Optimal Placement of Distributed Generation

Bansal Mk

Published on: 2023-10-09

Abstract

Power sector is the key for the growth of Indian economy. Increasing demand of power in the developing countries has presented two foremost challenges in power sector which are a) addition of new power generation capacity and b) Expansion of existing transmission and distribution infrastructure. Distributed generation has potential to deal with these challenges, especially in remote areas. Badly managed distribution of power lead to slower industrial growth and huge economy loss. India is 3rd major creator and fourth major user of electricity in world by way of installed power capacity touching 370029 MW as of 30th Sept 2020. The renewable energy resource power capacity is around 24.43 % of India's total installed capacity. The total electricity production in India during 2018-19 fiscal year including utilities and non-utilities was 1,547 TWh and electricity produced by utilities was 1,372 TWh. The total electricity consumption was 1,181 kWh per capital in India during 2018-19. In 2015-16, electricity consumption in agriculture that is around 17.89% was noted as the highest worldwide. The per capita consumption of electric power in India is around 1100 Kwhr/year that is very low as compared to most of other countries in spite of low electricity tariff in India. India realizes a challenge to confirm availability of reliable and current forms of energy to all its people. Around 85 % of rural houses rest on solid fuel for their cooking needs and only 55 % of all rural houses have access to electric power. The energy shortage at the end of financial year 2009-10 was around 10.1 %, whereas at the end of financial year 2020-21, it is reduced up to 0.4 %. (Ministry of New and Renewable Energy, GoI).

Keywords

Distributed generation; Power sector; Distribution infrastructure

Introduction

The reasons for very poor per capita consumption are many and main factor is poor distribution network leading to high aggregate technical and commercial losses (AT&C losses). This concept is very useful as it gives an accurate representation of energy & revenue loss condition at distribution level. The technical losses can be sub-grouped further on the basis upon the power transformation & transmission system stages as transmission losses (400kV/220kV/132kV/ 66kV) and as distribution losses (33kV and below). Transmission and Distribution losses are the percentage of electric energy lost in the electricity grid while transportation of electricity from generating station to consumption point. All countries present some percentage of technical losses. There are numerous causes for technical losses which are inherent to power transmission system. Technical losses are regarded as I 2R losses, Insufficient reactive compensation, ill maintained equipment and Transformer losses etc. High number of technical losses in the system are largely due to insufficient investments on system improvement works for number of years, unplanned enlargement of the distribution lines, absence of adequate reactive power support and overloading of the system elements like transformers and conductors etc. The main reasons of commercial losses are pilferage, defective meters, mistakes in meter reading and unmetered supply of energy.

The AT&C losses in electric power system should be ideally about 3 to 6%. In the developed countries, these losses are less than 10%. But power losses in developing countries are around 20%. The electricity prices in deregulated marketplaces are also linked to the system losses. Hence, electric sector is presently concerned in reducing it so as to be more competitive. The technical and non-technical losses in India are reported as 22.5% of the whole input energy as per 2018-19 data. India has 31 states including union territories. From the graph as shown below Delhi-NCR (Major cities are Faridabad, Gurugram, Noida, Gr. Noida, Ghaziabad) has AT&C losses of 9.07% which is lowest in 2018-19 data as received from Ministry of Power and Annual Report of Power Finance Corporation (PFC) but there are many districts in Delhi-NCR which are having more than 20% AT&C Losses which shall be the focus of the study. Arunachal Pradesh has the highest AT&C losses in 2018-19 of more than 55% and the Indian average is 22.5%. (2018-19 data). As per 2018-19 World Bank data, 54 Percent of Countries have AT&C losses of 10 to 72 Percent. Loss is 2 percent in Singapore, 4 percent in China, 5.9 percent in United States, 6.4 percent in European Union, 10 percent in Russia, 15.8 percent in Brazil, 22.5% in India, 72.5 percent in Togo. The average losses in world are 8.3 percent as per World Bank report.

Figure 1: Source Ministry of Power and Power Finance Corporation.

