The Evolving Landscape of Home Energy Management
The demand for electrical energy has risen significantly in recent years due to global population growth and economic progress. To tackle this issue, utility regulators are exploring different approaches to reduce end-user consumption, such as implementing demand response (DR) techniques and high tariff rates, including time of use (TOU) tariffs, due to power delivery constraints. In addition, energy authorities are taking steps to reduce reliance on fossil fuels due to depletion, price fluctuations, and environmental concerns regarding CO2 emissions.
To decrease energy usage, users must adopt energy-efficient products and appliances and adjust their energy consumption habits. Governments worldwide are promoting electricity conservation by raising awareness, incentivizing users who conserve energy, and advocating the use of renewable energy alternatives. An advanced framework called a home energy management system (HEMS) controls and monitors indoor device operations, allowing for peak shaving and load transformation to meet specific requirements. Additionally, reducing energy consumption and enhancing energy efficiency are top priorities for HEMS customers.
The smart grid (SG) refers to the electricity grid combined with a modern infrastructure that enables efficient, comfortable, stable, and reliable electricity use and is a highly positive concept for building an intelligent society. A smart grid (SG) consists of controls, automation, computers, new technologies, and equipment working together. The smart grid boasts several features, including the transmission of electricity with high efficiency, quick restoration of electricity following a power outage, lowered management costs that reduce energy costs for users, decreased demand during peak hours, which also decreases electricity consumption costs for the consumer, integration of renewable energy systems with traditional sources of generation, and improved security and protection of the electrical network.
Demand-Side Management: Strategies for Energy Optimization
Demand side management (DSM) refers to a variety of techniques and methods that enable end-users to control and manage their energy usage. It involves adopting measures to decrease overall energy demand during peak periods of consumption through diverse approaches such as energy efficiency, load shifting, and demand response.
Energy efficiency measures seek to improve the performance of energy-consuming devices and systems, such as HVAC, lighting, and appliances, to reduce energy consumption. Demand response entails modifying energy usage patterns in response to changes in energy supply or demand, often through incentives or time-of-use pricing. Load shifting aims to shift energy usage to low-demand periods, typically through the use of energy storage systems. DSM can enhance the efficiency and dependability of the power grid, minimize energy costs, and reduce the need for additional infrastructure and generation capacity.
DSM Strategies Explained
- Peak Clipping: A demand-side management approach that aims to decrease peak electricity demand during high consumption periods. It involves implementing measures that reduce energy consumption during peak periods, such as turning off or decreasing the output of energy-consuming devices and systems.
- Valley Filling: A strategy that aims to increase electricity consumption during off-peak periods when demand is low.
- Load Shifting: A demand-side management strategy that involves shifting the timing of electricity consumption from peak periods to off-peak periods. This can help to reduce electricity demand during peak periods, which can delay or avoid the need for additional generation capacity.
- Energy Efficiency: A strategy that aims to reduce energy consumption while maintaining or enhancing the level of service or output. This can be achieved through various methods, such as upgrading to more efficient lighting and appliances, improving building insulation, optimizing industrial processes, and implementing efficient transportation systems.
With the continuous rise in electricity costs and the emergence of smart grids, home energy management systems (HEMSs) have become a feasible solution to address demand response. The primary goal of these systems is to monitor, control, and optimize energy usage and flow. HEMS aims to shift demand and minimize it to enhance the energy consumption and production profile for consumers. Demand-side management (DSM) controls a multitude of loads that can be managed across different sectors, including industrial, commercial, government agencies, agricultural, and residential, among others.
The Evolution of Home Energy Management Systems (HEMS)
The concept of home energy management system (HEMS) was initially introduced in 1979 by Moen (1979). Since the introduction of personal computers in 1980 (Capehart et al. 1982), HEMS has undergone significant advancements. In 1982, the optimization algorithm was developed for power management, aimed at reducing electricity costs by minimizing demand and usage time (Rahman and Bhatnagar 1986).
