A NOVEL APPROACH FOR OPTIMAL PLACEMENT OF …ijric.org/volumes/Vol7/Vol7No5.pdf · · 2011-09-12The siting of distributed generator in distribution ... central generating facilities

International Journal of Reviews in Computing 30th September 2011. Vol. 7

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ISSN: 2076-3328 www.ijric.org E-ISSN: 2076-3336

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A NOVEL APPROACH FOR OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION & FACTS CONTROLLERS IN

POWER SYSTEMS: AN OVERVIEW AND KEY ISSUES

BINDESHWAR SINGH1, K.S. VERMA1, DEEPENDRA SINGH1, AND S.N. SINGH2

1Kamla Nehru Institute of Technology, Sultanpur-228118, U.P., India,

2Indian Institute of Technology, Kanpur, U.P., India

Email: [email protected], [email protected], [email protected], [email protected]

ABSTRACT

This paper presents a state-of-the-art on enhancement of different performance parameters of power systems by optimally placed Distributed Generation (DG) & FACTS controllers in power Systems. Also this paper presents the current status on enhancement of different performance parameters of power systems by optimally placed Distributed Generation & FACTS controllers in power Systems. Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of the enhancement of different performance parameters of power systems by optimally placed Distributed Generation (DG) & FACTS controllers in power Systems. Keywords: Distributed Generation (DG), Flexible AC Transmission Systems (FACTS), FACTS

Controllers, Power Systems. 1. INTRODUCTION DG may play an increasingly important role in the electric power system infrastructure and market. The siting of distributed generator in distribution feeders is likely to have an impact on the operations and control of power system, a system designed to operate with large, central generating facilities. Distributed generator benefits are site specific. DG devices can be strategically placed in power systems for grid reinforcement, reducing power losses and on-peak operating costs, improving voltage profiles and load factors, differing or eliminating for system upgrades, and improving system integrity, reliability, and efficiency [1]-[3]. Generalized reduced gradient method or the second order method is previously used to compute the amount of resources in selected buses to make up a given total to achieve the desired optimizing objectives [1],[4]. The benefit expressed as a performance index can be the minimization of active power losses, VAr losses, or loading in selected lines.

Introduction of generation resources such as DGs on the distribution system can significantly impact

the flow of power and voltage conditions at customers and utility equipment. Voltage regulation for maintaining the voltage conditions within a permissible range is normally achieved using LCT (load-tap Changing Transformer) and LDC (Line Drop Compensator) at substation bus [5],[6]. DG sources have attracted serious attention due to their potential solution for some issues, like the deregulation in power system, increasing the power consumption and the shortage of transmission capacities. The optimal placement of DG is necessary for maximizing the DG potential benefits in power system such as maintaining and/or improving reliability and stability. There are several research studies to determine the optimal DG location by their imposed constraints and objectives. However, the systematic principle for this issue is still an unsolved problem. This paper is reviewed some of the most popular DG placement methods, including 2/3 Rule, Analytical Methods, Optimal Power Flow and Evolutionary Computational Methods (Genetic Algorithm, Ant Colony Search (ACS), Particle Swarm




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Optimization (PSO), Fuzzy Systems (FZ) and Tabu Search (TS)). The related applications and advantages of each technique are expressed briefly. This literature provided helpful information and resources for the future studies in this area. Several techniques have been proposed in determining the optimal location of DG in open literatures. The major objective of DG placement techniques is to minimize the losses of power systems. However, other objectives like improving the voltage profile, reliability, maximizing DG capacity, cost minimization and etc have also been considered in different studies. DG is a kind of electricity production which is on-site or close to the load center and is interconnected to the distribution system. The plenitude of the advantages of DG justifies the planning of electric systems at presence of DG. Some important reasons for the increasingly widespread use of DG could be summarized as follows:

• DG units are closer to customers. Therefore Transmission and Distribution (T&D) costs are reduced;

• The latest technology has made available plants with high efficiency and extended ranging in capacity (ranging in capacity from l0kW to 15MW );

• It is easier to find sites for small generators;

• Natural gas as fuel in DG stations is easily accessible and prices are more stable;

• Usually DG plants require shorter installation times and the investment risk is not too high;

• DG plants yield fairly good efficiencies especially in cogeneration and in combined cycles (larger plants);

• The liberalization of the electricity market contributes to creating opportunities for new utilities in the power generation sector;

• DG offers greater values as it provides a flexible way to choose a wide range of combinations of cost and reliability.

DG impacts different parameters of a power system, comprising voltage profile, line losses, short circuit current, amount of injected harmonic, and system reliability and stability. These parameters have to be appropriately investigated prior to installation of DG units. The problem of

allocating DG units to optimal places and also sizing of them is of higher priority amongst all issues which affect on the mentioned parameters. However, installation of DG units in non-optimal places may results in an increase in system losses and a bad effect on voltage profile and other parameters which may lead to a growth of costs, and consequently an opposite effect on what is expected. Selecting the best places for installation of DG units and their preferable sizes in large distribution systems is a complex multimodal and combinatorial optimization problem. Thus, using an optimization method which is capable of indicating the best solution for a given distribution network, would help system planning engineers. DG is a small generator scatter throughout a power system. Due to locally available resources and the small scale, DG units are mostly installed in demand system and directly connected to distribution system. Distribution generation has many types including wind turbines, photovoltaic cells, photovoltaic cells, fuel cells, battery energy, storage systems, cogeneration system, fuel-cells, biomass, micro turbines, small hydroelectric plant, etc. Embedded generation (EG) can be defined as small-scale generation, which is not directly connected to the transmission system and is not centrally dispatched. Generation is now being connected at distribution level, which has led to the characteristics of the network being changed. If increasing levels of generation are to be accommodated, then there must be a change of thinking regarding the planning and design of the distribution network. There is increasing demand for the development of the distribution network from a passive network to an active one in order to facilitate increasing levels of embedded generation. A power system is a complex network comprising numerous generators, transmission lines, transformers and variety of loads. Because of the economic as well as environmental problems associated with adding new transmission lines to keep pace with the growing loads and generation, the major task facing utility industry of today is the proper and efficient utilization of the existing transmission system. With the growing demand of electrical power, the existing transmission systems are being pushed closed to their stability and thermal limits and the focus on the quality of power delivered is greater




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than ever. This increased loading of transmission lines has lead to various problems associated with stability and maintenance of appropriate voltage levels across the system. A reliable solution of these problems can be achieved with the help of FACTS (Flexible Alternating Current Transmission System) controllers. However, these controllers are effective only when they are placed at carefully determined locations. The literatures [7]-[152] are reviews for a novel approach for Optimal Placement of DG in Power Systems. The literatures [153]-[163] are reviews for a novel approach for Optimal Placement of FACTS controllers in Power Systems. The literature [164] is reviews for a novel approach for Optimal Placement of DG and FACTS controllers in Power Systems. The proposed works aim to investigate in depth the optimal location, and sizing of DG and FACTS controllers in power System environments. 2. A BRIEF REVIEW ON OPTIMALLY

