Advertisement

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

  • E. Jafari
  • S. Soleymani
  • B. Mozafari
  • T. Amraee
Original Paper

Abstract

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.

Keywords

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

List of symbols

\(i\)

Index of each energy resources,

\(M(i,t)\)

Commitment state of i-th generation unit

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

Maximum load which can be shifted

\(H(t)\)

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

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

Amount of increased load at hour t

\(BATT_{COST}\)

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

\(SU(i,t)/SD(i,t)\)

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

NCLO

The number of colonies

\(\rho_{s}\)

The probability of scenario s

\(T/t\)

Total number/index of time intervals

\(Y\)

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

\({\text{AH(t)}}\)

Available heat in the buffer tank

\(\delta\)

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

\(\hat{y}_{in}\)/\(y_{in}\)

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

References

  1. Abedinia O, Amjdy N (2016) Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm. Int Trans Electr Energy Syst 26(7):1511–1525CrossRefGoogle Scholar
  2. Alipour M, Mohammadi-Ivatloo B, Zare K (2015) Stochastic scheduling of renewable and CHP-based microgrids. IEEE Trans on Ind Inform 11(5):1049–1058CrossRefGoogle Scholar
  3. Amjdy N, Keynia F, Zareipour H (2011) A new hybrid iterative method for short-term wind speed forecasting. Int Trans Electr Energy Syst 21(1):581–595Google Scholar
  4. Askarzadeh A (2014) Voltage prediction of a photovoltaic module using artificial neural networks. Int Trans Electr Energy Syst 24(12):1715–1725CrossRefGoogle Scholar
  5. Azizipanah-Abarghooee R, Niknam T, M M, Bavafa F, Kaji M (2015) Optimal power flow based TU/CHP/PV/WPP coordination in view of wind speed, solar irradiance and load correlations. Energ Convers Manag 96:131–145CrossRefGoogle Scholar
  6. Baziar A, Kavousi-Fard A (2013) Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices. Renew Energ 59:158–166CrossRefGoogle Scholar
  7. Gazafroudi AS, Bigdeli N, Ramandi MY, Afshar A (2014) A hybrid model for wind power prediction composed of ANN and imperialist competitive algorithm (ICA). In: The 22nd Iranian conference on electrical engineering (ICEE 2014), May 20–22Google Scholar
  8. Jiang W, Yan Z, Feng DH, Hu Z (2012) Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model. Int Trans Electr Energy Syst 22(5):662–673Google Scholar
  9. Jin M, Feng W, Marnay C, Spanos C (2017) Microgrid to enable optimal distributed energy retail and end-user demand response. Appl Energy. doi: 10.1016/j.apenergy.2017.05.103 CrossRefGoogle Scholar
  10. Khodayar ME, Shahidehpour M (2013) Stochastic price-based coordination of intrahour wind energy and storage in a generation company. IEEE Trans Sustain Energ 4:554–562CrossRefGoogle Scholar
  11. Khorani V, Forouzideh N, Nasrabadi AM (2011) Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: comparing performances. IEEE Conf, 61–67Google Scholar
  12. Lund H (2010) The implementation of renewable energy systems lessons learned from the Danish case. Energy 35:4003–4009CrossRefGoogle Scholar
  13. Malheiro A, Castro PM, Lima RM et al (2015) Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems. Renew Energy 83:646–657CrossRefGoogle Scholar
  14. Mohammadi S, Mozafari B, Solimani S, Niknam T (2013) An adaptive modified firefly optimisation algorithm based on Hong’s point estimate method to optimal operation management in a microgrid with consideration of uncertainties. Energy 51:339–348CrossRefGoogle Scholar
  15. Mohammadi S, Soleymani S, Mozafari B (2014) Scenario-based stochastic operation management of MicroGrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices. Int J Electr Power Energy Syst 54:525–535CrossRefGoogle Scholar
  16. Morales JM, Conejo AJ, Perez-Ruiz J (2010) Short term trading for a wind power producer. IEEE Trans Power Syst 25(1):554–564CrossRefGoogle Scholar
  17. Niknam T, Golestaneh F, Shafiei M (2013) Probabilistic energy management of a renewable microgrid with hydrogen storage using self-adaptive charge search algorithm. Energy 49:252–267CrossRefGoogle Scholar
  18. Renewable Energy Organization of Iran. (2015) [Online]. Available:www.suna.org
  19. Rose A, Stoner R, Pérez-Arriaga I (2016) Prospects for grid-connected solar PV in Kenya: a systems approach. Appl Energy 161:583–590CrossRefGoogle Scholar
  20. Sakaguchi T, Tabata T (2015) 100% electric power potential of PV, wind power, and biomass energy in Awaji island Japan. Renew Sustain Energy Rev 51:1156–1165CrossRefGoogle Scholar
  21. Sarkhani S, Soleymani S, Mozafari B (2014) Strategic bidding of an electricity distribution company with distributed generation and interruptible load in a day-ahead electricity market. Arab J Sci Eng 39:3925–3940CrossRefGoogle Scholar
  22. Schmidt J, Cancella R, Pereira AO (2016) An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil. Renew Energy 85:137–147CrossRefGoogle Scholar
  23. Shayeghi H, Ghasemi A (2013) Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Conversion and Management 74:482–e491CrossRefGoogle Scholar
  24. Tahani M, Babayan N, Pouyaei A (2015) Optimization of PV/Wind/Battery stand-alone system, using hybrid FPA/SA algorithm and CFD simulation, case study: Tehran. Energy Convers Manag 106:644–659CrossRefGoogle Scholar
  25. The Ontario Electricity System Operator (IESO) (2016) [Online].Available: http://www.ieso.ca/
  26. Voronin S, Partanin J, Kauranne T (2014) A hybrid electricity price forecasting model for the Nordic electricity spot market. Int Trans Electr Energy Syst 24(5):736–760CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2017

Authors and Affiliations

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

Personalised recommendations