Abstract—As renewable energy penetrates the utility market, the electricity industry is undergoing a paradigm shift that will change the industry to generate electricity using distributed generation systems. Consequently, there are new opportunities for enhancing the utility company’s power quality and network reliability, as well as increasing their level of automation. This paper surveys the most recent advances in planning, optimization, and automation for distributed generation systems. Our emphasis is on explaining how renewable sources of energy, such as wind power and solar photovoltaic, are integrated into the distributed generation systems. Our review covers both deterministic and probabilistic methods of planning. Automation in power quality monitoring, network control, and fault detection are discussed. There is an increased interest on using smart grids for distributed energy production, distribution, and consumption. Therefore, our paper identifies critical directions for future research in this area.

Keywords: distributed generation, renewable energy,

stochastic optimization, green automation, smart grid

I. INTRODUCTION ISTRIBUTED generation (DG), also called decentralized generation, produces and supplies

electricity from a significant amount of small energy sources. As opposed to centralized generation (CG), DG-based sources generate electricity near the end users. The generator is frequently located in the same building where the end users are located. These small energy sources, also called distributed energy resources (DER), could be wind turbine generators (WTG), photovoltaic (PV), fuel cells, microturbines, and internal combustion engines. DER capacity currently ranges from 3 kW to 10 MW, which in perspective is considerably less than the 500 MW capacity of existing CG-based gas turbines.

Because they are renewable, sustainable, and environmentally friendly, wind power and PV are emerging as promising sources of energy that will help close the gap between the supply and the demand of energy in the next couple of decades. However, the availability of wind energy and solar radiation is unpredictable and stochastic since their output power depends significantly on whether conditions, geographical location, and other season-dependent factors.

Manuscript received February 14, 2010. Tongdan Jin is with the Ingram School of Engineering, Texas State

University-San Marcos, San Marcos, TX 78666 USA (telephone: 512-245-1826; fax: 512-245-7771; e-mail: tj17@ txstate.edu).

Jesus A. Jimenez is with the Ingram School of Engineering, Texas State University-San Marcos, San Marcos, TX 78666 USA (e-mail: jj30@txstate.edu).

In wind farms, for instance, each WTG has an average capacity of 34.5 kV, but this power output frequently varies due to changing wind speeds [1]. Similar power output variability is observed in systems that use PV technology, especially because these systems do not receive solar electricity at night, or during poor weather conditions. Furthermore, the efficiency of PV may be influenced by humidity and air pollution [2]. Although the production costs for wind energy have dropped significantly to nearly 3-5 cent per kWh, the investment cost and the reliability uncertainty in PV still prevent the wide deployment of PV in the utility market.

The techniques for integrating different types of DER units into the distribution systems have been extensively analyzed [5-14]. These studies usually applied optimization techniques to determine the optimal equipment capacity and placement within the distribution network. The design criteria considered by these optimization techniques usually include minimizing the power loss, minimizing the equipment cost, and maximizing the generation capacity. Optimization methods such as gradient methods, genetic algorithms, and heuristic search are used for finding the optimal DER sitting and sizing within the DG network.

Modeling techniques used for DG planning can be classified into two categories: deterministic modeling and stochastic modeling. Deterministic models commonly assume that the network load or the output power of a DER unit is constant [5-9]. The worst scenario using the peak load conditions is frequently utilized as the design criteria. On the other hand, probabilistic models characterize loads and DER output power using statistics and probability theory [10-14]. In other words, it allows planners to represent the loads and the DER output powers by considering the load variations and the power uncertainty of DER units. Recently, the stochastic planning methods have received more attention because of the increasing integration of renewable energy sources into the grid system.

This paper reviews the deterministic and stochastic modeling techniques that are available in the literature for planning distributed generation systems. The advantages and the disadvantages of both modeling techniques will be discussed and their solution methodologies will be explained as well.

