their output, consumers (both traditional and EV users) experience no disruptions, and the entire distribution network operates at optimal efficiency.
[00036] In an embodiment, while the primary function of the system revolves around managing power dispatch and EV scheduling, its potential applications extend beyond this. With tweaks and refinements, similar systems could be employed in other renewable energy scenarios, such as solar or hydroelectric powered grids. Furthermore, as the world increasingly turns towards smart cities and integrated urban management solutions, systems like the one described here will play pivotal roles in ensuring these futuristic visions become a reality.
[00037] In an embodiment, the NLP formulation module is intricately designed to serve a dual purpose: maximize the Hosting Capacity (HC) and minimize network losses. By emphasizing these dual objectives, the system ensures that it can accommodate the maximum amount of distributed generation from the wind turbines without jeopardizing the operational integrity of the network. Simultaneously, it takes proactive measures to curtail energy losses within the distribution system. Such delicate balance helps in maintaining a consistent and reliable power supply, even as demands fluctuate, ensuring both energy producers and consumers benefit from heightened efficiency and reliability.
[00038] In an embodiment, the system adeptly manages uncertainties related to the generated wind power, system load demand, and the behavior of EV drivers using Probability Density Functions (PDFs). These PDFs offer a statistical means to quantify the variability and unpredictability inherent in these parameters. By modeling these uncertainties with PDFs, the system gains a clear mathematical understanding of potential variations, allowing it to make informed and precise decisions even in the face of seemingly erratic wind patterns, unpredictable power demands, or erratic EV charging behaviors.
[00039] In an embodiment, the scenario generation module, pivotal to the system, is specifically crafted to alleviate computational burdens. While PDFs are invaluable in capturing uncertainties, their continuous use could challenge computational feasibility. Recognizing this, the system leverages the scenario generation methodology to provide a distilled, concise range of plausible uncertainty scenarios. Such approach ensures that while the system retains the depth and nuance of understanding from PDFs, it is not bogged down by computational strain, ensuring smooth and efficient operation.
[00040] In an embodiment, the EV aggregator model, an integral component of the system, adopts a clustered approach. The genius behind the approach lies in its capacity to dramatically reduce the search space when tackling the optimization problem. Instead of individually considering every EV, the model identifies patterns and behaviors that allow it to group EVs, rendering the optimization challenge more manageable. The pattern identification not only enhances efficiency but also ensures that the system can respond in real-time to demands without lag or delay.
[00041] In an embodiment, the clustering strategy is refined further, with EVs being clustered at designated nodes within the distribution network. These clusters, rather than being autonomous, are meticulously supervised by a designated entity known as an aggregator. The EV ggregator’s role is pivotal: it provides oversight, manages the power demands of the clustered EVs, and

