Abstract
The day-ahead Energy Resource Management (ERM) problem with the aim to backing the functioning decisions of Virtual Power Player (VPP) in the microgrid environment. The aim of the VPP is to manage the available distributed energy resources as practically as possible with the objective of minimizing the operational cost and maximizing profits by reducing the need to buy energy from the external supplier or electricity market at high prices. The day-ahead ERM is executed the day before the energy trades are due. Typically, the considered trades periods are one-hour corresponding to 24 scheduling periods. A vital input to the ERM is each hour forecasting demand, which can be done using correct forecasting methods. VPP can aggregate the all types of energy resources like, DGs, PV, electric vehicles, energy storage, demand response and electricity market. The use of Vehicle to Grid (or G2V), PV, and energy storage technology can help to increase the penetration of non dispatchable uncertain renewable based DGs. The drawback of large DERs penetration is that the optimal scheduling problem turns into a complex optimization problem and becomes hard to be addressed by deterministic techniques, because these techniques can take a large execution time for obtaining the optimal solution. On the other hand, the VPP has its own optimal scheduling related time constraints. For these reasons, metaheuristic techniques are very useful to support the VPP in the computation of a good solution with a low execution time. This paper proposed the new metaheuristic algorithm called Cross-Entropy Variable Neighborhood Differential Evolutionary Particle Swarm Optimization (CE-VNDEPSO) for addressing the Energy Resource Management (ERM) problem of 25-bus microgrid systems. The effectiveness of CE-VNDEPSO algorithm is finding out by comparing its performance with the well-known optimization algorithms like, Variable Neighborhood Search (VNS), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Particle Swarm Optimization (PSO) and Differential Evolution (DE).
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Abbreviations
- d:
-
Distributed generation (DG) units
- p:
-
Photovoltaic units
- s:
-
External suppliers
- e :
-
Energy storage systems (ESSs)
- v:
-
Electric vehicles (EVs)
- L:
-
Loads
- mt:
-
Markets
- se:
-
Scenarios
- t :
-
Time Periods
- P Dg :
-
Active power generation (kW)
- P Ext :
-
External supplied power (kW)
- P es − :
-
Discharge power of ESS (kW)
- P ev − :
-
Discharge power of EV (kW)
- P es + :
-
Charge power of ESS (kW)
- P ev + :
-
Charge power of EV (kW)
- P cut :
-
Power reduction of load (kW)
- P nsd − :
-
Non-supplied demand for load (kW)
- P eg + :
-
Excess generation of DG unit (kW)
- P Buy :
-
Power buy to the market (kW)
- P Sell :
-
Power sell to the market (kW)
- x DG :
-
Binary variable for DG status
- N d :
-
Number of DG
- N p :
-
Number of PV
- N s :
-
Number of external suppliers
- N e :
-
Number of ESSs
- N v :
-
Number of EVs
- N L :
-
Number of loads
- Nmt:
-
Number of markets
- N se :
-
Number of scenarios
- T :
-
Number of periods
- C Dg :
-
Generation cost of DG (m.u./kWh)
- C Ext :
-
Cost of external supplier (m.u./kWh)
- C p.v :
-
Cost of PV generation (m.u./kWh)
- C es − :
-
Discharging cost of ESS (m.u./kWh)
- C ev − :
-
Discharging cost of EV (m.u./kWh)
- C cut :
-
Load curtailment cost (m.u./kWh)
- C nsd − :
-
Non-supplied demand cost (m.u./kWh)
- C eg + :
-
Excess generation cost (m.u./kWh)
- π(se):
-
Probability of scenario se
- P p.v :
-
Photovoltaic generation (kW)
- P load :
-
Forecasted load
- MP :
-
Electricity market price (m.u./kWh)
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Dabhi, D., Pandya, K. (2020). Metaheuristic Optimization Algorithm for Day-Ahead Energy Resource Management (ERM) in Microgrid Environment of Power System. In: Mehta, A., Rawat, A., Chauhan, P. (eds) Recent Advances in Communication Infrastructure. Lecture Notes in Electrical Engineering, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-15-0974-2_11
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DOI: https://doi.org/10.1007/978-981-15-0974-2_11
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