Distributed Energy Resources and Supportive Methodologies for their Optimal Planning under Modern Distribution Network: a Review

  • Umesh AgarwalEmail author
  • Naveen Jain
Review Paper


Rapid growth in electrical load demand with lack in generation of electrical power and transmission line congestion has set the trend for smart electrical system. In smart electrical system, need arises to deploy more non-conventional energy sources, which include Renewable Energy Sources (RES) as well as non-RES. Though, the RES are getting more encouragement due to several advantages over non-RES. In recent past, there is significant increase in the penetration of small units of local generation in existing distribution system. These small units (RES and non-RES), usually known as Distributed Generation (DG), may offer several technical, economic and environmental benefits like reduction in power loss, improvement in power quality, reliability, system security, reduction in capital cost investment at large level, reduction in emission of green-house gases and many more. However, these advantages are difficult to achieve due to some technical and non-technical barriers. To extract maximum potential benefits from the DG, the optimal planning of such sources in distribution network has always been a topic of great interest. Though, fresh researchers face many problems in carrying out research in this area due to lack of knowledge about suitable research software, standard test networks, types of renewable/non-renewable sources, appropriate literature, etc. This paper uses a systematic approach to discuss the DG and its technologies with advantages, disadvantages and effects on end users as well as on the utility. A comparative study of all optimization techniques for planning of DG in existing power system considering optimal size and location is also included. This paper also involves the details about some standard test systems along with details of useful software’s (licensed & open source) for DG planning. The present study can add worthful information and serve as a base for the fellow working in this area.


Distributed generation Distributed generation planning Modern distribution system Optimization approach Power system Renewable energy sources 


According to an estimation of United States (US) energy information administration, it is expected that electricity generation may increase by a very high percentage during the period 2016 to 2040. The growth may be significantly higher than global energy consumption. The RES will contribute greatly to the power generation mix with non-RES and other conventional sources to achieve the target as shown in Fig. 1 [1].
Fig. 1

Evolution of global electricity generation by various sources of energy (Trillion kWh) [1]

In the last few years, penetration of the RES has been increased by tremendous rate. There are several factors such as government motivation in term of several incentives, environmental consciousness of society and advancement in technologies. Further, the key factors are changing the pace of power generation as the system is moving towards local generation near to their locality such as generation at home by solar, bio-mass and wind energy sources.

The local generation is affecting power flow direction as compared to the network with traditional centralized power generation sources, which has unidirectional power flow. In modern distribution systems, real power flow with the DG can be bi-directional. Since, the DG (renewable/non-renewable type) includes a broad range starting from 1 kW to 100 MW [2, 3], which can reverse the direction of real power at light load or no load condition. In [4], Khatod suggested a broad classification as following:
  1. (a)

    Micro DG (1 W < 5 kW),

  2. (b)

    Small DG (5 kW < 5 MW),

  3. (c)

    Medium DG (5 MW < 50 MW)

  4. (d)

    Large DG (50 MW < 300 MW).


The above classification was also discussed in [5, 6, 7, 8]. The DG units provide technical, economical and environmental advantages subject to planning strategy and technologies used for the DG. The technical advantage is an important concern as it reflects system health in terms of power loss, voltage profile, reliability and power quality. The DG can improve system performance. Further, it can also mitigate harmonics, voltage sag and swell significantly along with reduced investment in transmission and distribution [9, 10, 11, 12, 13].

There are certain challenges with DG technologies such as stability issues of power system due to intermittent source of energy, protection problem due to bi-directional flow of real power, frequency stability, islanding difficulties [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105]. Therefore, some factors need to be considered for planning of sources in distribution networks [5, 6], [8], [14, 15].
  • Power injection pattern from the DG is very important as it depends upon type of generation source, whether renewable or non-renewable. Hence, researchers must take care while choosing any renewable/non-renewable source for their study [16, 17].

  • The optimal planning has its importance in improving overall performance of the system for getting the best possible potential from the DG.

  • There is a great issue with the DG as it can cause bi-directional flow of real power. Therefore, suitable protection schemes need to be considered with load growth.

This work is prepared considering the importance and the necessity of the DG in existing power system. It includes a vast overview of the work carried out in the DG planning. Further, there are some important distribution systems, which required in planning of distribution system with the DG, are discussed with schematic figures. Moreover, a detailed section is given to discuss various open source and licensed software, which can be great help to the researchers.

This paper is organized as: Section II represents the details of the DG such as DG techniques, potential benefits and impacts of the DG. Section III introduces a brief overview of techniques used for planning of the DG in power system to extract maximum potential advantages. Section IV, chronologically, represents the involvement of the reviewed work. Section V includes the key issues for the DG integration in existing power system. In Section VI includes important test systems that are considered in several well established literatures. Some key supportive tools both open source and licensed (planning of the DG) are discussed in Section VII. Finally, Section VIII covers discussion and conclusion.

Distributed Generation

In [8], the DG is represented as a source of electrical energy that is connected to the radial structure of distribution system near the customer end.

According to International Council on Large Electric System, any generation units, connected to distribution network and having capacity from 50 MW to 100 MW, without facility of central planning and dispatchability is termed as DG [18].

Institute of Electrical and Electronics Engineer (IEEE) considers the DG as facility, comparatively smaller than central power plant and can be allocate at anywhere in power system [19].

The Electric Power Research Institute (EPRI) defines the DG as generation unit having maximum capacity up to 50 MW along with energy storage devices connected at consumers end or at distribution or sub-transmission substations [20]. Considering all the above views about the DGs, it can be concluded that the DG is a small source of electric power, connected near the load point or in the distribution network. The size of the DG is sufficiently smaller than the central power generation source.

A significant development in technology is making loads more sensitive. In addition, present polluted environment is attracting people towards the use of renewable energy. These are some factors providing momentum to go for renewable energy based DG. The DG has become a matter of interest for researchers, academicians and environmentalists due to its numerous advantages over conventional generating sources [5], [8, 9], [14, 15].

Key DG Technologies

Renewable energy sources as DG are beneficial in contrast of reduction in green-house gases, but uncertainties in power supply is also an issue. Some of the Renewable technologies require large space but most can be concise at small place like bio-gas plant for installation and initial cost is high, however, still less than centralized power generating source. Currently, to a certain extent, some of the DG technologies are still in research and under development phase. The major DG technologies with their range of electric power generation, primary source of energy, cost of installation is shown in Tables 1 and 2 [4, 7, 14, 21].
Table 1

A summary of major renewable DG technologies

S. No.

DG Technologies

Power Generation Range

Energy Conversion

Dispatchability (Avoiding Grid Expansion)

Primary Source of Energy

Capital Cost/kW

Merits & Demerits


Solar photo-voltaic (SPV)

1 kW–80,000 kW

Solar radiation to electrical




These are represented in [4].


Small hydro

5 kW–100,000 kW

Gravitational potential energy to electrical





Micro hydro

1 kW–1000 kW

Gravitational potential energy to electrical




Wind turbine

200 W- 3000 kW

Wind energy to electrical





Bio-mass energy

100 kW–20,000 kW

Chemical to electrical, thermal and in bio-fuels





Geothermal energy

5000 kW- 100,000 kW

Heat to electrical


Hot water



Tidal energy

0.1–1 MW

Kinetic energy to electrical


Ocean water


Hydrogen energy scheme

40–400 MW

Chemical to electrical


Water, organic compounds, biomass, and hydrocarbons


marine energy

100 kW–1000 kW

Kinetic to electrical


Ocean wave

Table 2

A brief overview of major non-renewable DG technologies

S. No.

DG Technologies

Power Generation Range

Energy Conversion

Fuel Type

Capital Cost/kW

Merits & Demerits


Integrated gasification combined gas turbine

30 kW-3000+ kW

Fuel to gas then to electricity

Gas, diesel or coal


These are represented in [4].


