Description:FIELD OF INVENTION
User is interested in load detection and prioritization within energy management systems to optimize performance. This involves real-time monitoring, demand response strategies, and intelligent allocation of resources to ensure efficient energy distribution. The focus is on integrating advanced algorithms and IoT technology to enhance grid stability, reduce energy costs, and improve overall system reliability in dynamic environments.
BACKGROUND OF INVENTION
The invention relates to energy management systems (EMS) that focus on load detection and prioritization to enhance performance in power distribution networks. As energy demands grow and fluctuate, ensuring optimal allocation of resources becomes crucial to maintain grid stability and efficiency. Traditional energy management systems often struggle to handle real-time load variations, leading to inefficiencies, higher costs, and potential grid instability. This invention addresses these challenges by introducing advanced algorithms and smart technologies to detect, monitor, and prioritize loads dynamically. By leveraging IoT devices, sensors, and real-time data analytics, the system can accurately assess energy consumption patterns and forecast demand. The system then prioritizes critical loads while optimizing the distribution of available power, thus preventing overloading and minimizing energy wastage. Moreover, this invention integrates demand response strategies, allowing it to adjust power delivery based on real-time grid conditions and consumer preferences. This ensures not only the efficient operation of the power grid but also enhances energy conservation efforts. The system is designed to be scalable and adaptable to various environments, from residential to industrial, making it a versatile solution for modern energy challenges. This approach significantly improves the overall reliability, sustainability, and cost-effectiveness of energy management systems.
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SUMMARY
The invention focuses on an advanced energy management system (EMS) designed to detect and prioritize electrical loads for optimal performance. Unlike conventional systems, this EMS employs sophisticated algorithms and real-time data analysis to monitor energy consumption across various loads, ensuring efficient and reliable power distribution. The system integrates IoT sensors and devices that continuously track energy usage, identifying patterns and predicting future demand. Central to the invention is its ability to prioritize loads based on criticality and efficiency, dynamically adjusting power distribution to match real-time conditions. This prioritization ensures that essential loads receive uninterrupted power, while non-essential loads are managed to prevent overloading and reduce energy waste. Additionally, the system incorporates demand response capabilities, allowing it to adjust power delivery in response to grid conditions, pricing signals, and consumer preferences. The EMS is adaptable to various settings, from residential to industrial environments, and is scalable to meet the needs of different grid sizes. By optimizing load detection and prioritization, the system enhances grid stability, reduces operational costs, and supports sustainability initiatives by minimizing unnecessary energy consumption. This invention represents a significant advancement in energy management, offering a smarter, more responsive approach to handling the complexities of modern power distribution.
DETAILED DESCRIPTION OF INVENTION
Load Detection
1. Smart Meters and Sensors
• Function: Smart meters and sensors are deployed throughout the electrical system to measure the power consumption of individual loads in real-time.
• Technology: These devices use advanced metering infrastructure (AMI) to collect and transmit data on energy usage to a central system for analysis.
• Benefits: Provides accurate, real-time data on energy consumption, enabling precise load detection and monitoring.
2. Machine Learning Algorithms
• Function: Machine learning algorithms process the data collected by smart meters and sensors to identify patterns and classify different loads.
• Techniques:
o Neighborhood Component Analysis (NCA): Used for feature selection and dimensionality reduction, improving the accuracy of load classification.
o Regularized Extreme Learning Machine (RELM): A fast and efficient algorithm for training neural networks, used for load recognition.
• Benefits: Enhances the accuracy and efficiency of load detection, enabling better energy management.
3. Non-Intrusive Load Monitoring (NILM)
• Function: NILM disaggregates the total power consumption into individual appliance usage without needing separate sensors for each device.
• Technology: Uses algorithms to analyze the overall power consumption data and identify the unique signatures of different appliances.
• Benefits: Cost-effective and less intrusive method for load detection, suitable for both residential and commercial applications.
Load Prioritization
1. Critical Load Identification
• Function: Identifies which loads are essential and must be powered continuously, such as medical equipment, security systems, and critical infrastructure.
• Technology: Uses data from load detection systems to classify loads based on their importance and criticality.
• Benefits: Ensures that essential services remain operational during power outages or peak demand periods.
2. Demand Response Strategies
• Function: Implements techniques to shift or reduce power usage during peak times, often incentivized by utility companies.
• Techniques:
o Time-of-Use Pricing: Encourages users to shift their energy consumption to off-peak times by offering lower rates.
o Direct Load Control: Utility companies remotely control certain appliances (e.g., air conditioners, water heaters) to reduce demand during peak periods.
• Benefits: Reduces peak demand, lowers energy costs, and improves grid stability.