In Delhi, Faridabad, Noida, Gurugram, Gr. Noida, and Ghaziabad, AT&C losses are less than 10% where as in Palwal and Bhiwani, considered under the study in the thesis, it is more than 28% which is above national average of 22% ( Source MoP and PFC 2018-19). This is the reason to choose the particular regions for the study.

Literature Review

There is no common definition of distributed generation because it uses numerous technologies and applications. Different countries use different names like “decentralized generation”, “embedded generation”, “and dispersed generation” etc. (C.Fortoul et al; 2005). The definition used in this thesis is, “Distributed generation is defined as an electrical source coupled to the power system, in a point near/or at consumer´s site, which is small scale as compared to centralized power plants”. Presently, there is no agreement on what rating and voltage level of DGs should be at the “point of common coupling (PCC)” to the grid. These two parameters to some extent differ among countries research groups. For example, Portugal defines DGs capacity limit up to 10 MW which can be connected at all voltage level, Austria defines DGs capacity limit up to 10 MW which can be connected at MV level, France defines DGs capacity limit greater than 40 MW which can be connected at 225 kV voltage level, Spain agrees that DGs capacity limit can go up to 50% of feeder capacity. Until now, only Australia has accomplished to install the largest size DGs of 130 MW at 132 kV voltage level on its power grid and Denmark has allowed the utility to determine the voltage level for connecting DGs units size which range from less than 2 MW to greater than 50 MW. The “International Council on Large Electric Systems CIGRE” defines DGs as an electric source of energy with maximum capacity less than 50–100 MW. Though the capacity limit of DG are not clearly defined for UK, Netherlands and Germany and but their voltage levels are defined as up to 132 kV for UK, up to 150 kV for Netherlands and up to 110 kV for Germany (Favuzza et al. 2007). Cano (2007) proposed classification of DGs on the basis of their capacities as 1 W to 5 kW micro DGs, 5 kW to 5 MW small DGs, 5 MW to 50 MW medium DGs and 50 MW to 300 MW are large DGs.

Table 1: Previous Studies on Distributed Generation.