A Review on Planning and Automation Technologies for Distributed Generation Systems Tongdan Jin, Jesus A. Jimenez

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6th annual IEEE Conference on Automation Science andEngineeringMarriott Eaton Centre HotelToronto, Ontario, Canada, August 21-24, 2010

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Compared with the central generation infrastructure, DG systems integrated with renewable sources are more sensitive to energy reliability and power stability. It is thus critical to develop and implement intelligent mechanisms to automatically monitor the network reliability and the power quality in a real-time basis. As such, utility suppliers or distribution companies can guarantee the overall network performance and quickly respond to emerging issues by pro-actively taking necessary measures. Therefore, this paper also provides a summary of such technologies.

The remainder of the paper is organized as follows. Section II briefly introduces the network infrastructure, as well as the operational principle of DG systems. Section III reviews existing deterministic approaches to planning DG systems. Section IV discusses several stochastic models used for planning DG systems. Section V provides a survey of intelligent automation and control technologies, with application in distribution networks. Section VI concludes the paper with some remarks on future research.

II. DISTRIBUTED GENERATION With the growth of WTG and PV energy, DG has

become a promising technology that will help utility companies meet future energy demand while reducing the levels of greenhouse gasses emissions. Furthermore, DG can reduce the amount of energy losses during delivery because electricity is generated near the location where it is consumed. Therefore, the infrastructure (i.e. distribution lines, transformers, etc.) needed for energy delivery would be considerably reduced. Fig. 1 depicts a typical DG system comprising one substation, one PV, and two WTG units to support eight load lines (i.e. L1 to L8).

Fig. 1: DG Integrating Substation, WTG and PV

Smaller and environmentally friendly energy sources,

such as WTG and PV, can be incorporated into the existing electric network to form a DG system. A major challenge to the implementation of DG systems is the high cost associated with initial installation, operation, and maintenance. Many wind farmers are located in remote regions or off-shore areas, creating tremendous challenges for spare logistics, maintenance, and equipment repair. Nevertheless, utility companies are consistently seeking new technologies to reduce DG cost, yet still guarantee a high reliability of the electric grid. With continuous advancement in technology, the costs of DER equipment will eventually

decrease, thus DG systems will be able to compete with traditional centralized generation.

III. DETERMINISTIC PLANNING MODELS As was explained above, the deterministic planning

method treats both the load and the DER output power as time-independent parameters with no variation. This section reviews the deterministic planning models.

Rau & Wan [3] and AlHajri & El-Hawary [4] studied the allocation of distributed resources in electrical networks to minimize power losses, line loadings, and reactive requests. In their formulation, the electricity network is assumed to have a constant load and the power of injected DER units is treated as a deterministic value. Vallem & Mitra [5] and Kamalinia et al. [6] solve the optimal DER sitting and sizing problem to minimize a weighted objective function consisting of power losses, voltage variation, active and reactive powers on the bus lines. In their model, it is assumed that the bus line loads and DER output powers are constant. Simulated annealing, genetic algorithms, and data envelopment analysis (DEA) are used for searching the optimal solutions in their models.

Other researchers [7-11] approach the DG planning problem from the design perspective by considering the worst case scenario. These authors use the peak load as the key criterion to determine the optimal placement and capacity for DER units in order to satisfy anticipated design objectives. More specifically, these objective functions aims to minimize the power losses, minimize the installation/operation cost, or maximize the network reliability and power quality. For instance, the objective function presented by El-Khattam et al. [9] is given as follows Problem P1: Minimize:

(1)

Where =the capacity limit for DG i =the actual output power for DG i

=power purchased by the distribution utility i

=power not served for load bus i Vi=voltage for bus i

=feeder segment impedance from bus i to j cfi=hourly DG investment cost at load bus i cri=hourly DG operation cost at load bus i cuei=cost for unserved power at load bus i p=electricity market price γ=system power factor M= Total number of load buses N= Total number of system buses S=number of substations and DER units

For more information about the constraints used in this model, interested readers can refer to [9]. The optimal solution to the planning problem is often found by using

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genetic algorithms [7, 8, 10] and heuristic methods [9, 11]. Usually, the optimal solution derived from the worst-case scenario is conservative in terms of system cost, network reliability, and power quality. The reason for such conservative estimate is because the peak load usually represents 20-30% of the daily time, but in practice, electric networks usually operate in off-peak mode in most time of the day.

