International Journal of Clean Coal and Energy, 2013, 2, 17 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 Email: ke.meng@uon.edu.au, zydong@ieee.org Received 2013 ABSTRACT Power system planning is one of the essential tasks in th e power system operation management, which requires indepth knowledge of the system under consideration. It can be regarded as a nonlinear, discontinuous, constrained multiobjective 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 stateoftheart 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 marketdriven 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 shortterm plan ning, midterm planning, and longterm 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 shortterm 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]. Medterm planning provides the guidance for making market decisions and system opera tions. Longterm 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 verticallyintegrated 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]. Copyright © 2013 SciRes. IJCCE
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 [58]. 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 selflearning, selfadjusting, 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 [1921].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 stateoftheart 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 Copyright © 2013 SciRes. IJCCE
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 largescale, nonlinear 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 multiobjectives [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 largescale 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) [33,3842], 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 [4749]. 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, multiobjective optimization problem. In previou s research, as far as optimization approaches used for transmission expansion planning, the linear programming was most frequently applied [5558]. Since the transmis sion expansion planning problem is essentially of a dis crete nature, it can be defined via a mixedinteger pro gramming formulation. The applications of mixedinte ger programming methods can be found in [5964]. 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 Copyright © 2013 SciRes. IJCCE
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 [7173], 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 multiobjective, 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. 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