Title	Authors (Year)	Journal	Outcome
1.Optimal Placement and Sizing of Distributed Generation for Power Loss Reduction using Particle Swarm Optimization	Bhumkittip,et al (2013)	Energy Procedia, 43(01), 307-317	Particle Swarm Optimization (PSO) methodology used is fast and accurate to find the optimal DG location and size satisfying constraint of minimizing the total real power loss in transmission line. In this study, the methodology used was tested on 26 bus system to find best location of DG where the total power loss of the system reduced extremely with significant improvement in system voltage profile.
2. Optimal placement of distributed generations considering voltage stability and power losses with observing	Esmaili,et al (2013)	Applied Energy, 113 (01), 1252-1260	Separately optimized method is used to find optimal position of distribution generation. Installation of DGs not always help to reduce power losses so number of DGs should be selected such that voltage profile is in given acceptable limits and grid losses are minimum.
3. Distributed Power Generation Rural India – A Case Study	Bharadwaj and Tongia (2000)	Computer Science	Load flow analysis is used to simulate the line settings for existing rural feeders in India, and examine the benefits of decentralized generation in terms of loss reduction and system improvement so as to meet rural loads.
4. An approach to quantify the technical benefits of distributed generation	Chiradeja & Ramakumar (2004)	IEEE Transactions on Energy Conversion	Presented the technical gains with the placement of DG in a quantitative manner. They worked on the 12-bus test system to show the usefulness of the developed indices of voltage reduction index, line loss reduction index, and DG performance index.
5.Determining the Impact of Distributed Generation on Power Systems	Barker and de Mello (2000).	Power Engineering Society Summer Meeting, Seattle, 1645-1656	Examined the potential of DG to enhance distribution system performance
6. The influence of the connection technology of dispersed energy sources on grid stability	Thong et al. (2004)	Second International Conference on Power Electronics, Machines and Drives (PEMD 2004),742-745	Described the interconnection of Distributed Generation and its impact on power system.
7. Review of the Distributed Generation Concept: Attempt of Unification	Gonzalez-Longatt and C. Fortoul (2005)	Renewable energy and power quality journal	Various distributed generation concept was reviewed by authors and optimization techniques were not used in any study.
8. Research of Distribution Generation Planning in Smart Grid Construction	Xia et al. (2010)	Asia-Pacific Power and Energy Engineering Conference	Authors discussed how to integrate renewable Energy based distributed generation safely and reliably in a smart grid by using adaptive genetic algorithm.
9. Power quality improvement of distribution system by optimal placement and power generation of DGs using GA and NN	Reddy et al. (2012)	European Journal of Scientific Research	Discussed the benefits of optimal placement of DG in power quality improvement using GA and NN technique
10. Impact of Distributed Generation on Reliability of Distribution System	Das & Deka (2013)	IOSR Journal of Electrical and Electronics Engineering	Discussed the effect of DG on the reliability of distribution system and found that If DG units were placed optimally then it improves the reliability of distribution network
11. Voltage Control and Voltage stability of Power distribution systems in the presence of DG	viawan (2008)	PhD Thesis	Presented analysis on voltage control and stability in the distribution systems with and without integration of induction and synchronous distributed generation (DG).
12. Distributed generation issues and standards	Vaziri et al. (2011)	IEEE international conference on information reuse and integration	In current paper, authors investigated DG standards with special emphasis on frequency and voltage and interconnection concerns at high penetration stages.
13. Analysis of the Distributed Generation System and the Influence on Power Loss	Wang bo and Lan Ka (2011).	Asia-Pacific Power and Energy Engineering Conference	Discussed the role of distributed generation system to meet with the higher demand and improve power quality.
14. A study of the impact of Distributed Generation on power quality	Olatoke and Darwish (2012)	in universities power Engineering Conference (UPEC), 47th international	Authors discussed about multi-DG distribution network and analysed the power quality of such distribution network
15. Impact of Distributed Generation on Smart Grid Transient Stability	Hidayatullah et al. (2014)	Smart Grid and Renewable Energy, 2, 99-109	Studied impact of distributed generation systems on smart grid stability using ‘Flexible AC Transmission System (FACTS)’ machine particularly ‘Static VAR Compensator (SVC)’
16. Effects of Distributed Generation penetration on system power losses and voltage profiles	Kilongi & Abungu (2013)	International Journal of Scientific and Research Publications	The authors used Genetic Algorithm approach on IEEE 57-bus test system to find optimal location and Size of Multi type DG for reducing power losses and improving voltage profile. Further authors discussed that though with the introduction of first DG the losses are reduced and voltage profile improved but with further increase in number of DG, distribution network reached a point where power losses increased and voltage profile is distorted
