Abstract
A combined electric vehicles (EVs) and controllable loads scheduling framework is presented in this paper for a microgrid aimed at minimizing the operating cost and emissions. The microgrid is equipped with renewable power generation by using wind turbines and solar photovoltaic panels. In this respect, EVs would be used for load profile flattening and controllable loads would be used to address the reserve requirements of the system mainly due to intermittent renewable power generation. The problem is formulated as a two-stage scheduling program to specify the expected operating cost and reserve. The first stage aims to minimize the total costs including the generation and reserve costs. The second stage seeks to minimize the redispatch costs due to volatile renewable power generation. The resulting optimization problem is then solved by using the modified manta ray foraging optimization algorithm known as "MMRFO". This algorithm is an efficacious one being capable of handling various types of optimization problems. The findings obtained from a 24-h analysis of an MG model demonstrate the superior performance of the MMRFO algorithm when compared to other established methodologies. The obtained results by applying the MMRFO method indicate high efficiency of this algorithm in comparison with some other well-known algorithms when tackling the combined EV and controllable loads scheduling problem in the presence of wind and solar power generation.
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Abbreviations
- ECt :
-
The expected cost of the system at time t
- \(C_{si}^{t}\) :
-
The cost incurred for the establishment of unit i at a specific point in time, denoted as t
- \(C_{{si_{mn} }}^{t}\) :
-
The actual costs associated with the initialization of unit i in scenario t
- \(T\) :
-
The total number of hours
- N g :
-
The total amount of generating units
- \(U_{i}^{t}\) :
-
The condition of unit i during the time frame t, if it is in an activated or deactivated state
- \(P_{{{\text{G}}i}}^{t}\) :
-
The active power output of the i-th unit at time t
- \(C_{{{\text{G}}i}}^{t}\) :
-
The energy costs at a given time t, as supplied by unit i
- \(R_{{{\text{G}}i.u}}^{t}\) :
-
At time t, unit i's topmost spinning reserve
- \(R_{{{\text{G}}i.d}}^{t}\) :
-
At time t, unit i's lower spinning reserve
- \(\lambda_{{{\text{G}}i.u}}^{t}\) :
-
The cost of reserve spinning up, referred to as unit-i, over a specific period of time
- \(\lambda_{{{\text{G}}d.u}}^{t}\) :
-
The cost of reserve spinning down, referred to as unit-i, over a specific period of time
- N EV :
-
The collective figures of electric vehicles
- \(P_{{{\text{EV}}_{j} }}^{t}\) :
-
The electricity generation at a given time t originating from aggregator j
- \(C_{{{\text{EV}}_{j} }}^{t}\) :
-
The energy costs at a given time t, as supplied by aggregator j
- \(P_{{\text{w}}}^{t}\) :
-
The power generated by a wind turbine at a specific time t
- \(P_{mn}^{r}\) :
-
The probability of the mth wind power scenario and the nth PV power scenario materializing
- \(r_{{{\text{G}}i_{mn} }}^{t}\) :
-
The energy supply of scenario m and n for unit i's reserve deployed at time t
- \({\text{VOLL}}_{L}^{t}\) :
-
The load loss value for customer L during the time frame t
- N L :
-
The number of loads
- \(r_{{{\text{EV}}^{mn} }}^{t}\) :
-
The energy supply of scenario m and n for aggregator j's reserve deployed at time t
- \(P_{{{\text{ws}}_{mn} }}^{t}\) :
-
The magnitude of power dissipation from wind turbines in the given scenario, denoted by m and n, at a specific time t
- \(C_{{{\text{ws}}}}^{t}\) :
-
The financial implications of wind power reduction in the given scenario involving variables m and n at a specific moment in time, denoted as t
- \(P_{{{\text{PV}}_{{S_{mn} }} }}^{t}\) :
-
The magnitude of power dissipation from Pv generators in the given scenario, denoted by m and n, at a specific time t
- \(C_{{{\text{PV}}_{S} }}^{t}\) :
-
The financial implications of PV power reduction in the given scenario involving variables m and n at a specific moment in time, denoted as t
- \(P_{{{\text{G}}i,\min }} (t)\) :
-
At time t, the minimum power output that can be provided by DGs
- \(P_{{\text{Grid,min}}} (t)\) :
-
At time t, the minimum power output that can be provided by the grid
- \(P_{{{\text{PV}}}}^{t}\) :
-
The power output of a PV system during the charging or discharging process at a given time t
- \(C_{{{\text{PV}}}}^{t}\) :
-
The PV system offers costs for energy at a given time t
- \(P_{{{\text{grid}}}}^{t}\) :
-
The quantity of active power being transmitted to or received from the utility at time t
- \(C_{{{\text{grid}}}}^{t}\) :
-
The utility bid at a specific time t
- \(P_{{\text{w}}}^{t}\) :
-
The power generated by a wind turbine at a specific time t
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No. ZK[2022]549), the Natural Science Foundation of Education of Guizhou province (No. [2019]203, No. KY[2019]067), and the Funds of Qiannan Normal University for Nationalities (No.qnsy2019rc09).
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Li, M., Aksoy, M. & Samad, S. Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization. Soft Comput 28, 2015–2034 (2024). https://doi.org/10.1007/s00500-023-09168-8
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DOI: https://doi.org/10.1007/s00500-023-09168-8