The multi-energy microgrid (MEMG) comprises heterogeneous distributed generators (DGs) such as wind turbines, diesel generators, combined cooling, heat and power plants etc. Proper placement of these DGs is critical for the system energy efficiency and network reliability performance. This study proposes a two-stage coordinated method for optimally placing heterogeneous DGs in an MEMG project considering the uncertainties from renewable energy sources (RESs). Apart from optimising the traditional DG size and location, this method considers the optimal DG type and investment year simultaneously by maximising the project net present value (NPV), which consists of investment costs and operation costs. The whole problem is modelled as a two-stage coordinated stochastic optimisation model, where the long-term DG investment is determined at the first stage and operation decisions are determined at the second stage. The proposed method is verified on a test MEMG system. The simulation results show that its NPV is positive, which means the method is effective and should be implemented. Compared with the conventional DG placement approaches, the proposed method is more robust against the RES uncertainties and can better coordinate the heterogeneous energies with higher dispatch flexibility and economic profits.

Li, Z., Xu, Y., Fang, S., Mazzoni, S. (2019). Optimal placement of heterogeneous distributed generators in a grid-connected multi-energy microgrid under uncertainties. IET RENEWABLE POWER GENERATION, 13(14), 2623-2633 [10.1049/iet-rpg.2019.0036].

Optimal placement of heterogeneous distributed generators in a grid-connected multi-energy microgrid under uncertainties

Mazzoni S.
2019-01-01

Abstract

The multi-energy microgrid (MEMG) comprises heterogeneous distributed generators (DGs) such as wind turbines, diesel generators, combined cooling, heat and power plants etc. Proper placement of these DGs is critical for the system energy efficiency and network reliability performance. This study proposes a two-stage coordinated method for optimally placing heterogeneous DGs in an MEMG project considering the uncertainties from renewable energy sources (RESs). Apart from optimising the traditional DG size and location, this method considers the optimal DG type and investment year simultaneously by maximising the project net present value (NPV), which consists of investment costs and operation costs. The whole problem is modelled as a two-stage coordinated stochastic optimisation model, where the long-term DG investment is determined at the first stage and operation decisions are determined at the second stage. The proposed method is verified on a test MEMG system. The simulation results show that its NPV is positive, which means the method is effective and should be implemented. Compared with the conventional DG placement approaches, the proposed method is more robust against the RES uncertainties and can better coordinate the heterogeneous energies with higher dispatch flexibility and economic profits.
2019
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-IND/09
English
renewable energy sources
investment
optimisation
power distribution economics
power grids
power generation reliability
power generation economics
distributed power generation
wind turbines
investment year
project net present value
investment costs
operation costs
two-stage coordinated stochastic optimisation model
long-term DG investment
test MEMG system
conventional DG placement approaches
RES uncertainties
heterogeneous energies
optimal placement
heterogeneous distributed generators
grid-connected multienergy microgrid
wind turbines
diesel generators
power plants
proper placement
system energy efficiency
network reliability performance
two-stage coordinated method
heterogeneous DGs
MEMG project
renewable energy sources
Li, Z., Xu, Y., Fang, S., Mazzoni, S. (2019). Optimal placement of heterogeneous distributed generators in a grid-connected multi-energy microgrid under uncertainties. IET RENEWABLE POWER GENERATION, 13(14), 2623-2633 [10.1049/iet-rpg.2019.0036].
Li, Z; Xu, Y; Fang, S; Mazzoni, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/350444
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