17. Multiple distributed generator placement in primary distribution networks for loss reduction	Hung and Mithulananthan, (2013)	IEEE Transactions on Industrial Electronics	The authors studied how to place multi-DG units so as to achieve high drop in losses in large scale prime distribution system. Improved analytical method is proposed to compute size of four different type of DG and best location to place them. Optimal power factor for DG units to provide real and reactive power for minimizing losses is calculated in this method and this method is more effective as compare to exhaustive load flow method (ELF) and loss sensitivity factor method (LSF).
18. Optimization of Distributed Generation Systems using a New Discrete PSO and OPF	M. Gomez-Gonzalez and López (2012)	Electric Power Systems Research The simulation results have shown that this method allows reduced losses and costs.	In this paper authors used new hybrid techniques like optimal power flow (OPF) and particle swarm optimization (PSO) to find optimal location and size combination for predefined number of DG in the distribution network. This results in reduced transmission losses and cost.
19. A Local Loss Reduction Method Based on Injected Power Sensitivity	Wen et al (2012)	Power and Energy Engineering Conference	This paper proposed Injected Power Sensitivity that is local loss reduction method to minimize the local line losses quickly and safely.
20. Maximal optimal benefits of distributed generation using genetic algorithms	Abou El Ela et al (2010).	Electric Power Systems Research	The Linear programming technique (LP) using genetic algorithm (GA) was applied on real section of the ‘West Delta sub-transmission network’ to find optimal site and size of DG considering multiple system constraints to attain single or multiple objectives. The results showed improvement in efficiency in terms of Power flow Reduction (PFR), Voltage Profile Improvement (VPI), Line-Loss Reduction (LLR) and spinning reserve increasing (SRI).
21. Evaluation of the impact of distributed generation on power losses by using a sensitivity-based method	Ayres et al (2009)	Power & Energy Society General Meeting, 2009. PES'09. IEEE	The authors used sensitivity-based method to find impact of DG on active and reactive power losses. The analytical method is used on 70-bus distribution network to assess the effects of varying location, generation level and operating type of generators so as to maintain stability and reliability. Also, to enumerate the effect of multi-DG on power losses, a numerical index is proposed.
22. Selection of optimal location and size of multiple distributed generations by using Kalman filter algorithm	Lee and Park (2009).	Power Systems, IEEE Transactions	The authors used Kalman filter algorithm to select the optimal size of multi- DG. Using KF algorithm method, only few samples were taken so computational requirement was reduced dramatically in the optimization method.
23. Economic Operation and Optimization of a Standalone Microgrid system	Wang et al (2012)	Autom. Electr. Power Syst	The researchers used analytical methods in this paper for optimal location of DG to reduce the power loss in radial and networked system. Three diverse load distributions like uniformly, centrally and uniformly rising distributed load are considered to find the effectiveness of DG in terms of loss reduction.
24. Real time analysis of the control structure and management functions of a hybrid microgrid System	Gaztanaga et al (2006)	IEEE Industrial Electronics 32nd Annual Conference, Paris.	In this study, authors suggested multi objective formulation technique for finding optimal location and sizing of DG units into prevailing distribution networks by minimizing objective functions such as energy losses cost, Service interruptions cost, network upgrading cost and energy purchased cost.
25. A Centralized Optimal Energy Management System for Microgrids	Daniel E. Olivares et al (2011).	IEEE PES	The optimal Power management is analysed on the basis of two foremost approaches which are Centralized Power Management System (CPMS) and Distributed Power Management System (DPMS). The CPMS system is proposed being better power management technique for autonomous microgrid by considering advantages and disadvantages of both systems. CPMS has mainly two blocks which are economic load dispatch and optimal dispatch block of distributed power resource.
26. Optimal Energy Management of Wind battery Hybrid Power System with Two-scale Dynamic Programming	Zhang and Yaoyu (2013)	IEEE Transactions on Sustainable Energy	Authors suggested optimal power management approach for wind-battery hybrid power system (WBHPS) using two-scale dynamic programming, grounded on multistage estimation of load, wind power generation and utility price.
27. Optimal power flow management for grid connected PV systems with batteries	Riffonneau et al (2011)	IEEE Trans. Sustain. Energy	A predictive optimization method using dynamic programming is used to forecast loads consumption, electricity price, solar irradiance and ambient temperature.
28. Multi objective optimal allocation of a distributed generation unit in distribution network using PSO	Maruthi Prasanna et al (2014)	International Conference on Advances in Energy Conversion Technologies	Multi objective method using PSO is used for Optimal assignment of DG in the distribution network to achieve two objectives that are minimizing power losses and maximizing tail end voltage.
29. Particle Swarm Optimization based Method for Optimal Placement and Estimation of DG capacity in Distribution Networks	Aref et al (2012)	International Journal of Science and Technology	Discussed about finding optimal position and capacity of DG in radial distribution network to minimize real power losses and improve the voltage profile by using PSO method. This method was tested on IEEE 13-bus network.
30. Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications	Kancev et al (2011)	IEEE Transactions on Industrial Electronics	The authors recommended a deterministic power management system with advanced PV generators used for smart grid functions.