Micro turbine

30 kW–1000 kW

Chemical to mechanical then electrical

Biogas, propane or natural gas



Internal combustion (IC) engine

5 kW–10,000 kW

Diesel, gas or natural gas



Fuel cell (FC) technologies


Chemical to electrical


Alkaline FCs

100 W- 50000 W

Alkaline electrolyte like KOH


Phosphoric-acid FCs

200 kW–2000 kW

Acidic solution like H3PO4


Molten carbonate FCs

250 kW–2000 kW

Molten carbonate salt electrolyte


Solid oxide FCs

250 kW–5000 kW

Ceramic ion conducting electrolyte in solid oxide form


Proton exchange FCs

1–250,000 W

Proton exchange membrane


Battery storage

500–5000 kW



Non-renewable DG technologies, mentioned in Table 2 can avoid expansion of grid if these are dispatchable

After-Effects of DG

The impacts of the DG can be generally classified into three categories as technical, financial and environmental impacts.
  • Technical issues: Insertion of the DG in existing distribution network is beneficial in many technical aspects. The DG is installed near load centre, therefore, reduces power loss and at the same time improves voltage profile by keeping the voltage in limits. The DG improves reliability, system security and energy efficiency of the supply. All these benefits appears only if the DG is planned optimal, otherwise, the DG may produce several technical problems as presented in [2, 5], [14, 15], [7, 8, 9], [22, 23, 24, 25, 26, 27].

  • Financial issues: Installation of the DG is beneficial for the utility as well as the customer. Since, the DG reduces the capital cost by delaying the need for investment in new transmission and distribution infrastructure. It also reduces depreciation costs of the fixed assets in the network, loss in the system network, operation & maintenance costs. The DG reduces electricity tariffs by creating favourable market environment for new agents [3, 5, 14, 28].

  • Environmental issues: Major DG technologies are associated with renewable sources; therefore, it is possible to generate green energy. As per the published literature, fuel burning is the main cause of around 80% pollution all over the world [23, 24, 25]. Many researchers have proved that the DG technologies, mainly renewable energy based, are capable of reducing the emission of carbon, technology and capable to cut the emission of carbon by approximately 40% [7]. As per the above mentioned definitions, it is clear that the DG can be installed near the load centres. Hence, there is no need of large space and it reduces deforestation. Though, there are some adverse impacts of renewable technologies on environment. Wind turbines are particularly not favorable to the bird species. Moreover, wind turbine required to be dug deep into the earth, which off-course has negative effect on underground habitats. In addition, it creates noise pollution. Similarly, ocean wave energy can be harmful to local water species during energy production.

Popular Techniques for Optimal Sizing and Sitting of the DG

The DG planning depends upon the requirement of the system such as: (a) Technical Issues (b) Economic Issues (c) Environmental Issues. In technical issues, key issues of the DG planning are voltage profile improvement, energy loss minimization, harmonics reduction, mitigating the issues of intermittent nature of the DG and maximization of reliability. There are several economic issues related to distribution system where the DG can help in mitigating such issues. Therefore, economic issues can be as key objective of the planning, whereas sometimes it can be merely a constraint. In several developed country, environmental issues are so important that the DG planning primarily considers it. Thus, the RES are mainly considered in such countries even they are costlier option.

The DG can be planned to address single or multiple issues, which may be combination of above said issues. This makes planning as single objective or multi-objective planning with or without constraint. In continuation, selection of the optimizing tool is based on nature of the planning and system constraints.

To maximize the requirement of the system, it is necessary to place the DG with proper sizing and siting considering the key constraints in distribution system. Such planning can result as desirable output. Hence, there are a lot of techniques available in the literature as per the objectives of the planning [125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135].

The following techniques have been adopted by the researchers to serve the objectives of sizing and siting of the DG in appropriate manner. The major acting techniques can be categorized as follows [12, 29].
  • Analytical Techniques

  • Classical Optimization Techniques

  • Artificial Intelligent (Meta-heuristic) Techniques

  • Miscellaneous Techniques

  • Other Techniques for Future Use

Analytical Techniques

In Analytical techniques, mathematical replica is used to represent the system and numerical solution of the system, which can be computed reliably. Beauty of this technique is less computation time, high efficiency and simplicity of system with less state variables. Accomplishment of the technique has been reported in [2, 3], [10, 11, 12], [15, 16], [29, 30, 31, 32]. However, Analytical techniques may have some restrictions for bulky and difficult systems. These optimization techniques broadly include many key techniques and some of them are shown in Fig. 2.
Fig. 2

Analytical techniques

Classical Optimization Technique

These optimization techniques are utilized to expand the advantage of the system according to the created formulation as per the necessity under given circumstances with system limitations. In this way, it needs to apply an appropriate optimization technique to get the required aimed function. These optimization techniques mainly cover important methods as shown in Fig. 3.
Fig. 3

Classical optimization techniques

Artificial Intelligent Techniques

The beauty of Artificial Intelligence (AI) technique lies in getting well-organized, precise and best possible solutions wisely. The supposition extracted from the AI technique is the most up to date and adorable meta-heuristic explore technique. There are some other family optimization algorithms that have been adopted in meta-heuristic as shown in Fig. 4 [4, 5, 6], [13, 22], [25, 26, 27, 28], [2, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45].
Fig. 4

Artificial intelligent techniques

Miscellaneous Techniques

There are many more verities of methods observed in the work of literature, which are kept under miscellaneous techniques as shown in Fig. 5.
Fig. 5

Miscellaneous techniques

Future Promising Optimizing Techniques

There might be several new optimization techniques, with capabilities to have room for the multifaceted questions of the DG planning with multi-objective function, which can be classified as shown in Fig. 6.
Fig. 6

Future promising techniques

Significant Contribution in the Reviewed Planning of the DG

Table 3 describes the main contribution of the published DG works reviewed in this paper in a chronological order.
Table 3

Main contribution of the published Optimal DG Planning works in chronological order

S. No.

Goal of the Planning

Planning Variables





Minimization of total electrical power losses (PL)

Optimal placement of multiple DG units


Stud Krill herd Algorithm



Minimization of total electrical PL

Optimal placement of different types of DG units


Bat Algorithm



Minimization of total electrical PL

Optimal location and sizing of DG unit


Intelligent Water Drop algorithm along with loss sensitivity factor



Minimization of total electrical PL

Optimal siting and sizing of DG units


Analytical method



Minimization of total electrical PL

Optimal site and size


Sequential Quadratic Programming and Branch and Bound algorithm



Minimization of total electrical PL

Optimal allocation and sizing of different types of DG units


PSO-based algorithm and also analytical method



Minimization of total electrical PL

ODGP and sizing of multiple DG


Kalman Filter Algorithm, optimal locator index



Minimization of total electrical PL

Optimal allocation (sizing and siting) of DG and capacitor


Method based on analytical approach with heuristic curve fitting technique



Minimization of total electrical PL

Optimal placement of DGs and size of the DG’s


PSO technique



Minimization of total electrical PL

Optimal allocation of three types of DG (Solar parks, wind farms and power stations)





Minimization of total electrical PL

Optimal placement and size of DG units


Modified Teaching Learning Based Optimization Algorithm



Minimization of total electrical PL

Size and Location of DG


Immune Algorithm with active model of DG in the smart network including all kind of cast factors.



Minimization of total electrical PL

Optimum sizes and operating strategy of DG units


Three alternative analytical expressions (Elgerd’s loss formula, branch current loss formula, branch power flow loss formula)



Minimization of total electrical PL

Optimal DG-unit’s size, power factor, and location


Meta-heuristic, population-based optimization methodology with an Artificial Bee Colony (ABC) algorithm



Reduction in power loss along with voltage stability enhancement

Optimum DG placement





Addition of cost of real power and energy loss cost with power loss optimization

Optimal location and size of multi DG


Adaptive Differential Search Algorithm



Comparison of three optimization techniques for reduction of the real power loss and voltage profile improvement

Optimal placement of DG





Real power loss minimization and voltage improvement and improvement of voltage stability index

Optimal location and size of DG


Teaching learning based optimization algorithm



Network power losses, achieve better voltage regulation and improve the voltage stability

Optimal location and sizing of DG unit


Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine



Diminishing real power disaster, working expense and improving voltage steadiness

Optimal location and sizing of DG unit


IWO along with the loss sensitivity factor



Reducing power losses and improving voltage profile

Optimal location


Loss reduction sensitivity method

Voltage improvement sensitivity method



Minimizing power losses and generation costs

Optimal location and sizing of DG unit


Relaxed MINLP



Minimum annual investment and operation (I&O) cost of DG, purchasing electricity cost & voltage deviation

Optimal sitting and sizing


Improved Non-dominated Sorting GA-II



Energy loss minimization considering the random nature of some distributed resources and the time varying loads

Optimum allocation of DG


Refined parallel Monte Carlo method



Optimal multiple DG



Rank Evolutionary PSO By hybridizing the Evolutionary Programming in PSO algorithm.