A HEMS was later developed for home-based applications that incorporated various technologies, such as video, radio frequency, and ultrasonic sensors to track customers and locate missing objects (Kidd et al. 1999). An innovative tactic involved developing a home automation communication system to manage and control electronic devices (Wacks 1991). In Japan, a power-controlled system was implemented in 2003 to manage the energy consumption of 20 individuals. The system utilized gateways from each residence, which functioned as automation data loggers for power consumption and information controls. The power line communication network architecture was subsequently upgraded as an energy management controller to serve the same function. The controller was integrated with PCs to allow for the monitoring and control of electrical devices (Inoue et al. 2003).
Further HEMS strategies were created that utilized smart algorithms to track the activities of individual occupants and determine their status in similar environmental conditions. These techniques were discovered to reduce energy consumption, improve occupant comfort, and enhance performance levels (Das et al. 2006). Whirlpool Corporation abandoned a patent in 2006 for a HEM controller that could manage the energy consumption of home-based appliances (Ghent 2006).
In 2012, advanced HEMS technology was introduced with demand response (DR) capabilities aimed at reducing power consumption and costs for home appliances, including electric vehicles and air conditioning systems (Han et al. 2011). This marked a trend toward using sophisticated technology in HEMSs. Smart HEM systems were also developed around the same time to provide optimal electric energy in residential areas, using a controller based on event-driven binary linear optimization (Giorgio and Pimpinella 2012).
The HEMS was further improved with the integration of advanced optimization algorithms that factored in big storage systems, renewable resources, and variable electricity costs to minimize energy consumption and keep a home functional. Additional advancements included the use of artificial bee colony as an optimization strategy and the application of intelligent lookup tables using associative neural networks to determine optimal energy efficiency (Squartini et al. 2013).
While HEMSs improved over time, early versions relied on infrared remote controls to manage lighting and electrical outlets, which could not cover the full distance between the central controller and outlets (Shahgoshtasbi and Jamshidi 2014; Missaoui et al. 2014). Studies have investigated real-time energy control to facilitate the implementation of smart HEMS (Dittawit and Aagesen 2013). Additionally, a fuzzy controller has been proposed to optimize energy scheduling and regulate battery power consumption using rolling optimization techniques (Zhou et al. 2014).
Cutting-Edge Algorithms for Smart Home Energy Management
Various innovative algorithms have been developed to promote smarter electricity management and reduce electricity consumption through different energy management models. Some studies have focused on incorporating renewable energy resources in HEMS (Boynuegri et al. 2013), while others have developed adaptive smart HEMS that consider dynamic variables like weather patterns and electricity prices when scheduling power usage.
Several companies have taken steps toward developing intelligent HEMS for energy management, including Honda in the US, which has deployed a hardware system for monitoring, controlling, and optimizing electricity generation and appliance usage at home (Honda 2018). General Electric Co. has also designed home appliances that can be managed using smartphones (Ge 2018; Hong et al. 2015). These home automation systems include energy storage batteries that can be used during peak hours of the day and solar PV.
Numerous algorithms have been utilized for home energy management system (HEMS), including:
- Internet of Things (IoT)-based Optimization: A novel approach that employs a multi-objective optimization strategy considering both consumer comfort and energy consumption cost as primary objectives, designed to operate in a smart grid environment (Wang et al. 2021).
- Predictive Energy Management: A multi-objective approach using machine learning techniques for grid-connected hybrid energy systems in residential areas (Shivam et al. 2021).
- Stochastic Many-Objective Solution Framework: A method relying on lexicographic optimization and scalarizing functions, using a mixed-integer linear programming formulation (Tostado-Véliz et al. 2022).
- Distributed Coordination Technique: A method that effectively coordinates transactive HEMS with electric baseboard heater thermostats enabled for demand response, focusing on achieving consensus to satisfy both individual and collective objectives (Etedadi et al. 2023).
- Stochastic Approach for Optimal Day-Ahead Operation: Considering solar photovoltaic resources, electric water heaters, and batteries (Correa-Florez et al. 2018).
- Load Sector Incorporation: Dividing smart housing loads into various sectors to incorporate diverse demand response capabilities (Nan et al. 2018).