PLACED DG IN POWER SYSTEMS The following open literatures are reviews for optimal placement of DG in power systems for different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. DG sometimes provides the most economical solution to load variations. Under voltages or overloads that are created by load growth may only exist on the circuit for a small number of hours per day or/ month or/ year. There may be many locations that do not have overload or voltage problems, where the DG can be located and provide the necessary control [7]. An optimization technique should be employed for the design of engineering systems, allowing for the best allocation of limited financial resources. In electric power systems, most of the electrical energy losses occur in the distribution systems. It is a tool that can be used both for the design of a new distribution system and for the resizing of an existing system [8]. Analytical approaches for optimal placement of DG with unity power factor is to minimize the power loss of the system. A “2/3 rule” is used to place DG on a radial feeder with uniformly distributed load, where it is suggested to install DG of approximately 2/3 capacity of incoming generation at approximately 2/3 of the

length of line. In above approaches size of DG is not optimized. Line loading constraint is not considered during optimization [9]. Reference [10], has been addressed a novel technique for placement of distributed generation (DG) in electric power systems. A GA based approach for sizing and placement of DG keeping in view of system power loss minimization in different loading conditions is explained. Minimal system power loss is obtained under voltage and line loading constraints. Proposed strategy is applied to power distribution systems and its effectiveness is verified through simulation results on 16, 37-bus and 75-bus test systems is presented in this literature. In [11], has been suggested a multi-objective performance index-based size and location determination of distributed generation in distribution systems with different load models. Normally, a constant power (real and reactive) load model is assumed in most of the studies made in the literature. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The simulation technique based on genetic algorithms is studied in this literature. The studies have been carried out on 16-bus and 37-bus distribution systems are presented in this literature. In [12], has been presented a new optimization algorithm based on integrating the use of genetic algorithms and tabu search methods to optimal allocation of dispersed generation resources in distribution networks. The proposed algorithm finds how much distribution losses can be reduced if dispersed generations are optimally allocated at the demand side of power system. A significant improvement of the proposed algorithm results, over that obtained by genetic algorithm, has been achieved in this literature. In [13], a method for placement of distributed generation (DG) units in distribution networks has been presented. This method is based on the analysis of power flow continuation and determination of most sensitive buses to voltage collapse. This proposed method in this literature is executed on a typical 34-bus test system and yields efficiency in improvement of voltage profile and reduction of power losses; it also may permit an increase in power transfer capacity, maximum loading, and voltage stability margin is presented in this literature. The effect of load models on DG planning in distribution system is investigated in this literature. It is shown that load models can significantly affect the DG planning. Normally a constant power (real and reactive) load model is assumed in most of the studies is presented in open literatures. Such assumptions may lead to




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inconsistent and misleading results about deferral values, loss reduction, payback period, and other subsequent calculations. It has been demonstrated that DG planning based on such assumptions would not be effective after implementation. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. A comparative study of real and reactive power loss, real and reactive power intake at the main substation and MVA support provided by installing DG resources for different type of loads models has been performed in [14]. DGs sometimes provide the lowest cost solution to handling low-voltage or overload problems. In conjunction with handling such problems, a DG can be placed for optimum efficiency or optimum reliability. Such optimum placements of DGs are investigated in [15]. The concept of segments, which has been applied in previous reliability studies, is used in the DG placement. The optimum locations are sought for time-varying load patterns. It is shown that the circuit reliability is a function of the loading level. The difference of DG placement between optimum efficiency and optimum reliability varies under different load conditions. Observations and recommendations concerning DG placement for optimum reliability and efficiency are provided in [15]. Economic considerations are also addressed [15]. In [16], has been presented the two new methodologies for optimal placement of DG in an optimal power flow (OPF) based wholesale electricity market. DG is assumed to participate in real time wholesale electricity market. The problem of optimal placement, including size, is formulated for two different objectives, namely, social welfare maximization and profit maximization. The candidate locations for DG placement are identified on the basis of locational marginal price (LMP). Obtained as lagrangian multiplier associated with active power flow equation for each node, LMP gives the short run marginal cost (SRMC) of electricity. Consumer payment, evaluated as a product of LMP and load at each load bus, is proposed as another ranking to identify candidate nodes for DG placement. The proposed rankings bridges engineering aspects of system operation and economic aspects of market operation and act as good indicators for the placement of DG, especially in a market environment. In order to provide a scenario of variety of DGs available in the market, several cost characteristics are assumed. For each DG cost characteristic, an optimal placement and size is identified for each of the objectives in [16]. In

[17], proposed an analytical expression to calculate the optimal size and an effective methodology to identify the corresponding optimum location for DG placement for minimizing the total power losses in primary distribution systems. The analytical expression and the methodology are based on the exact loss formula. The effect of size and location of DG with respect to loss in the network is also examined in detail in [17]. In [18], distributed generation (DG) reduces losses and eliminates some of the transmission and distribution costs. It may also reduce fossil fuel emissions, defer capital costs, and improve the distribution feeder voltage conditions. The calculation of the effects of small residential photovoltaic and wind DG systems on various feeder operating variables is complicated by both the probabilistic nature of their output and the variety of their possible spatial allocations. A method based on a combination of clustering techniques and a convex hull algorithm is proposed that may reduce the computational burden by an order of magnitude, while still allowing accurate estimation of DG-enhanced feeder operation. In [19], the allocation of the system losses to suppliers and consumers is a challenging issue for the restructured electricity business. Meaningful loss allocation techniques have to be adopted to set up appropriate economic penalties or rewards. The allocation factors should depend on size, location, and time evolution of the resources connected to the system. In the presence of distributed generation, the variety of the power flows in distribution systems calls for adopting mechanisms able to discriminate among the contributions that increase or reduce the total losses. Some loss allocation techniques already developed in the literature have shown consistent behavior. However, their application requires computing a set of additional quantities with respect to those provided by the distribution system power flow solved with the backward/forward sweep approach. This paper presents a new circuit-based loss allocation technique, based on the decomposition of the branch currents, specifically developed for radial distribution systems with distributed generation. The proposed technique is simple and effective and is only based on the information provided by the network data and by the power flow solution. With the rapid increase in DG, the issue of voltage regulation in the distribution network becomes more significant and centralized voltage control (or active network management) is one of the proposed




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methods. Alternative work on intelligent distributed voltage and reactive power control of DG has also demonstrated benefits in terms of the minimization of voltage variation and violations as well as the ability to connect larger generators to the distribution network. This lierature uses optimal power flow to compare the two methods and shows that intelligent distributed voltage and reactive power control of the DG gives similar results to those obtained by centralized management in terms of the potential for connecting increased capacities within existing networks [20]. In [21], high penetration of DG resources is increasingly observed worldwide. The evolution of this process in each country highly depends on the cost of traditional technologies, market design, and promotion programs and subsidies. Nevertheless, as this trend accelerates, higher levels of penetration will be achieved and, in turn, a competitive market integration of DG will be needed for an adequate development of the power sector. In [22], recent years, DG, as clean natural energy generation and cogeneration system of high thermal efficiency has increased due to the problems of global warming and exhaustion of fossil fuels. Many of the distributed generations are set up in the vicinity of the customer, with the advantage that this decreases transmission losses. However, output power generated from natural energy, such as wind power, photovoltaics, etc., which is DG, is influenced by meteorological conditions. Therefore, when the distributed generation increases by conventional control techniques, it is expected that the voltage change of each node becomes a problem. Proposed in this paper is the optimal control of distribution voltage with coordination of distributed installations, such as the load ratio control transformer, step voltage regulator (SVR), shunt capacitor, shunt reactor, and static var compensator. In this research, SVR is assumed to be a model with tap changing where the signal is received from a central control unit. Moreover, the communication infrastructure in the supply of a distribution system is assumed to be widespread. The genetic algorithm is used to determine the operation of this control. In order to confirm the validity of the proposed method, simulations are carried out for a distribution network model with distributed generation (photovoltaic generation). In [23], addressed the problem of voltage rise mitigation in distribution networks with distributed generation. A distributed automatic control approach is proposed to alleviate the voltage rise caused by active power injection. The objective of the proposed approach is not to