Power loss, line flow maximum value, and voltage summary and voltage steadiness directory combined using weighting coefficients.

Optimal siting and sizing of DG units


Chaotic Artificial Bee Colony Algorithm



Mitigation of losses, improving the voltage profile and equalizing the feeder load balancing in distribution systems

Optimal siting and sizing of multiple DG units


Hybrid Fuzzy-IWD Approach



Network real loss and enhance the voltage profile combined power factor and reduction in network reactive power loss

Optimal allocation of DG


Backtracking Search Optimization Algorithm



Costs are minimized and profits are maximized

Optimal locations, sizes and mix of dispatchable & discontinuous DGs


Column and Constraint Generation framework



Improving voltage profile and stability, power-loss reduction, and reliability enhancement, economic analysis

Optimal DGs places, sizes, and their generated power contract price





Enhancement of power quality includes improvement of voltage and reduction of line losses

Optimal placement and sizing of Distribution Static Synchronous Series Compensator





Removal of susceptible nodes to maintain the voltage level of system

Optimal location and capacity of DG units





Voltage stability and loss minimization

Optimal location and size of DGs


Maximum Power Stability Index and PSO Algorithm



Promoting energy competence

Optimal network capacity and distribution of the CHP-based DG


Integrated System Dispatch model



System loss minimization and voltage profile improvement

Optimal siting and sizing of DG units





Reduce the total power loss and to improve the voltage profile

Optimal siting and sizing of DG units


Bacterial Foraging Optimization Algorithm Modified Bacterial Foraging Optimization Algorithm



Reduction in power system losses, maximization of system load ability and voltage quality improvement.

Multi-DG placement and sizing


Hybrid PSO



Voltage constancy, power losses and network voltage fluctuations

ODGP and sizing


Pareto Frontier Differential Evolution algorithm



Voltage regulation problem considering random nature of lower heat value of biomass and load.

Optimal location of biomass- fuelled gas engines


Frog-Leaping Algorithm and three phase probabilistic load flow combined with the Monte Carlo method



Uncertainties considered:

(i) the future load growth in the power distribution system,

(ii) the wind generation,

(iii) the output power of photovoltaic’s,

(iv) the fuel costs and

(v) the electricity prices

Optimal siting and sizing


Point estimate method embedded GA



Minimizing annual energy losses

Optimal location, size and power factor of dispatchable and non-dispatchable DG units


Analytical Approach



To make mini hydro scheme a cost-effective renewable energy option

New designs of turbines, electrical equipment’s and governor controllers





Improvement in power system parameters

Optimal sitting and sizing of DG


ICA and GA



Minimize real power losses by maintaining the fault level and the voltage variation within the acceptable limit.

Optimal sizing and siting of DG


CS technique



Minimize energy loss considering time- varying characteristics of both load and wind-generation profile

Optimal size of wind turbine


Weighting factor based methodology incorporating genetic algorithm with power flow analysis with fuzzy–c means clustering to reduce execution time.



Minimize the annual energy losses and reduce the harmonic distortions

Optimally allocating different types of DG (i.e. wind-based DG, solar DG and non-renewable DG)


Probabilistic planning approach



Reduced number of DG, less power loss and maximizing voltage stability

Optimal sizing and siting of DG


Non-Linear Programming with fuzzification to avoid problem of selection of weighting factors.



High loss reduction in large-scale primary distribution networks

Optimal size and location of 4 types of DG


Improved analytical method including loss sensitivity factor and exhaustive load flow method.



Power losses and voltage profile

DG placement and sizing


Improved Multi-Objective Harmony search



Total imposed costs, total network losses, customer outage costs, private investments

Optimal location of DG


Non-dominated Sorting GA-II



Minimizing total electrical energy losses, total electrical energy cost and total pollutant emissions produced

Optimal placement and sizing of DG units


Interactive fuzzy satisfying method, which is based on Hybrid Modified Shuffled Frog Leaping Algorithm,



Reduce the real power losses and cost of the DG. The paper also focuses on optimization of weighting factor, which balances the cost and the loss factors

Placement of DG


Population based meta heuristic approach namely Shuffled Frog Leaping Algorithm



Network losses reduction & voltage profile and stability enhancement.

DG placement and sizing


Line voltage stability index



Improve voltage profile and reduce power loss

Optimal DG allocation





Improving the stability margin considering system voltage limits, feeders’ capacity, and the DG penetration level

DG placement and sizing


Modified voltage index method using mixed-integer nonlinear programming



Minimize the costs of losses with voltage profile and reliability enhancement

Optimal DG allocation and sizing


Hybrid method based on improved PSO algorithm and Monte Carlo simulation




Optimal DG allocation and sizing


GA with the inclusion of weighting factors.



Power losses and voltage levels

Optimal DG allocation


Bellman-Zadeh algorithm with DiGSILENT software



Reduce the network energy loss, energy cost, and energy not supplied

Optimal size and location DG & of remote controllable switches


GA generation worth index and annual DG operation strategy



Minimize network power losses, better voltage regulation and improve the voltage stability.

Optimal location and sizing of DG


Combination of GA and PSO



Reduction in line loss, voltage sag problem and economical factors like installation and maintenance cost of the DGs

Optimal location and sizing of DG


GA supported weighting method



Optimal number of DGs, along with sizes and bus locations

Optimal location and sizing of DG





Minimizing power loss of the system with enhanced reliability and voltage profile.

Optimal location and sizing of DG


Dynamic Programming



Losses, voltage profile and short circuit level

Optimal location and sizing of DG


Appropriate weight factors based algorithm


Challenges with Distributed Generation

Integration of the DG in distribution network has lots of benefits including technical, economic and environmental. Still, integration of the DG unit with existing power system is facing some challenges [4, 8, 28, 36], [90, 91, 92]. These are divided into three categories as:
  • Technical issues

  • Economical issues

  • Operational & connection issues

These all issues are discussed in detail under this section.

Technical Issues

The prime objective of the DG integration with existing distribution network is to overcome technical troubles like reliability, power loss, harmonics, voltage fluctuation, stability and power quality [4]. The DG can successfully mitigate these problems; still the DG integration has some technical issues. These issues are discussed as follows.

Power Handling Issue

Addition of the DG at the distribution level can significantly affect the amount of the power to be handled by types of equipments such as cables, lines, transformer and many other [93]. In [93], it is discussed that the transformer is the mainly affected during power generation increases with power utilization. The system’s peak hours are more critical as both base and peak distributed generators will operate to cash in the price premium.

Power Quality Issue

This issue depends on the technique, which are used for the DG and their modes of the operation. The key cause of harmonics is frequent on/off or frequent change in voltage and current, which adds non-linearity. In addition, too much use of power electronics devices and modern automatically controlled devices produce power quality issues. Though, these devices are very sensitive to voltage-frequency fluctuations [90].

Short Circuit Capacity

Integration of the DG in existing distribution network increases the short circuit capacity of the system by increasing the steady state current at fault. This depends on size, type and remoteness of the DG from the location where fault occurs. This adversely affects the system reliability as well as its safety. Although, sometimes it is desirable to have high short circuit capacity in case of inverter of a line commutated HVDC station, but in general increase in short circuit capacity dominantly indicated problems [90].

Power Conditioning Issues

The power output pattern of the DG, either AC or DC depends upon the DG technology. The DG source with DC output needs converter to convert DC into AC. In some cases, Cyclo-converters are required to have variable frequency AC supply. The converters may generate harmonics in the system.

