Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Wu, Hongyu; Pratt, Annabelle; Munankarmi, Prateek; Lunacek, Monte; Balamurugan, Sivasathya Pradha; Liu, Xuebo; Spitsen, Paul

doi:10.1016/j.adapen.2022.100094

Cited by 18 publications

(6 citation statements)

References 28 publications

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“…EMS coupled with the presence of EV has been extensively investigated in the scientific literature. The relevance of those systems has been enabled by recent advances in communication techniques [11], the large-scale integration of EVs into the grid [1,3], smart sensing, and smart metering [12]. Energy management problems can include a wide variety of entities and sizes.…”

Section: Electric Vehicle Integration In Energy Management Systemmentioning

confidence: 99%

“…Private users' perspective is not the only possible approach: in [16], an optimal EV charging strategy was calculated for one charging station; in [17] optimal sizing and energy management strategy were conducted for a grid-connected EV workplace charging stations considering PV sources and flywheel energy storage system. Modelingwise, EMS archetypes can be mainly broken down into rule-based, optimization [1,8,[11][12][13][14], and heuristics [3]. It is commonly agreed that optimization techniques can be used to solve scheduling problems in EMS and can provide accurate solutions; they are normally time-consuming, especially when solving complex optimization problems [15].…”

Section: Electric Vehicle Integration In Energy Management Systemmentioning

confidence: 99%

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optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Caminiti,

Merlo,

Fotouhi Ghazvini

et al. 2024

Energies

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This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty.

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Section: Electric Vehicle Integration In Energy Management Systemmentioning

confidence: 99%

Section: Electric Vehicle Integration In Energy Management Systemmentioning

confidence: 99%

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Caminiti,

Merlo,

Fotouhi Ghazvini

et al. 2024

Energies

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“…The residential sector consumes over one-quarter of electricity worldwide as of 2021. The HEMS proves to be an effective tool in monitoring the power consumption levels of smart appliances, reducing peak loads, and saving energy costs [135]. An HEMS comprise various components, including distributed PV modules, smart appliances, user interaction terminals, ESSs, central control units, and SMs [62].…”

Section: Home Energy Management System (Hems)mentioning

confidence: 99%

Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review

Irfan,

Deilami,

Huang

et al. 2023

Energies

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The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner’s economic benefit.

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“…With the development of artificial intelligence, deep reinforcement learning is applied to islanded microgrid control [16][17][18]. One of the actor-critic algorithms is widely used because of its strong adaptive decisionmaking capability.…”

Section: Energy Proceedingsmentioning

confidence: 99%

Deep reinforcement learning based adaptive energy management for islanded microgrids considering multi-objective optimization

Jiawen¹,

Li²,

Tao³

2023

Preprint

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In order to reduce the frequency deviation and unit generation cost of an isolated microgrid, an adaptive load frequency control (ALFC) method is proposed in this paper. The method employs an adaptive proportionalintegral-derivative (PID) controller to achieve adaptive control by adaptively adjusting the parameters of the controller to output the regulation command. In addition, to achieve adaptive regulation of the control parameters, a deep actor-critic (DAC) algorithm is proposed in this paper, which introduces multiple critics and Gaussian noise exploration techniques to enhance the quality of the ALFC strategy. The performance of the proposed method is tested in the Zhuzhou isolated microgrid of the China Southern Grid(CSG), which can effectively reduce frequency deviation and generation cost.

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Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Cited by 18 publications

References 28 publications

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review

Deep reinforcement learning based adaptive energy management for islanded microgrids considering multi-objective optimization

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