control bus voltage but to guarantee that generator injections alone do not cause significant voltage rise: a solution in which distribution network operators (DNOs) are kept to their traditional task of voltage regulation for load demand. The examine some of the most important economic benefits brought about by distributed generation technologies to the distribution utility and the power system. Models are developed that allow the quantification of those benefits in economic terms. In some cases, industry regulators or utilities charge connection fees to the owners of distributed generators, even if they are saving the local utility considerable amounts of money every year in deferred network upgrades, reduced losses, avoided wholesale market purchases and others. Efficient economic systems dictate that a proper share of the indirect benefits created by a given economic activity leads to overall optimal independent decision-making by its participants. Quantifying and allocating the benefits of distributed generation to the owners improves the economic performance of their investments and encourages the implementation of those DG applications most valuable to the system [24]. In [25], a method for placement of DG units in distribution networks has been presented. This method is based on the analysis of power flow continuation and determination of most sensitive buses to voltage collapse. This method is executed on a typical 34-bus test system and yields efficiency in improvement of voltage profile and reduction of power losses; it also may permit an increase in power transfer capacity, maximum loading, and voltage stability margin. Increase in power consumption can cause serious stability problems in electric power systems if there are no ongoing or impending construction projects of new power plants or transmission lines. Additionally, such increase can result in large power losses of the system. In costly and environmentally effective manner to avoid constructing the new infrastructures such as power plants, transmission lines, etc., the DG has been paid great attention so far as a potential solution for these problems. The beneficial effects of DG mainly depend on its location and size. Therefore, selection of optimal location and size of the DG is a necessary process to maintain the stability and reliability of existing system effectively before it is connected to a power grid. However, the systematic and cardinal rule for this issue is still an open question. In this paper, a method to determine




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the optimal locations of multiple DGs is proposed by considering power loss. Also, their optimal sizes are determined by using the Kalman filter algorithm [26]. Despite its promises, installing the DG to an electric power grid is not a simple plug-and-play problem. Indeed, as well as operation of the DG itself, it requires a careful consideration for the interaction with existing power network with respect to stability, reliability, protection coordination, power loss, power quality issues, etc. [27]–[28]. First of all, it is important to determine the optimal location and size of a given DG before it is connected to a power system. Moreover, if multiple DGs are installed, an optimal approach for selection of their placement and sizing is imperative in order to maintain the stability and reliability of an existing power system effectively. This study presents an ordinal optimisation (OO) method for specifying the locations and capacities of DG such that a trade-off between loss minimisation and DG capacity maximisation is achieved. The OO approach consists of three main phases. First, the large search space of potential combinations of DG locations is represented by sampling a relatively small number of alternatives. Second, the objective function value for each of the sampled alternatives is evaluated using a crude but computationally efficient linear programming model. Third, the top-s alternatives from the crude model evaluation are simulated via an exact non-linear programming optimal power flow (OPF) programme to find the best DG locations and capacities. OO theory allows computing the size s of the selected subset such that it contains at least k designs from among the true top-g samples with a pre-specified alignment probability AP. This study discusses problem-specific approaches for sampling, crude model implementation and subset size selection. The approach is validated by comparing with recently published results of a hybrid genetic algorithm OPF applied to a 69-node distribution network operating under Ofgem (UK) financial incentives for distribution network operators [29]. DG technologies are among the main vehicles for the reduction of carbon emissions in accordance with the Kyoto agreement on climate change. Other benefits of DG include the reduction of losses and the deferment of investment for network upgrades. The different technologies that are

classified as DG in the UK are categorised into renewable and fossil fuel-based sources. The renewable sources comprise wind turbines, biomass, photovoltaics, geothermal, small hydro, and tidal and wave. Fossil fuel-based sources consist of fuel cells, internal combustion engines and combustion turbines. The installation of DG in distributed networks has to be coordinated such that early connections do not effectively sterilise parts of the network by constraining the development of other, potentially larger, plant connections [30]. This necessitates providing the distribution network operator with tools for determining the DG locations and capacities that make best use of the existing network infrastructure. The information provided by such tools could be transferred to potential developers in the form of spare connection sites and sizes. The problem of DG planning has recently received much attention by power system researchers. Different formulations have been solved using calculus-based methods, search-based methods and combinations of the previous techniques. The calculus-based methods include linear programming (LP)[31], second-order algorithms[32] and OPF-based approaches[33]. These optimisation methods treat the DG capacities as continuous variables while their locations remain fixed. However, the problem of placing DG in practical networks belongs to the complexity category of non-deterministic polynomial (NP) complete, that is, it is almost certain that solving for its global optimum cannot be done efficiently on a computer. Solutions can be obtained by analytical approaches only under simplifying assumptions, such as placing a single DG [34]. Search-based methods have been proposed to seek the optimal (or near-optimal) DG locations and capacities from candidate sites and sizes. The search-based methods comprise various artificial intelligence techniques such as genetic algorithms (GA) [35, 36], combined fuzzy-GA [37], multi-objective evolutionary approaches [38] and tabu search [39]. Combinations of search-based and calculus-based techniques that handle discrete and continuous variables have been also proposed to improve the modeling of DG sites and sizes. An example of such a hybrid technique is the combination of GA and OPF [40-42]. Another approach is the heuristic method of [43] for DG investment planning. It is also possible to handle both discrete and continuous variables via Benders decomposition. Such an approach has been proposed in [44] within the context of placing




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static var compensators for maximising the loading margin. The additional power demand caused by cold load pickup (CLPU) condition restricts the simultaneous restoration of all the loads of a network due to excessive loading on the network elements and violation of limits. Therefore, step-by-step restoration is the most adopted approach. However, it requires long time for the complete restoration. The main cause of CLPU problem is the loss of diversity amongst the loads which causes the enduring phase of CLPU condition. The conservation of load diversity reduces the demand during restoration. In the proposed approach, the diversity is conserved by using distributed generation (DG) for quick restoration and the reduction of additional power demand caused by CLPU condition. The capacity required for DG is evaluated on the basis of the required additional power demand and the load-diversity preserved. The proposed approach utilizes the genetic algorithm for determination of the optimal size and location of the required DG. The approach is demonstrated on a 33-bus, 12.66-kV primary distribution network [45]. DG [46] is increasing in penetration on power systems across the world. In rural areas, voltage rise limits the permissible penetration levels of DG. Another increasingly important issue is the impact on transmission system voltages of DG reactive power demand. Here, a passive solution is proposed to reduce the impact on the transmission system voltages and overcome the distribution voltage rise barrier such that more DG can connect. The fixed power factors of the generators and the tap setting of the transmission transformer are determined by a linear programming formulation. Despite the technical challenges in properly accommodating DG, one of the major and well recognized benefits is the ability of DG to defer future demand-related network investment. It is, however, often poorly represented in existing planning approaches and analysis ignores the potential security of supply benefits. Here, a novel, more integrated, approach is presented wherein reinforcements required by system security standards (e.g., N-1) are also taken into account. The DG contributions to system security provided by U.K. Engineering Recommendation P2/6 are adopted, enabling the methodology to quantify the deferment produced by DG considering both demand growth- and system security-related investment [47]. Mendez et al. [48] demonstrated