Economical Issues

Cost of the DG is the key factor in its growth and adoption as a new technology [94]. The DG has many advantages; still cost of the DG unit is barrier in its growth. Further, the DG is lagging behind due to regulatory plus policy issues. These issues are point wise discussed here.

Electricity Pricing Issues

In the present scenario, as price of the electricity is increasing continuously due to increasing demand by all types of the consumers. There is a possibility that distribution companies and industrial load may install their own generation units to partially fulfil their energy demand. This will reduce purchasing of electricity from grid. Therefore price will get affected as consumers having option to choose power supply either from grid or from own generation unit. This will reduce the market price of electricity and create good competition between different electricity generation companies [8].

Demand Response Effect

Several countries are having electricity market and for its better financial status, demand response is a major tool. The demand response is less effective in case of the RES due to its intermittent nature [90].

Regulatory Issues

The DG is more beneficial, if integrated at proper location in distribution system. Still due to lack of transparent policies and regulatory instruments which are associated with DG treatment, this technology is at brimming stage. In order to promote green energy it is necessary to develop new schemes that support integration and implementation of the DG. An appropriate regulatory policy of Government must be developed for future growth of the DGs.

Operation & Connection Issues

The DG integration in an existing system may introduce protection and power flow related issues. Further, non-optimal location as well as size also creates many problems, therefore, the optimal location with size should be globally optimized. These issues are point wise discussed below.

Protection system Co-Ordination Issue

Earlier distribution systems were radial distribution network where power flow was unidirectional, however, after the DG integration, power can flow in both directions and this may cause some critical challenges in existing network.

The DG units can modify fault current level and disturb the settings of protection devices, making it harder to detect fault. Further, it is complicating co-ordination among the protection devices. Presence of the DGs affects speed of reclosing of switch and it may lead to other serious problems. Since, higher reclosing speed may lead to failure of some DG.

The overall protection schemes and their modification depend upon size, type and location of the DG. In order to avoid major modification, the total capacity of the DG should be 5% [95]. Therefore, a balance is required to manage successful operation of distribution system with RES/non-RES DGs.

Islanding Issues

Islanding issue comes when power is required to continuously deliver to a part of the system by the DG during grid supply is off. It may be challenging for the utility as workers may work on a charged line and it prevents automatic operation of the switching devices. Islanding can be great challenge for synchronization of renewable sources, which results in false tripping at the moment of re-closer operation [8].


Traditionally, the distribution systems were passive with radial topology. Moreover, it needs not to be analysed on the basis of stability as system remains stable during most of the circumstances. However, increased penetration of the DG makes necessary to consider system stability including short duration transient and long term steady state stability [90].

Distribution Test Systems and Load Representation

In present era, looking at the power crisis problem and several other technical and non-technical issues, RES and non-RES DGs are placed near to electrical load centers considering types of loads as: Uniformly distributed, increasingly distributed, centrally distributed, and randomly distributed loads. It is observed in the literature that the majority of the planning of distribution system was carried out for following test systems shown in Table 4.
Table 4

Important test systems in literature

S. No.

Test System

Base power (MVA)

Base voltage (kV)


Data reference


12-Bus network




[96, 97, 98]


16-Bus network



See Appendix

[16, 42]


30-Bus network




33-Bus network


[16, 42, 100]


41-Bus network




69-Bus network


[16, 42, 97, 102, 103]


85-Bus network




141-Bus network




IEEE Test System


In [96, 97, 98], 12-bus (Indian) System was popularly used in testing of several research works. The 12-bus system data is given in [96, 97, 98]. For load flow study, a power base of 0.01 MVA and voltage base of 11 kV can be taken. The one line diagram of 12-bus system is given in Appendix Fig. 7. The 16-bus system was mainly considered in [16, 42]. For study, 100 MVA and voltage base of 23 kV can be suitable base values for power and voltage, respectively. The one line diagram of 16-bus system is presented in Appendix Fig. 8. This system has six capacitors to maintain the system voltage profile at rated value. The 33-bus system data can be obtained from [16, 42, 100]. For load flow study, a power base of 100 MVA and voltage base of 12.66 kV can be considered. The one line diagram of 33-bus system is comprises in Appendix Fig. 9. The 41-bus system data is given in reference [101]. For study, a power base of 100 MVA and voltage base of 33 kV were taken in the literature. The one line diagram of 41-bus system is represented in Appendix Fig. 10. The 69-bus system data is taken from references [16, 42, 97, 102, 103]. For study, a power base of 100 MVA and voltage base of 12.66 kV can be taken. The one line diagram of 69-bus system is presented in Appendix Fig. 11. The 85-bus system data is given in [104]. For study, a power base of 100 MVA and voltage base of 11 kV can be taken. The one line diagram of 85-bus system is presented in Appendix Fig. 12. The 141-bus system data is given in [105]. For study, a power base of 100 MVA and voltage base of 12.47 kV can be considered. The one line diagram of 141-bus system is shown in Appendix Fig. 13.

Supportive Tools for Distributed Generation Planning

Researchers working in the power system have used various research tools to analyse the planning problem for the DG. Therefore, some useful supportive tools, which help greatly in research related to the planning of the DG, have been presented in this section. Tables 5 and 6 can help researchers in working with distribution system including distributed energy sources.
Table 5

Important software tools and their brief description (Open source)

S. No.




The Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST)

The Engineering, Economic & Environmental Electricity Simulation Tool (E4ST) was presented in [107].


Panda Power (Load Flow Programme)

Radial distribution system has been used to implement this power flow programme, which is based on backward/forward sweep approach. In [108], tutorials to use this software are given. Also, the panda power flow programme may be suitable software for power system analysis [109].


Electric Grid Test Cases

This webpage is intended to provide a repository of publicly available, non-confidential power system test cases [110].



The iPST is open-source software which was designed to provide a stage for examination of security and safety of expanded power system. It is an active power system simulator for simulating the dynamics of the system. It additionally encourages the power grid data-mining utilizing huge-data databases that permit storing time-series of power system related information’s [111].



MATPOWER is a software package for solving load flow and system optimization related problems. It was primarily developed as part of the Power Web project [112].


PSAT (Power System Analysis Toolbox)

The PSAT is an obliging software for power system examination and modeling. It can assist in power system stability problems with real time analysis, wind turbine models, change of information records from a few configurations. It provides interfaces to GAMS and UWPFLOW programs [113].


Open DSS

The Open DSS is a comprehensive electrical power system simulation tool. It supports nearly all frequency domain (sinusoidal steady-state) analyses, which are commonly performed on electric utility power distribution systems. In addition, it supports many new types of analyses that are designed to meet future needs related to smart grid, grid modernization and renewable energy research. Other features support analysis of such things as energy efficiency in power delivery and harmonic current flow. The Open DSS has room for changes to meet future needs [114].


Smart Residential Load Simulator (SRLS)

The SRLS facilitates the study of energy management systems in smart grids. This provides a complete set of user-friendly graphical interfaces to properly model thermostats, air conditioners, furnaces, water heaters, refrigerators, stoves, dish washers, cloth washers, dryers, lights and pool pumps as well as wind, solar, and battery sources of power generation in residential houses. The simulator allows modeling, the way appliances consume power and helps to understand how these contribute to peak demand providing individual and total energy consumption and costs and allowing assessment of generated power by residential power sources. This platform can be a useful tool for researchers and educators to validate and demonstrate models for residential energy management and optimization. It can also be used by residential customers to model and understand energy consumption profiles in households [115].


Grid LAB-D

Grid LAB-D is a power distribution system simulation and analysis tool that provides valuable information to users to design and operate distribution systems. It incorporates the most advanced modelling techniques to deliver the best in end-use modelling. The Grid LAB-D can be integrated with three-phase unbalanced power flow and retail market systems [116].


Miscellaneous Data Set

Several public data sets available from IEEE-PES ISS at [117].

Data related to energy and water [118].

The data related to house hold electric consumption having resolution of 1-min [119].

The data related to house hold electric consumption having resolution of 15-min [120].

Table 6

Key features of some popular licensed software

S. No.





The Power Factory Monitor (PFM) is multi-functional Dynamic System Monitor, which can be fully integrated with DIgSILENT Power Factory software. The beauty of PFM is grid and plant monitoring, fault data record, grid characteristics analysis by offering easy access to recorded information, analysis trends, verification of system upset responses and test results [121].