ensures that their behaviors are in line with the broader objectives of the system, guaranteeing harmonized operation within the network.
[00042] In an embodiment, the system delves deeper into the nuances of EV behavior. At each node, where EVs are clustered, the system employs k-means clustering to categorize their behavior, which includes key parameters like arrival time, departure time, and daily mileage. The categorization allows the system to make even more nuanced and precise decisions regarding power dispatch and EV scheduling, optimizing operations to a granular level.
[00043] In an embodiment, the system boasts an added feature: a multi-objective NLP problem formulation module. The module is specifically crafted to tackle the complexities that arise when multiple objectives, such as maximizing HC and minimizing losses, come into play. By analyzing the resultant Pareto front, which represents a set of non-dominated solutions, the system can discern the optimal solution that ensures that the system doesn’t just operate efficiently, but operates at its peak potential, extracting the utmost value from its resources.
[00044] In an embodiment, the system's prowess is further amplified by its ability to manipulate controllable features of EVs, a strategy designed to boost the HC of the distribution system. By adjusting parameters like charging rates or delaying charging to off-peak hours, the system can better manage the energy load. The manipulation not only optimizes the HC but also diminishes the computational evaluation efforts required, ensuring that the system remains agile, responsive, and efficient.
[00045] FIG. 2 illustrates a method 200 for power management in a distribution network, in accordance with an embodiment of the present disclosure. At step 202, the method initiates by employing a wind turbine sizing module to determine the appropriate size of wind turbines to be integrated into the distribution network. The step 202 ensures the optimal utilization of wind energy resources. At step 204, the method addresses uncertainties related to wind power generation, system load demand, and EV behavior using a dedicated module based on Non-Linear Programming (NLP) formulation. The step allows for the formulation of mathematical models that can handle these uncertainties. At step 206, continuous Probability Density Function (PDF) representations are converted into a finite set of scenarios using a scenario generation module. The discrete representation facilitates the analysis and consideration of uncertainties in subsequent steps. At step 208, the method involves clustering electric vehicles (EVs) at specific nodes within the distribution network. These clusters are then supervised using an EV aggregator model, allowing for coordinated management of EV charging and discharging activities. At step 210, within each node, EV behaviors, including arrival, departure, and daily mileage patterns, are categorized using k-means clustering. The categorization provides valuable insights into EV usage patterns and aids in optimizing the network configuration. At step 212, the method formulates a multi-objective Non-Linear Programming (NLP) problem based on the gathered data and scenarios. By solving the NLP problem, an optimal distribution network configuration is obtained, taking into account the various objectives and constraints identified in the process.
[00046] FIG. 3 illustrates an exemplary electric vehicle (EV) aggregator model, in accordance with an embodiment of the present disclosure. As illustrated, the model aspires to augment the Hosting Capacity (HC) of the distribution system. However, addressing the intricacies of each EV introduces an expansive search space, complicating the optimization problem, especially with a considerable EV population. To circumvent the aforesaid, the EV aggregator model adopts a clustered methodology, strategically grouping EVs at specific nodes, dramatically reducing both the search space and computational demands. Overseeing the clusters is the EV aggregator, an entity adept at supervising and managing the collective behaviors of the EVs within its purview. Employing k-means clustering, the system efficiently categorizes arrival, departure times, and daily mileages of EVs at a given node, providing a streamlined approach that sidesteps the complexities of individual EV driving parameters.
[00047] FIG. 4 illustrates a multi-objective optimization methodology, in accordance with an embodiment of the present disclosure. Drawing from an intricate matrix of inputs such as network data, load demand, prevailing wind scenarios, and the strategically established EV clusters, the methodology computes the hosting capacity and associated losses within the distribution system. By integrating the diverse data sources, the model ensures an accurate representation of the current system state. As a result, optimal decisions can be rendered that reconcile the often-competing objectives of maximizing hosting capacity while concurrently minimizing system losses. Such confluence of data-driven insights and advanced computational techniques positions the methodology to effectively address the complexities and challenges of modern distribution networks, underpinning more resilient and efficient power system operations.
[00048] FIG. 5 illustrates a validation of multi-objective optimization on modified 33-bus radial distribution system, in accordance with an embodiment of the present disclosure. Notably, wind DG units are strategically positioned at nodes 14, 26, and 31. In line with the system's conventional load demand, the number of EVs is proportionally evaluated. The system incorporates a total of 1,140 EVs, and to ensure a thorough analysis, varying levels of EV penetration - 20%, 40%, and 60% (which correspond to 190, 380, and 570 EVs, respectively) - are explored across diverse case studies. Each EV boasts a battery capacity of 24 kWh, and they exhibit charging and discharging powers of 4 kWh and 2 kWh, respectively, operating with impeccable efficiency. The aggregated EV entities are found at nodes 18, 22, and 33. By positioning the EVs towards the terminal ends of the distribution system and in proximity to the DG, the design aspires for resilient solutions, particularly at vital junctures. Furthermore, a planning horizon spanning one year, segmented into 24-hour increments, is adopted. Within the 24-hour frame, an impressive 1,000 scenarios are generated, accounting for variations in wind speed, conventional load demand, EV connection statuses, and their state of charge, all derived from established PDFs.
[00049] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00050] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00051] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00052] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00053] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00054] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A system for optimizing the Hosting Capacity (HC) of wind turbines (WT) and reducing network losses in a distribution system, the system comprising:
a Nonlinear Programming (NLP) formulation unit configured to determine the size and power dispatch of wind turbines and schedule Electric Vehicles (EVs);
an uncertainty characterization module, operatively connected to the NLP formulation unit, designed to capture uncertainties in wind power generation, traditional system load demand, and EV driver behavior using Probability Density Functions (PDFs);
a scenario generation mechanism linked to the uncertainty characterization module, facilitating the conversion of continuous PDFs into finite uncertainty realizations; and
an Electric Vehicle (EV) aggregator modeling component for managing and optimizing the controllable features of clustered EVs in the distribution system.

2. The system of claim 1, wherein the NLP formulation unit utilizes a multi-objective problem framework to balance between the maximization of HC and the minimization of network losses.
3. The system of claim 1, wherein the uncertainty characterization module employs continuous PDFs to represent the variability and randomness inherent in wind power generation, system load demands, and EV driving patterns.
4. The system of claim 1, wherein the scenario generation mechanism is configured to synthesize a broad spectrum of possibilities into a limited set of representative scenarios, thereby streamlining computational processes.
5. The system of claim 1, wherein the EV aggregator modeling component utilizes a clustered approach to manage EVs, reducing the optimization problem's search space and computational requirements.
6. The system of claim 5, wherein the EVs are clustered at specific nodes in the distribution system, with each cluster supervised by an aggregator entity.
7. The system of claim 6, wherein the EV aggregator employs k-means clustering to group EVs based on similar arrival and departure times and daily mileage, thereby avoiding the challenges of individual EV driving parameters.