the impact of different DG penetration and concentration levels and technology mixes on allowable load growth without the need for reinforcements. While the results clearly show the impact DG has on postponing investment, this particular study cannot be used for quantifying the relative benefit that a generation unit may bring about according to its location. Gil and Joos [49] developed an approach based on the amount that network radial feeder currents are reduced by a DG unit. Their definition of reinforcement deferment was based on the time required for feeder currents to reach the pre-DG level. This calculation of the deferment, however, is not appropriate since the economic benefits of DG can only be quantified accurately when deferment is measured relative to the time when the reinforcement costs would be incurred [50]. In [51], has been proposed a bilevel programming approach to determine the optimal contract price of dispatchable DG units in distribution systems. Two different agents are considered in the model, namely, the distribution company (DisCo) and the owner of the DG. The former seeks the minimization of the payments incurred in attending the forecasted demand, while the latter seeks the maximization of his profit. To meet the expected demand, the DisCo has the option to purchase energy from any DG unit within its network and directly from the wholesale electricity market. A traditional distribution utility model with no competition among DG units is considered. DG can be broadly defined as the production of energy by typically small-size generators located near the consumers [52]. The technical and economic implications of DG have been widely studied in the literature. Recent research includes stability [53], voltage control [54], power quality issues [55], loss reduction [56], and the potential of DG to provide ancillary services [57]. Also, as the potential benefits of DG largely depend on its location and size, many of the studies regarding DG address the problem of its optimal placement and sizing [58]–[59]. Given its small capacity, DG units are mainly installed in distribution networks. These units are typically no larger than 1 or 2MWand might be installed by the utility itself or by end users [60]. While it is well known that in most cases DG cannot compete with centrally dispatched generation, it is also true that it can provide benefits to the distribution utility such as reduction of power losses, deferral of investments, and improvement of voltage profile [61]. Bearing this in mind, the utility might be willing to buy energy from a DG unit that is strategically located in the distribution network.




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To supply the demand of its network, a DisCo purchases energy from the point of interconnection with the transmission system (substation). Most of this energy is bought through longterm bilateral contracts at a price based on the wholesale electricity market price. Depending on specific market rules, the market clearing price may be different for every hour and node, being in general higher during peak hours. In this literature, consider the DisCo to be the sole owner and operator of a distribution system. Furthermore, the DisCo can purchase energy form the wholesale electricity market and form DG units owned by independent producers. Only dispatchable DG units are considered in the model. The DisCo receives a contract price offer and a declared capacity of the DG units located in its network, and must decide the amount of energy to be bought in the wholesale electricity market and from the DG units. The DisCo must weigh the DG energy contract price offer with the potential benefits obtained from the dispatch of these units. For instance, if the power injected by a DG unit contributes to the enforcement of a voltage constraint and/or has a positive impact reducing power losses, then, even if the DG energy contract price offer is slightly higher than the wholesale market price, the DG unit is likely to be dispatched. Conversely, if the DG unit has a negative impact in the distribution network, it might not be dispatched, even if its contract price is lower than the wholesale market price. On the other hand, the owner of the DG must find the optimal contract price that would maximize his profit. If he bids a high price, he might not be dispatched by the DisCo; conversely, if he bids a low price, he might not perceive maximum profits. In order to obtain an optimal contract price offer that would render maximum profits, the DG owner must consider the reasoning performed of the DisCo when deciding over the dispatch of the DG units. This two-agent relationship is modeled in this paper as a bilevel programming problem (BLPP) [62]. A BLPP is composed by an outer and an inner optimization problem. The outer problem corresponds to the optimization performed by the owner of the DG who, as stated above, procures the optimal contract price that would result in the maximization of his profits. The inner optimization problem corresponds to the DisCo which procures the minimization of the payments incurred in attending the forecasted demand.

The problem of minimizing losses in distribution networks has traditionally been investigated using a single, deterministic demand level. This has proved to be effective since most approaches are generally able to also result in minimum overall energy losses. However, the increasing penetration of (firm and variable) DG raises concerns on the actual benefits of loss minimization studies that are limited to a single demand/ generation scenario. Here, a multiperiod AC OPF is used to determine the optimal accommodation of (renewable) DG in a way that minimizes the system energy losses. In addition, control schemes expected to be part of the future Smart Grid, such as coordinated voltage control and dispatchable DG power factor, are embedded in the OPF formulation to explore the extra loss reduction benefits that can be harnessed with such technologies. The trade-off between energy losses and more generation capacity is also investigated. The methodology is applied to ageneric U.K. distribution network and results demonstrate the significant impact that considering time-varying characteristics has on the energy loss minimization problem and highlight the gains that the flexibility provided by innovative control strategies can have on both loss minimization and generation capacity [63]. In [64] operation of distribution system, DGs are installed as an alternative of extension and establishment of substations and transmission and distributed lines according to increasing power demand.In operation planning of DGs, determining optimal capacity and allocation gets economical profit and improves power reliability. This paper proprses determining a optimal number, size and allocation of DGs needed to minimize operation cost of distribution system. Capacity of DGs for economical operation of distribution system estimated by the load growth and line capacity during operation planning duration, DG allocatipns are determined to minimize total cost with power buying cost, operation cost of DG, loss cost and outage cost using GA(Genetic Algorithm). In [65], presented a voltage analysis method of distribution systems interconnected with DG. Nowadays, small scale DG becomes to be introduced into power distribution systems. But in that case, it is difficult to properly maintain the terminal voltage of low voltage customers by using only ULTC (Under Load Tap Changer). This paper presents a voltage analysis method of distribution systems with DG for proper voltage regulation of power distribution systems with ULTC. In order to develop the voltage analysis method, distribution




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system modeling method and advanced load flow method are proposed method has been applied to a 22.9 kV practical power distribution systems. In [66], Introduced DG in a distribution network has considerable advantages such as reducing power loss and cost, environmental friendliness, voltage improvement, postponing system upgrades and enhancing system reliability and continuity of service. Practical application of the DG, however, proves difficult. Social, economic and political factors affect the final optimal solution. Solution techniques for DG deployment rely on optimisation methods. The technique proposed here finds the optimal location and size of the DG to minimise the total system power loss for radial distribution feeder systems by solving two independent sub-problems: (i) location and (ii) size. A sufficient sensitivity test for the first problem is suggested. Determining the optimal DG size is done using a new heuristic curve-fitted technique that reduces the search-space by selecting fewer DG-tests. Four DG sizes, which are carefully selected based on the system’s total load demand percentages, are used to determine the optimal solution. There has been an increased interest in installing DG at the distribution systems due to considerable advantages such as power loss reduction, cost reduction, environmental friendliness, voltage improvement, postponement of system upgrades and increasing reliability. To achieve such advantages, DG has to be operated and placed at the optimal size and location, respectively. Many optimisation tools have been utilised to solve different DG problems. Tools such as deterministic [67], heuristic [68] and hybrid [69] methods are promising and still evolving in this field. In addition, simplifying assumptions have been used in [67] to enhance the solution performance. Besides power loss reduction, locating a DG may be based on cost reduction [68]. The sequential quadratic programming method was proposed in [70] to obtain the optimal size and location of the DG. The objective function was to reach a predetermined power loss reduction level (up to 25%) with a minimum net DG cost. The method constrained the DG size to 40% of peak loads. Maximising the voltage support by DG was discussed in [71] using a voltage sensitivity index to determine the optimal location of the DG using Thevenin’s theorem. Subsequently, the values of the DG’s active and reactive powers were adjusted to satisfy the objective function and constraints. Minimising the power loss by finding the optimal