The GAMS is an advanced-level mathematical optimization modeling tool for linear, nonlinear, and mixed-integer optimization problems. They can be efficiently modeled and solved using GAMS. The system is tailored for and allows the user to build large maintainable models that can be adapted to new situations and complex, large-scale modeling applications. The GAMS develop models in concise pattern and human-understandable algebraic statements [122].



PSCAD/EMTDC provides the facility to researchers to build, simulate and model power system networks with ease and limitless possibilities for simulation. The PSCAD/EMTDC also incorporates a comprehensive library of system models ranging from simple passive elements and control functions to electric machines and other complex devices [123].



ETAP is the wide-ranging electrical engineering software platform for the design, simulation, operation, and computerization of generation, transmission, distribution, and industrialized systems. As a fully integrated model-driven enterprise solution, The ETAP extends its scope from modeling to operation in real-time power management system [124].


This paper focuses on optimal planning of DG considering various objective functions and constraints in distribution networks planning. In addition, it also covered the impacts of DG integration on distribution network’s voltage, protection scheme, reliability and security. It is evident from literature that DG installation influences technical, environmental as well as economical benefits in distribution network. Thus, this article also discussed the key benefits and shortcomings (technical, environmental and economical) of addition of DGs. Moreover, renewable energy technology with their comparative study is also presented to make this paper more useful. Further, brief overview of several test systems and open source as well as licensed software presented in this article.

This paper also covered application of modern optimization techniques such as Bacterial Foraging Optimization Algorithm, Simulated Annealing Algorithm, Intelligent Water Drop Algorithm, Shuffled Frog Leaping Algorithm and Invasive Weed Optimization Algorithm in optimal siting and sizing of the DG.