In contrast to the peak load design, Harrison & Nehrir [12] planned the optimal DG system by maximizing the overall capacity based on the minimum load criterion. To represent the dynamics of the loading conditions, Popovi et al. [13] and Soroudi & Ehsan [14] divide the load conditions into multiple levels. These levels allow the planner to decide which DER units should be optimally placed in the network such that the total loss and equipment cost are minimized.

In summary, deterministic planning techniques can yield a reliable solution when the load conditions and the DER output power are relatively stable. The method can be applied for applications where microturbines or internal combustion engines are used as DER equipment. Since the DG planning issues are formulated as deterministic optimization models, gradient methods, genetic algorithms, and heuristic algorithms are generally effective and efficient to search the optimal or near-optimal solutions for the sitting and sizing issue.

IV. STOCHASTIC PLANNING MODELS In the probabilistic approach, the load conditions and the

DER output powers are modeled as random variables varying over the time. Stochastic planning techniques are gaining the popularity in recent years due to the penetration of WTG and PV into the distribution network. The behavior of these units is stochastic and intermittent since their output powers are highly correlated with wind speeds, solar radiations, and local temperatures. The volatility of these renewable energy sources must be explicitly incorporated into the planning procedure in order to obtain a robust DG system.

In this section, the load dynamics and the power volatility of WTG units will be examined prior to reviewing most popular stochastic models of planning. The reader should notice that the method used for analyzing wind power volatility can be appropriately extended to PV. A. Time-Varying Loads

The load of a DG system usually exhibits a non-stationary behavior that varies over the time. Fig. 2 shows a typical residential load profile of a bus line during a 24-hour period. In this figure, P1 represents the valley and the P2 represents the peak loads, respectively. It indicates that the daily load varies between P1 and P2. The peak demand occurs in the evening, and the valley demand takes place in the early morning. If the DG system is planned based on P2, the system configuration will be too conservative as it requires a large amount of DER capacity. On the other hand, if the system is designed to meet the requirement of P1, both the reliability and the power quality will be jeopardized when

the actual load increases in the evening. Therefore, the probabilistic planning method emerged as an effective tool to cope with the time-varying demand profile.

Fig. 2: Typical Residential Daily Load Profile

B. Characterizing Wind Power Volatility

The power output for WTG is highly correlated with the wind speed across the location where the turbine is installed. The wind speed, denoted as X, can be modeled as a Normal, Weibull, or Rayleigh random variable, depending on the fit of the underlying distribution [15-17].

Given the wind speed, the output power for a WTG can be determined from its power curve (i.e. a plot of the output power against the wind speed). Fig. 3 shows a typical WTG power curve with a maximum or rated power of Pm. The power curve comprises four operating phases: standby phase (i.e. no power output when 0<x<vc); nonlinear power phase with vc<x<vr; rated power phase vr<x<vs; and the cut-off phase (i.e. the generator is disconnected for the protection when x>vs). Notice that vc is the cut-in speed, vr is the rated speed, and vs is the cut-off speed.

Fig. 3: A Typical WTG Power Curve

Cubic power curves were proposed based on the kinetic

theory of the air flow dynamics across the turbine blades [18]. Basically, the actual power for the WTG is proportional to the cube of the wind speed when vr≤x≤vs, and the mathematical expression is given below:

(2)

Equation (2) shows that the output power from a wind

stream is directly proportional to the air density, the cross area, and the cube of the velocity. In this equation, ρ is the air density, A is the area covered by the blades, and ηmax describes the conversion rate between total wind energy and the actual produced electrical power. The theoretical value

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for ηmax is 0.5926, but the actual rate could be lower, ranging from 0.4 to 0.5.