size, location and operation point of the DG was suggested in[67]. A sensitivity analysis relating the power loss with respect to DG current injection was used to identify the DG size and operation point. Loads in one of the tested systems were assumed to be uniformly distributed, which is rare in practical feeder systems. The location of the DG was based on the assumption of downstream load buses, which may not be appropriate for different feeder configurations. In [72], the authors proposed an analytical method to calculate the optimal size and location of a DG. The power flow calculation was employed twice, and the employed DG restricted to supply real power only. The sensitivity factor deployed to determine the optimum location and size of DG in order to minimise the total power loss was proposed in [73]. In [74], the authors employed the genetic algorithm (GA) and OPF to minimise the cost of DG active and reactive powers. Significant reduction in the search-space was achieved by eliminating the DG size. However, dispatchable DGs can cause operational problems on distribution feeders if it is not utility owned. An algorithm was offered in[75] to maximise the reduction of load supply costs as well as the operational schedules for all feeder load levels exploiting the evolutionary programming method. The optimal solution was selected based on maximising the cost reduction, which was attained through evaluating the cost of DG supply scenarios based on the base case. The authors in [68] proposed a methodology to determine the optimal location and size of a DG in distribution systems to maximise the benefit/cost ratio. The objective function was to minimise the power loss subject to acceptable reliability and voltage levels. The method utilised a combination of GA techniques with other methods to evaluate the DG impacts on system loss, reliability and voltage profile. The proposed optimal sitting and sizing of the DG was based on a preselected list of candidate places, and a respective list of candidate units that can be installed at each location. In [69], a hybrid method was proposed to determine the optimal (predefined) number of deployed DG in the distribution networks. The suggested GA–OPF technique solved the static optimisation problem (single-time period analysis) utilising the economic dispatch formulation. The method was reported to enable distribution network operators to strategically connect a predefined number of DGs among a large number of potential combinations. In [76], the author offered some general rules related to the impact of a DG on power flow,




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power loss, voltage regulation and fault currents for uniformly distributed loads only. Although the impacts of single DG on different aspects of operation are reviewed, uniformly distributed loads are not common in distribution feeder systems. Therefore some of these aspects will be violated in other applications. Analysing the system’s state after a contingency due to different DG locations and sizes was proposed in [77]. The objective function was to find the optimal location and size of a DG in order to minimise the voltage deviations from a predetermined profile. Three DG sizes were considered, that is, one-third, half, and two-thirds of total load capacity. The authors stated that the best location of the DG may be size and topology dependent. Improper placement of distributed generation (DG)[78] units in power systems would not only lead to an increased power loss, but could also jeopardise the system operation. To avert these scenarios and tackle this optimisation problem, this study proposes an effective method to guide electric utility distribution companies (DISCOs) in determining the optimal size and best locations of DG sources on their power systems. The approach, taking into account the system constraints, maximises the system loading margin as well as the profit of the DISCO over the planning period. These objective functions are fuzzified into a single multi-objective function, and subsequently solved using genetic algorithm (GA). In the GA, a fuzzy controller is used to dynamically adjust the crossover and mutation rates to maintain the proper population diversity (PD) during GA’s operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The results obtained on IEEE 6-bus and 30-bus test systems with the proposed method are evaluated with the simulation results of the classical grid search algorithm, which confirm its robustness and accuracy. This study also demonstrates DG’s economic viability relative to upgrading substation and feeder facilities, when the incremental cost of serving additional load is considered. A number of approaches have been proposed by different researchers in determining the optimal size and location of DG in power networks. For instance, a ‘2/3 rule’ method often used in siting shunt capacitors in distribution systems, was adopted by Willis[79] to place a DG unit in a radial distribution feeder. He proposed that 2/3 capacity of the incoming generation be placed at approximately 2/3 of the length of the feeder. However, apart from the fact that this method cannot be applied to a meshed network, it also cannot guarantee optimal size and best

location, since DG size is better determined relative to the system power demand rather than system generation, as adopted in the approach. Also, a DG whose size is equal to the capacity of the total network load is proposed by Gozel et al.[80]. Our concern is that this size seems to be too large as it could result in voltage and line loading MVA limits violations on the system. Acharya et al.[81] used the exact loss formula that involves the calculation of the bus admittance from its impedance to determine the optimal size and location of a single DG unit. However, the large size and complexity of distribution networks would render this approach to fall short of robustness requirements. In the same vein, Elnashar et al.[82] categorised the impacts of DG on the distribution systems as positive, such as voltage profile improvement and negative, such as increased system loss and short-circuit level, and used these to formulate a multi-objective function. Nevertheless, this problem formulation is flawed, because DG would lead to increase in loss only when it is sited in improper locations, and so its impact cannot be considered negative in all cases. Hedayati[83] used continuation power flow to determine the most sensitive buses to voltage collapse and proposed DG installation on three of the most sensitive buses. In[84] the Tabu search approach was employed, and in[85]-[88] the simple genetic algorithm (SGA) approach was adopted, while Haghifam et al.[89] used the concept of Pareto optimality based on the non-dominant sorting genetic algorithm (NSGA-II) for the optimisation problem. In [90], proposed analytical expressions for finding optimal size and power factor of four types of distributed generation (DG) units. DG units are sized to achieve the highest loss reduction in distribution networks. The proposed analytical expressions are based on an improvement to the method that was limited to DG type, which is capable of delivering real power only. Three other types, e.g., DG capable of delivering both real and reactive power, DG capable of delivering real power and absorbing reactive power, and DG capable of delivering reactive power only, can also be identified with their optimal size and location using the proposed method. The method has been tested in three test distribution systems with varying size and complexity and validated using exhaustive method. Results show that the proposed method requires less computation, but can lead optimal solution as verified by the exhaustive load flow method.




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There have been many studies on the reconfiguration of distribution systems for loss reduction. A switch exchange algorithm was proposed in [91]. In [92], an approximate power-flow technique was developed for analyzing loss reduction from network reconfiguration. In [93], Fan et al. formulated the reconfiguration problem as a linear programming problem and applied a single-loop optimization method to solve network reconfiguration. Other techniques such as the genetic algorithm (GA) [94], simulated annealing (SA) [95], improved Tabu Search (TS) [96], and ant colony search (ACS) algorithm [97] have been used for the purpose of network reconfiguration for reducing losses. For optimal capacitor placement for loss reduction, a well known “Golden Rule,” or “2/3 rule” is presented in [98]. This method would yield good solutions in system where the loads are uniformly distributed. Many researchers have applied other techniques such as dynamic programming [99], fuzzy expert system [100], Tabu Search [101], and Genetic Algorithm [102] for finding best locations for capacitors to reduce losses. In distribution systems, DG can deliver a portion of real and/or reactive power so that the feeder current is reduced and voltage profile can be improved with reduction in losses. However, studies indicate that poor selection of location and size would lead to higher losses than the losses without DGs [103], [104]. A technique for DG placement using “2/3 rule” has been presented in [104]. Although the 2/3 rule is simple and easy to apply, this technique may not be effective in distribution with not uniformly distributed loads. Besides, if a DG is capable of delivering real and reactive power, applying the method that was developed for capacitor placement may not work. In [105], an analytical approach has been presented to identify appropriate location to place single DG in radial as well as loop systems to minimize losses. But, in this approach, optimal sizing is not considered. GA was applied to determine the size and location of DG in [106] and [107]. Though GA is suitable for multi objective problems and can lead to a near optimal solution, they demand computational time. Recently, an analytical approach based on exact loss formula was presented to find the optimal size and location of DG [103]. In this method, the load flow is required to be conducted only twice. The first load-flow calculation is needed to calculate the loss of base case. The second load-flow solution is required to find the minimum total loss after DG