31. Smart Mini Grid: An innovative distributed generation based energy system	Gujar et al (2013)	Innovative Smart Grid Technologies-Asia	Discussed the design of smart minigrid systems which incorporated several distributed generation technologies such as Solar PV, small wind generator, biomass, diesel so as to improve the efficiency and reliability of the whole system
32. Microgrids Management	Katiraei et al (2008)	IEEE Power Energy Mag	proposed multiple DG units microgrid system for real and reactive electric power management
33. System modelling and online optimal management of microgrid using mesh adaptive direct search	Mohamed and Koivo (2010)	International Journal of Electrical Power and Energy System	Authors proposed mesh adaptive optimization technique for optimal sizing of hybrid energy system taking into account reliability and cost factors.
34. Modelling and simulation of the micro sources within a micro grid	Yubing et al (2008)	international journal of power	Examined an interface problem that may be generated from distributed generators connected to a microgrid modelled and simulated by using Matlab.
35. Future energy systems, integrating renewable energy sources into the smart power grid through industrial electronics	Liserre et al (2010)	IEEE Ind. Electron Mag	Described future energy system which put together renewable energy sources with smart power grid by industrial electronics.
36. A standalone photovoltaic super capacitor battery hybrid energy storage system	Glavin et al (2008)	in Proc. 13th Power Electronics Motion Control Conf	Suggested photovoltaic superior capacitor battery hybrid power storage system
37. Strategic framework of an energy management of a micro grid with photovoltaic-based active generator	Lu and Francois (2009)	in Proc. Electro Motion EPE Chapter	Described an energy management framework of micro grid using photovoltaic active generator
38. Energy loss minimization of electricity networks with large wind generation using FACTS	Zhang (2008)	IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century	Author had discussed in this paper about sensitivity analysis to find distribution generation size and the operating point so as to minimize energy losses in power system based on renewable energy-based DG.
39. Distributed generation for minimization of power losses in distribution systems	Kashem et al (2006)	IEEE Power Engineering Society General Meeting	Discussed optimization techniques in a distribution feeder to find size, location and operating point of distribution generation unit so as to minimize losses.
40. Optimization model for loss minimization in a deregulated power distribution network	Davidson and Ijumba (2002)	IEEE AFRICON. 6th Africon Conference in Africa	Authors suggested an optimization model for placing distribution generation units for minimizing losses.
41. Allocation of losses in distribution systems with embedded generation	Mutale et al. (2000)	IEEE proceedings of generation, transmission and distribution	Authors suggested a methodology to determine the impact of integration distribution generation units on loss reduction by calculating loss allocation coefficient.
42. The impact of embedded generation on voltage regulation and losses of distribution networks	Salman (1996)	IEE Colloquium on the Impact of Embedded Generation on Distribution Networks	Authors by using some techniques found impact of distribution generation size and positioning on reliability, losses and voltage profile of distribution system
43. Optimal allocation of embedded generation on distribution networks	Keane and O’Malley (2005)	IEEE Transactions on Power Systems	The authors proposed linear programming methodology for finding optimal location and size of DG considering maximum generation capacity objective and constraint of fixed load.
44. A method for placement of DG units in distribution networks	Hedayati et al (2008)	IEEE PES Power Systems Conference and Exposition	Authors did study on 34 bus test system for finding optimal location of DG units to reduce power losses, increase the voltage stability and load
45. Optimal distributed generation allocation in MV distribution networks	Celli & Pilo (2001)	22nd IEEE PES International Conference on Power Industry Computer Application	Genetic algorithm method is used for allocation of distributed generation and its sizing in distribution network that minimize cost by doing significant savings.
46. Effective method for optimal allocation of distributed generation units in meshed electric power systems	Akorede et al (2011)	IET Generation, Transmission Distribution	The authors used genetic algorithm and fuzzy method for effective allocation of DG units in meshed power network so as to maximise the loading margin and profit of distribution companies considering voltage and feeder power flow as constraints in the optimization process
47. A multi-objective approach for the optimal distributed generation allocation with environmental constraints	Celli et al (2008)	Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems	Multi objective optimization technique is used to find optimal location of distributed generation considering uncertainties because of renewable energy resources to make the study more realistic.
48. Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization	Prommee and Ongsakul (2010)	European Transactions On Electrical Power	Improved reinitialized social structures PSO is used for optimal allocation of Multiple DG units in microgrid system
49. Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation	Zonkoly (2011)	IET Generation, Transmission Distribution	Multi-objective index based technique is used for finding the multiple DGs capacity taking into account diverse load models so as to minimize the real and reactive power losses and improve voltage .

