Distributed Energy Resources and Supportive Methodologies for their Optimal Planning under Modern Distribution Network: a Review
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Abstract
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 nonconventional energy sources, which include Renewable Energy Sources (RES) as well as nonRES. Though, the RES are getting more encouragement due to several advantages over nonRES. 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 nonRES), 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 greenhouse gases and many more. However, these advantages are difficult to achieve due to some technical and nontechnical 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/nonrenewable 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.
Keywords
Distributed generation Distributed generation planning Modern distribution system Optimization approach Power system Renewable energy sourcesIntroduction
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, biomass and wind energy sources.
 (a)
Micro DG (1 W < 5 kW),
 (b)
Small DG (5 kW < 5 MW),
 (c)
Medium DG (5 MW < 50 MW)
 (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].

Power injection pattern from the DG is very important as it depends upon type of generation source, whether renewable or nonrenewable. Hence, researchers must take care while choosing any renewable/nonrenewable 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 bidirectional 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 subtransmission 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
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 

1  Solar photovoltaic (SPV)  1 kW–80,000 kW  Solar radiation to electrical  Difficult  Sun  70,000  These are represented in [4]. 
2  Small hydro  5 kW–100,000 kW  Gravitational potential energy to electrical  Difficult  Water  650,000845,000  
3  Micro hydro  1 kW–1000 kW  Gravitational potential energy to electrical  Difficult  650,000845,000  
4  Wind turbine  200 W 3000 kW  Wind energy to electrical  Difficult  Wind  45,000–68,500  
5  Biomass energy  100 kW–20,000 kW  Chemical to electrical, thermal and in biofuels  Difficult  Biomass  45,000–50,000  
6  Geothermal energy  5000 kW 100,000 kW  Heat to electrical  Difficult  Hot water  170,000–350,000  
7  Tidal energy  0.1–1 MW  Kinetic energy to electrical  Difficult  Ocean water  –  
8  Hydrogen energy scheme  40–400 MW  Chemical to electrical  Difficult  Water, organic compounds, biomass, and hydrocarbons  –  
9  marine energy  100 kW–1000 kW  Kinetic to electrical  Difficult  Ocean wave  – 
A brief overview of major nonrenewable DG technologies
S. No.  DG Technologies  Power Generation Range  Energy Conversion  Fuel Type  Capital Cost/kW  Merits & Demerits 

1  Integrated gasification combined gas turbine  30 kW3000+ kW  Fuel to gas then to electricity  Gas, diesel or coal  55,000–116,200  These are represented in [4]. 
2  Micro turbine  30 kW–1000 kW  Chemical to mechanical then electrical  Biogas, propane or natural gas  78,000–110,500  
3  Internal combustion (IC) engine  5 kW–10,000 kW  Diesel, gas or natural gas  17,000–37,000  
4.  Fuel cell (FC) technologies  Chemical to electrical  –  –  
Alkaline FCs  100 W 50000 W  Alkaline electrolyte like KOH  >12,965  
Phosphoricacid FCs  200 kW–2000 kW  Acidic solution like H_{3}PO_{4}  194,479  
Molten carbonate FCs  250 kW–2000 kW  Molten carbonate salt electrolyte  >12,965  
Solid oxide FCs  250 kW–5000 kW  Ceramic ion conducting electrolyte in solid oxide form  64,826  
Proton exchange FCs  1–250,000 W  Proton exchange membrane  97,240  
Battery storage  500–5000 kW  6500–13,000 
AfterEffects of DG

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 offcourse 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 multiobjective 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].

Analytical Techniques

Classical Optimization Techniques

Artificial Intelligent (Metaheuristic) Techniques

Miscellaneous Techniques

Other Techniques for Future Use
Analytical Techniques
Classical Optimization Technique
Artificial Intelligent Techniques
Miscellaneous Techniques
Future Promising Optimizing Techniques
Significant Contribution in the Reviewed Planning of the DG
Main contribution of the published Optimal DG Planning works in chronological order
S. No.  Goal of the Planning  Planning Variables  SO/MO  Algorithm  Ref. 

