International Journal of Clean Coal and Energy, 2013, 2, 1-7
doi:10.4236/ijcce.2013.22B001 Published Online May 2013 (http://www.scirp.org/journal/ijcce)
Swarm Intelligence in Power System Planning
Ke Meng, Z.Y. Dong, Yichen Qiao
Centre for Intelligent Electricity Networks, The University of Newcastle, Australia
Received 2013
ABSTRACT
Power system planning is one of the essential tasks in th e power system operation management, which requires in-depth
knowledge of the system under consideration. It can be regarded as a nonlinear, discontinuous, constrained
multi-objective optimization problem. Although the traditional optimization tools can be used, the modern planning
problem requires more advanced optimization tools. In this paper, a survey of state-of-the-art mathematical optimiza-
tion methods that facilitates power system planning is provided, and the needs of introducing swarm intelligence ap-
proaches into power system planning are discussed.
Keywords: Power System Planning; Swarm Intelligence
1. Introduction
Power system planning plays a significant role in main-
taining power system stability and reliability. It deter-
mines the right schedule of introducing additional gen-
eration facilities, transmission lines, substations, trans-
formers, reactive compensations, and control devices;
also it covers the direction of replacement needs with
respect to aging existing power system devices, where all
of these are directed at increased stability and reliability
through market-driven augmentations [1].
Generally speaking, in terms of objectives, the plan-
ning issues may be categorized as generation planning
and transmission planning; while in term of periods, the
planning problems can be classified into short-term plan-
ning, mid-term planning, and long-term planning [2].
Generation planning is intended to determine the optimal
timing, locations, generation equipments and associated
capacities required to satisfy various system constraints
and operation conditions, which can maximize profit and
minimize risk [1]. Similarly, transmission planning aims
to select the best time, siting, and transmission facilities
to meet the rising customer demand, which minimizes
investment and maintains stability [2]. From planning
period point of view, the major purpose of short-term
planning is to devise operation plans for power plants or
single generating unit so as to ensure the balance of sup-
ply and demand [3]. Med-term planning provides the
guidance for making market decisions and system opera-
tions. Long-term planning ensures adequate generation
capacities and delivery capabilities will be available to
meet the expected future demand increases.
In a word, planning is one of the most important re-
search areas in power system analysis, which needs
careful and extensive studies and it should be carried out
in a timely and effective manner.
2. New Challenges
Nowadays, power industries worldwide have been un-
dergoing profound changes with system deregulations
and reconstructions. In particular, the traditional, verti-
cally monopolistic structures have been reformed into
competitive markets in pursuit of increased efficiency in
electricity productions and utilizations. Along with the
introduction of competitive and deregulated electricity
markets, many power system problems have become
difficult to be analyzed with traditional methods, espe-
cially when power system planning issues are involved.
Since the change in the competition environment, the
perspectives of power system planning are also change
correspondingly. In the traditional vertically-integrated
structure, the whole power system is operated by single
system and service provider, who owns all the generation
and transmission assets. Therefore, when conducting
power system planning, the generation and transmission
planning can be carried out simultaneously. However,
after deregulations, the conventional monopolistic struc-
ture has been separated into three independent parts:
generation, transmission, and distribution. This separa-
tion results in a situation that transmission companies
have no direct role in deciding the patterns of power
generation and distribution. Furthermore, some invest-
ment and operation information about generation and
distribution companies becomes business confidential
and cannot be obtained by the planner [4].

K. MENG ET AL.
2
Moreover, the modern power system planning process
requires a wide array of input information, such as
weather forecasts, load forecasts, market forecasts, ex-
pected water supply and fuel price variations. In addition,
system constraints, single unit constraints, and various
chance constraints, along with other environmental and
physical influence, are taken into account. Besides tech-
nical information, there are social and governmental or-
ganizations to be consulted in the process of planning so
as to ensure that every decision is well rounded and
completed. All these have made existing problems even
more complex. As a consequence, more advanced and
effective techniques need to be introduced into planning
issues.
3. Swarm Intelligence
Swarm intelligence is an artificial intelligence technique
involving the study of collective behavior in decentral-
ized system, which is made up by populations of simple
individuals interacting locally with each other and with
external environment [5-8]. Several examples of these
systems can be found in the nature, for example, colonies
of ants, flocks of birds, schools of fish, groups of bees,
packs of wolves, and so on. An interesting phenomenon
of swarms is that collective swarm behavior can emerge
on a global scale even when all individuals have only a
restricted view of the system and interactions between
individuals and their environment occur only on a local
scale [9]. Owning to these outstanding characteristics, the
principles of swarm behavior have been studied exten-
sively and been widely applied into many fields. Com-
putational swarm intelligence is the algorithmic models
that imitate the principles of large groups of simple
swarm individuals working together to achieve a goal
through self-learning, self-adjusting, and mutual coop-
eration manners. These algo rithms have shown to be able
to adapt well in changing environments, and are im-
mensely flexible and robust [8,10]. Two of the computa-
tional swarm intelligence techniques are ant colony op-
timization (ACO) [11] and particle swarm optimization
(PSO) [6]. In the next section, these two swarm intelli-
gence algorithms will be discussed in detail.
