Planning and Operation of Distributed Generation in Distribution Networks

International Journal of Emerging Technology and Advanced Engineering

381

Planning and Operation of Distributed Generation in

Distribution Networks

Gopiya Naik. S

_{, D. K. Khatod}

_{, M. P. Sharma}

1 _{Research Scholar, Alternate Hydro Energy Centre, IIT Roorkee, Roorkee-247667, India.}

2 _{Assistant professor,} 3_{Associate professor, Alternate Hydro Energy Centre, IIT Roorkee, Roorkee-247667, India}

. Abstract— Optimal siting and sizing of Distributed Generation (DG) and other power compensating devices in distribution networks are the two crucial and also very important factors to get maximum benefits such as technical, economical, and environmental both for the utility and customers, especially, in sites where the central generation is impracticable or where there are deficiencies in the transmission system. This paper discusses an overview on different DG technology, available capacity and their merits and demerits. The state-of-art-of literature on operational (network reconfiguration in the presence of DG) and planning (optimal sizing and siting of DG, distribution system expansion planning with DG, and the DG-Capacitor placement) aspects in the presence of DG on the distribution networks has also been reviewed and presented. The various optimization techniques employed in the DG planning and operation with their merits and demerits has been discussed.

Keywords— Distributed Generation, DG Technology,

Distribution System Planning, Optimization

Techniques.

I. INTRODUCTION

For many years, power systems were vertically and centralized operated systems. The large thermal and nuclear power plants generate most of the power due to their scale and economic merits. The electric power is transmitted and distributed to consumers over long distances at different voltage levels. The centralized and hierarchical control is applied to allow real time monitoring and control of the system. The existing power system structures are changing due to: geographical and environmental constraints, stability and security problems of large plants, rapidly growing demand related investment, privatization, deregulation, competitive energy markets and emergence of advanced generation techniques with small ratings employed with environmental benefits and increased profitability[1].

A distribution system is meant to provide reliable power in cost effective manner to the consumers. Conventional distribution system planning follows well-established strategies such as expanding existing substations, building new substations, adding new feeders and/or reconfiguring the existing distribution system, load switching and capacitor placement which need additional investment in generation and transmission infrastructure to meet the increasing load demand [2-3]. In recent years, deregulation and liberalization of energy market, increasing petroleum fuel prices and associated environmental

concerns has attracted the attention of

researchers/developers to incorporate distributed generation (DG) in distribution system planning. DG is a relatively small power generation source (from a few KW upto 10 MW), usually, connected in the distribution network or at the consumer side for the purpose of reducing power losses, improving voltage profile and power quality, peak shaving, eliminating the need of reserve margin with improved environmental concerns and increasing the network capacity. The disadvantages of DG include the stability, complex protection strategies and the islanding problems [4]. The major driving forces for the increasing penetration of DG in distribution system are technical, economical and environmental benefits [5].

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A. DISTRIBUTED GENERATION TECHNOLOGIES

The advances in DG technologies and increase in their sizes play significant role in power distribution systems. As per the current definition, DG is very diverse and range from 1kW PV installation, 1 MW engine generators to 1000 MW offshore wind farms or more [6]. Table- I, briefly overviews the most commonly used DG and their typical module sizes [7].

TABLE- I: DISTRIBUTED GENERATION SYSTEM WITH MODULAR SIZE [6]

No. DG Technology Typical available

power module size

1 Combined Cycle Gas Turbine 35-400 MW 2 Internal Combustion Engines 5 kW -10 MW 3 Combustion Turbine 1-250 MW 4 Micro-Turbines 35 kW-1 MW 5

5 a Fuel Cells Phos. Acid 200 kW -2 MW b Molten Carbonate 250 kW -2 MW c Proton Exchange 1-250 kW d Solid Oxide 250 kW-5 MW

6 Battery Storage 0.5-5 MW 7

7 a

Hydro power

Small Hydro 1-25 MW 7 b Micro Hydro 25 kW -1 MW

8 Wind Turbine 200 W -3 MW 9 Solar Photovoltaic power plants 20 W-100 kW 10 Solar Thermal power plants based

on central Receiver

1-10 MW 11 Solar Thermal ( Lutz System) 10-80 MW 12 Biomass Gasification based power

plants

100 kW-20 MW 13 Geothermal 5-100 MW 14 Ocean Energy 0.1-1 MW

The above table shows that all DG based on hydro, solar biomass, ocean and geothermal energy are renewable DGs while others are conventional DGs. For centralized generation, synchronous generator, asynchronous generator and power electronic converter interfaces can also be used as DG [8-10]. Table-II compares the merits and demerits of various DG technologies [11].