There are more number of researches and findings in this area. But due to some practical difficulties in implementation the methods are not able to be adopted in the power sectors. So a real time study is required to find solution for the practical problems while implementation and hence the study was carried out. Limited studies are available showing the comparative analysis of with and without DG scenario.

So different optimization and MCDM tools and techniques are used to determine the optimal size and location of the DG units and further assessing the impact on the behaviour of an electric system with and without presence of distributed generation.

The difference between the results obtained for these two operating conditions provides important information to both, customers as well as companies in the electric sector. So the main problems encountered after incorporation of DG in the distributed network is presented.

Objective of Study

The foremost objective of current study is to explore the technical impact that the incorporation of distribution generation unit has on the protection coordination of distribution power network. This impact can be evaluated by investigating the behaviour of power system, with and without the existence of DG. The comparison between the results attained from these different operating conditions provides important information to power sector companies.

The major problem today in Power sectors are losses in transmission and distribution network. In India Electricity Board the Transmission & Distribution (T&D) losses is around 22% and Aggregate Technical and Commercial (AT & C) losses is around 19.5%. The Transmission and distribution losses in the advanced countries of the world have been ranging between 6 to 11%. Delhi NCR also need to reduce and bring the T&D losses below 10% as per standard. So, the first objective is

To survey on T&D and AT &C losses and methods and techniques to mitigate these losses.

The Power quality problems tend to be localized phenomena and are not often system wide concerns. With the increasing use of electronic components for appliances and equipment in homes, offices, and factories, customers are increasingly concerned about power quality, and potential damages to equipment and business operations. In certain instances, DG can be used to address power quality problems, particularly when the systems involve the use of energy storage, power electronics, and power conditioning equipment. So, the second objective is

To survey various Distributed Generation technologies and renewable energy generation techniques and methods for reduction of line losses and improve power quality.

The DG systems can produce reductions in peak load electricity requirements, depending on how the DG is operated. Because most investment decisions for new plant and equipment in the electric power industry are driven by peak load requirements. Reductions in peak load can displace or defer capital investments. Properly planned and operated DG can provide consumers and society wide benefits of improved environmental performance also. So, the third objective is

To determine the optimal solar renewable energy mix DG to meet the load demand, minimize the installation cost of renewable resources, maximizing energy savings & efficiency and improved environmental performance.

However, there are also examples where the use of DG has led to power quality problems. The power loss of the system can be considerably reduced by finding optimum location of a decentralized power generator. There is a significant reduction of technical distribution losses, reactive and real power losses, and improvement in voltage profiles. So, the fourth objective is

To determine the optimum location for the placement of a renewable distributed generation unit for minimizing the technical distribution losses, reactive and real power losses, and improvement in voltage profile.

Electric system reliability is an aggregate measure used by electric system planners and operators to evaluate the level and quality of service to customers. The use of DG directly to the end user increase electric system reliability. A renewable energy-based DG impact on the power system will be compared with traditional system. So, the fifth objective is

To investigate the potential benefits of renewable energy-based DG on increased electric system reliability by comparing this with traditional distribution system.

Research Methodology

It is expected to attract to get results for reducing real power losses by using DGs units. Better understanding of the flow of electric power on transmission lines investigate the power losses along transmission lines. The help of optimization and multi criteria decision making tools gives an insight into the major problems on electric power transmission and provides a solution in a compact form for meeting the desired objectives. In this research, a thorough analysis of power loss characteristics with variations in DG capacity is performed and discussed. The Analytical and Descriptive research design has been considered wherein the research is undertaken on two areas viz Palwal (NCR region) and Bhiwani region. Following tools and techniques have been used:

Optimization based on weighted aggregate method used to determine the size of the DG unit.
Multicriteria Decision Making: TOPSIS used to identify the optimal place for DG unit installation.
Reliability indices such as SAIDI, EENS, and E-Cost are used to compute the reliability of the system with and without DG.

