Optimization strategies for microgrid based on generation scheduling considering cost reduction and electric vehicles

One of the main issues in power systems relates to scheduling of energy resources. With the ever-increasing penetration of renewable energies with intermittent power output, this issue has turned into an even more significant problem. Renewable energy sources (RESs) have captured attention due to their low environmental emission and also low running cost. One drawback that may be brought into power systems is the surplus power generation by such generation technologies that should be carefully addressed in power system-related problems. This paper proposes the unscented transform modeling to consider the stochastic behavior of charge and discharge of EVs, random performance of photovoltaic, load demand and wind turbine systems. Due to the unpredictable nature of solar and wind power outputs, as well as plug-in electric vehicle owners' behavior when supplying or receiving power from the grid, a stochastic programming-based approach is proposed to operate microgrids in grid-connected configuration mode. The integration of vehicle to grid (V2G) has a good ability to minimize the operating cost of the MG. An integrated optimization model is presented in this study for optimal operation of the MG with high penetration of PEVs and RESs. Modified sunflower optimization algorithm (MSFO) algorithm is applied in this paper to address the optimization problem. The single-objective stochastic optimization is used for minimizing the total operating cost over the day taking into consideration the uncertainties due to the RESs’ power output intermittency, including wind speed and solar irradiance and load demand forecast error. Several case studies are taken into account to show the efficiency of the optimal operation with PEVs.

Enquiries about data availability should be directed to the authors.

C _Grid :

Grid-supplied energy cost, PEV

C _ENS :

Load interruption cost ($/kW)

C _PEV :

The cost of PEV

Ct _DG,k :

The energy cost bought from DGs

C _Bat :

The battery investment cost ($)

E _bat :

Available energy of battery (kWh)

\(E_{v}^{t}\) :

The amount of energy for fleet v in time t

\(E_{D,v}^{t}\) :

Fleet v energy to drive in time t

\(E_{v}^{\max } \;\& \;E_{v}^{\min }\) :

The lower and upper limits of energy in batteries

N _Cus :

The number of supplied consumers

Ndis :

The number of battery discharge cycles

Nv :

The number of PEVs in each fleet

\(P_{{{\text{Grid}}}}^{t} \& P_{{{\text{Grid}}}}^{\max }\) :

The power exchange and the highest possible power exchange

\(P_{c,v}^{t} \& P_{d,v}^{t}\) :

Charging and discharging capacity of fleet v

\(P_{d,v}^{\min } \& P_{d,v}^{\max }\) :

The minimum and maximum bounds of the discharging capacity

\(P_{c,v}^{\min } \& P_{c,v}^{\max }\) :

The minimum and maximum bounds of the charging capacity

\(S_{ij}^{t} \;\& \;S_{ij}^{max}\) :

The apparent power and maximum apparent power flowing from bus i to bus j in time t

\(P_{v}^{t}\) :

The minimum and maximum bounds of power capacity of ith DG in time t

\(Q_{i}^{t} \;\& \;P_{i}^{t}\) :

The reactive and active power injected to bus i in time t

rand :

Operator for generating random values

m :

Uncertain parameters number

T :

Scheduling period

This study was not funded by any institution or organization.

Authors and Affiliations

1. Institute of Information Technology, Changchun Automotive Vocational and Technical University, Changchun, 130051, China

   Yingjie Jiang

2. School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

   Yingjie Jiang & Jasni Mohamad Zain

3. Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

   Jasni Mohamad Zain

4. Solar Energy and Power Electronic Co, Ltd, Istanbul, Turkey

   Arman Nasr

Corresponding authors

Correspondence to Jasni Mohamad Zain or Arman Nasr.

Conflict of interest

The author declares that he has no conflict of interest.

Research involving human participants and /or animals

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

The processes of program coding, numerical execution, and statistical analysis were based on personal computers. All authors agreed to publish this paper, if accepted.

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