Form the table-II, it can be seen that fuel cell, wind, solar PV and small hydro are emission free DGs and require no fuel and are environmental friendly. The most suitable DGs considering environmental concerns, fuel cost, maintenance costs and output power are identified as wind, SPV, biomass, small hydro etc.

B. OPTIMIZATION TECHNIQUES

Optimization is a mathematical formulation that is concerned with finding of minima or maxima of functions subject to the so called constraints. Some decision making analysis involves determining the action that best achieves a desired goal or objective. This finding means the actions that optimizes (i.e. minimizes or maximizes) the value of an objective function. Optimization is applied in the deregulated power industry to find best allocation of DG and other devices. There are many optimization techniques available for the distribution system planning in the presence of DG as discussed below. For determining global optimal solution to the complex multi-objective optimization problem, one has to consider the basic conflicts resulting between accuracy, reliability and computational time. So, some trade-off is necessary to arrive at the compromised solution by satisfying all the objectives.

Literature has revealed various solution

techniques/methodologies that can be employed for optimal allocation and are classified into four categories: (a) Analytical approaches (b) Artificial intelligent search techniques, like Genetic Algorithm (GA), Particle Swarm Optimization(PSO), Ant Colony Algorithm(ACO), Tabu Search(TS), Evolutionary programming (EP), Fuzzy Logic (FL), and Differential Evolution (DE) (c) Conventional techniques Such as Probabilistic based Mixed Integer Non-Linear Programming (MINLP), Monte-Carlo (MC) simulation, Artificial Bee Colony (ABC), Distribution Load Flow (DLF), Optimal Power Flow (OPF), Continuation Power Flow (CPF), and index based planning, and (d) Hybrid based techniques like GA-OPF, GA-PSO, GA-TS, Fuzzy-GA, PSO-Ordinal optimization. A comparison of these optimization techniques is given in Table-III. Fig. I classifies various optimization techniques.

Classification of Optimization Techniques

Analytical Artificial Intelligent Conventional Hybrid

e.g. Mathematical GA, ACA, TS, PSO, MINLP, OPF, DLF, GA-OPF, GA-PSO,

model EP, FL, etc. Index based, etc. GA-TS, Fuzzy-GA, etc.

and Numerical

Solution

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TABLE-II: COMPARISON BETWEEN MAIN DG TECHNOLOGIES TABLE- III: COMPARISON OF OPTIMIZATION TECHNIQUES

Optimizatio n Technique

Merits Demerits References

Analytical  Simple and useful in Capacitor and DG placement

 Non-iterative hence no convergence problem

 Can’t get exact solution but only approximate solution is obtained

 Can’t be directly applied given the size, complexity and the specific characteristics of distribution systems [15,16, 17, 32,36] Genetic

algorithm  They function with discrete and continuous parameters

 Lack of accuracy when high-quality solution is required [19,29,41, 42, 52,60, 62,79] Combinatio n of genetic algorithm techniques

 Uses probabilistic rule instead of deterministic

rule

 Less susceptible to local minima

 Computationally inexpensive

 Not suitable for placing DG under peak load condition [13,22,24,25, 35, 37,48] Artificial intelligent approaches

 The procedure provides the best DG siting and sizing taking into account uncertainties introduced by DGs  Robustness

 Fast processing and accurate result

[41]

Tabu search  Simple, robust and easy to modify

 Highly successful in finding near-optimal solutions in many practical optimization problem than their subordinate heuristic  Some assumptions can’t be satisfied or approximated in many real cases

 The ability to prove optimality of solutions is lost and approximate solution are obtained [37, 66,68] Particle swarm optimization  Computationally inexpensive in terms of memory and speed

 Can’t work out the problems of scattering and optimization [32, 40, 53,58,76,77] Sl. No. DG Technology