1  Minimization of total electrical power losses (PL)  Optimal placement of multiple DG units  SO  Stud Krill herd Algorithm  [46] 
2  Minimization of total electrical PL  Optimal placement of different types of DG units  SO  Bat Algorithm  [47] 
3  Minimization of total electrical PL  Optimal location and sizing of DG unit  SO  Intelligent Water Drop algorithm along with loss sensitivity factor  [48] 
4  Minimization of total electrical PL  Optimal siting and sizing of DG units  SO  Analytical method  [49] 
5  Minimization of total electrical PL  Optimal site and size  SO  Sequential Quadratic Programming and Branch and Bound algorithm  [50] 
6  Minimization of total electrical PL  Optimal allocation and sizing of different types of DG units  SO  PSObased algorithm and also analytical method  [32] 
7  Minimization of total electrical PL  ODGP and sizing of multiple DG  SO  Kalman Filter Algorithm, optimal locator index  [51] 
8  Minimization of total electrical PL  Optimal allocation (sizing and siting) of DG and capacitor  SO  Method based on analytical approach with heuristic curve fitting technique  [52] 
9  Minimization of total electrical PL  Optimal placement of DGs and size of the DG’s  SO  PSO technique  [43] 
10  Minimization of total electrical PL  Optimal allocation of three types of DG (Solar parks, wind farms and power stations)  SO  GA  [53] 
11  Minimization of total electrical PL  Optimal placement and size of DG units  SO  Modified Teaching Learning Based Optimization Algorithm  [40] 
12  Minimization of total electrical PL  Size and Location of DG  SO  Immune Algorithm with active model of DG in the smart network including all kind of cast factors.  [54] 
13  Minimization of total electrical PL  Optimum sizes and operating strategy of DG units  SO  Three alternative analytical expressions (Elgerd’s loss formula, branch current loss formula, branch power flow loss formula)  [39] 
14  Minimization of total electrical PL  Optimal DGunit’s size, power factor, and location  SO  Metaheuristic, populationbased optimization methodology with an Artificial Bee Colony (ABC) algorithm  [55] 
15  Reduction in power loss along with voltage stability enhancement  Optimum DG placement  MO  [56]  
16  Addition of cost of real power and energy loss cost with power loss optimization  Optimal location and size of multi DG  MO  Adaptive Differential Search Algorithm  [57] 
17  Comparison of three optimization techniques for reduction of the real power loss and voltage profile improvement  Optimal placement of DG  MO  PSO, GA and PSO + ABC  [58] 
18  Real power loss minimization and voltage improvement and improvement of voltage stability index  Optimal location and size of DG  MO  Teaching learning based optimization algorithm  [59] 
19  Network power losses, achieve better voltage regulation and improve the voltage stability  Optimal location and sizing of DG unit  MO  QuasiOppositional Swine Influenza Model Based Optimization with Quarantine  [60] 
20  Diminishing real power disaster, working expense and improving voltage steadiness  Optimal location and sizing of DG unit  MO  IWO along with the loss sensitivity factor  [61] 
21  Reducing power losses and improving voltage profile  Optimal location  MO  Loss reduction sensitivity method Voltage improvement sensitivity method  [62] 
22  Minimizing power losses and generation costs  Optimal location and sizing of DG unit  MO  Relaxed MINLP  [63] 
23  Minimum annual investment and operation (I&O) cost of DG, purchasing electricity cost & voltage deviation  Optimal sitting and sizing  MO  Improved Nondominated Sorting GAII  [64] 
24  Energy loss minimization considering the random nature of some distributed resources and the time varying loads  Optimum allocation of DG  SO  Refined parallel Monte Carlo method  [65] 
25  Optimal multiple DG  Location  SO  Rank Evolutionary PSO By hybridizing the Evolutionary Programming in PSO algorithm.  [66] 
26  Power loss, line flow maximum value, and voltage summary and voltage steadiness directory combined using weighting coefficients.  Optimal siting and sizing of DG units  MO  Chaotic Artificial Bee Colony Algorithm  [34] 
27  Mitigation of losses, improving the voltage profile and equalizing the feeder load balancing in distribution systems  Optimal siting and sizing of multiple DG units  MO  Hybrid FuzzyIWD Approach  [67] 
28  Network real loss and enhance the voltage profile combined power factor and reduction in network reactive power loss  Optimal allocation of DG  MO  Backtracking Search Optimization Algorithm  [68] 
29  Costs are minimized and profits are maximized  Optimal locations, sizes and mix of dispatchable & discontinuous DGs  MO  Column and Constraint Generation framework  [69] 
30  Improving voltage profile and stability, powerloss reduction, and reliability enhancement, economic analysis  Optimal DGs places, sizes, and their generated power contract price  MO  PSO  [70] 
31  Enhancement of power quality includes improvement of voltage and reduction of line losses  Optimal placement and sizing of Distribution Static Synchronous Series Compensator  MO  PSO  [71] 
32  Removal of susceptible nodes to maintain the voltage level of system  Optimal location and capacity of DG units  SO  GA  [72] 