  1. 1.
    International Energy Outlook (2013). U.S Energy Information Administration, July 2013Google Scholar
  2. 2.
    Wen-Shan Tan, Mohammad Yusri Hassan, Md Shah Majid, and Hasimah Abdul Rahman, "Optimal distributed renewable generation planning: a review of different approaches," Renew Sust Energ Rev, vol. 18, February 2013, pp. 626–645Google Scholar
  3. 3.
    Angel A.Bayod-Rújula, "Future development of the electricity systems with distributed generation," Energy, vol. 34, no. 3, March 2009, pp. 377–383Google Scholar
  4. 4.
    Prem Prakash and Dheeraj K. Khatod, "Optimal sizing and siting techniques for distributed generation in distribution systems: a review," Renew Sust Energ Rev, vol. 57, May 2016, pp. 111–130,Google Scholar
  5. 5.
    G. Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer, "Distributed generation: definition, benefits and issues ," Energy Policy , vol. 33, no. 6, April 2005, pp. 787–798Google Scholar
  6. 6.
    Bindeshwar Singh, V. Mukherjee, and Prabhakar Tiwari, "A survey on impact assessment of DG and FACTS controllers in power systems," Renew Sust Energ Rev, vol. 42, February 2015, pp. 846–882Google Scholar
  7. 7.
    Rajkumar Viral and D.K. Khatod, "Optimal planning of distributed generation systems in distribution system: a review," Renew Sust Energ Rev, vol. 16, no. 7, September 2012, pp. 5146–5165Google Scholar
  8. 8.
    Thomas Ackermann, GöranAndersson, and Lennart Söder, "Distributed generation: a definition," Electr Power Syst Res , vol. 57, no. 3, April 2001, pp. 195–204Google Scholar
  9. 9.
    N. Hadjsaid, J. F. Canard, and F. Dumas, "Dispersed generation impact on distribution networks," IEEE Computer Applications in Power, vol. 12, no. 2, April 1999, pp. 22–28Google Scholar
  10. 10.
    M.M. Aman, G.B. Jasmon, A.H.A. Bakar, and H. Mokhlis, "A new approach for optimum simultaneous multi-DG distributed generation units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, vol. 66, March 2014, pp. 202–215Google Scholar
  11. 11.
    N. Khalesi, N. Rezaei, and M.-R. Haghifam, "DG allocation with application of dynamic programming for loss reduction and reliability improvement," Int J Electr Power Energy Syst, vol. 33, no. 2, February 2011, pp. 288–295Google Scholar
  12. 12.
    Chandrasekhar Yammani, Sydulu Maheswarapu, and Sailajakumari Matam, "Multiobjective optimization for optimal placement and size of DG using shuffled frog leaping algorithm," Energy Procedia, vol. 14, March 2012, pp. 990–995Google Scholar
  13. 13.
    A. A. Abdelsalam and E. F. El, "Probabilistic approach for optimal planning of distributed generators with controlling harmonic distortions," IET Generation, Transmission Distribution, vol. 7, no. 10, October 2013, pp. 1105–1115Google Scholar
  14. 14.
    W El-Khattam and M.M.A Salama, "Distributed generation technologies, definitions and benefits," Electr Power Syst Res, vol. 71, no. 2, October 2004, pp. 119–128Google Scholar
  15. 15.
    J.A. Peças Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins, "Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities," Electr Power Syst Res, vol. 77, no. 9, July 2007, pp. 1189–1203Google Scholar
  16. 16.
    Hung DQ, Mithulananthan N, Bansal RC (August 2010) Analytical expressions for DG allocation in primary distribution networks. IEEE Transactions on Energy Conversion 25(3):814–820CrossRefGoogle Scholar
  17. 17.
    S. Kansal, B. B. R., B. Tyagi, and V. Kumar, "Optimal placement of wind-based generation in distribution networks," in IET conference on renewable Power generation (RPG 2011), September 2011, pp. 1–6Google Scholar
  18. 18.
    "Impact of incresing contribution of dispersed generation on the power system," CIGRE study committee, 2003Google Scholar
  19. 19.
    IEEE, "IEEE, Institute of Electeical and Electronics Engineers," 1547–2003 – IEEE Draft Standard for Interconnecting Distributed Resources with Electric Power SystemsGoogle Scholar
  20. 20.
    EPRI, "The Electrical Power Research Institute,".“About Distributed Reosurses”. Full study at: Accessed 27 May 2017
  21. 21.
    Greg Hiemstra van and Alice J. Hovorka, "Fuelwood: the other renewable energy source for Africa?," Biomass Bioenergy, vol. 33, no. 11, November 2009, pp. 1605–1616Google Scholar
  22. 22.
    J. A. P., "Integration of dispersed generation on distribution networks-impact studies," in 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), vol. 1, August 2002, pp. 323–328Google Scholar
  23. 23.
    Maurizio Delfanti, Davide Falabretti, and Marco Merlo, "Dispersed generation impact on distribution network losses," Electr Power Syst Res, vol. 97, April 2013, pp. 10–18Google Scholar
  24. 24.
    Sujatha Kotamarty, Sarika Khushalani, and Noel Schulz, "Impact of distributed generation on distribution contingency analysis," Electr Power Syst Res, vol. 78, no. 9, September 2008, pp. 1537–1545Google Scholar
  25. 25.
    Chiradeja P, Ramakumar R (November 2004) An approach to quantify the technical benefits of distributed generation. IEEE Transactions on Energy Conversion 19(4):764–773CrossRefGoogle Scholar
  26. 26.
    Barin A, Canha L, Abaide A, Machado R (April 2012) Methodology for placement of dispersed generation systems by analyzing its impacts in distribution networks. IEEE Lat Am Trans 10(2):1544–1549CrossRefGoogle Scholar
  27. 27.
    D. T. C., L. F. Ochoa, and G. P. Harrison, "DG impact on investment deferral: network planning and security of supply," IEEE Trans Power Syst, vol. 25, no. 2, May 2010, pp. 1134–1141Google Scholar
  28. 28.
    Gianfranco Chicco and Pierluigi Mancarella, "Distributed multi-generation: a comprehensive view," Renew Sust Energ Rev, vol. 13, no. 3, April 2009, pp. 535–551Google Scholar
  29. 29.
    T. Jin, Y. Tian, C. W. Zhang, and D. W. Coit, "Multicriteria planning for distributed wind generation under strategic maintenance," IEEE Transactions on Power Delivery, vol. 28, no. 1, January 2013, pp. 357–367Google Scholar
  30. 30.
    Seyed Amir Hosseini, et al., "Optimal sizing and siting distributed generation resources using a multiobjective algorithm," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, May 2013, pp. 825–850Google Scholar
  31. 31.
    Dehghanian P, Hosseini SH, Moeini-Aghtaie M, Arabali A (October 2013) Optimal siting of DG units in power systems from a probabilistic multi-objective optimization perspective. Int J Electr Power Energy Syst 51:14–26CrossRefGoogle Scholar
  32. 32.
    Peyman Karimyan, G.B. Gharehpetian, M. Abedi, and A. Gavili, "Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type," Int J Electr Power Energy Syst, vol. 54, January 2014, pp. 277–287Google Scholar
  33. 33.
    M. A. Kashem, A. D. T. Le, M. Negnevitsky, and G. Ledwich, "Distributed generation for minimization of power losses in distribution systems," in 2006 IEEE Power Engineering Society General Meeting, October 2006Google Scholar
  34. 34.
    N. Mohandas, R. Balamurugan, and L. Lakshminarasimman, "Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos," Int J Electr Power Energy Syst, vol. 66, March 2015, pp. 41–52Google Scholar
  35. 35.
    M. Kefayat, A. Lashkar Ara, and S.A. Nabavi Niaki, "A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources ," Energy Convers Manag , vol. 92, March 2015, pp. 149–161Google Scholar
  36. 36.
    Antonio Colmenar-Santos, Cipriano Reino-Rio, David Borge-Diez, and Eduardo Collado-Fernandez, "Distributed generation: a review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks," Renew Sust Energ Rev, vol. 59, June 2016, pp. 1130–1148Google Scholar
  37. 37.
    Mohab M. Elnashar, Ramadan El Shatshat, and Magdy M.A. Salama, "Optimum siting and sizing of a large distributed generator in a mesh connected system," Electr Power Syst Res, vol. 80, no. 6, June 2010, pp. 690–697Google Scholar
  38. 38.
    Sh. Abdi and K. Afshar, "Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing," Int J Electr Power Energy Syst, vol. 44, no. 1, January 2013, pp. 786–797Google Scholar
  39. 39.
    Duong Quoc Hung, N. Mithulananthan, and R.C. Bansal, "Analytical strategies for renewable distributed generation integration considering energy loss minimization," Appl Energy, vol. 105, May 2013, pp. 75–85Google Scholar
  40. 40.
    Juan AndrésMartín García and Antonio JoséGil Mena, "Optimal distributed generation location and size using a modified teaching learning based optimization algorithm," Int J Electr Power Energy Syst, vol. 50, September 2013, pp. 65–75Google Scholar
  41. 41.
    K. Nekooei, M. M. Farsangi, H. Nezamabadi-Pour, and K. Y. Lee, "An improved multi-objective harmony search for optimal placement of DGs in distribution systems," IEEE Transactions on Smart Grid, vol. 4, no. 1, March 2013, pp. 557–567Google Scholar
  42. 42.
    D. Q. Hung and N. Mithulananthan, "Multiple distributed generator placement in primary distribution networks for loss reduction," IEEE Trans Ind Electron, vol. 60, no. 4, April 2013, pp. 1700–1708Google Scholar
  43. 43.
    Satish Kansal et al., "Optimal placement of different type of DG sources in distribution networks," Int J Electr Power Energy Syst, vol. 53, December 2013, pp. 752–760Google Scholar
  44. 44.
    Mohammad H. Moradi, Arash Zeinalzadeh, Younes Mohammadi, and Mohammad Abedini, "An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialist competitive algorithm and genetic algorithm," Int J Electr Power Energy Syst, vol. 54, January 2014, pp. 101–111Google Scholar
  45. 45.