Merits Demerits

1 Fuel Cell  High efficiency

 Low noise

 Nearly zero emission

 Fast load response

 Pure hydrogen need

 High cost

 Low durability

 Fuel required processing 2 Micro

Turbine

 Low noise

 Low emission

 Light weight

 Small size

 High cost

 Limited to low temperature

 Relatively low efficiency

3 Wind Turbine  Low production cost

 Low energy loss

 Environmental friendly

 Save land use

 No fuel demand

 Affected by wind speed

 Variable power output

 Noise

 High investment cost

 Harm birds 4 Solar PV  Low maintenance

 Environmental friendly

 No fuel demand

 High investment cost

 Affected by solar radiation

5 CHP  High efficiency

 Low emission

 Save energy loss

 Integration various fuels

 Increased investment cost

 Need reasonable plan

 Decrease flexibility

 Complex technology need

6 Gas turbine  High reliability

 Low emission  High pressure gas need

 Low efficiency at low load

7 Reciprocating engine

 Fast start- up

 Low investment

 Relatively higher emission

 High maintenance 8 Small hydro  Free and renewable

source of energy

 No impact on river eco-system

 Short installation time

 Environmental friendly

 Power output depends on availability of water

 Affected by flood

 They can be suited where potential site exists

 Can’t meet required load demand

 Continuous maintenance is required

9 Biomass plant  Uses renewable source

 Reduces

dependency on fossil fuel

 Reduces green house gas emissions

 Expensive

 Causes pollution

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Optimizatio n Technique

Merits Demerits References

Ant Colony  Reliable and

gives good result  Theoretical analysis is difficult

 Uncertain convergence time

[63,69]

Heuristic method

 Iterative and easy to understand

 Does not produce accurate results

[45,46]

Combined GA-OPF

 The hybrid method are better than SGA in terms of solution quality

and number of iteration

 Computationall y Demanding, Less robust

[51]

Combined GA and simulated annealing

 Approach is effective for variable and intermittent forms of generation

 Computational efficiency reducing the world models of into a set of linear equations is usually very difficult Probabilistic

approach using MINLP

 The proposed technique can closely mimic the actual loss calculations resulting in more accurate also considered the uncertainty

 Computationall y demanding

 Less robust

[30, 58]

GA-TS  Better solution in terms of solution quality and number of iteration

[37]

GA- PSO  Escapes from local minima.

 Increases the diversity of variable values

[25]

From the table-III, it is concluded that analytical approaches are not suitable for multi-objective complex optimization problems. When optimization problems are solved by conventional technique like mixed integer nonlinear programming, the nonlinear and integer variables

will demand more computation time and are less robust. One of the first and most widely used optimization technique is GA, but it suffers from divergence and local optima. PSO is the next popular technique used because of its simplicity, less computation time and fast convergence characteristics. PSO is efficient for solving those problems for which the accurate mathematical modeling is difficult but prone to local minima and premature convergence. Artificial intelligence based optimization techniques like simulated annealing, evolutionary programming, tabu search, particle search algorithms, and ant colony search algorithms can handle the integer variables very well. Simulated annealing provides better solution but the computation time is excessively large. Tabu search is an efficient technique to achieve either optimal or sub-optimal solution in the short duration. ACS algorithm is more heuristic than the conventional technique and needs further investigation on its performance. Many recent publications use hybrid optimization techniques to obtain an efficient and reliable solution to the problem by adding their strengths and discarding the weaknesses. In majority of the hybrid techniques, GA is used along with other optimization techniques as seen from the table.

II. OPERATIONALANDPLANNINGSTRATEGIES

OFDG

It consists of three planning strategies: optimal siting and sizing of DG, distribution system expansion planning with DG, DG-Capacitor placement and an operational strategy (i.e. network reconfiguration in presence of DG). Each one is briefly discussed below:

A

.

International Journal of Emerging Technology and Advanced Engineering

385 GA based methods and PSO technique was also presented [19, 29 and 33]. EP based method and FL based method are also presented in [34] and [39]. The other methods like probabilistic based MINLP [30], probabilistic based MC simulation [26], ABC [32], DLF [38], and OPF [18, 20, 22]

are also presented by various authors. The hybrid based

methods, like GA-PSO, GA-TS, and Fuzzy-GA are suggested by [25, 37, 35], respectively.

B. DISTRIBUTION SYSTEM EXPANSION PLANNING WITH DG

Apart from expansion of existing substations, building new transmission lines and new substations, DG can be used to accommodate new load growth and provide relief for overloaded components. Research work has reported different techniques to achieve the goal of distribution system planning with DG. The GA based optimization techniques, [41, 42, 52, 60], PSO based method[40, 53] and other conventional methods like OPF[51], probabilistic based MINLP[45], probabilistic based MC simulation[54, 59], Successive elimination [49,55], probabilistic based reliability evaluation model[50] and mathematical model [47] were also suggested by various authors including

Fuzzy-GA and PSO-Ordinal optimization hybrid

methods[48,58] proposed in [40, 59].

