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Optimization approach for planning hybrid electrical energy system: a Brazilian case

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Abstract

The continuous proliferation of distributed generation is leading end users to look for new tools that help to design hybrid electrical energy systems (HEES). Thus, this work proposes a novel approach for optimal planning of HEES, which comprises the optimization of the type and capacity of distributed generation connected to the end user. The main objective is to minimize the project’s total cost, considering the net metering scheme. To this end, the bioinspired meta-heuristic artificial immune system is proposed to optimally determine the number and type of photovoltaic panels. In addition, a nonlinear programming model is proposed to optimize the diesel generator and BESS capacity, considering the energy supply to the consumer by the HEES and the main distribution grid. Case studies involving commercial and residential customers in Brazil are introduced considering the normative resolutions from ANEEL, the Brazilian Regulatory Agency. Comparative analyses are made concerning an exhaustive search procedure and the commercial software Homer Pro, designed to optimize the operation of HEES systems. An important conclusion is that the proposed approach is as effective as the cutting-edge tools, with reasonable computational effort.

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Notes

  1. State value-added tax on sales and services (ICMS) on semi-finished products.

  2. Contribution to the Social Integration Plan.

  3. Contribution for Social Security Financing.

Abbreviations

k :

Type of PV panel

max:

Maximum limit

a :

Ambient

Be:

Battery energy storage system

ch:

BESS charging

dg:

Diesel generator

disch:

BESS discharging

ins:

Instantaneous

inv:

Inverter

mod:

Module

op:

Off-peak load period

pe:

Peak load period

pv:

Photovoltaic

ref:

Reference

sim:

Simulation

u :

Period

x :

Period related to the Brazilian tariff scheme

\(P_{\text{re,u}}\) :

Power flow from the main grid to HEES

\(x_k\) :

Defines what PV panel type has been chosen

A :

Area

Cc:

Cost of connecting cables, support structures and protection devices

CC:

Fuel cost

CCE:

Energy consumption cost

CE:

Energy consumption proposed by HEES

CI:

Initial investment cost

CO:

Maintenance cost

CP:

Total cost of PV panels

Ct:

Cost of transportation and installation

CV:

Energy provided to the main grid

CVE:

Energy “sale” cost

\(E_{\text{be}}\) :

BESS capacity

FC:

Fuel consumption function

N :

Number of energy sources

TC:

Total cost

\({\text{OBF}}\) :

Objective function

P :

Power;

SOC:

State of charge

t :

Time

\(z_{\text{be}}\) :

Binary variable that prevents the battery from charging and discharging simultaneously

\(A_{\text{d}}\) :

Coefficient of consumption function

\(B_{\text{d}}\) :

Coefficient of consumption function

\(T_{\text{u}}\) :

Number of hours of the period u

\({\text{ud}}_{\text{op}}\) :

Cost for the contracted demand at off-peak load periods

\({\text{ud}}_{\text{pe}}\) :

Cost for the contracted demand at peak load periods

Au:

Module area

Cb:

Price term related to the Brazilian

CD :

Cost for the contracted demand;

cdf:

Charging/discharging factor

D :

Contracted load demand

fp:

Power factor

ie:

Annual energy readjustment rate

Im:

Nominal current capacity of the general protection device

ip:

Annual inflation rate

na:

Number of years of the planning horizon

nf:

Number of phases

NK:

Number of candidate types of PV panels

\(N_{\text{pv,k}}^{\text{max}}\) :

Maximum number of type-k PV panels

NU:

Number of periods;

pc, pm, pr:

Parameters that control the cloning, somatic hypermutation and receptor editing mechanisms of the AIS algorithm;

\({p}_{x}\) :

Probability occurrence of each tariff flag;

PD:

Demand

Pp:

Nominal power of photovoltaic panel unit

Rad:

Radiation

\({{\text{SOCf}}}^{\text{max}}\) :

Maximum SOC factor

\({{\text{SOCf}}}^{\text{min}}\) :

Minimum SOC factor

T :

Temperature;

uc:

Unit cost

UO:

Off-peak load period

UP:

Peak load period

Vn:

Nominal voltage of the installation

α :

Capacity cost;

β :

Temperature coefficient;

η :

Efficiency

\( \eta _{{{\text{be,rt}}}} \) :

BEES round-trip efficiency

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Acknowledgements

The authors would like to thank CNPq, FAPEMIG, CAPES e INERGE. In addition, this work is partially supported by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project ESGRIDS—Desenvolvimento Sustentável da Rede Elétrica Inteligente/SAICTPAC/0004/2015—POCI-01-0145-FEDER-016434.

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Authors and Affiliations

  1. Federal University at Juiz de Fora (UFJF), Juiz de Fora, MG, Brazil

    D. T. Kitamura, L. W. Oliveira, J. G. Oliveira & B. H. Dias

  2. Federal Technological and Education Center of Minas Gerais, Belo Horizonte, Brazil

    K. P. Rocha

  3. Centre for Power and Energy Systems, INESC TEC, 4200-465, Porto, Portugal

    T. A. Soares

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  1. D. T. Kitamura
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Kitamura, D.T., Rocha, K.P., Oliveira, L.W. et al. Optimization approach for planning hybrid electrical energy system: a Brazilian case. Electr Eng 104, 587–601 (2022). https://doi.org/10.1007/s00202-021-01316-3

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