    J.A.Laghari, H. Mokhlis, A. H. A. Bakar, Hasmaini Mohammad, "A comprehensive overview of new designs in the hydraulic, electrical equipments and controllers of mini hydro power plants making it cost effective technology," Renew Sust Energ Rev, vol. 20, April 2013, pp. 279–293Google Scholar
  46. 46.
    S.A. ChithraDevi, L. Lakshminarasimman, and R. Balamurugan, "Stud krill herd algorithm for multiple dg placement and sizing in a radial distribution system,“Engineering Science and Technology, an Internation Journal, Vol. 20, Issue 2, April 2017, pp. 748–759Google Scholar
  47. 47.
    S. Remha, S. Chettih, and S. Arif, "Optimal placement of different DG units type in distribution networks based on voltage stability maximization and minimization of power losses," in 2016 8th international conference on modelling, identification and control (ICMIC), November 2016, pp. 867–873Google Scholar
  48. 48.
    Rama Prabha D, Jayabarathi T, Umamageswari R, Saranya S (September 2015) Optimal location and sizing of distributed generation unit using intelligent water drop algorithm. Sustainable Energy Technologies and Assessments 11:106–113CrossRefGoogle Scholar
  49. 49.
    Rajkumar Viral and D.K. Khatod, "An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization," Int J Electr Power Energy Syst, vol. 67, May 2015, pp. 191–201Google Scholar
  50. 50.
    Sandeep Kaur, Ganesh Kumbhar, and Jaydev Sharma, "A MINLP technique for optimal placement of multiple DG units in distribution systems," Int J Electr Power Energy Syst, vol. 63, December 2014, pp. 609–617Google Scholar
  51. 51.
    S. H. Lee and J. W. Park, "Optimal placement and sizing of multiple DGs in a practical distribution system by considering Power loss," IEEE Trans Ind Appl, vol. 49, no. 5, Sep.-Oct. 2013, pp. 2262–2270Google Scholar
  52. 52.
    S. Gopiya Naik, D.K. Khatod, and M.P. Sharma, "Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks," Int J Electr Power Energy Syst, vol. 53, December 2013, pp. 967–973Google Scholar
  53. 53.
    Rene Prenc, Davor Škrlec, and Vitomir Komen, "Distributed generation allocation based on average daily load and power production curves," Int J Electr Power Energy Syst, vol. 53, December 2013, pp. 612–622Google Scholar
  54. 54.
    Ma Junjie, Wang Yulong, and Liu Yang, "Size and location of distributed generation in distribution system based on immune algorithm," Systems Engineering Procedia, vol. 4, January 2012, pp. 124–132Google Scholar
  55. 55.
    F. S. Abu-Mouti and M. E. El-Hawary, "Optimal distributed generation allocation and sizing in distribution systems via artificial bee Colony algorithm," IEEE Transactions on Power Delivery, vol. 26, no. 4, October 2011, pp. 2090–2101Google Scholar
  56. 56.
    U. Sultana, Azhar B. Khairuddin, M.M. Aman, A.S. Mokhtar, and N. Zareen, "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renew Sust Energ Rev, vol. 63, September 2016, pp. 363–378Google Scholar
  57. 57.
    Belkacem Mahdad and K. Srairi, "Adaptive differential search algorithm for optimal location of distributed generation in the presence of SVC for power loss reduction in distribution system," Engineering Science and Technology, an International Journal, vol. 19, no. 3, September 2016, pp. 1266–1282Google Scholar
  58. 58.
    Pushpendra Singh Renu, "Optimum size and location of distributed generation and capacitor for loss reduction using different optimization technique in Power distribution network," International Research Journal of Engineering and Technology (IRJET), vol. 03, March 2016, pp. 709–713Google Scholar
  59. 59.
    Banaja Mohanty and Sasmita Tripathy, "A teaching learning based optimization technique for optimal location and size of DG in distribution network," Journal of Electrical Systems and Information Technology, vol. 3, no. 1, May 2016, pp. 33–44Google Scholar
  60. 60.
    Sharmistha Sharma, Subhadeep Bhattacharjee, and Aniruddha Bhattacharya, "Quasi-oppositional swine influenza model based optimization with quarantine for optimal allocation of DG in radial distribution network," Int J Electr Power Energy Syst, vol. 74, January 2016, pp. 348–373Google Scholar
  61. 61.
    Rama Prabha D, Jayabarathi T (June 2016) Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm. Ain Shams Engineering Journal 7(2):683–694CrossRefGoogle Scholar
  62. 62.
    A.K. Singh and S.K. Parida, "Novel sensitivity factors for DG placement based on loss reduction and voltage improvement," Int J Electr Power Energy Syst, vol. 74, January 2016, pp. 453–456Google Scholar
  63. 63.
    Antonio JoséGil Mena and Juan AndrésMartín García, "An efficient approach for the siting and sizing problem of distributed generation ," Int J Electr Power Energy Syst , vol. 69, July 2015, pp. 167–172Google Scholar
  64. 64.
    Ke yan Liu, Wanxing Sheng, Yuan Liu, Xiaoli Meng, and Yongmei Liu, "Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs," Int J Electr Power Energy Syst, vol. 69, July 2015, pp. 430–440Google Scholar
  65. 65.
    J. A. Martinez and G. Guerra, "A parallel Monte Carlo method for optimum allocation of distributed generation," IEEE Trans Power Syst, vol. 29, no. 6, November 2014, pp. 2926–2933Google Scholar
  66. 66.
    J.J. Jamian, M.W. Mustafa, and H. Mokhlis, "Optimal multiple distributed generation output through rank evolutionary particle swarm optimization ," Neurocomputing , vol. 152, March 2015, pp. 190–198Google Scholar
  67. 67.
    H. Bagheri Tolabi, M. H. Ali, M. Rizwan, "Novel hybrid fuzzy-intelligent water drops approach for optimal feeder multi objective reconfiguration by considering multiple-distributed generation," Journal of Operation and Automation in Power Engineering, vol. 2, Autumn 2014, pp. 91–102Google Scholar
  68. 68.
    El-Fergany A (January 2015) Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int J Electr Power Energy Syst 64:1197–1205CrossRefGoogle Scholar
  69. 69.
    Wang Z, Chen B, Wang J, Kim J, Begovic MM (September 2014) Robust optimization based optimal DG placement in microgrids. IEEE Transactions on Smart Grid 5(5):2173–2182CrossRefGoogle Scholar
  70. 70.
    A. Ameli, S. Bahrami, F. Khazaeli, and M. R. Haghifam, "A multiobjective particle swarm optimization for sizing and placement of DGs from DG Owner's and distribution Company's viewpoints," IEEE Transactions on Power Delivery, vol. 29, no. 4, August 2014, pp. 1831–1840Google Scholar
  71. 71.
    S. Devi and M. Geethanjali, "Optimal location and sizing of distribution static synchronous series compensator using particle swarm optimization," Int J Electr Power Energy Syst, vol. 62, November 2014, pp. 646–653Google Scholar
  72. 72.
    Y. Zhao, Y. An, and Q. Ai, "Research on size and location of distributed generation with vulnerable node identification in the active distribution network," IET Generation, Transmission Distribution, vol. 8, no. 11, November 2014, pp. 1801–1809Google Scholar
  73. 73.
    Ruhaizad Ishak, Azah Mohamed, Ahmed N. Abdalla, and Mohd Zamri Che, "Optimal placement and sizing of distributed generators based on a novel MPSI index," Int J Electr Power Energy Syst, vol. 60, September 2014, pp. 389–398Google Scholar
  74. 74.
    X. Zhang, G. G. Karady, and S. T. Ariaratnam, "Optimal allocation of CHP-based distributed generation on urban energy distribution networks," IEEE Transactions on Sustainable Energy, vol. 5, no. 1, January 2014, pp. 246–253Google Scholar
  75. 75.
    C. Yammani, S. Maheswarapu, and S. K. Matam, "Optimal placement and sizing of DER's with load models using BAT algorithm," in 2013 international conference on circuits, Power and computing technologies (ICCPCT), June 2013, pp. 394–399Google Scholar
  76. 76.
    S. Devi and M. Geethanjali, "Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation," Expert Syst Appl, vol. 41, no. 6, May 2014, pp. 2772–2781Google Scholar
  77. 77.
    Mohammad H. Moradi, S.M. Reza Tousi, and Mohammad Abedini, "Multi-objective PFDE algorithm for solving the optimal siting and sizing problem of multiple DG sources," Int J Electr Power Energy Syst, vol. 56, March 2014, pp. 117–126Google Scholar
  78. 78.
    M. Gomez-Gonzalez, F.J. Ruiz-Rodriguez, and F. Jurado, "Probabilistic optimal allocation of biomass fueled gas engine in unbalanced radial systems with metaheuristic techniques," Electr Power Syst Res, vol. 108, March 2014, pp. 35–42Google Scholar
  79. 79.
    V. A. Evangelopoulos and P. S. Georgilakis, "Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm," IET Generation, Transmission Distribution, vol. 8, no. 3, March 2014, pp. 389–400Google Scholar
  80. 80.
    Duong Quoc Hung, N. Mithulananthan, and Kwang Y. Lee, "Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss," Int J Electr Power Energy Syst, vol. 55, February 2014, pp. 179–186Google Scholar
  81. 81.
    W. Buaklee and K. Hongesombut, "Optimal DG allocation in a smart distribution grid using cuckoo search algorithm," in 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, July 2013, pp. 1–6Google Scholar
  82. 82.
    FarukUgranlı and Engin Karatepe, "Optimal wind turbine sizing to minimize energy loss," Int J Electr Power Energy Syst, vol. 53, December 2013, pp. 656–663Google Scholar
  83. 83.
    M. Esmaili, "Placement of minimum distributed generation units observing power losses and voltage stability with network constraints," IET Generation, Transmission Distribution, vol. 7, no. 8, July 2013, pp. 813–821Google Scholar
  84. 84.
    Hasan Doagou-Mojarrad, G.B. Gharehpetian, H. Rastegar, and Javad Olamaei, "Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm," Energy, vol. 54, June 2013, pp. 129–138Google Scholar