Renewable Energy Resources Mix for Sustainable Development: A Revenue Optimization Model

It is very complicated to determine the mix of rating of renewable energy sources (Solar in NCR). So the researcher has discussed the potential benefits of optimal solar renewable energy mix DG to meet the load demand using the Multi-Objective Optimization Model (MOOM). The model was formulated to minimize the installation cost of renewable resources, maximizing energy savings and efficiency. To achieve the optimal solution of the multiple conflicting objectives, a multi-objective linear programming model has been formulated as:

Weighted Sum Technique

In order to determine tradeoff-based solution, the researcher scalarize a set of objectives into a single objective by adding each objective pre multiplied with a suitably determined weight.

Table 2: Data of Solar Panel with Three Different Range.

Solar Panel Mix	Energy Savings (MW)	Cost of Installation (Rs)	Energy Production (MW)	Efficiency (%)
Small Range	0.0001	87.000	1	10
Medium Range	0.0015	1,30,500	1.5	14
Large Range	0.0002	1,74,000	1.75	17.5

Model for Palwal Region

Using the data given in Table, the multi-objective linear programming problem can be represented as follows:

Model for Bhiwani Region

Using the data given in Table, the multi-objective linear programming problem can be represented as follows:

The solution for the Palwal and Bhiwani models are obtained using optimization software LINGO-17 as follows:

Interpretation

The Multi-Objective Optimization Model (MOOM) was developed to determine the optimal solar renewable energy mix that needs to be installed to meet the load demand. The model was formulated to minimize the installation cost of renewable resources, maximizing energy savings and efficiency. The solar renewable resource mix obtained using weighted sum technique depicts the installation of 10 units of medium solar panels in Palwal region and 7 units in Bhiwani region to meet the total demand of 15 and 10 MW.

Optimal Placement of Renewable Energy Based DG

DG units can reduce line losses and provide environmental benefits. But the problem is to decide the finest position for the placement of DG units which provides the optimal results. The problem of optimal position of DG units is formulated using alternatives and criteria. The alternatives, in this case, are potential buses on which DG units can be connected. Therefore, 15 and 10 MW DG unit has been determined using optimization technique as an optimal sizing. Here the focus is on four criteria: losses, real power, reactive power, and voltage. Analysis has been done, how 15 MW (Palwal region) and 10 MW (Bhiwani region) induction of DG help to reduce line losses in IEEE 8 and 6 Bus system. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to determine the preference importance for each location.

Figure 2: Technique for Order of Preference by Similarity to Ideal Solution.

Table 3: For IEEE 8 Bus System (Palwal Region).

Bus Number	Relative Closeness to Ideal Solution	Rank
0	0.211	4
2	0.332	3
4	0.036	5
6	0.935	1
8	0.688	2

Results shows that if the DG unit is placed at 6th bus, then losses, real power, and reactive power are minimum, whereas voltage is maximum.

Table 4: For IEEE 6 Bus System (Bhiwani Region).

Bus Number	Relative Closeness to Ideal Solution	Rank
0	0.0361	7
1	0.1234	6
2	0.4798	5
3	0.7462	4
4	0.9639	1
5	0.8726	2
6	0.8045	3

Results shows that if the DG unit is placed at 4th bus, then losses, real power, and reactive power are minimum, whereas voltage is maximum.

The Potential Benefits of Dg on Increased Electric System Reliability

DG can be used by electric system planners and operators to improve reliability in both direct and indirect ways. For example, DG could be used directly to support local voltage levels and avoid an outage that would have otherwise occurred due to excessive voltage sag. DG can improve reliability by increasing the diversity of the power supply options. DG can improve reliability in indirect ways by reducing stress on grid components to the extent that the individual component reliability is enhanced. Reliability indices are used by system planners and operators as a tool to improve the level of service to customers. Planners use them to determine the requirements for generation, transmission, and distribution capacity additions.