3. Load Shedding
• Function: Temporarily turns off non-essential loads to prevent overloading the system and ensure the stability of the power grid.
• Technology: Automated systems detect when the load exceeds a certain threshold and initiate load shedding protocols.
• Benefits: Prevents blackouts and maintains the reliability of the power grid.
Benefits of Load Detection and Prioritization
• Energy Efficiency: Optimizes the use of available energy, reducing waste and improving overall efficiency.
• Cost Savings: Lowers energy bills by managing peak demand and utilizing off-peak rates.
• System Reliability: Ensures critical loads are prioritized, maintaining system stability and preventing outages.
• Environmental Impact: Reduces the carbon footprint by optimizing energy usage and integrating renewable energy sources.
Applications
• Residential: Home Energy Management Systems (HEMS) use load detection to optimize household energy consumption, integrating smart appliances and renewable energy sources.
• Commercial: Businesses can manage their energy use more effectively, reducing operational costs and improving sustainability.
• Industrial: Factories and large facilities can prevent downtime, improve efficiency, and integrate renewable energy sources through effective load management.

Figure 1: National Electricity Consumption Breakdown by Sector
In countries, heavily reliant on imported oil, has established policies to promote biofuels, cogeneration, and renewable energy to address increasing energy demand, rising prices, and environmental challenges like climate change. The government introduced the 20-year Energy Efficiency Development Plan (EEDP) from 2011 to 2030, aiming to reduce energy intensity by 25% by 2030, equating to a 20% reduction in final energy consumption. The transportation and industrial sectors are projected to contribute over 80% of the total energy savings, while the commercial and residential sectors hold more than 60% of the electricity savings potential. The study emphasizes the importance of adopting high-efficiency appliances and fostering public awareness to meet these targets. A smart demand-responsive energy management system is proposed to optimize energy use in residential areas, with simulations showing potential bill savings through load shifting based on time-of-use pricing.
Proposed Smart Demand-Responsive Energy Management System in the Residential Sector
Demand Response
Demand response (DR) refers to the modifications in electricity usage by end-use customers in response to changes in electricity prices over time or through incentive payments that encourage reduced electricity use during periods of high demand. This research focuses on analyzing the demand response potential under a time-of-use (TOU) pricing model, specifically in the context of Country without the use of enabling technology. TOU rates are a type of static time-varying rate, where electricity prices are higher during peak periods to reflect the increased cost of providing electricity and lower during off-peak periods when it is cheaper to supply electricity. In the Thai power system, the peak period is from 09:00 to 22:00, Monday to Friday, while the off-peak period is from 22:00 to 09:00 on weekdays and all day on Saturdays, Sundays, and public holidays. The TOU rates in Country are summarized in Table 3. It is observed that during peak hours, the demand charge is 5.2674 Baht/kWh, whereas during off-peak hours, it is 2.1827 Baht/kWh for voltage levels below 22 kV, which is typically used in residential and small business sectors.
Country Residential Electricity Consumption
According to data from the Ministry of Energy, Country's residential sector consumed 32,920 GWh in 2011, accounting for 22.1% of the nation's total electricity consumption. The Provincial Electricity Authority (PEA) classifies household electricity consumers into two categories: those using less than 150 kWh/month and those exceeding 150 kWh/month. This study targets the latter category, which constitutes the majority of PEA's customers. Figure 2 illustrates the load profile for the residential sector in November 2012, divided by peak days, Sundays, Saturdays, and weekdays. The data reveals that peak loads typically occur in the evening between 19:00 and 21:00, with additional high demand in the morning from 06:00 to 07:00. The household load profile is generally consistent across the country.
Load Characterization and Load Prioritization for Demand Response Actions
This section characterizes household loads in Country based on surveys, considering the absence of energy performance standards for small commercial buildings and residential groups. Load characterization is categorized into seven groups:
1. Lighting: Includes fluorescent tubes and incandescent bulbs.
2. News, Entertainment, and Office Equipment: Includes televisions, VCD/DVD players, stereos, radios, and computers.
3. Cooking Appliances: Includes rice cookers, electric stoves, microwave ovens, and refrigerators.
4. Washing Machines: Includes clothes washers and dishwashers.
5. Air-Conditioning Systems
6. Heating Appliances: Includes electric water heaters and irons.
7. Others: Includes electronic equipment, tablet chargers, and mobile phone chargers.
To implement a demand-responsive program, load prioritization is essential. A smart demand system can prioritize household loads to shift certain uses to off-peak periods, thereby saving on electricity bills. The system can prevent the use of specific loads during peak periods unless authorized or adjust parameters to reduce energy consumption during peak hours. Load prioritization is based on technical limitations and user flexibility and is categorized into four groups: Uncontrollable Loads, Reparameterizable Loads, Interruptible Loads, and Shiftable Loads.