33  Voltage stability and loss minimization  Optimal location and size of DGs  MO  Maximum Power Stability Index and PSO Algorithm  [73] 
34  Promoting energy competence  Optimal network capacity and distribution of the CHPbased DG  SO  Integrated System Dispatch model  [74] 
35  System loss minimization and voltage profile improvement  Optimal siting and sizing of DG units  MO  BA  [75] 
36  Reduce the total power loss and to improve the voltage profile  Optimal siting and sizing of DG units  MO  Bacterial Foraging Optimization Algorithm Modified Bacterial Foraging Optimization Algorithm  [76] 
37  Reduction in power system losses, maximization of system load ability and voltage quality improvement.  MultiDG placement and sizing  MO  Hybrid PSO  [10] 
38  Voltage constancy, power losses and network voltage fluctuations  ODGP and sizing  MO  Pareto Frontier Differential Evolution algorithm  [77] 
39  Voltage regulation problem considering random nature of lower heat value of biomass and load.  Optimal location of biomass fuelled gas engines  SO  FrogLeaping Algorithm and three phase probabilistic load flow combined with the Monte Carlo method  [78] 
40  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  MO  Point estimate method embedded GA  [79] 
41  Minimizing annual energy losses  Optimal location, size and power factor of dispatchable and nondispatchable DG units  SO  Analytical Approach  [80] 
42  To make mini hydro scheme a costeffective renewable energy option  New designs of turbines, electrical equipment’s and governor controllers  SO  [45]  
43  Improvement in power system parameters  Optimal sitting and sizing of DG  MO  ICA and GA  [44] 
44  Minimize real power losses by maintaining the fault level and the voltage variation within the acceptable limit.  Optimal sizing and siting of DG  SO  CS technique  [81] 
45  Minimize energy loss considering time varying characteristics of both load and windgeneration profile  Optimal size of wind turbine  SO  Weighting factor based methodology incorporating genetic algorithm with power flow analysis with fuzzy–c means clustering to reduce execution time.  [82] 
46  Minimize the annual energy losses and reduce the harmonic distortions  Optimally allocating different types of DG (i.e. windbased DG, solar DG and nonrenewable DG)  MO  Probabilistic planning approach  [13] 
47  Reduced number of DG, less power loss and maximizing voltage stability  Optimal sizing and siting of DG  MO  NonLinear Programming with fuzzification to avoid problem of selection of weighting factors.  [83] 
48  High loss reduction in largescale primary distribution networks  Optimal size and location of 4 types of DG  SO  Improved analytical method including loss sensitivity factor and exhaustive load flow method.  [42] 
49  Power losses and voltage profile  DG placement and sizing  MO  Improved MultiObjective Harmony search  [41] 
50  Total imposed costs, total network losses, customer outage costs, private investments  Optimal location of DG  MO  Nondominated Sorting GAII  [31] 
51  Minimizing total electrical energy losses, total electrical energy cost and total pollutant emissions produced  Optimal placement and sizing of DG units  MO  Interactive fuzzy satisfying method, which is based on Hybrid Modified Shuffled Frog Leaping Algorithm,  [84] 
52  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  MO  Population based meta heuristic approach namely Shuffled Frog Leaping Algorithm  [12] 
53  Network losses reduction & voltage profile and stability enhancement.  DG placement and sizing  MO  Line voltage stability index  [10] 
54  Improve voltage profile and reduce power loss  Optimal DG allocation  MO  CS  [85] 
55  Improving the stability margin considering system voltage limits, feeders’ capacity, and the DG penetration level  DG placement and sizing  MO  Modified voltage index method using mixedinteger nonlinear programming  [86] 
56  Minimize the costs of losses with voltage profile and reliability enhancement  Optimal DG allocation and sizing  MO  Hybrid method based on improved PSO algorithm and Monte Carlo simulation  [38] 
57  Optimal DG allocation and sizing  GA with the inclusion of weighting factors.  [30]  
58  Power losses and voltage levels  Optimal DG allocation  MO  BellmanZadeh algorithm with DiGSILENT software  [26] 
59  Reduce the network energy loss, energy cost, and energy not supplied  Optimal size and location DG & of remote controllable switches  MO  GA generation worth index and annual DG operation strategy  [87] 
60.  Minimize network power losses, better voltage regulation and improve the voltage stability.  Optimal location and sizing of DG  MO  Combination of GA and PSO  [88] 
61.  Reduction in line loss, voltage sag problem and economical factors like installation and maintenance cost of the DGs  Optimal location and sizing of DG  MO  GA supported weighting method  [89] 
62.  Optimal number of DGs, along with sizes and bus locations  Optimal location and sizing of DG  MO  GA  [17] 
63  Minimizing power loss of the system with enhanced reliability and voltage profile.  Optimal location and sizing of DG  MO  Dynamic Programming  [11] 
64  Losses, voltage profile and short circuit level  Optimal location and sizing of DG  MO  Appropriate weight factors based algorithm  [37] 
Challenges with Distributed Generation