The power volatility can be characterized by its mean and the variance of P(x). Both metrics are relatively simple, yet they can assess the average output power and its variation based on the distribution of wind speeds. The mean power can be estimated as follows:

(3)

where q= is the power curve coefficient. Hence, f(x) and F(·) represent the probability density function and the cumulative distribution function for X, respectively. In general, the closed form for (3) is difficult to obtain when X follows the Normal or Weibull distributions. Numerical methods can be adopted to compute the mean power. The variance for P(X) can also be obtained by computing the 2nd moment of P(X), and then subtracting the mean square value. That is

(4)

(5) Although Equation (5) is simple, it helps estimating and

characterizing the power variability of a WTG for a given wind speed distribution. Interested readers are referred to [19] for more details about the derivation of the power variance. To characterize WTG power output more precisely, higher moments of P(X) can be computed accordingly. C. Probabilistic Planning Models

Willis [20] and Wang & Nehrir [21] use statistical methods to minimize the loss of the DG system. In the former, the load is approximated by a uniform distribution. In the latter, time-varying models are adopted to represent the load condition. Strictly speaking, these methods belong to a semi-probabilistic modeling technique because the output power of DER units are still treated as constant. They solve the optimal DER placement problem using conventional non-linear optimization techniques. Recently, Falaghi & Haghifam [22] and Haghifam et al. [23] solve a similar problem using ant colony optimization and generic algorithms, respectively. In their models, load conditions and DER powers are treated as random variables.

According Dugan et al. [24], the new DG planning process needs to consider multiple factors, such as load variation, contingencies, dispatch, and control action, in order to determine capacity and their related costs. Equally important is the evaluation of economic risk due to the uncertainties introduced by load growth and power

volatility, especially when these systems are integrated with the wind power and solar energy.

In [25], Jin & Novoa propose a stochastic planning method to minimize the DG system cost while satisfying the stringent reliability criteria. Both the load condition and the WTG powers are treated as random variables for which only the mean and the variance are known. The stochastic optimization model, denoted as P2, is given as follow

Problem P2:

Minimize : (6)

Subject to: (7)

(8)

(9)

where xij=decision variable for DER type i placed in node j Pij=power generated by ith type of DER at node j P=total power generated by all DER in the system L=system load, a random variable Lm=load for the mth distribution line with m=1, 2, …, l α= confidence level d=total number of DER types n= total number of the nodes in the network aij =cost coefficients for equipment installation bi=cost coefficients for equipment maintenance ci=penalty cost for choosing type i

Three cost items are considered in the objective function in (6), these are installation costs, operation & maintenance, and an environmental penalty. The penalty cost is used to comprehend the trade-off between renewable DER units and traditional substations. Specifically, if a node adopts a substation instead of WTG or PV, penalties will be incurred due to the emission of greenhouse gases. Equation (7) controls the reliability of the DG system. It states that the probability that the generated power is larger than the total load should be greater than α . We can obtain a design solution that meets the expected energy reliability goal by adjusting α. Because of the central limit theorem, both P and L tend to be normally distributed regardless of the distribution of individual Pij and Lm.

The reliability criterion in P2 did not differentiate the impact of power loss between peak time and valley time. This is the deficiency of the current model. A power shortage in the peak time and an interruption in the off-peak period are different. This factor should be incorporated into the future work.

In conjunction with the formulation in P2, intelligent optimization algorithms are needed to find the best solution for highly complex non-linear and stochastic optimization problems. Techniques such as approximate dynamic programming, simulation-based optimization, and data envelopment analysis can be applied for solving the stochastic optimization problems. In addition, heuristic

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optimization techniques, such as genetic algorithms, ant colony optimization, and particle swarm optimization can be used for solving DG planning problems. In general, the computational complexity of stochastic models is higher than that of deterministic modeling. However, the availability of modern computing resources can facilitate the search of the solution.