• Uncontrollable Loads: These loads cannot be managed by automated demand response actions. Examples include lighting, entertainment equipment, and cooking appliances, which are essential for users and must be available on demand.
• Reparameterizable Loads: These are loads that can be controlled by adjusting temperature settings, such as air conditioning systems, refrigerators, and electric water heaters.
• Interruptible Loads: These loads can be paused for a short period at specific points in their operation cycle, such as thermos and fans.
• Shiftable Loads: These loads can be rescheduled to operate during off-peak hours without user intervention, such as washing machines.

Figure 2: Impact of Demand Response on Household Load Profile
Structure of a Smart Demand-Responsive Energy Management System
As part of Country's Smart Grid initiative, the Provincial Electricity Authority (PEA) has outlined a strategic objective to integrate new technologies that enhance organizational performance and utility. The Smart Grid plan aims to overlay the conventional electrical grid with an advanced information system and net-metering capabilities, incorporating smart meters. This infrastructure supports more detailed monitoring of electricity flow and the effective integration of renewable energy sources like solar and wind power.
One critical aspect of this initiative is the development of demand-responsive energy management systems, particularly within the residential sector. Despite the challenges posed by managing a large number of residential units, these systems are expected to provide significant benefits in optimizing energy usage, especially when paired with smart meters. The proposed smart energy management system leverages a home area network (HAN) based on wireless sensor technology, specifically the ZigBee/IEEE 802.15.4 standard.
System Framework and Operation
The smart energy management system framework is composed of data collection nodes and a Personal Area Network (PAN) coordinator. Each node is connected to an individual home appliance, enabling various functions such as energy usage monitoring, on-off control, signal processing, and data transmission to the PAN coordinator. The coordinator then relays this information to a central base station, where it is processed by an advanced energy management algorithm. This algorithm analyzes the data, determines appropriate demand-responsive actions for each appliance, and suggests ways to control and manage energy usage according to load-shaping strategies.
By implementing this system, households can optimize their energy consumption, particularly during peak hours. Users can monitor and manage their electricity usage in real-time, leading to more informed decisions and potential behavioral changes that contribute to overall energy efficiency.
Impact on Residential Load Profiles
To evaluate the potential impact of this demand-responsive energy management system, a survey was conducted with 30 households associated with Walailak University, who are PEA customers. A simulation was also performed to assess the impact of demand-responsive actions on household energy usage. For instance, in a high-usage household (over 500 kWh per month), the load profile indicated significant appliance usage during peak hours (19:00 to 22:00), leading to high electricity bills.
By applying the smart energy management system, consumers could shift the usage of high-energy appliances to off-peak periods, such as using cooling fans instead of air conditioners during peak hours or scheduling washing machines to run automatically during off-peak times. These adjustments can lead to substantial savings on electricity bills. For example, one household saw a 24.54% reduction in their bill without changing their energy consumption, while another reduced their usage and bill by 34.13% through load shifting.
However, not all households experienced bill savings; in some cases, the electricity bill increased due to the nature of the appliances and their usage patterns during peak periods. This highlights that while time-of-use pricing and demand-responsive programs can be beneficial, they may not suit all consumers equally.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1: National Electricity Consumption Breakdown by Sector
Figure 2: Impact of Demand Response on Household Load Profile , Claims:1. Load Detection and Prioritization in Energy Management Systems for Optimal Performance claims that smart demand-responsive energy management system using ZigBee-based wireless communication is proposed for the Thai residential sector.
2. The implementation of this system presents both opportunities and challenges specific to the Thai residential sector.
3. The study investigates the potential of demand response in the residential sector in Country, particularly focusing on time-of-use (TOU) pricing without the use of enabling technologies.
4. The proposed load characterization and prioritization within the energy management system can demonstrate significant bill savings through demand response programs.
5. To effectively implement price-based demand response, it is essential to have wireless and smart metering capabilities, along with data management systems, in place for all customers.
6. The results of the study provide valuable insights for future energy management and planning efforts in Country.
7. The findings could assist the Provincial Electricity Authority (PEA) in optimizing electricity supply for households.
8. The implementation of the proposed system can contribute to achieving the energy savings targets set in Country's 20-year Energy Efficiency Development Plan (EEDP).
9. New energy technologies that have been successfully implemented in other countries should be considered for application in Country.
10. Integrating both energy efficiency and demand response principles into education programs and action plans can enhance consumer understanding and improve the effectiveness of demand-responsive programs.