8. The system of claim 1, further comprising a Pareto front solution extraction mechanism that processes the results from the NLP formulation unit to determine optimal or near-optimal solutions for the given problem.
9. The system of claim 1, wherein the system is configured to dynamically adjust and adapt to changes in wind turbine generation, system load demand, and EV charging patterns.
10. A method for optimizing the Hosting Capacity (HC) of wind turbines in a distribution system using a system, comprising the steps of:
determining the size and dispatch of wind turbines using the NLP formulation unit;
capturing and characterizing uncertainties associated with wind power, system load, and EV driving behaviors;
generating a set of representative scenarios based on continuous PDFs;
utilizing the EV aggregator modeling component to manage and optimize the controllable features of clustered EVs; and
extracting an optimal or near-optimal solution from the NLP formulation results using the Pareto front solution extraction mechanism.

SYSTEM AND METHOD FOR OPTIMIZING THE HOSTING CAPACITY (HC) OF WIND TURBINES (WT) AND REDUCING NETWORK LOSSES IN A DISTRIBUTION SYSTEM
Abstract
The disclosure introduces a system and method to optimize the Hosting Capacity (HC) of wind turbines (WT) in a distribution system. The system employs a Nonlinear Programming (NLP) formulation to determine WT size, power dispatch, and Electric Vehicle (EV) scheduling. The captures uncertainties using Probability Density Functions (PDFs), which are then transformed into representative scenarios for computational efficiency. The system also implements a clustered approach for EVs, managed by an EV aggregator, to reduce optimization search space. The approach aims to maximize HC while minimizing network losses, catering to the dual challenges of renewable integration and the growing presence of EVs.
, Claims:Claims
I/We Claim:
1. A system for optimizing the Hosting Capacity (HC) of wind turbines (WT) and reducing network losses in a distribution system, the system comprising:
a Nonlinear Programming (NLP) formulation unit configured to determine the size and power dispatch of wind turbines and schedule Electric Vehicles (EVs);
an uncertainty characterization module, operatively connected to the NLP formulation unit, designed to capture uncertainties in wind power generation, traditional system load demand, and EV driver behavior using Probability Density Functions (PDFs);
a scenario generation mechanism linked to the uncertainty characterization module, facilitating the conversion of continuous PDFs into finite uncertainty realizations; and
an Electric Vehicle (EV) aggregator modeling component for managing and optimizing the controllable features of clustered EVs in the distribution system.

2. The system of claim 1, wherein the NLP formulation unit utilizes a multi-objective problem framework to balance between the maximization of HC and the minimization of network losses.
3. The system of claim 1, wherein the uncertainty characterization module employs continuous PDFs to represent the variability and randomness inherent in wind power generation, system load demands, and EV driving patterns.
4. The system of claim 1, wherein the scenario generation mechanism is configured to synthesize a broad spectrum of possibilities into a limited set of representative scenarios, thereby streamlining computational processes.
5. The system of claim 1, wherein the EV aggregator modeling component utilizes a clustered approach to manage EVs, reducing the optimization problem's search space and computational requirements.
6. The system of claim 5, wherein the EVs are clustered at specific nodes in the distribution system, with each cluster supervised by an aggregator entity.
7. The system of claim 6, wherein the EV aggregator employs k-means clustering to group EVs based on similar arrival and departure times and daily mileage, thereby avoiding the challenges of individual EV driving parameters.

8. The system of claim 1, further comprising a Pareto front solution extraction mechanism that processes the results from the NLP formulation unit to determine optimal or near-optimal solutions for the given problem.
9. The system of claim 1, wherein the system is configured to dynamically adjust and adapt to changes in wind turbine generation, system load demand, and EV charging patterns.
10. A method for optimizing the Hosting Capacity (HC) of wind turbines in a distribution system using a system, comprising the steps of:
determining the size and dispatch of wind turbines using the NLP formulation unit;
capturing and characterizing uncertainties associated with wind power, system load, and EV driving behaviors;
generating a set of representative scenarios based on continuous PDFs;
utilizing the EV aggregator modeling component to manage and optimize the controllable features of clustered EVs; and
extracting an optimal or near-optimal solution from the NLP formulation results using the Pareto front solution extraction mechanism.