placement. The technique requires less computation. However, the analytical approach can be applied to DG capable of delivering only real power. In [108], the operation of distribution systems, DGs (Distributed Generations) are installed as an alternative to extension and the establishment of substations, transmission and distribution lines according to the increasing power demand. In the operation planning of DGs, determining optimal capacity and allocation achieves economical profitability and improves the reliability of power distribution systems. This paper proposes a determining method for the optimal number, size and allocation of DGs in order to minimize the operation costs of distribution systems. Capacity and allocation of DGs for economical operation planning duration are determined to minimize total cost composed with power buying cost, operation cost of DGs, loss cost and outage cost using the GA (Genetic Algorithm). Reactive power planning (RPP) [109] is one of the most important task in operation and control of power systems. This paper presents a methodology, based on genetic algorithms (GA), for optimal placement of DG units in power networks to achieve both effects: to guarantee the voltage profile and to maximize the voltage loadability under normal and/or contingency conditions. The methodology aims in finding the configuration, among a set of system components, which meets the desired system reliability requirements taking into account stability limits. Results shown in the paper indicate that the proposed formulations can be used to determine which the best buses are where the addition of small distributed generator units can greatly enhance the voltage stability of the whole network and power transfer capability under contingencies. The following Evolutionary Computational Methods cover a wide range of Artificial Intelligence (AI) techniques such as Genetic Algorithm [110], Fuzzy Systems [111], Tabu Search [112] and etc which have been applied in most optimization problems as well as DG optimal placement. The applications and goals of these techniques vary owing to their great potentials to optimize technical and economical DG challenges. The Genetic Algorithm (GA) [113] is an optimization procedure or stochastic search based on the application of natural selection and genetics. It is a powerful search algorithm which has been solved many non-linear and large-scale problems of power systems. The GA is initialized with a population of individuals (solution-optimal




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location) and a binary representation of the decision variables to perform the search by using genetic operators i.e. selection, crossover and mutation. The quality of an individual is assessed by its fitness, which is based on fitness function. In case of DG placement, this fitness is evaluated based on minimizing real power losses, to reduce investments and operational costs, and providing optimal size. The [114] optimal placement of Distributed Generation(DG) has attracted many researchers’ attention recently due to its ability to obviate defects caused by improper installation of DG units, such as rise in system losses, decline in power quality, voltage increase at the end of feeders and etc. This paper presents a new advanced method for optimal allocation of DG in distribution systems. In this study, the optimum location of DG units is specified by introducing the power losses and voltage profile as variables into the objective function. Particle Swarm Optimization (PSO) and Clonal Selection Algorithm (CLONALG) are two methods which have been applied to optimize different objective functions in previous studies. In this paper, the Combination of Particle Swarm Optimization and Clonal Selection Algorithm (PCLONALG) is utilized as a solving tool to acquire superior solutions. Considering the fitness values sensitivity in PCLONALG process, it is necessary to apply load flow for decision making. Finally, the feasibility of the proposed technique is demonstrated for a typical distribution network and is compared with the PSO and CLONALG methods. The experimental results illustrate that the PCLONALG method has a higher ability in comparison with PSO and CLONALG, in terms of quality of solutions and number of iterations. The approach method has the preferences of both previous methods. Via immunity operation, the diversity of the antibodies is maintained and; the speed of convergence is ameliorated by operating particle swarm intelligence. Many conventional optimization techniques such as gradient method, linear programming, dynamic programming, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Clonal Selection Algorithm (CLONALG) have been employed to attain the aforementioned goals. Nevertheless, due to complexity of the problems, these methods may fail to find the global optimal solution and also may converge in an Un-reasonable time [115], [116]. The optimal placement and sizing of DG units in distribution networks have been vastly studied in order to

achieve different targets. The intent may be the minimization of active losses of the feeders [117], [118], the minimization of total network supply costs, which includes generators operation and losses compensation [119], the best utilization of available generation capacity [120], THD reduction [121], and improving voltage profile [122]. In order to achieve the aforementioned benefits, the DG size has to be optimized. The optimal DG size is dealt with as a nonlinear programming (NLP) problem with both nonlinear objective function and constraints. Such DG NLP problem was solved via heuristic and deterministic techniques. The heuristic methods utilized in the attempt of solving the DG optimal size were Genetic Algorithm (GA) [123]-[124], hybrid GA – Fuzzy [125]-[126] and Particle Swarm Optimization [127]. Authors of [128] have presented a technique to evaluate the impact of DG size and placement on losses, reliability and voltage profile of distribution networks. Davidson et al. [129] have presented an optimization model for loss minimization in a distribution network with DG. An algorithm has been proposed in [130] to determine the near optimal placement of distributed generation with respect to system losses. Mutale et al. [131] has presented a methodology to evaluate the impact of DG on power loss minimization by examining loss allocation coefficients. Optimal placement [132] of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. Especially, ant colony search-based algorithms are a kind of outstanding discrete optimizers [133]–[135]. In this study, an ant colony system (ACS) algorithm is proposed to optimize the recloser (or DG) placement for a fixed DG (or recloser) allocation. The idea can be extended to the simultaneous placement of both reclosers and DGs. In [136] proposed an adaptive weight particle swarm optimization (APSO) for solving optimal distributed generation (DG) placement. APSO has ability to control velocity of particles. The objective is to minimize the real power loss within acceptable voltage limits. Four types of DG are considered including DG supplying real power only, DG supplying reactive power only, DG supplying real power and consume reactive power, DG supplying real power and reactive power,




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representing photovoltaic, synchronous condenser, wind turbines, and hydro power, respectively. The test systems include 33-bus and 69-bus radial distribution systems. With a given number of DGs in each type, APSO could find the optimal sizes and locations of multi-DG which result in less total power system loss than basic particle swarm optimization (BPSO) and repetitive load flow. Moreover, if the number of DG increases from one to three, the total power loss will decrease for all types. In [137], presented a methodology for optimal placement of DG units in power networks to guarantee the voltage profile, maximize loadability conditions in normal and in contingencies situations. The methodology aims in finding the configuration, among a set of system components, which meets the desired system reliability requirements taking into account stability limits. Results shown in the paper indicate that the proposed formulations can be used to determine which the best buses are where the addition of small distributed generator units can greatly enhance the voltage stability of the whole network and power transfer capability under contingencies. In [138], Sensitivity indices based on voltage stability improvement with respect to change in injected active and reactive power at a load bus were used to identify DG suitable location. DG [139] is a small source of electric power generation (typically range from less than a kW to tens of MW) that is not a part of central power system and is located close to the load. The loads of the system are uncontrolled and depend on voltage and frequency of the system. Therefore, analysis assuming constant power loads will give misleading and inaccurate results. This paper addresses the voltage sensitiveness of the loads by incorporating voltage dependent load models in the analysis. Three types of DGs are considered for implementation and DGs are modelled as PQ bus. The suitable location for placing DG is identified through loss sensitivity factors and L index. Evolutionary programming is used to find the optimal size of distributed generation (DG).The objective of this paper is to minimize the total losses by optimal siting and sizing of three types of DG for a mixed realistic load model. In [140], proposed a methodology for placement of sectionalizing switches in distribution networks in the presence of distributed generation sources. The multi-objective considerations are handled using a fuzzy approach. The primary objective is reliability