V. DISTRIBUTED GENERATION AUTOMATION Utility companies measure the performance of electric

grid systems in terms of the energy reliability and power quality. Reliability is characterized by the number of interruptions or outages during a given time period. The following metrics are often used to measure the network reliability: loss of load probability (LOLP), loss of load expectation (LOLE), loss of expected energy (LOEE), and expected value of demand not served (eDNS). Power quality is measured in terms of voltage drops and frequency fluctuations around the nominal value. The state-of-art techniques for monitoring DG reliability and energy distribution automation are reviewed below.

A. Automatic Monitoring of Grid Reliability

Bollen et al. [26] propose several methods for calculating the reliability of intentional islanding systems (i.e. microgrid) with DER, including distributed generation and distributed storage units. Different levels of automation are considered, starting from the current technology and continuing with increasing levels of intelligence in the network.

Luo et al. [27] develop an auto-monitoring system to track voltage deviation, three-phase unbalance, and electromagnetic transient. The system consists of hardware and software components. The hardware performs the signal sampling and stores the sampling data. On the other hand, the software carries out data processing and displays functions. The authors apply discrete wavelet transformation (DWT) in order to decompose the real-time voltage signal into fundamental, high harmonic frequencies, seeking to extract abnormal voltage signatures.

Sun et al. [28] use event-based Monte Carlo simulation to analyze the reliability of intentional islanding or microgrid systems with DER units. Exponential and Weibull distributions are used for modeling the lifetime of certain components (i.e. generators, switchers, and cables), as well as their corresponding repair times. They found that the presence of circuit breakers and the reliability of the DER units have a large influence on the entire network reliability.

Hermanns & Wiechmann [29] investigate the stochastic energy balancing problem as a result of the penetration of renewable energy into the distribution network. They suggest the total load can be viewed as the composition of a deterministic and a stochastic component. The deterministic load is guaranteed through reliable supplying sources and the stochastic load is handled in a real-time manner. However, the implementation of these strategies relies on real-time state information of the entire grid. As the authors suggest, such information could be realized through demand

side management and advanced metering management (AMM).

B. Distribution Automation

Distribution Automation (DA) aims to develop and implement intelligent control functions for power distribution. Normally, electric utilities with SCADA (supervisory control and data acquisition) systems have extensive control over transmission-level facilities, and fairly good control over distribution lines. However, they are unable to control smaller entities such as those DER units, buildings, and homes that are close to end users. Extending and enhancing existing control functions to these entities is becoming increasingly important, especially for DG systems.

Mano et al. [30] provides an overview on electric distribution automation, such as automatic synthesis of alarms, fault location and network reconfiguration. The authors also emphasize the importance of the distribution automation associated with network interoperability and synergies of AMM technology with DER units. Kezunovic [31] discusses the implementation of intelligent electronic devices at the substations in order to facilitate fault identification, prevention, and correction.

Brown [32] discusses the design for future distribution systems with the capabilities of self-healing, dynamic pricing, and energy conservation. The paper advocates the importance of the incorporation of new technologies, such as information technology, automation, and distributed generation and storage, into the new distribution infrastructure,.

Geisler et al. [33] describe an intelligent energy delivery management system to achieve secure, reliable, and cost-effective distribution of energy to the end customers. The management system can automatically model the distribution elements, synthesize all necessary real-time information, manage connectivity and network resources, and proactively recommend operational changes to achieve service quality.

VI. CONCLUSION This paper reviews recent advances in planning and

automation of distributed generation systems. Two planning techniques, i.e. deterministic and stochastic methods, were introduced; their advantages and limitations have been discussed. The deterministic planning techniques are effective for designing DG systems if the load condition and DER output power are time-independent or with small variations. When renewable energy resources such as WTG and PV are integrated into the DG system, the power volatility becomes a key issue in terms of minimizing power losses, maximizing network reliability, and maintaining quality services. Stochastic planning techniques generally outperform the deterministic planning method because of its ability to incorporate uncertainties into the decision-making process, resulting in a more realistic system design.

