1. SOLUTIONS TO POWER SYSTEM OPERATION PROBLEMS USING
ADAPTIVE REAL CODED BIOGEOGRAPHY BASED OPTIMIZATION
ABSTRACT
The objective of the present research work is to develop an adaptive real coded
biogeography based optimization (ARCBBO) for solving various power system operation
problems. The proposed ARCBBO algorithm is developed from original biogeography-based
optimization (BBO). BBO is a bio-inspired algorithm which is structured by two operations;
such as migration and mutation operation. These operations function in the basic probabilistic
manner. It fails to achieve good population diversity and exploration ability. So the original BBO
algorithm is modified to improve the quality of the solutions. In the proposed ARCBBO
algorithm, migration operation of BBO is migrated with the mutation operation of differential
evolution (DE), to improve the diversity of the population. Furthermore, the mutation operation
of BBO is adapted with Gaussian mutation, to enhance the exploration ability. Hence, the
proposed ARCBBO algorithm ensures more reliable, accurate and smooth convergence
characteristics in obtaining a global solution.
In the present work, the proposed ARCBBO algorithm is applied to solve the various
power system operation problems, such as economic load dispatch (ELD), optimal power flow
(OPF), multi-objective optimal power flow, dynamic optimal power flow, congestion
management, enhancement of loadability limit.
Initially, the economic load dispatch problem is solved using BBO, differential
evolution algorithm (DE) and the proposed ARCBBO algorithm. Here, fuel cost minimization is
the main objective function and also considered some real time operational constraints such as
prohibitive operating zones, ramp rate limits and valve point effects. The performance of the
algorithms is validated at the standard 6 unit, 15 unit and 40 unit test systems. The simulation
results obtained by ARCBBO algorithm are compared with results from the original BBO and
DE algorithm, and also compared with results reported in the recent literature survey.

2. The proposed ARCBBO algorithm is used to solve the optimal power flow problems
with various objective functions, such as fuel cost minimization, active power loss
minimization, emission minimization, voltage profile improvement and enhancement of voltage
stability with intact and contingency conditions. Here, OPF problem is considered as a single
objective optimization, simultaneously minimizing multi-objective optimization and also
dynamic optimal power flow problems. The algorithm is tested on IEEE 30-bus, IEEE 57-bus
and IEEE 118-bus systems.
The ARCBBO algorithm is extended to solve various congestion management
problems, such as a transmission line outage, generator outage, transformer outage and overload.
Here, the generator sensitivity factor is used to identify the contribution of generators, to relieve
the congestion. The simulation results obtained by ARCBBO, BBO and DE algorithms are
compared to each other. The performance of the algorithms is validated on IEEE 30-bus, IEEE
57-bus and IEEE 118-bus systems.
Finally, the ARCBBO algorithm is implemented to enhance the loadability limit of
the power systems without and with FACTS devices (and also to identify the optimal location
and capacity of the FACTS device). Here, the power systems are considered as an intact and
contingency condition. The performance of the algorithms is validated on IEEE 30-bus and IEEE
118-bus systems. The simulation results obtained by ARCBBO algorithm are compared with
results reported in the recent literature survey.
From the comparative analysis, we know that the proposed ARCBBO algorithm ensure
reliable, accurate and smooth convergence characteristics than BBO, DE and some algorithms
reported in the literature. Moreover, the effectiveness of the proposed ARCBBO algorithm is
proved with single and multi-objective function, linear and nonlinear constraints and large scale
power systems. Hence, it is concluded that the proposed ARCBBO algorithm is applicable to
solve any power system problems.