3.1. Ant Colony Optimization (ACO)
ACO is a metaheuristic inspired by the foraging behavior
of ants [12- 14 ]. In o rde r to f ind the shor test path f rom th e
nest to food source, ant colonies exploit a positive feed-
back mechanism: they use a form of indirect communi-
cation called stigmergy, which is based on the laying an d
detection of pheromone trails [15,16]. These ants deposit
pheromone on the ground in order to mark some favor-
able path that should be followed by other members of
the colony [13]. ACO takes inspirations from the collec-
tive behavior of ants and exploits a similar mechanism
for solving optimization problems. In ACO, firstly colo-
nies of artificial ants with given size are generated, and
each ant denotes a potential solution, whose performance
is measured based on a quality function. Many different
paths can be constructed by ants walking on the graph,
and these paths encode the target problem. The cost of
the generated paths is used to modify the pheromone left
by ants, and therefore to bias the generation of further
paths towards promising regions of the search space
[17,18]. Among these feasible paths, ACO attempts to
find the one with min imum cost.
3.2. Particle Swarm Optimization (PSO)
PSO is a heuristic algorithm developed in [19-21].The
algorithm is inspired by the social behavior of a bird
flock. It has been found to be successful in a wide variety
of optimization tasks. In PSO, each solution is a bird in
the flock and is referred to as a particle, which denotes a
candidate solution to the optimization problem. The birds
in the population evolve their social behavior and ac-
cordingly their movement towards a destination. In a
PSO system, each particle flies through the multidimen-
sional search space, adjusts its position in search space
according to its own experience and that of neighbor par-
ticles. A particle makes use of the global best position
which the current particle has visited so far, as well as
the population best position which the entire population
has found so far and the process repeats until the swarm
reaches a desired destination. The effect is that particles
fly toward a minimum, while still searching a wide area
around the best solution. The performance of each parti-
cle is measured by using a predefined fitness function,
which encapsulates the characteristics of the optimization
problem. Two parameters inertia weight and constriction
factor are used to control over the previous velocity of
the particles [22]. In short, PSO is characterized as a
simple heuristic of well balanced mechanism with flexi-
bility to enhance and adapt to both global and local ex-
ploration abilities, which gains lots of attention in power
system applications [23].
4. Power System Planning
In this section, a survey of state-of-the-art mathematical
optimization approaches that facilitates power system
planning is provided and the merits and needs of intro-
ducing swarm intelligence methods into power system
planning are discussed.
4.1. Generation Expansion Planning
Generation expansion planning is intended to determine
the locations, cap acities, and expected op erations of gen-

K. MENG ET AL. 3
eration plants required to satisfy various requirements
and constraints imposed by future expectations and fore-
casting conditions, which is to be done in a manner that
maximizes profits and minimizes risks [1],[24]. Mathe-
matically, the consequent typical generation planning
challenge can be expressed as a large-scale, non-linear
optimization problem with the objectives of maximizing
profits and minimizing risks subject to a set of compli-
cated constraints.
To solve the complicated issues of generation expan-
sion planning, different mathematical methods have been
suggested and reported. The initial work started in [25],
where a linear programming method was applied to ne-
cessitate the linear approximation of objective function
and various constraints. Then linear programming model
was further enhanced to address the increasingly com-
plex planning issues with multi-objectives [26]. An ex-
tensive study of the applications of linear programming
methods in power system was given in [27]. Linear pro-
gramming methods are fast and reliable, but the main
drawback is that they are associated with the piecewise
linear cost approximation. Another great alternative for
generation planning is nonlinear programming methods
[28]. However, nonlinear programming methods have a
problem of algorithm convergence and complexity.
Along with the ever expanding large-scale interconnec-
tion of modern power network, both linear programming
and nonlinear programming techniques were not ade-
quate for most applications until dynamic programming
appeared, which overcame some limitations and received
wide acceptance [29]. In general dynamic programming
based methods have the advantage over the other tech-
niques, in that, nonlinearity in project capital costs and
engineering constraints, sophisticated techniques of pro-
duction costing such as probabilistic simulation, and an
adequate modelling of the reliability o f the system dur ing
its future stages, can all be more adequately accounted
for [30,31]. However, the curse of dimensionality prob-
lem of generation expansion planning afflicts the method
of dynamic programming particularly severely [32,33].
In many cases, the mathematical equations involved have
to be simplified or decomposed in order to obtain possi-
ble solutions because of the limited capability of existing
mathematical approaches [34,35].