Thanks to the ubiquitous information technology, the electric grids are becoming “smarter”. Different initiatives

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have been proposed in order to develop and deploy smart grid systems characterized by high reliability, self-healing, full controllability, and optimal asset management. DG systems embedded with this type of technologies enable utility companies to maintain market competitiveness by minimizing the distribution cost while offering high quality electricity to their customers. An interesting research direction is to combine the information technology, wireless networks, and existing grid infrastructure to form an interactive energy system that allows direct interactions among suppliers, market traders, and customers.

REFERENCES [1] T.J. Foxon, “Implications of seasonal and diurnal variations of wind

velocity for power output estimation of a turbine: a case study of Grenada,” International Journal of Energy Research, vol. 27, no. 13, 2003, pp. 1165-1179.

[2] R. Karki, R. Billinton, “Reliability/cost implications of PV and wind energy utilization in small isolated power systems,” IEEE Transaction on Energy Conversion, vol. 16, no. 4, 2001 pp. 368-373.

[3] N.S. Rau, Y.-H. Wan, “Optimum location of resources in distributed planning,” IEEE Transactions on Power Systems, vol. 9, no. 4, 1994, pp. 2014-2020.

[4] M.F. AlHajri, M.E. El-Hawary, “Optimal distributed generation siting via fast sequential quadratic programming", Proceedings of Large Engineering Systems Conference on Power Engineering, 2007, pp. 63-66.

[5] M.R. Vallem, J. Mitra, “Siting and sizing of distributed generation for optimal microgrid architecture,” Proceedings of 37th Annual North American Power Symposium, 2005, pp.611- 616.

[6] S. Kamalinia, S. Afsharnia, M.E. Khodayar, A. Rahimikian, M.A. Sharbafi, “A combination of MADM and genetic algorithm for optimal DG allocation in power systems,” Proceedings of 42nd University Power Engineering Conference, 2007, pp. 1031-1035.

[7] G. Carpinelli, G. Celli, F. Pilo, Russo, A. “Distributed generation siting and sizing under uncertainty,” Proceeding of 2001 IEEE Porto Power Tech Conference, vol. 4, pp. 1-7.

[8] G. Carpinelli, G. Celli, S. Mocci, F. Pilo, A. Russo, “Optimization of embedded generation sizing and sitting by using a double trade-off method,” IEE Proceedings, Generation. Transmission and Distribution, vol. 152, no. 4, 2005, pp. 503-513.

[9] W. El-Khattam, K. Bhattacharya, Y. Hegazy, M. Salama, “Optimal investment planning for distributed generation in a competitive electricity market,” IEEE Transactions on Power Systems, vol. 19, no. 3, 2004, pp. 1674-1684.

[10] G. Celli, E. Ghaiani, S. Mocci, F. Pilo, “A multiobjective evolutionary algorithm for the sizing and siting of distributed generation,” IEEE Transactions on Power Systems, vol. 20, no. 2, 2005, pp. 47-55.

[11] C. Tautiva, A. Cadena, “Optimal placement of distributed generation on distribution networks,” Proceedings of Transmission and Distribution Conference and Exposition, 2008, pp. 1-5.

[12] G. Harrison, M.H. Nehrir, “Optimal power flow evaluation of distribution network capacity for the connection of distributed generation,” IEE Proceedings, Generation, Transmission and Distribution, vol. 152, no. 1, 2005, pp. 115-122.

[13] D.H. Popovic, J.A. Greatbanks, M. Begovic, A. Pregelj, “Placement of distributed generators and reclosers for distribution network security and reliability,” International Journal of Electrical Power & Energy Systems, vol. 27, no. 5/6, 2005, pp. 398-408.

[14] A. Soroudi, M. Ehsan, “Multi objective distributed generation planning in liberalized electricity markets,” IEEE/PES Transmission and Distribution Conference and Exposition, 2008, pp. 1-7.

