Conditional value-at-risk model for smart home energy management systems

Javadi, Mohammad Sadegh; Nezhad, Ali Esmaeel; Gough, Matthew; Lotfi, Mohamed; Anvari‐Moghaddam, Amjad; Nardelli, Pedro H. J.; Sahoo, Subham; Catalào, João P. S.

doi:10.1016/j.prime.2021.100006

Cited by 14 publications

(13 citation statements)

References 36 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%

“…Table 9 lists the references related to single and multiobjective optimization. Among these articles, the single objective approach is presented in [51], [53], [62], [63], [79], [87], [94], [97], [99], [101], [104], [105], [109], [113], [115], [118], and [119]. The single objective formulations mainly include electricity costs minimization [63], [79], [94], [97], [99], [101], self-scheduling [104], [105], total profit maximization [62], maintenance cost minimization [109], load peak minimization [113], PAR reduction [114], energy balance [115], user's satisfaction maximization [53], energy consumption minimization [51], [119], and multi-home coordinated load scheduling [118].…”

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: ) Single Objective Functions and Multi-objective Functionsmentioning

confidence: 99%

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

et al. 2023

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“…Smart meters or electricity meters use digital technology rather than electromechanical technology [17]. The major difference between the smart meter and modern measuring or metering equipment is that they take control over functions [18]. The smart meter is a device that was developed to act as a gateway for smart grid to household devices [19].…”

Section: Smart Metermentioning

confidence: 99%

A Concealed Based Approach for Secure Transmission in Advanced Metering Infrastructure

2022

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The smart grid has an important subsystem known as the Advanced Metering Infrastructure (AMI), responsible for measuring customer consumption of electricity. The AMI subsystem has smart meters as one of the components and they play a vital role in enabling communication between the utility provider and consumers. Smart meters can report real-time electricity consumption readings of the consumer to the utility. Securing this communication link from attacks remain very important for the secure transmission of readings. Different methods or approaches have been developed in the past and most of the works have high computational overheads. This paper proposes a lightweight Concealed Based Security Scheme (CBSS) for secure transmission within the AMI, providing authentication, reducing the computational overheads and energy consumption during transmission. The CBSS method is compared with the AMI Data Communication Scheme (ADCS) which does not have authentication process. The network is built in the Network Simulator 2 environment, showing the communication between the nodes. Further security is provided using a simple encryption/decryption method of 2 random numbers. The contribution of this paper is the proposed lightweight method for the AMI that authenticates the transfer of data between smart meters and other components of the AMI system. The paper also contains a simulation evaluation for the chosen design parameters of the resemblance of AMI network. The simulations show an improvement of 5% in delivery ratio, 3% throughput and 4% on energy consumed when adding security to the network.

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“…From ( 16), the initial indoor temperature is set to the desired temperature. In (17), a restricted margin is defined for the indoor temperature (i.e., 1°C). Equations ( 18) and ( 19) calculate the electricity consumption of the SH.…”

Section: Shmentioning

confidence: 99%

“…Inclusion of CVaR does not complicate the solution process due to its convex formulation. It should be noted that the closest works to this paper are [16], [17], where the authors have used a CVaR-based energy management scheme for optimal scheduling of a smart home, while they use the CVaR method to reduce the risk of loss of load, and solar generation, respectively. In comparison with these recent works, this paper provides the following contributions:…”

Section: Introductionmentioning

confidence: 99%

CVaR-based Stochastic Energy Management of a Smart Home

Akbari-Dibavar

Zare

Mohammadi‐Ivatloo

et al. 2022

2022 IEEE International Conference in Power Engineering Application (ICPEA)

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Considering recent developments in photovoltaic (PV) systems, storage, and electrical vehicles, not unexpected that one day smart homes will also take part in energy markets directly. In this regard, the presented paper proposes a stochastic programming approach to manage the consumption of a smart home according to intermittent PV system production and uncertain energy prices to make the smart home available for taking part in the local day-ahead (DA) energy market. A battery storage system is integrated to make flexibility against price fluctuations. Furthermore, modeling of plug-in electric vehicles (PEV) is also provided, where the traveling pattern is modeled through scenarios. The goal is to maximize the daily profit of the smart home while the welfare of the inhabitants is satisfied by considering comfort constraints. In addition, the conditional value at risk (CVaR) risk index is considered to manage associated risk with gained profit. The obtained results show the effectiveness of the optimization framework, in which the expected daily profit of the homeowner can reach $ 1.72 per day in the risk-neutral condition.

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Conditional value-at-risk model for smart home energy management systems

Cited by 14 publications

References 36 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 Concealed Based Approach for Secure Transmission in Advanced Metering Infrastructure

CVaR-based Stochastic Energy Management of a Smart Home

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