Optimal operation of a micro-grid containing energy resources and demand response program

  • E. Jafari
  • S. SoleymaniEmail author
  • B. Mozafari
  • T. Amraee
Original Paper


In profit-based unit commitment, the objective of programming is to maximize profit and optimize generation. Practically, the gross profit depends not only on the revenue but also on the total expenditures. In this article, an efficient algorithm is suggested to assess the effect of uncertainties in determining 24-hour optimal strategy of a microgrid (MG) containing wind farms, photovoltaic, fuel cell, combined heat and power units, boiler, and energy storage devices (ESDs). The optimization problem is presented to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. The uncertainties include wind speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT)-artificial neural network (ANN)-imperialist competitive algorithm (ICA). In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study, programing MG is isolated from the grid. In the second case study, which is grid-connected mode the WT-ANN-ICA and WT-ANN uncertainties predictions methods are compered. In the third case, which is grid-connected mode the effect of DR programs on the expected profit of energy resources is assessed.


Micro-grid Wind farm Photovoltaic Combined heat and power Expected profit 

List of symbols


Index of each energy resources,


Commitment state of i-th generation unit

\(DR_{\hbox{max} }\)

Maximum load which can be shifted


Real heat which the buffer tank could be supplied at hour t

\(\varepsilon_{increasrd} (s,t)\)

Amount of increased load at hour t


The cost of buying energy for battery charging

\(P_{BATT}^{CH} (K,t),\;\;P_{BATT}^{\hbox{max} CH} (K)\)

The amount of charging power of k-th electrical energy storage device at hour t and its maximum limit

\(\alpha ,\beta ,\theta ,\lambda\)

The four marginal points of the electrical-thermal characteristic of combined heat and power

\(t_{on} (FC,t)/t_{off} (FC,t)\)

Duration for which fuel cell had been continuously up/down till period t

\(R_{\hbox{max} }^{up}\)/\(R_{\hbox{max} }^{down}\)

Maximum ramp up/down rate of fuel cell


Startup/Shutdown status of i-th unit at hour t

\(W\), \(CHP\), \(PV\), \(FC\), \(K\), \(B\)

Index of wind farm, combined heat and power, photo-voltaic, fuel cell, electrical energy storage device, and boiler

\(P_{G}^{W} (s_{W} ,t)\), \(P_{G,CHP} (t)\),\(P_{G}^{FC} (t)\)

Power generation of wind farm, heat and power, and fuel cell

\(E_{P} (s_{p} ,t)\)

Price of the market for energy for \(s_{p} - th\,\) scenario of price


The number of colonies


The probability of scenario s


Total number/index of time intervals


Sufficient large number

\(L_{shift} (s,t)\)

Shifted load from other hours to hour t

\(\varepsilon_{\hbox{max} }\)

Maximum amount of load which can be increased at hour t


Available heat in the buffer tank


Efficiency factor of electrical energy storage device

\(P_{BATT}^{DCH} (K,t)\)\(P_{BATT}^{\hbox{max} DCH} (K)\)

The amount of power delivered while discharging k-th electrical energy storage device at hour t and its maximum limit

\(P_{WN} (W)\)

Rated power of W-th wind farm

\(U_{\hbox{min} } /D_{\hbox{min} }\)

Minimum up/down time of fuel cell

\(R^{up} (FC,t)/R^{down} (FC,t)\)

Ramp up/down capacity of fuel cell at hour t

\(Z_{CH} (PV,t)/Z_{DCH} (PV,t)\)

Charge/Discharge state of energy saving device of photo-voltaic unit at hour t

\(A_{W}\), \(A_{PV}\), \(A_{CHP} - F_{CHP}\), \(A_{FC}\), \(B_{FC}\)

Cost coefficients of wind farm, photo-voltaic, combined heat and power, and fuel cell

\(s_{p}\), \(s_{W}\), \(s_{PV}\), \(s_{PL}\), \(s_{HL}\)

Index of scenarios for market price, wind speed, photo-voltaic power generation, power and thermal load demand


Predicted/ Real value for in-th input

\(U_{COST} (i,t)/D_{COST} (i,t)\)

Startup/shutdown cost of \(i - th\) generation unit

\(P_{sale} (s,t)\)/\(P_{buy} (s,t)\)

the amount of power sold and bought to/from the market


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Copyright information

© Islamic Azad University (IAU) 2017

Authors and Affiliations

  • E. Jafari
    • 1
  • S. Soleymani
    • 1
    Email author
  • B. Mozafari
    • 1
  • T. Amraee
    • 1
  1. 1.Department of Electrical and computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran

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