[15] R. Karki, P. Hu, R. Billinton, “A simplified wind power generation model for reliability evaluation,” IEEE Transaction on Energy Conversion, vol. 21, no. 2, 2006, pp. 533-540.

[16] F. Vallee, J. Lobry, O. Deblecker, “Impact of the wind geographical correlation level for reliability studies,” IEEE Transactions on Power Systems, vol. 22, no. 4, 2007, pp. 2232-2239.

[17] A.B. Morales, J. Trecat, M. Crappe, “Impact of decentralized wind generation in power systems reliability:variable wind speed turbines,” European Transactions on Electrical Power, vol. 11, no. 3, 2001, pp. 189-193.

[18] A. Kusiak, H. Zheng, Z. Song, “Short-term prediction of wind farm power: a data mining approach,” IEEE Transactions on Energy conversion, vol. 24, no. 1, 2009, pp. 125-136.

[19] T. Jin, Z. Tian, “Uncertainty analysis for wind energy production with dynamic power curves,” The 11th International Conference on Probabilistic Models Applied to Power Systems, June 14-17, 2010, Singapore.

[20] H.L. Willis, “Analytical methods and rules of thumb for modeling DG distribution interaction,” Proc. IEEE Power Engineering Society Summer Meeting, Seattle, USA, 16-20 July 2000, pp. 1643-1644.

[21] C. Wang, M.H. Nehrir, “Analytical approaches for optimal placement of distributed generation resources in power systems,” IEEE Transactions on Power Systems, vol. 19, no.4, 2004, pp. 2068-2076.

[22] H. Falaghi, M.-R. Haghifam, “ACO based algorithm for distributed generation sources allocation and sizing in distribution systems,” in Proc. 2007 Power Tech, pp. 555-560.

[23] M.-R. Haghifam, O.P. Malik, “Genetic algorithm-based approach for fixed and switchable capacitors placement in distribution systems with uncertainty and time varying loads,” IET Proceedings in Generation Transmission & Distribution, vol. 1, no. 2, 2007, pp. 244-252.

[24] R.C. Dugan, T.E. McDermott, G.J. Ball, “Distribution planning for distributed generation,” Rural Electric Power Conference, 2000, pp. C4/1 - C4/7.

[25] T. Jin, C. Novoa, “Reliability centered planning for distributed generation considering wind power volatility,” (working paper, 2010).

[26] M.J. Bollen, Y. Sun, G.W. Ault, “Reliability of distribution networks with DER including intentional islanding,” Proceedings of The 2005 International Conference on Future Power Systems, 2005, pp. 1-6.

[27] X. Luo, S. Su, G. Liu, P. Peng, L. Fan , “Power Quality Auto-monitoring for Distributed Generation Based on Virtual Instrument,” International Conference on Intelligent Computation Technology and Automation, 2008, vol. 1, pp. 1106-1110.

[28] Y. Sun, M.J. Bollen, G.W. Ault, “Probabilistic Reliability Evaluation for Distribution Systems with DER and Microgrids,” Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on 11-15 June 2006 Page(s):1-8.

[29] H. Hermanns, H. Wiechmann, “Future design challenges for electric energy supply,” IEEE Conference on Emerging Technologies & Factory Automation, 2009, pp. 1-8.

[30] X. Mamo, S. Mallet, T. Coste, S. Grenard, “Distribution automation: The cornerstone for smart grid development strategy,” IEEE Power & Energy Society General Meeting, 2009, pp. 1-6.

[31] M. Kezunovic, “Automated fault analysis in a smart grid,” Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009, pp. 1-3.

[32] R.R. Brown, “Impact of Smart Grid on distribution system design,” 2008 IEEE Power and Energy Society General Meeting, 2008, pp. 1-4.

[33] K.I. Geisler, T.D. Nielsen, D.F. Hall, R. Frowd, “The rise of energy delivery management systems,” IEEE/PES Transmission and Distribution Conference and Exposition, 2001, vol. 2, pp. 895-900.

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