Efficient solution of many-objective Home Energy Management systems

Tostado‐Véliz, Marcos; Gurung, Samundra; Jurado, Francisco

doi:10.1016/j.ijepes.2021.107666

Cited by 57 publications

(19 citation statements)

References 48 publications

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“…Cost is the most common objective in the HEMS because it is the primary motivation for residents to use HEMSs to manage household appliances by minimizing the electricity costs while considering available electricity prices and renewable micro-generation. As shown in Table 5, the objective of cost refers to any financial term related to energy management mainly including electricity costs minimization [63], [77], [98], [103], [79], [91], [97], the self-scheduling between the grid, renewable energy generators and loads [104], [105], retailer's profit maximization [102], total profit maximization [62], [106], [107], the start-up costs of the home system [108], and the maintenance cost [109].…”

Section: ) Costmentioning

confidence: 99%

“…There are six different strategies of load profiling which are peak shaving, valley filling, strategic conservation, load shifting, strategic growth, and flexible load shape [13], [98], [111], [112] Compared with the first three strategies, load shifting, strategic growth, and flexible load shape provide more systematic and large-scale changes in load management. Table 6 represents the objective function of the load profiling, which mainly includes load peak minimization [98], [113], peak-to-average ratio (PAR) reduction [91], [103], [114], self-consumption [106] and energy balance [115].…”

Section: ) Load Profilingmentioning

confidence: 99%

“…Therefore, to efficiently utilize the benefits of DR incentives while maintaining user comfort, there is a need to consider more parameters that directly affect the user. In order to improve the participation of DR projects, there are several methods to enhance user comfort by using HEMS for example maximizing the comfort factor, including the thermal comfort of heating, ventilation, and air conditioning (HVAC) system [91], [116], visual comfort of the lighting system [116], suitable load shifting [77], and quick response [93], and discomfort minimization including thermal discomfort of air conditioner and domestic hot water [117], waiting for time [91] and unreasonable load shifting [92].…”

Section: ) User Comfortmentioning

confidence: 99%

“…The multi-objective method is considered [77], [91], [92], [93], [98], [103], [106], [108], [116], [117]. In [77], the authors present a multi-objective to minimize the electricity cost and maximize user satisfaction.…”

Section: ) Single Objective Functions and Multi-objective Functionsmentioning

confidence: 99%

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Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies

et al. 2023

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Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies' power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.INDEX TERMS Demand response, home appliances, home energy management system, optimization, renewable energy resources, smart grid.

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Section: ) Costmentioning

confidence: 99%

Section: ) Load Profilingmentioning

confidence: 99%

Section: ) User Comfortmentioning

confidence: 99%

Section: ) Single Objective Functions and Multi-objective Functionsmentioning

confidence: 99%

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Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies

et al. 2023

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“…Mixed Integer Linear Programing formulation is used as the optimisation method in Ref. [31], where the author assumes that one of the objectives, that is electricity bill, has a higher importance than the others. Heuristic methods, for example, genetic algorithms [28,32] and particle swarm optimisation [33], offer greater flexibility than traditional mathematical optimisation techniques.…”

Section: Artificial Intelligence For Residential Energy Managementmentioning

confidence: 99%

A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling

Mannion

Mason

2022

IET Smart Grid

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Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand. Demand-side management is crucial to reducing this demand placed on the grid and increasing renewable utilisation. This research study presents a multi-objective tunable deep reinforcement learning algorithm for demand-side management of household appliances. The proposed tunable Deep Q-Network (DQN) algorithm learns a single policy that accounts for different preferences for multiple objectives present when scheduling appliances. These include electricity cost, peak demand, and punctuality. The tunable Deep Q-Network algorithm is compared to two rule-based approaches for appliance scheduling. When comparing the 1-month simulation results for the tunable DQN with an electricity cost rule-based benchmark method, the tunable DQN agent provides a statistically significant improvement of 30%, 18.2%, and 37.3% for the cost, peak power, and punctuality objectives. Moreover, the tunable Deep Q-Network can produce a range of appliance scheduling policies for different objective preferences without requiring any computationally intensive retraining. This is the key advantage of the proposed tunable Deep Q-Network algorithm for appliance scheduling.

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An improved bald eagle search optimization algorithm for optimal home energy management systems

et al. 2023

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In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer’s daily activities based on actual time and next day’s energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm’s performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience.

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Efficient solution of many-objective Home Energy Management systems

Cited by 57 publications

References 48 publications

Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies

Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies

A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling

An improved bald eagle search optimization algorithm for optimal home energy management systems

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