Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid

Ouammi, Ahmed; Achour, Yasmine; Zejli, Driss; Dagdougui, Hanane

doi:10.1109/tase.2019.2910756

Cited by 76 publications

(33 citation statements)

References 30 publications

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“…Schedulable loads are controlled at the slot scale. The shorttime scheduling problem at time slot h can be optimized by minimizing the cost function as expressed in (24). It should be mentioned that the cost function minimizes the cost of the current time slot h and all the other slots in the duration.…”

Section: A) Upper-level: Discrete Mpc Loadsmentioning

confidence: 99%

“…The capability of economic MPC to absorb time-varying costs and peak demand charges for electricity consumers is verified in [21], and the asymptotic stability of the economic MPC algorithm for economic load dispatch and load frequency control is derived in [22]. To date, the MPC-based optimization method has already been applied to the energy sector in a number of fields, such as electric vehicle charging [23], microgrid operation [24], and energy scheduling [25].…”

Section: Introductionmentioning

confidence: 99%

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Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

et al. 2020

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Smart home scheduling, facilitated by advanced metering, monitoring, and manipulation technology, plays an important role in the energy transition in terms of accommodating intermittent renewable energy and improving energy consumption efficiency. The key functionalities of home energy scheduling are usually implemented by leveraging the flexibility of household appliances, such as thermostatically controlled loads (TCLs) and energy storage units, to improve the peak-to-average ratio for utilities and reduce energy bills for customers. However, the consumption patterns of appliances are greatly influenced by a variety of factors, including real-time tariffs, ambient temperature profiles, indoor activities, and solar irradiance. Hence, smart home energy scheduling is a challenging task because most of these impacting factors are stochastic and difficult to predict. To properly model and manage the uncertainty factors associated with smart home appliance scheduling, an economic model predictive control (MPC)-based bilevel smart scheduling scheme is proposed in this paper. The comprehensive modeling of distributed generation and household appliances is performed at the single-household level. The home energy scheduling problem is formulated on two levels, with the upper level emphasizing the economic impact and the lower level focusing on capturing TCL responses. The correlations among different TCLs and their performance under the influence of various uncertainty factors, such as environmental impacts and user behaviors, are considered. The efficiency of the proposed MPC-based bilevel optimization model and the effectiveness of the home energy scheduling strategy in managing uncertainties are validated and illustrated in numerical studies.

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Section: A) Upper-level: Discrete Mpc Loadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

et al. 2020

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“…Model predictive control of power converters for robust and fast operation of ac microgrids was analysed by Dragičević [26]. Sajadian et al, Panda et al, and Ouammi et al also reported about MPC application in islanded and grid-connected condition of microgrid [27][28][29]. Despite many developments in MPC, some problems are still exist.…”

Section: Introductionmentioning

confidence: 99%

“…Despite many developments in MPC, some problems are still exist. New theoretical methods have proven that MPC is better than many other control strategies with an intermediate computational complexity [24][25][26][27][28][29]. The non-linear dynamics were approximated as a piecewise linear (INTEGRAL CONSTANT) 13.62 model [30], and then the MPC is applied.…”

Section: Introductionmentioning

confidence: 99%

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

Panda

Ghosh

2020

Technol Econ Smart Grids Sustain Energy

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Power electronics converter and inverter systems for microgrid operations are always stochastic. When the system is operating in grid-tied mode, the attached load is not constant, so the current which drawn from the DC grid also varies leads to a voltage drop and that causes steady-state error in between the actual and desired output voltage in microgrid. DC-DC bidirectional converters are widely used in microgrid application that facilitates bidirectional energy convertion which having boost mode operation in one direction and bucking mode in other operation. The Interfacing converter is controlled by using the Maximum Power Point Tracking (MPPT) control technique for obtaining maximum power during different atmospheric conditions. In this work, a Model Predictive Control (MPC) strategy is proposed which having an improved cost function, single prediction horizon, and an observer to keep tracking the output voltage. Here, MPC is proposed for cumulative control in between the DC grid side converters and AC grid side inverter, and for improving the power quality and reliability of the utility grid. Both continuous time and discrete time domain models are formed for the whole system for applying different control strategies. Along with MPC, the system has been controlled by using other controllers like as PI controller, Sliding Mode Controller (SMC). Luenberger observer is used to check the DC grid voltage when disturbances are implemented. Here, all the computational results are compared for justifying and validate a better controlling method for microgrid application. These kind of work is first time reported in this manuscript.

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“…In the midday hours (9-16 h) of the day, the CHP and FC generate the maximum power. In the last hours(17)(18)(19)(20)(21)(22)(23)(24) h), also the CHP and FC generate the maximum power. In this situation, if the generated power of MG exceeds the load demand, MG can sell some power to the main grid.…”

mentioning

confidence: 99%

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

et al. 2019

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This paper proposes an efficient hybrid approach-based energy management strategy (EMS) for grid-connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasioppositional-chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS-RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid-connected MG system is continuously tracked by the RF technique.The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two-strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques. KEYWORDS energy management strategy, grid-connected MG, power flow constraints, random forest, quasioppositional-chaotic symbiotic organisms search algorithm How to cite this article: Chen J, Zhou Z, Karunakaran V, Zhao S. An efficient technique-based distributed energy management for hybrid MG system: A hybrid QOCSOS-RF technique. Wind Energy. 2020;23:575-592. https://doi.

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Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid

Cited by 76 publications

References 30 publications

Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

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