Multiobjective Scheduling-based Energy Management System Considering Renewable Energy and Energy Storage Systems: A Case Study and Experimental Result

  • I. El KafaziEmail author
  • R. Bannari


The fast-growing development of smart grid and renewable energy increases the challenge in balancing the production on local energy consumption. The power scheduling of energy storage has directed to growing interests in energy storage system to increase the use of renewables. In this study, a practical laboratory energy management system considering renewable energy and battery is established. Besides, two control strategies including ‘scheduling’ and ‘ON/OFF’ operation of the grid in the photovoltaic–wind–battery hybrid systems are modeled. This paper proposes a day-ahead optimizing planning using mixed-integer linear programming, aiming to achieve economic benefit by reducing operational costs of the grid. Related to demand-side management, a control technique is developed for a proper scheduling of the power from the hybrid system. The ultimate objective of the aimed strategy is to maximize the advantages of renewable energy in different running conditions such as weather fluctuation and grid support. In addition, a day-ahead optimization for operational costs, as well as a prediction model for PV and WT, is used. The data of renewable productions and load demand are used. The obtained results prove that applying the scheduling strategy for PV–WT–battery and grid operation control models, significant grid decreasing can be achieved related to the case where the grid is managed alone to satisfy the same load demand.


Day ahead Renewable energy Optimization Forecasting Multi objective scheduling Energy management 

List of Symbols



Objective function


Time scheduling

\( \Delta t \)

Duration of interval


Index of units of res

\( C_{\text{sell}}^{\text{grid}} \)

Cost of selling energy

\( C_{\text{buy}}^{\text{grid}} \)

Cost of buying energy

\( P_{\text{res}} \)

Power from renewables (kW)

\( P_{\text{grid}} \)

Power from the utility (kW)

\( P_{\text{grid/buy}} \)

Power bought from the utility (kW)

\( P_{\text{grid/sell}} \)

Power sold to the utility (kW)

\( P^{\text{bat}} \)

Power of the battery (kW)

\( P_{\text{l}} \)

Power load (kW)


\( ES_{ \hbox{max} }^{\text{bat}} \)

Maximum battery energy level

\( ES_{ \hbox{min} }^{\text{bat}} \)

Minimum battery energy level

\( \eta^{{{\text{bat}},{\text{ch}}}} \)

Battery charging efficiency

\( \eta^{{{\text{bat}},{\text{disch}}}} \)

Battery discharging efficiency

\( \lambda_{\text{grid}} \left( t \right),\; B_{t} \)

Binary variable

\( X_{\text{load}} \)

Connection load demand





Renewable energies


Energy storage system


Energy management system




Wind turbine


State of charge


Distributed energy resources



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Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Laboratory Systems Engineering, ENSAIbn Tofail University KenitraKenitraMorocco

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