A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue

Shafie‐khah, Miadreza; Siano, Pierluigi

doi:10.1109/tii.2017.2728803

Cited by 228 publications

(115 citation statements)

References 28 publications

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“…Constraint (47) places bounds on the electricity generated by the CHP. Constraint (48) relates to the CHP operation. Constraint (49) measures the CHP fuel cost.…”

Section: Combined Heat and Power Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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“…Constraint (47) places bounds on the electricity generated by the CHP. Constraint (48) relates to the CHP operation. Constraint (49) measures the CHP fuel cost.…”

Section: Combined Heat and Power Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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“…Conversely, some methods are faced with the challenge of uncertainties with respect to consumer behavior as well the RES. As a consequence, a stochastic model for HEMS while considering the limited size of renewable energy generation is proposed in [16]. This model optimizes the consumer's electricity cost of several DRPs and occupant satisfaction is acquired via a response fatigue index.…”

Section: Related Workmentioning

confidence: 99%

“…However, they ignore PAR, electricity consumption and also do not take into account the need of RES. Work in [16] presents a strategy that considers the limited size of RES for electricity cost optimization. However, it is insufficient to address the computational complexity of the proposed stochastic model.…”

Section: Problem Statementmentioning

confidence: 99%

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Dahunsi

Javaid

et al. 2018

Energies

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Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

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“…Another interesting contribution is found in [56], which presents a setting similar to the references above, and in addition proposes a soft peak power-limiting strategy consisting in the integration into the MILP problem of a critical peak pricing scheme, something which is generalized by the present paper. Additional and similar recent MILP formulations of a residential EMS are presented in [57], which uses it to assess the DR-driven load pattern elasticity of smart households, in [58], which minimizes the response fatigue of the controlled devices and considers uncertainties of PEV availability and small-scale renewable energy generation, and in [59], which aims at minimizing costs and maximizing user convenience in the context of real-time and capacity-based pricing schemes (for the capacity-based tariff case, Park et al [59] considers a quadratic tariff function and proposes an approximate technique).…”

Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

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This paper presents a two-level control scheme for the energy management of an electricity prosumer node equipped with controllable loads, local generation, and storage devices. The main control objective is to optimize the prosumer's energy bill by means of intelligent load shifting and storage control. A generalized tariff model including both volumetric and capacity components is considered, and user preferences as well as all technical constraints are respected. Simulations based on real household consumption data acquired with a sampling period of 1 s are discussed. The proposed control scheme bestows the prosumer node with the flexibility needed to support smart grid use cases such as bill optimization (i.e., local energy trading), control of the profile at the point of connection with the grid, demand response, and reaction to main supply faults (e.g., islanding operation), etc.

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A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue

Cited by 228 publications

References 28 publications

A realistic energy optimization model for smart‐home appliances

A realistic energy optimization model for smart‐home appliances

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

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