A Hybrid Optimization Approach for Residential Energy Management

Huang, Yantai; Zhang, Jinjiang; Mo, Yujie; Lu, Shuijin; Ma, Junchao

doi:10.1109/access.2020.3044286

Cited by 14 publications

(17 citation statements)

References 35 publications

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“…The commercial nonlinear solver KNITRO is then used to solve the proposed scheme. KNITRO is a commercial solver that solves large-scale MINLP problems by using a nonlinear branch and bound algorithm [23]. It is widely used in business and other industries because of its efficiency and robustness.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…a 6 are the polynomial coefficients. This paper adopts the fuel cell presented in [23]. Using curve fitting to construct a mathematical function describing the relationship between the electric efficiency and PLR of the fuel cell, it is obtained that a 1 = 0.9033, a 2 = −2.9996, a 3 = 3.6503, a 4 = −2.0704, a 5 = 0.4623 and a 6 = 0.3747.…”

Section: Electric and Thermal Efficiencies Of Fuel Cellsmentioning

confidence: 99%

“…In [22], an analytical rule-based power dispatch strategy was proposed to optimize the power dispatch results according to the time-of-use prices as well as with electric and thermal demands considering the nonlinear variation of the electric efficiency for the fuel cell. In [23], an electric power and thermal power dispatch optimization scheme was presented to optimize the electric and thermal power dispatch of a DFCCHP system considering the nonlinear variation of the electric efficiency for the fuel cell. A similar optimization scheme was also presented in [24].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

Yao

Teo

2022

IEEE Access

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This study proposes an optimization scheme of thermal power and electric power dispatch integrated with load scheduling for the domestic fuel cell-based combined heat and power (DFCCHP) system. The scheme is implemented in home energy management systems installed in smart homes. To provide accurate energy cost evaluation for the optimization scheme, the nonlinear electric efficiency characteristics of the fuel cell are approximated with a polynomial expression. Along with the inclusion of thermal power dispatch in the scheme, the nonlinear relationship between the thermal power and the electric power of the fuel cell is incorporated to design temperature constraints in the DFCCHP system. On top of the nonlinearity and complexity of the fuel cell, residential electric load scheduling and other energy sources including the power grid, PV panels, and battery energy storage are considered to ensure that the scheme takes a comprehensive energy management approach. Because of the nonlinearity that exists in the modeling of the DFCCHP system, a mixed-integer nonlinear programming formulation is utilized to solve the optimization problem. The scheme is tested in a day-ahead environment with time-varying electricity prices and natural gas prices. The optimization aims to minimize the electricity cost and natural gas cost. It is shown in the simulation that optimization scheme dispatches the electric power and thermal power in an optimal way so that the energy cost due to time-varying electricity prices and natural gas prices is minimized. The electricity cost optimization puts both power purchase and power selling into consideration. It is also shown in the simulation that the household loads are scheduled in an optimal way to the time slots with lower electricity prices in accordance with the optimal thermal and electric power dispatch.INDEX TERMS Home energy management system, nonlinear optimization, combined heat and power, load scheduling, power dispatch, fuel cell, PV panels.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Electric and Thermal Efficiencies Of Fuel Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

Yao

Teo

2022

IEEE Access

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“…The high share of integrated RES contributes more economical benefits, reduces greenhouse emissions from the environment, and provides better energy security. To analyze the economic and environmental benefits, a DR program was conducted on the residential MG system [27][28][29][30][31], CHP-based reconfigurable MG system [32], demand response analysis framework with multiple BSS [33], and CHP-based MG with multiple markets [34]. The cost of the MG emitted emission and the demand cost are optimized simultaneously by linear programming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the solution of such a highly complex constrained optimization problem, researchers have applied various analytical as well as nature-inspired (NI) algorithms until now. Among the analytical approaches, game theory [8], linear programming (LP) [11,13,34], Monte-Carlo simulation [15], and mixed integer nonlinear programming (MINLP) [31,33,34] were used to solve this problem.. The NI algorithms include particle swarm optimization (PSO) [7,17], grey wolf optimizer [9], Genetic Algorithm (GA) [10,26], Jaya algorithm [22], Artificial Bee Colony Algorithm (ABC) [26], and NSGA-III [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

Kujur¹,

Dubey²,

Salkuti

2023

Sustainability

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In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach.

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Real-time application of a demand-side management strategy using optimization algorithms

Tüysüz,

Okumuş,

Aymaz

et al. 2024

Applied Energy

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A Hybrid Optimization Approach for Residential Energy Management

Cited by 14 publications

References 35 publications

Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

Real-time application of a demand-side management strategy using optimization algorithms

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