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Hybrid neuro-swarm optimization approach for design of distributed generation power systems

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Abstract

The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this sort of power source was done by integrating (or hybridizing) multiple different alternative energy sources (e.g., wind turbine generators, photovoltaic cell panels, and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability, and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system were carried out successfully in the previous work using particle swarm optimization (PSO). The goal was to minimize cost, maximize reliability, and minimize emissions (multi-objective function) subject to the requirements of the power balance and design constraints. In this work, the optimization was performed further using Hopfield neural networks (HNN), PSO, and HNN-PSO techniques. Comparative studies and analysis were then carried out on the optimized results.

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Abbreviations

COST ($/year):

Total cost

w, s, b:

Wind, solar, and battery storage indices

I i, S pi, OMpi ($/year):

Initial cost, present worth of salvage value, present worth of operations, and maintenance cost

N p (year):

Lifespan of the project

C g :

Annual cost of purchasing power from the utility grid

α w, α s, α b ($/m2):

Initial cost of WTG, PV panels, and storage battery

A w, A s (m2):

Swept area of WTG and PV panels

S w, S s ($/m2):

Salvage value of WTG and solar per square meter

β, Υ, ν :

Inflation rate, interest rate, and escalation rate

αOMw, αOMs, αOMb ($/m2/year):

Yearly operation and maintenance cost for wind, solar, and storage batteries

N p, N w, N s, N b (year):

Lifespan of project, WTG, PV, and storage batteries

ηs, ηw, ηb :

Efficiency of PV, WTG, and storage batteries

P g, t (kW):

Purchased power from the utility at hour t

psi ($/kW h):

Grid power price

EIR:

Energy index of reliability

EENS (kW h/year):

Expected energy not served

E (kW h/year):

Total power demand per annum

k :

Ratio of purchased power with respect to the hourly insufficient power

PE (ton/year):

Pollutant emission

Ω, φ, Г :

Coefficients approximating the generator emission characteristic coefficients

P bcap (kW):

Capacity of storage batteries

P bsoc (kW):

State of charge of storage batteries

P bmax (kW):

Maximum conversion capacity

P bmin (kW):

Minimum permissible storage level

P bcapmax (kW):

Allowed storage capacity

P br (kW):

Rated battery capacity

P b(t) (kW):

Discharge power from the storage batteries

P gmax (kW):

Maximum annual power allowed to be purchased from the utility grid

P gmin (kW):

Minimum annual power allowed to be bought from the utility grid

T (h):

Period under observation, 8,760 h (per year)

P bsup (t) (kW):

Surplus power at hour t

P d (t) (kW):

Load demand during hour t

P total (t) (kW):

Total power from WTG, PV, and FFG

P g (kW):

Power from the FFG

P w (kW):

Power from the WTG

P s (kW):

Power from the PV

R :

Ratio of maximum permissible unmet power

P dump (kW):

Dumped power

P WTG (kW):

Output power from th WTG

V, V ci, V r, V co (m/s):

Wind speed, cut-in wind speed, rated wind speed, and cutoff wind speed

P r (kW):

Rated WTG power

A wmax, A wmin (m2):

Maximum and minimum swept area of WTGs

A smin, A smax (m2):

Minimum and maximum swept area of PVs

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Acknowledgments

This work was supported by STIRF grant (STIRF CODE NO: 90/10.11) of University Technology Petronas (UTP), Malaysia. The authors sincerely thank the anonymous referees for their valuable and constructive comments and suggestions for the improvement of the overall quality on this paper.

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  1. University Technology Petronas, Tronoh, Malaysia

    T. Ganesan, P. Vasant & I. Elamvazuthi

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  1. T. Ganesan
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  3. I. Elamvazuthi
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Correspondence to P. Vasant.

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Ganesan, T., Vasant, P. & Elamvazuthi, I. Hybrid neuro-swarm optimization approach for design of distributed generation power systems. Neural Comput & Applic 23, 105–117 (2013). https://doi.org/10.1007/s00521-012-0976-4

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