- Game Theory for Multi-Objective Optimization: Scheduling home appliances using an enhanced normalized normal constraint (ENNC) method and fuzzy compromising methods to improve gaseous emissions and overall energy cost (Waseem et al. 2021).
- Metaheuristic Optimization Algorithms: A comparison of the Estimation of Distribution Algorithm (EDA) and Tabu Search for scheduling home loads (Alfageme et al. 2021).
- Machine Learning for Optimal Scheduling: A method for anticipating consumer priorities in optimal scheduling of smart devices (Sadat-Mohammadi et al. 2021).
These cutting-edge algorithms and techniques aim to optimize home energy management systems, addressing various objectives such as load profiling, cost reduction, consumer convenience, and environmental sustainability.
Balancing Objectives: Optimizing Consumption and Production Schedules
Through the optimization of consumption and production schedules, HEMS can achieve various objectives, such as load profiling, cost reduction, convenience for consumers, and environmental sustainability. Researchers have investigated smart home energy management systems and addressed unnecessary energy consumption, revealing a deficiency in quality attributes such as security and privacy (Aliero et al. 2021).
A comprehensive approach using a mixed-integer quadratic programming model predictive control scheme based on the building energy management system and the thermal building model has been presented (Killian et al. 2018). An efficient demand-side management model for residential areas has been introduced, aiming to optimize user comfort while minimizing electricity costs, execution time, and peak-to-average ratio (PAR) (Rahim et al. 2016). The study proposes an energy management controller (EMC) for residential energy management (HEM), which performs better with a genetic algorithm (GA) than ant colony optimization (ACO) and binary particle swarm optimization (BPSO) in terms of PAR and cost reduction. GA is also the fastest optimization method in terms of execution time.
Other studies have classified appliances and users into different groups based on their energy usage patterns (Jovanovic et al. 2016) and utilized plug-in electric vehicles (PEVs) and game theory for an autonomous energy management system that aimed to minimize energy costs, but did not consider consumer convenience (Gao et al. 2014). Researchers have also used electric vehicles and renewable energy sources to optimize house demand and reduce grid dependence, ultimately stabilizing the microgrid (Mesarić et al. 2015).
Additionally, studies have investigated scheduling power demand to reduce peak demand, accounting for both power compression and request postponement, with each scenario resulting in different social welfare and average electricity cost (Vardakas et al. 2016). A new HEMS was proposed that allows residents to independently implement demand response programs, utilizing a photovoltaic system and battery to avoid peak hours throughout the day (Shakeri et al. 2017). Finally, the impact of interactive home energy management systems on the electrical network was investigated (Pratt et al. 2016).
Optimizing Electrical Systems: Challenges and Opportunities
While various studies have made significant contributions to the field of home energy management systems, there are still several challenges and opportunities to be addressed. The issue of peak-to-average ratio (PAR) and its impact on utility and generation units, as well as seasonal price signals or future pricing, was not taken into account by some researchers, creating problems in managing energy consumption during peak and non-peak hours.
Additionally, some studies did not prioritize consumer convenience in their research, focusing more on energy cost reduction and load management. Addressing both energy efficiency and user comfort is crucial for the widespread adoption and success of home energy management systems.
Ongoing efforts are necessary to enhance or introduce novel algorithms that can provide optimal solutions for complex mathematical problems. Given the shortcomings of current methods, the development of improved techniques, such as the Improved Bald Eagle Search (IBES) algorithm, can help achieve the desired objectives of minimizing energy consumption, reducing electricity costs, and maximizing user comfort and convenience.
The proposed IBES algorithm aims to address these challenges by coordinating the scheduling of household appliances, minimizing electricity bills, reducing the peak-to-average ratio, and enhancing user comfort. By leveraging the advanced features of the IBES algorithm, homeowners can optimize their electrical systems and enjoy increased comfort and convenience in their smart homes.
To learn more about the IBES algorithm and how it can transform your home energy management, visit Volt Watt Electric today. Our team of electrical experts is dedicated to providing practical solutions and in-depth insights to help you achieve your energy efficiency goals.