improvement with consideration of economic aspects. Thus, the objectives are defined as reliability improvement and minimization of the cost of sectionalizing switches. A fuzzy membership function is defined for each term in the objective function according to relevant conditions. Some considerations incorporated in the proposed model are relocation of existing switches and operating constraints on distribution networks and DG sources during post-fault service restoration. The Ant Colony Optimization (ACO) algorithm is adopted to solve the fuzzy multi-objective problem efficiently. Shunt capacitors [141] installation in distribution systems requires optimal placement and sizing. More harmonics are being injected into distribution systems. Adding shunt capacitors may lead to high distortion levels. The capacitor placement and sizing problem is a nonlinear integer optimization problem, with locations and ratings of shunt capacitors being discrete values. The goal is to minimize the overall cost of the total real power loss and that of shunt capacitors while satisfying operating and power quality constraints. This paper proposes to solve the problem using particle swarm optimization (PSO). A discrete version of PSO is combined with a radial distribution power flow algorithm (RDPF) to form a hybrid PSO algorithm (HPSO). The former is employed as a global optimizer to find the global optimal solution, while the latter is used to calculate the objective function and to verify bus voltage limits. To include the presence of harmonics, the developed HPSO was integrated with a harmonic power flow algorithm (HPF). The concept of installing dispersed resources[142] in a utility distribution or sub-transmission system is gaining interest in the industry. Such resources could be photovoltaic cells, wind generation, battery storage, fuel cells or demand-side management solicitations. Research by others has suggested that the benefits of distributed resources could be substantial. However, these distributed benefits are site specific. A method to optimally locate such resources in a meshed network for maximizing the potential benefits is outlined in this paper. The proposed second order algorithm computes the amount of resources in selected nodes to make up a given total to achieve the desired optimizing objectives. The network under consideration could be a transmission, sub-transmission, or distribution network. The benefit expressed as a performance index can be the minimization of losses, var losses, or loadings in selected lines. The primary objective of any electric utility [143] company in the new




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competitive environment would be to increase the market value of the services it provides with the right amount of reliability, and at the same time, lower its costs for operation, maintenance, and construction of new facilities in order to provide lower rates for customers. The electric utility company will strive to achieve this objective via many different means, one of which is to defer the capital distribution facility requirements in favor of a distributed generation (DG) solution by an independent power producer (IPP) to meet the growing customer load demand. In this case, the distribution capital investment deferral credit received by the IPP will be dependent on the incremental system reliability improvement rendered by the DG solution. In other words, the size, location and the reliability of the DG will be based on the comparable incremental reliability provided by the distribution solution under considerations. This paper presents a reliability model for determining the DG equivalence to a distribution facility for use in distribution system planning studies in the new competitive environment. Reference [144] states that many DG technologies are expected to see 25%–40% decreases in capital costs and 10%–15% increases in efficiency. In addition, [144] predicts that over the next ten years, DG will emerge worldwide in many different shapes and sizes, possibly accounting for 8%–14% of all additions. As a result of the restructuring of electricity markets and the targets laid down for renewable energy, increasing amounts of embedded generation (EG) are being connected to distribution networks. To accommodate this new type of generation, the existing distribution network should be utilized and developed in an optimal manner. This paper explains the background to the technical constraints faced by EG projects, and a new methodology is developed using linear programming to determine the optimal allocation of EG with respect to these constraints. The methodology is implemented and tested on a section of the Irish distribution network [145]. In [146], the authors employ an optimal power flow technique to maximize EG capacity with respect to voltage and thermal constraints. SCLs, SCRs, equipment ratings, and losses are not considered. The effect of network sterilization is clearly demonstrated by comparison between allocating generation to buses individually rather than as a group. In [147], genetic algorithms were used to place generation such that losses, costs, and network disruption were minimized and the rating of the generator maximized. The constraints

considered were voltage, thermal, short circuit, and generator active and reactive power capabilities. Generation is placed in single units at individual buses, while ignoring the interdependence of the buses and the network sterilization that can result from improper EG placement. In [148], the authors use a heuristic approach to determine the optimal EG size and site from an investment point of view. Once again, short circuit constraints are not considered, and the focus of the objective function is on optimal investment rather than maximizing renewable energy. It uses a cost benefit analysis to evaluate various placements of EG. In [149], proposed a method for the allocation of fixed (capital and non-variable operation and maintenance) costs at the medium voltage (MV) distribution level. The method is derived from the philosophy behind the widely used MW-mile methodology for transmission networks that bases fixed cost allocations on the “extent of use” that is derived from load flows. Calculate the “extent of use” by multiplying the total consumption or generation at a bus-bar by the marginal current variations, or power to current distribution factors (PIDFs) that an increment of active and reactive power consumed, or generated in the case of distributed generation, at each bus-bar, produces in each circuit. These PIDFs are analogous to power transfer distribution factors (PTDFs). Unlike traditional tariff designs that average fixed costs on a per kWh basis across all customers, the proposed method provides more cost-reflective price signals and helps eliminate possible cross-subsidies that deter profitable (in the case of competition) or cost-effective (in the case of a fully regulated industry) deployment of DG by directly accounting for use and location in the allocation of fixed costs. All these different aspects of distributed generation sources in a low- or medium-voltage grid will influence the important question of where to place the production units. Different objectives can be put forward, such as highest efficiency (i.e. lowest power losses), minimal cost (installation and operation), highest reliability, etc. This optimization problem can be solved in different ways like exhaustive searches [150], Lagrangian based approaches [151] or tabu searches [152]. The promise of a new dawn of widespread DG sources proliferated throughout distribution systems has lead to a certain degree of hype in the electricity industry. Nonetheless, DG does offer many benefits as well as presenting numerous challenges. A large amount of research




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has been carried out investigating the impact of DG. A selection of the more relevant publications is given here. 3. A BRIEF REVIEW ON OPTIMALLY

PLACED FACTS CONTROLLER IN POWER SYSTEMS

The following open literatures are reviews for optimal placement of FACTS controllers in power systems for different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems. The SVC is one of the first FACTS devices. This local compensation system uses shunt connected elements that absorb or inject reactive power, like controlled reactors and switched capacitors. The SVC aims to maintain a set point of one or more elements of the power equation. Typically, the SVC modifies the voltage at the supply point. The SVC is characterized by fast response time, low power losses, high reliability, and low maintenance. Distributed Generation (DG): On practical networks, at low voltage levels, one or more SVC units of power rate similar to a lab-scale unit can be used. The SVC units could support power quality requirements by voltage regulation needs at low voltage level distribution networks. This is also valid for distribution networks with integration of DG. The impact of an SVC unit can be approached in two different states: steady-state and fault conditions. A small distribution network could use SVC units interacting on the spread points of connection. Lack of local VAr compensation can be a limiting factor of some DG technologies, such as the induction machine directly coupled to the grid and the permanent magnet synchronous machine. In this case, reactive power compensation and/or voltage regulation could be carried out using a small-scale SVC unit coupled to the DG point of connection. On the other hand, when the compensation is not local, the SVC unit can work as a coordinated distributed resource that receives reactive power set points depending on the power quality requirements of the network feeder. The SVC units can be located on strategically predefined points of

the distribution network to mitigate several unbalance problems, minimize negative- sequence currents, and do power factor correction. The mathematical modeling of FACTS controllers such as SVC and TCSC are presented in [153]-[156].