C. NETWORK RECONFIGURATION IN PRESENCE OF DG

The structure of the distribution systems is generally constructed as radial networks in order to have operational simplicity and protection coordination. The radiality can also change the network structure by using automatic or manual switches in such a way that the goal of supply to all consumers, minimization of total power losses and improvement in power quality can be achieved satisfactorily.

Looking at the impact of DG on power distribution networks, the distribution system reconfiguration is found to have more line losses and reduction of terminal voltage compared to transmission network. For optimizing power loss, the new reconfiguration can be used as the feeder reconfiguration as a systematic method to operate the distribution system at minimum cost with improved system reliability and security. By opening / closing the feeder switches, load currents can be transferred from feeder to feeder and can help to study the effect of DG on the distribution networks with reference to network reconfiguration problems.

The algorithms dealing with feeder reconfigurations like heuristic based and modern optimization methods have been proposed to solve the problem. The GA based method and ACO were proposed for optimal reconfiguration in presence of DG for distribution system power loss reduction [62, 63 and 66, 69] respectively. The other conventional methods like Honey Bee optimization [65], a two stage method [64], and DigSilent software [67] were also suggested.

From the above, it is clear that the feeder reconfiguration and DG placement process not only reduce the power loss but also improve the voltage profile. DGs are not only employed to provide real and /or reactive power compensation in distribution systems but also to reduce the power losses and to maintain the voltage profile within acceptable limits.

D. DG-CAPACITOR PLACEMENT

Since the consumers and distribution network components like motors and transformers are inductive loads, so the network power factor will be lag and results in the reducing the system’s capacity, increase the system losses, and reduction of the voltage. Shunt capacitors are used to overcome these problems [70-72] as the shunt capacitors not only reduce the loss but also enhance the voltage profile, power factor and voltage stability of the system. DG can also be applied as an effective way to overcome high loss, low power quality and heavy load density problems in the distribution network [73-75]. Moreover, the capacitor can be used for the reactive power compensation and also the DG can be used for the active and/or reactive power compensation in the network.

International Journal of Emerging Technology and Advanced Engineering

386 Since the last few years, the interest in the placement of DG in utility network has increased due to its effective role in reducing the power loss of the distribution networks to serve remote loads. Placement of shunt capacitors improves voltage profile but unable to serve remote load as it can provide only reactive power. So, combination of both the options can provide feasible solution. In this connection, a method based on GA for optimal DG-capacitor placement with the objective to minimize both the cost of loss and DG-capacitor is presented by Raghthaicharoencheep [79]. With the same objective, PSO based technique [76, 77] and differential evolution technique [78] were also proposed.

The DG units should be applied in an effective manner without causing degradation of reliability, system operation and power supply quality. The integrated planning of DG-Capacitor into the distribution system therefore demands comprehensive techno-economic evaluation. The shunt capacitors, commonly, deployed by most utilities for reactive power compensation, can also be used in parallel with DG for distribution system expansion planning. The literature reveals that the reactive power injected by shunt capacitors can effectively reduce system energy losses, relieve feeder loading and improve supply reliability [81, 82]. The siting and sizing of shunt capacitors need to be investigated carefully to avoid problems like increase in voltage and reduction of operating cost of DG units.

III. CONCLUSIONS

The location and sizing of compensating units (DG, capacitor, etc.) to utility network like: when DG is employed alone or while performing the network reconfiguration in the presence of DG or application of combined DG-capacitor is of great importance to achieve maximum positive benefits. The non-optimal placement can increase the system losses, thereby implying an increase in the cost. DG can be used to accommodate new load growth and provide relief for the over loaded components. The present paper critically reviews of the operational and planning strategies that can be employed to address the various issues of DG planning at the distribution network. The analytical techniques may not be suitable to offer solution to complex problems but the artificial intelligent search techniques may offer flexible and simplified solutions with compromise between solution quality and computation time. A hybrid of two or more approaches can, however, offer a better solution by incorporating benefits of each and discarding their draw-

backs.

ACKNOWLEDGEMENT

The authors would like to acknowledge Quality Improvement Programme (QIP) Scheme and Principal, P.E.S. college of Engineering, Mandya (Karnataka) for

allowing me to pursue the PhD work

.

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