A hybrid algorithm for mixed integer nonlinear programming in residential energy management

Huang, Yantai; Wang, Wanliang; Hou, Beiping

doi:10.1016/j.jclepro.2019.04.062

Cited by 49 publications

(21 citation statements)

References 25 publications

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“…The HEMS is designed to minimize the energy cost (including the cost of electricity and natural gas). As the optimization scheme searches for the optimal generated power P j FC of the fuel cell in the DFCCHP system, the consuming rate ϕ j FC of natural gas to generate the required generated power is calculated on the basis of ( 23) with the nonlinear electric power efficiency η j FC given in (24). In other words,…”

Section: Electric and Thermal Efficiencies Of Fuel Cellsmentioning

confidence: 99%

“…The burner is a regular heating facility that heats inlet cold water to a preset temperature satisfying the constraint in ( 17) by directly burning natural gas. Different from the fuel cell efficiency in (24), the auxiliary burner's efficiency η aux is a constant because no chemical reaction is involved in the heating process. There is a constant relationship between the natural gas consuming rate ϕ j aux and the generated thermal power H j aux for the auxiliary burner as follows:…”

Section: Electric and Thermal Efficiencies Of Fuel Cellsmentioning

confidence: 99%

“…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]. In [25], the Real Coded Genetic Algorithm is utilized to determine the optimal electric and thermal power dispatch of a DFCCHP system considering the nonlinear variation of the electric efficiency for the fuel cell.…”

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: Electric and Thermal Efficiencies Of Fuel Cellsmentioning

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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“…However, how to handle the intermittent nature of renewable energy sources is not discussed. A combination of Sequential Quadratic Programming algorithm (SQP) and Binary Particle Swarm Optimization (BPSO)-based optimization model is proposed in [5]. The proposed combination model is applied for energy management of the residential sector.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

et al. 2021

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Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

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“…However, utilization of nonlinear models of switched systems in this context typically gives rise to mixed-integer nonlinear programs (MINLPs), which are hard so solve. Applications of mixed-integer nonlinear programming techniques can be found within optimal scheduling applications for energy systems, for example, for building energy systems 23,24 and combined heat and power plants. 25 For application within MPC, arising MINLPs need to be solved sufficiently fast for real-time control of a considered system.…”

Section: Introductionmentioning

confidence: 99%

Experimental operation of a solar‐driven climate system with thermal energy storages using mixed‐integer nonlinear model predictive control

Bürger

Bull

Sawant

et al. 2021

Optim Control Appl Methods

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This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC).The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed-integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed-integer nonlinear MPC application. K E Y W O R D Sexperimental study, mixed-integer nonlinear programming, model predictive control, renewable energy systems, solar thermal climate systemsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A hybrid algorithm for mixed integer nonlinear programming in residential energy management

Cited by 49 publications

References 25 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

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

Experimental operation of a solar‐driven climate system with thermal energy storages using mixed‐integer nonlinear model predictive control

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