  85. 85.
    Zahra Moravej and Amir Akhlaghi, "A novel approach based on cuckoo search for DG allocation in distribution network," Int J Electr Power Energy Syst, vol. 44, no. 1, January 2013, pp. 672–679Google Scholar
  86. 86.
    R. S. Al, E. F. El-Saadany, and Y. M. Atwa, "Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation," IEEE Trans Power Syst, vol. 28, no. 1, Feb. 2013, pp. 326–334Google Scholar
  87. 87.
    Mahdi Raoofat and Ahmad Reza Malekpour, "Optimal allocation of distributed generations and remote controllable switches to improve the network performance considering operation strategy of distributed generations," Electric Power Components and Systems, vol. 39, no. 16, Oct. 2011, pp. 1809–1827Google Scholar
  88. 88.
    M.H. Moradi and M. Abedini, "A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems," Int J Electr Power Energy Syst, vol. 34, no. 1, January 2012, pp. 66–74Google Scholar
  89. 89.
    Soma Biswas, Swapan Kumar Goswami, and Amitava Chatterjee, "Optimum distributed generation placement with voltage sag effect minimization," Energy Convers Manag, vol. 53, no. 1, January 2012, pp. 163–174Google Scholar
  90. 90.
    Naveen Jain, S.N. Singh, "Distributed Generation in Power Systems: An Overview and Key Issues," Proceedings of IEC, December 2009Google Scholar
  91. 91.
    Dondi P, Bayoumi D, Haederli C, Julian D, Suter M (April 2002) Network integration of distributed power generation. J Power Sources 106(1):1–9CrossRefGoogle Scholar
  92. 92.
    Kumawat M, Gupta N, Jain N, Saxena D (Sept. 2015) Optimal distributed generation placement in power distributed networks: a review. In: 2015 international conference on electrical, electronics, signals, communication and optimization (EESCO), pp 1–6Google Scholar
  93. 93.
    F. van Overbeeke, "Active networks: distribution networks facilitating integration of distributed generation," 2nd International Symposium on Distributed Generation. Stockholm: Royal Institute of Technology, 2002Google Scholar
  94. 94.
    Vivek Shrivastava Vikas, "A review on distributed generation definitions and DG impacts on distribution system," Nov. 2013Google Scholar
  95. 95.
    S.N. Liew and G. Strbac, "Maximizing penetration of wind generation in existing distribution networks," ser Generation Transmission and Distribution, IEE Proceedings, vol. 149, Aug. 2002Google Scholar
  96. 96.
    S.F Mekhamer, S.A Soliman, M.A Moustafa, and M.E El-Hawary, "Load flow solution of radial distribution feeders: a new contribution," International Journal of Electrical Power and Energy Systems, vol. 24, no. 9, Nov. 2002, pp. 701–707Google Scholar
  97. 97.
    Gözel T, Hocaoglu MH (June 2009) An analytical method for the sizing and siting of distributed generators in radial systems. Electr Power Syst Res 79(6):912–918CrossRefGoogle Scholar
  98. 98.
    Das D, Nagi HS, Kothari DP (Jul. 1994) Novel method for solving radial distribution networks. IEE Proc. Gener. Transm. Distrib. 141(4):291–298CrossRefGoogle Scholar
  99. 99.
    I. Totonchi, H. Al Akash, A. Al Akash, and A. Faza, "Sensitivity analysis for the IEEE 30 bus system using load-flow studies," in 2013 3rd international conference on electric Power and energy conversion systems, Oct. 2013, pp. 1–6Google Scholar
  100. 100.
    Eminoglu U, Hocaoglu MH (Dec. 2008) Distribution systems forward/backward sweep-based Power flow algorithms: a review and comparison study. Electr Power Compon Syst 37(1):91–110CrossRefGoogle Scholar
  101. 101.
    Kumar V, Gupta I, Gupta HO, Agarwal CP (Nov. 2005) Voltage and current sensitivities of radial distribution network: a new approach. IEE Proceedings Generation, Transmission and Distribution 152(6):813–818CrossRefGoogle Scholar
  102. 102.
    Ghosh S, Das D (Nov. 1999) Method for load-flow solution of radial distribution networks. IEE Proc Gener Transm Distrib 146(6):641–648CrossRefGoogle Scholar
  103. 103.
    Naresh Acharya, Pukar Mahat, and N. Mithulananthan, "An analytical approach for DG allocation in primary distribution network," Int J Elect Power Energy Syst, vol. 28, no. 10, Dec. 2006, pp. 669–678,Google Scholar
  104. 104.
    D. Das, D.P. Kothari, and A. Kalam, "Simple and efficient method for load flow solution of radial distribution networks," Int J Electr Power Energy Syst, vol. 17, no. 5, Oct. 1995, pp. 335–346Google Scholar
  105. 105.
    Khodr HM, Olsina FG, de Oliveira-De PM, Yusta JM (July 2008) Maximum savings approach for location and sizing of capacitors in distribution systems. Electr Power Syst Res 78(7):1192–1203CrossRefGoogle Scholar
  106. 106.
    (Feb. 1995) Bibliography on load models for power flow and dynamic performance simulation. IEEE Trans Power Syst 10(1):523–538Google Scholar
  107. 107.
    Biao Mao, Daniel Shawhan and Ray Zimmerman. Compendium of simulation software, E4ST [online]. Available: content/uploads/2016/02/ Accessed 27 May 2017
  108. 108.
    Panda Power, Compendium of load flow and power flow MATLAB codes [online]. Available: Accessed 27 May 2017
  109. 109.
    Panda Power, Compendium of load flow and power flow, tutorials [online]. Available: Accessed 27 May 2017
  110. 110.
    Ti Xu, Electric grid test cases [online]. Available: Accessed 27 May 2017
  111. 111.
    iTesla power system tools (Power system simulation tool) [online]. Available:
  112. 112.
    Ray D. Zimmerman, Carlos E. Murillo-Sánchez. MATLAB power system simulation package [online]. Available: Accessed 27 May 2017
  113. 113.
    Power System Analysis and simulation Tool Box (June, 2006) [online]. Available: Accessed 27 May 2017
  114. 114.
    Distribution System Simulator, distribution circuit simulator [online]. Available: Accessed 27 May 2017
  115. 115.
    Smart residential load simulator, [online]. Mail- J. Miguel Gonzalez. Available: Accessed 27 May 2017
  116. 116.
    Pacific Northwest National Laboratory (December 2014), Power distribution system simulation and analysis tool [online]. Available : Accessed 27 May 2017
  117. 117.
    Open data sets. Available at: Accessed 27 May 2017
  118. 118.
    Energy and water data. Available at: Accessed 27 May 2017
  119. 119.
    Regional energy consumption data. Available at: resources
  120. 120.
    Hourly electrical power consumption data. Available at: Accessed 27 May 2017
  121. 121.
    DigSILENT Gmbh, Germany, Power factory monitor, [online]. Available at: Accessed 27 May 2017
  122. 122.
    GAMS Development Corporation, algebraic mathematics simulator [online]. Available at: Accessed 27 May 2017
  123. 123.
    Manitoba. HVAC research centre, power system computer added designing tool [online]. Available at: Accessed 27 May 2017
  124. 124.
    Etap (1986), USA, Electrical Power System Analysis & Operation Software [online]. Available at: Accessed 27 May 2017
  125. 125.
    Mohammad Jafar Hadidian-Moghaddam, Saber Arabi-Nowdeh, Mehdi Bigdeli, and Davood Azizian, "A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique,” Ain Shams Engineering Journal, available online, March 2017Google Scholar
  126. 126.
    Othman MM, El-Khattam W, Hegazy YG, Abdelaziz AY (March 2015) Optimal placement and sizing of distributed generators in unbalanced distribution systems using supervised big bang-big crunch method. IEEE Trans Power Syst 30(2):911–919CrossRefGoogle Scholar
  127. 127.
    Mohamed Imran A and Kowsalya M, "Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization," Swarm and Evolutionary Computation, vol. 15, April 2014, pp. 58–65Google Scholar
  128. 128.
    P. S. Georgilakis and N. D. Hatziargyriou, "Optimal distributed generation placement in Power distribution networks: models, methods, and future research," IEEE Trans Power Syst, vol. 28, no. 3, Aug. 2013, pp. 3420–3428Google Scholar
  129. 129.
    Florina Rotaru, Gianfranco Chicco, Gheorghe Grigoras, and Gheorghe Cartina, "Two-stage distributed generation optimal sizing with clustering-based node selection," Int J Electr Power Energy Syst, vol. 40, no. 1, Sept. 2012, pp. 120–129Google Scholar
  130. 130.
    M.M. Aman, G.B. Jasmon, H. Mokhlis, and A.H.A. Bakar, "Optimal placement and sizing of a DG based on a new power stability index and line losses," Int J Electr Power Energy Syst, vol. 43, no. 1, Dec. 2012, pp. 1296–1304Google Scholar
  131. 131.
    Chandrasekhar Yammani, Sydulu Maheswarapu, and Sailajakumari Matam, "Multiobjective optimization for optimal placement and size of DG using shuffled frog leaping algorithm," Energy Procedia, vol. 14, March 2012, pp. 990–995, 2011 2nd International Conference on Advances in Energy Engineering (ICAEE)Google Scholar
  132. 132.
    Taher Niknam, Seyed Iman Taheri, Jamshid Aghaei, Sajad Tabatabaei, and Majid Nayeripour, "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources," Appl Energy, vol. 88, no. 12, Dec. 2011, pp. 4817–4830Google Scholar
  133. 133.
    M. Amjadi, S. J. Peighambardoust, "Review of the proton exchange membranes for fuel cell applications,"Int J Hydrog Energy, vol. 3 5, Sept. 2010, pp. 9349–9384Google Scholar
  134. 134.
    Adel M. Sharaf and Mustafa E. Şahin, “A flexible PV-powered battery-charging scheme for electric vehicles,” IETE Tech Rev, vol. 34, no. 2, April 2016, Pages 133–143Google Scholar
  135. 135.
    Joseph E. Aldy, William A. Pizer, and Keigo Akimoto, “Comparing emissions mitigation efforts across countries,” Clim Pol, vol. 17, no. 4, Jan. 2016, Pages 501–515Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringCollege of Technology and EngineeringUdaipurIndia

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