In [157], modal controllability indices based method has been addressed for determine the suitable locations of FACTS devices to damp out power system oscillations in power system environments.

A sensitivity based approach has been proposed to determine the placement of TCSC and UPFC for enhancing the power system loadability [158].

A genetic algorithm has been addressed for optimal location of multiple type FACTS controllers in a power system. The optimization are performed on three parameters; the location of the devices, their types and their values. The system loadability is applied as measure of power system performance. Four different kinds of FACTS controllers are used as models for steady state studies: TCSC, TCPST, Thyristor Controlled Voltage Regulator (TCVR) and SVC in order to minimizing the overall system cost, which comprises of generation cost and investment cost of FACTS controllers [159].

In [160], the Goal Attainment (GA) method based on the SA approach is applied to solving general multi-objective VAR planning problems by assuming that the Decision Maker (DM) has goals for each of the objective functions. The VAR planning problem involves the determination of location and sizes of new compensators considering contingencies and voltage collapse problems in a power system.

In [161], the application of particle swarm optimization (PSO) technique to find the optimal location of flexible AC transmission system (FACTS) devices with minimum cost of installation of FACTS devices and to improve system loadability (SL) has been presented. While finding the optimal location, thermal limit for the lines and voltage limit for the buses are taken as constraints. Three types of FACTS devices, thyristor controlled series compensator (TCSC), static VAR compensator (SVC) and unified power flow controller (UPFC) are considered. The optimizations are performed on the parameters namely the location of FACTS devices, their




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setting, their type, and installation cost of FACTS devices. Two cases namely, single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC and UPFC) are considered. In [162], the location of SVC (Static VAR Compensators) and other types of shunt compensation devices for voltage support is an important practical question. This literature considers a tool based on the determination of critical modes. Critical modes are computed by studying the system modes in the vicinity of the Point of Collapse. System participation factors for the critical mode are used to determine the most suitable sites for system reinforcement. Because the method does not rely on base case linearizations, the method is able to properly consider all system limits and nonlinear effects.

In [163], advanced load flow models for the static VAR compensator (SVC) are presented in this literature. The models are incorporated into existing load flow (LF) and optimal power flow (OPF) Newton algorithms. Unlike SVC models available in open literature, the new models depart from the generator representation of the SVC and are based instead on the variable shunt susceptance concept. In particular, a SVC model which uses the firing angle as the state variable provides key information for cases when the load flow solution is used to initialize other power system applications, e.g., harmonic analysis. 4. A BRIEF REVIEW ON OPTIMALLY

PLACED DG & FACTS CONTROLLER IN POWER SYSTEMS

The following open literatures are reviews for optimal placement of DG and FACTS controllers in power systems for different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems. Motivated by the development [164] of power semiconductor technologies, flexible ac transmission systems (FACTS) devices and their penetration in the field of electrical power systems, an educational challenge in complementing theoretical knowledge with practical experience is recognized. In this paper, the design and implementation of a lab-scale hardware and software setup is presented. Three small-scale devices, including a static VAr compensator (SVC) unit, a transmission line

model, and a substation, are developed. The SVC unit is validated by obtaining its operating characteristic. The lab setup is presented as a platform to carry out different experiments related to the SVC operation. Safety considerations in the design are discussed. Steady-state and dynamic analysis are presented showing the consistency between theory and practice. The potential use of small SVC units on low-voltage distributed generation schemes is discussed.

5. RESULTS AND DISCUSSIONS

The following tables give summary of the paper as:

S.No. No. of Literatures out of 164 literatures

% of Literatures out of 164 literatures

1 DG 146 92.41 2 FACTS

controllers 11 6.96

3 DG+FACTS controllers

01 00.64

From above tables it is concluded that the 92.41%, 6.96%, and 00.64% of total literatures are reviews based on enhancement of different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. by optimally placed DG, FACTS controllers, and DG+FACTS Controllers in power systems respectively. Finally it is concluded that the maximum research work carryout from the enhancement of different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. by optimally placed DG controllers in power systems point of view. 6. CONCLUSIONS This paper presented a state-of-the-art on enhancement of different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. by optimally placed DG, or FACTS




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controllers, or DG+FACTS controllers in power systems. Also this paper discussed the current status of the research and developments in the field of the enhancement of different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. by optimally placed DG, or FACTS Controllers, or DG+FACTS controllers in power systems. Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of the enhancement of different performance parameters of power systems such as minimize power losses, minimize cost of systems, and improve the voltage profile, provide more reactive power support to the systems, increased load ability, local marginal price etc. by optimally placed DG, or FACTS controllers, or DG+FACTS controllers in power systems,

ACKNOWLEDGMENT The authors would like to thanks Dr. S. C. Srivastava, and Dr. S. N. Singh, Indian Institute of Technology, Kanpur, U.P., India, and Dr. K.S. Verma, and Dr. Deependra Singh, Kamla Nehru Institute of Technology, Sultanpur, U.P., India, for their valuables suggestions regarding placement and coordination techniques for FACTS controllers form voltage stability, and voltage security point of view in multi-machine power systems environments. REFERENCES [1] N.S. Rau and Y.H. Wan, “Optimal location

of resources in distributed planning”, IEEE Trans. Power System. Vol. 9, pp. 2014-2020, Nov., 1994.

[2] W.EI-hattam, M.M.A. Salma, “Distributed Generation Technologies, Definitions and Benefits” Electric Power System Research, Vol. 71, pp. 119-1283, 2004.

[3] H. Zareipour, K .Bhattacharya and C. A. Canizares, “Distributed Generation: Current Status and Challenges” IEEE Proceeding of NAPS 2004, Feb 2004.

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AUTHOR PROFILES:

Bindeshwar Singh received the M.Tech. in electrical engineering from the Indian Institute of Technology, Roorkee, in 2001.He is now a Ph. D. student at GBTU, Lucknow, India. His research interests are in Optimal Placement of Distributed Generation & FACTS controllers in power Systems. Currently, he is an Assistant Professor with Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, U.P., India, where he has been since August’2009. Email: [email protected] K.S. Verma received his B. Tech. (Hons) in Electrical Engineering and M. Tech. in Electrical Engineering (Power Systems) both from KNIT Sultanpur (India) in 1987 and 1998, respectively. He obtained his Ph.D. degree in Electrical Engineering (Power Systems) from Indian Institute of Technology, Roorkee, Roorkee (India) in 2003. Presently, Prof Verma is Director, KNIT Sultanpur (India). His research interests include FACTS Technology, distributed generation, power system optimization & control, power quality and AI application in power system. Email: [email protected] Deependra Singh received the Ph.D. in electrical engineering from the U.P. Technical University, Lucknow,U.P., India, in 2009. Currently, he is a Registrar with, Kamla Nehru Institute of Technology, Sultanpur, U.P., India, where he has been since January2010. His interests are in the areas of FACTS control and Power systems. Email: [email protected] S. N. Singh received M. Tech. and Ph.D. from Indian Institute of Technology Kanpur, India in 1989 and 1995, respectively. He is a Professor in the Department of Electrical Engineering, Indian Institute of Technology Kanpur, India. Presently he is on leave from IIT Kanpur and working with the Centre for Electric Technology (CET), Technical University of Denmark, Denmark. His research interests include power system restructuring, power system optimization & control, voltage security and stability analysis, power system planning, and ANN application to power system problems. He is a Fellow of IE (India), Fellow of IETE (India) and senior member of IEEE. Email: [email protected]

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