Recently, the advent of global optimization techniques
provides another tool for solving power system genera-
tion expansion planning problems and satisfactory per-
formance has been reported in a number of references.
Typical modern heuristic methods include evolutionary
programming (EP) [36], simulated annealing (SA) [37],
genetic algorithm (GA) [33,38-42], immune algorithm
(IA) [43], PSO [44,45], and composite method [46]. Al-
though the heuristic methods do not always guarantee
discovering globally optimal so lutions in finite time, they
often provide a fast and reasonable solution. Generally
speaking, each method has its own merits and drawbacks.
Many attempts try to merge some of the individual im-
plementations together into a new algorithm, so that it
can overcome individual disadvantages and benefit from
each others’ advantages. Extensive reviews and com-
parisons of these techniques in power system generation
expansion planning are given in [47-49]. Based on the
experience, when compared with other methods, the PSO
is computationally inexpensive in terms of memory and
speed. The most attractive features of PSO could be
summarized as: simple concept, easy implementation,
fast computation, and robust search ability [50].
4.2. Transmission Expansion Planning
Transmission lines are key components in a power sys-
tem, especially where system stability and reliability
analysis is concerned. In a deregulated electricity market,
transmission network service providers make possible
the required competitive en vironment for the market par-
ticipants. Therefore, as the market grows, transmission
planning should be carried out in a timely and effective
manner. In a competitive mar ket, su ch plannin g is mainly
driven by market needs, with the proviso that certain
constraints, such as reliability, security, economic con-
siderations, and regulatory rules, are satisfied [51,52].
However, restructuring and deregulation of the power
industry have given rise to more and more system uncer-
tainties and have changed the objectives of transmission
planning. As a consequence, the process of transmission
planning requires an evaluation of the annual load shape
and of the cost effectiveness and financial performance
of programs and plants, together with an analysis of
product attributes, profitability niches, delivery prefer-
ences, and investment risks [53]. The intention of such
planning is to minimize revenue requirements, meet cus-
tomer needs, as well as maximize network profits [53,54].
Following these changes, new approaches and action
criteria are demanded. These should not only consider
the traditional constraints, but they should also promote
fair competition in the electricity market as well as en-
suring certain levels of reliability.
Transmission expansion planning is a complex multi-
period, multi-objective optimization problem. In previou s
research, as far as optimization approaches used for
transmission expansion planning, the linear programming
was most frequently applied [55-58]. Since the transmis-
sion expansion planning problem is essentially of a dis-
crete nature, it can be defined via a mixed-integer pro-
gramming formulation. The applications of mixed-inte-
ger programming methods can be found in [59-64]. Dy-
namic programming method [65] can also been applied
to this problem. Similar as generation expansion plan-
ning, the artificial intelligence based methods also pro-

K. MENG ET AL.
4
vide perfect options for transmission expansion planning.
Technical references can be classified according to the
methodolog ies used to solve th e problem, which includes
expert systems [66] and fuzzy theory [67]. Recently, dif-
ferent heuristic methods have been proved to be effective
with promising performance, such as SA [68,69], tabu
search (TS) [70], GA [71-73], differential evolution (DE)
differential evolution (DE) [4,74], and PSO [75,76].
4.3. Planning with Distributed Generation and
Renewable Energy resources
Today, more and more renewable energy resources are
being built up and connected to the power grid s at trans-
mission as well as distribution levels. Large scale wind
farms are connected to transmission networks and are so
far the largest renewable generation source except hydro
power generations. Planning of wind farms requires sig-
nificant amount of studies including the conventional
generation connection studies as well as very expensive
wind farm planning itself. Normally to connect a wind
farm, significant historical wind resource data are re-
quired to evaluate the suitability of the wind farm site.
Once the site is selected, further optimization planning is
required to design the exact scheme of the wind farm so
as to maximize the energy output of wind power. This is
normally a multi-objective, cons trained, highly nonlinear
problem. Computational intelligence such as PSO and
DE can be used to solve the optimization problem in de-
signing the wind farms, [77,78]. At distribution level, the
increasing penetrations of distributed renewable genera-
tion can potentially cause problems with some feeders.
The common problems are protection system design and
reactive power support issues with such heavily DG
connected feeders. Planning of DGs and power quality
control devices such as STATCOM is another complex
optimization problem, and evolutionary computation can
be used in the planning as well, [79,80].
5. Conclusions
Owning to these outstanding characteristics, the swarm
intelligence techniques have been studied extensively
and have been widely applied in many fields. In general,
the swarm intelligence techniques can be used to solve
the nonlinear, discontinuous, constrained, optimization
problem. In this paper, a comprehensive survey of state-
of-the-art mathematical optimization methods that facili-
tates power system planning is provided; the importance
of introducing swarm intelligence methods into power
system planning is discussed.
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