Supervisory predictive control of a hybrid solar panels, microturbine and battery power generation plant

Chalal, Lamine; Dieulot, Jean-Yves; Dauphin-Tanguy, G.; Colas, Frédéric

doi:10.3182/20120902-4-fr-2032.00067

Cited by 5 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another interest of the real-time simulator is the ability to emulate and reuse a previously measured power (typically here a solar power profile). Hence, a fair comparison of different algorithms is possible (see Chalal et al, 2012 for additional details).…”

Section: Power Cell Hil Platform and Component Modelsmentioning

confidence: 99%

“…In this case, the microturbine works in operating mode. Start-up is discarded and only a turbine speed PID control loop is considered (see details in Chalal et al, 2012). The torque C m is a function of the rotor speed N and of the fuel flowrate W f :…”

Section: Power Cell Hil Platform and Component Modelsmentioning

confidence: 99%

“…It allows us to deliver an overall smooth power when intermittent and renewable power sources (wind turbines, solar panels) are combined with conventional sources and storage units (batteries, supercapacitors, etc.). Hence, a predictive supervisor helps us to promote the integration of the renewable sources into the grid (Chalal, Dieulot, Colas, & Dauphin-Tanguy, 2012;Guerin, Lefebvre, & Loisel, 2012;Liu, Chen, Christofides, & Qi, 2011a;Qi, Liu, & Christofides, 2013;Valenciaga & Puleston, 2005). Generating a smooth active power in a short time window, in a time scale around 10-30 s, is mandatory for a power cell (see a list of demonstrators in Daguzan & Galland, 2012).…”

Section: Introductionmentioning

confidence: 98%

“…Typically, in Chalal et al (2012), this horizon was fixed around 10 s. However, whenever an abrupt change of the renewable power occurs, a possible false prediction can hamper the results given by the MPC algorithm. As an example, this could happen for solar panels when it quickly clouds over, or for wind turbines with squally conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, extra components can be integrated into the cell. It is sufficient to consider their dynamical models and to add their economic contribution to the objective function, which can depend on the operating regime as in the case of wind turbines (Chalal et al, 2012;Qi et al, 2013). Hence, a predictive approach allows for an (economically) optimal and modular way to tune a hybrid power cell supervisor.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Dieulot

Colas

Chalal

et al. 2015

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

Hardware in the loop Variable receding horizon a b s t r a c tThe supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.

show abstract

Section: Power Cell Hil Platform and Component Modelsmentioning

confidence: 99%

Section: Power Cell Hil Platform and Component Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Dieulot

Colas

Chalal

et al. 2015

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

show abstract

Industrial Cloud Automation for Interconnected Factories

Chalal

Saadane

Rhiat

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Economic supervisory predictive control of a hybrid power generation plant

Dieulot

Colas

Chalal

et al. 2015

Electric Power Systems Research

Self Cite

View full text Add to dashboard Cite

Hardware in the loop a b s t r a c tThis work deals with the development of an economic supervisory predictive control method for the management of a hybrid renewable energy system. The hybrid cell integrates solar panels, a gas microturbine and a storage unit. Tuning the predictive controller is easy: the optimal criterion encompasses the environmental, fuel, energy delivery and storage costs. Short time predictions of the solar power are embedded in the supervisor which yields smoother battery control and better power management. Real-time experiments are driven in a Hardware-in-the-Loop framework illustrating the relevance of the proposed supervisory predictive control design.

show abstract

Supervisory predictive control of a hybrid solar panels, microturbine and battery power generation plant

Cited by 5 publications

References 14 publications

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Industrial Cloud Automation for Interconnected Factories

Economic supervisory predictive control of a hybrid power generation plant

Contact Info

Product

Resources

About