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 nonlinearity. 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 voltagefrequency 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, Cycloconverters 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, nonoptimal 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 CoOrdination 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 coordination 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/nonRES 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 recloser operation [8].
Stability
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
Important test systems in literature
S. No.  Test System  Base power (MVA)  Base voltage (kV)  Figure  Data reference 

1  12Bus network  0.01  11  
2  16Bus network  100  23  See Appendix  
3  30Bus network  11  [99]  
4  33Bus network  12.66  
5  41Bus network  33  [101]  
6  69Bus network  12.66  
7  85Bus network  11  [104]  
8  141Bus network  12.47  [105]  
9  IEEE Test System  [106] 
In [96, 97, 98], 12bus (Indian) System was popularly used in testing of several research works. The 12bus 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 12bus system is given in Appendix Fig. 7. The 16bus 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 16bus system is presented in Appendix Fig. 8. This system has six capacitors to maintain the system voltage profile at rated value. The 33bus 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 33bus system is comprises in Appendix Fig. 9. The 41bus 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 41bus system is represented in Appendix Fig. 10. The 69bus 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 69bus system is presented in Appendix Fig. 11. The 85bus 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 85bus system is presented in Appendix Fig. 12. The 141bus 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 141bus system is shown in Appendix Fig. 13.
Supportive Tools for Distributed Generation Planning
Important software tools and their brief description (Open source)
S. No.  Tool  Description 

1.  The Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST)  The Engineering, Economic & Environmental Electricity Simulation Tool (E4ST) was presented in [107]. 
2.  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]. 
3.  Electric Grid Test Cases  This webpage is intended to provide a repository of publicly available, nonconfidential power system test cases [110]. 
4.  iPST  The iPST is opensource 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 datamining utilizing hugedata databases that permit storing timeseries of power system related information’s [111]. 
5.  MATPOWER  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]. 
6.  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]. 
7.  Open DSS  The Open DSS is a comprehensive electrical power system simulation tool. It supports nearly all frequency domain (sinusoidal steadystate) 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]. 
8.  Smart Residential Load Simulator (SRLS)  The SRLS facilitates the study of energy management systems in smart grids. This provides a complete set of userfriendly 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]. 
9.  Grid LABD  Grid LABD 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 enduse modelling. The Grid LABD can be integrated with threephase unbalanced power flow and retail market systems [116]. 
10.  Miscellaneous Data Set  Several public data sets available from IEEEPES ISS at [117]. 
Data related to energy and water [118].  
The data related to house hold electric consumption having resolution of 1min [119].  
The data related to house hold electric consumption having resolution of 15min [120]. 
Key features of some popular licensed software
S. No.  Tool  Description 

1.  DIgSILENT  The Power Factory Monitor (PFM) is multifunctional 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]. 
2.  GAMS  The GAMS is an advancedlevel mathematical optimization modeling tool for linear, nonlinear, and mixedinteger 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, largescale modeling applications. The GAMS develop models in concise pattern and humanunderstandable algebraic statements [122]. 
3.  PSCAD  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]. 
4.  ETAP  ETAP is the wideranging electrical engineering software platform for the design, simulation, operation, and computerization of generation, transmission, distribution, and industrialized systems. As a fully integrated modeldriven enterprise solution, The ETAP extends its scope from modeling to operation in realtime power management system [124]. 
Conclusion
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.
Notes
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