Reliability Indices

System Average Interruption Duration Index (SAIDI) =

Where,

i= Outage source (power line, transformer, switch, etc.)

j= Customers experiencing interruption by outage source

N_j= Number of isolated customers due to outage source i

N_t= Total number of customers

t_i= Interruption duration by outage source i (in minutes)

h_i= Failure rate by outage source i.

Calculation of system Expected Energy Not Supplied due to power outage (EENS) =

Where,

i= Outage source (power line, transformer, switch, etc.)

k= Load point

L_k=Load at load point k

t_i= Interruption duration by outage source i (in minutes)

h_i= Failure rate by outage source i.

Calculation of system Expected Outage Cost

ECOST=

Where,

i= Outage source (power line, transformer, switch, etc.)

k= Load point

L_k= Load at load point k

t_i= Interruption duration by outage source i (in minutes)

h_i= Failure rate by outage source i

C_ik(t_i)= Customer interruption cost due to outage source i with interruption duration t_i.

Table 5: Reliability Performance Comparison (Palwal Region).

Reliability	Without DG	With DG	% Reduction
SAIDI	1827.5	1316	27.99%
EENS (MWH)	85283.333	61413.333	27.99%
E COST Rs/yr)	385554.5	341175	11.51%

Table 6: Reliability Performance Comparison (Bhiwani Region).

Reliability	Without DG	With DG	% Reduction
SAIDI	1286	1028.8	20.00%
EENS (MWH)	46724.667	37379.733	20.00%
E COST (Rs/yr)	216630.99	200187.33	7.59%

Conclusions & Future Scopes

The research has focused on examining the opportunities for distributed power generation in Delhi NCR region of India. Decentralized power generation near the load centres has the capability of addressing the energy crisis faced by Delhi NCR India. There is a critical improvement in the voltage profiles and decrease of technical distribution losses. This makes a possibility of setting up micro-grids or electricity cooperatives with solar-based power generators. Improvement in quality of power supply also makes incentives for farming and industrial tariff reforms. The study is restricted to just in Palwal and Bhiwani region of India. In future work, the work can be extended to different other regions in India using the examination and methods to decrease the line losses and further improve the power quality in entire country and at the same time increment in the revenue of the utilities.

Commercial Implications

Renewable distributed generation units such as solar panels have become cost-effective for many commercial and residential sectors. Larger placement of renewable DG technologies is promoted because of their benefits like energy security, resiliency, and CO2 emissions reductions. Grid operators may trust on some businesses to run their onsite emergency generators to sustain consistent electricity service for customers at peak hour’s power use. Distributed generation diminishes or removes the line losses or wasted energy which occurs through transmission and distribution of electricity by use of local energy sources. Distributed generation reduce the amount of power which is produced at centralized power plant and benefit the environment. Distributed generation has provided new opportunities for industrial and commercial solar adopters with its applications for industrial units, office buildings, retail spaces, warehouses and agricultural operations.

Organization of Thesis

This thesis is structured into seven chapters includes content as follows.

Chapter 1 gives introduction of the study, includes aggregate and transmission losses, introduction of DG, objective and significance of study, Methodology, and organization of thesis.

Chapter 2 covers detailed literature review on distribution generation systems and technologies, impact of DG and methods implemented for reducing line losses and improving power quality.

Chapter 3 discusses the potential benefits of optimal solar renewable energy mix DG to meet the load demand using the Multi-Objective Optimization Model (MOOM). The model was formulated to minimize the installation cost of renewable resources, maximizing energy savings and efficiency.

Chapter 4 discuss about determination of best location for the placement of a renewable DG unit for minimizing the losses in power transmission and distribution. The multi-criteria decision-making technique named as TOPSIS has been employed to find optimum location.

Chapter 5 investigates the potential benefits of DG on increased electric system reliability. The comparison and analyses give the real fact of the distribution network.

Chapter 6 covers the conclusion and future research.

Chapter 7 covers the commercial implications. There are major commercial implications of the study for both service providers as well as customers. The study can be used to minimize installation cost, generate revenue sources, and maximize energy savings with evident commercial benefit for the organization and environment.