Evolutionary computing techniques for Distributed Generation: Survey

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Evolutionary computing techniques for Distributed Generation: Survey

Alka Yadav

Department of Electrical Engineering Maharana Pratap college of Technology

Madhav Singh Kaurav Department of Electrical Engineering Maharana Pratap college of Technology

Anshu Sharma

Department of Electrical Engineering Maharana Pratap college of Technology

Abstract-Distributed Generation is the generation of electricity from many small energy sources and is located closer to the user, or customer. In recently there has been large interest in the assimilation of distributed generation units at the distribution systems. The main purpose of using distributed generation is to improve voltage profile, voltage stability and to minimize power losses. Distributed generation (DG) is carried out to reduce losses, to improve voltage profile and to maximize voltage stability in a power system. In recent years, several evolutionary computing based optimization techniques have been proposed to solve the optimization problem of distributed generation. These techniques include Genetic Algorithm, Artificial Bee Colony, Particle Swarm Optimization and Differential Evolution etc. DG problem has been solved as a single objective as well as multi-objective optimization problem using these techniques. This paper presents an overview of research and development work carried out in the field of Distributed Generation. This paper presents a review of research work carried out for solving problem using evolutionary computing techniques. Types of distributed generation, technology used for distributed generation and related terms are also discussed.

Keywords- Distributed generation (DG); types and technology;

Single objective optimization problem and Multi objective optimization problem

I. INTRODUCTION

Distributed Generation (DG) is defined as „generation plants connected to the distribution system‟. In the same Directive, the distribution is defined as „the transport of electricity on high- voltage, medium voltage and low voltage distribution systems with a view to its delivery to customers, but not including supply‟ [1]. There are different classifications utilized at national level. Here, a brief overview of main definitions used at international level follows:

1. The Institute of Electrical and Electronics Engineers Inc.

(IEEE) defines the DG as generation of electricity by facilities sufficiently smaller than central plants, usually 10 MW or less, so as to allow interconnection at nearly any point in the power system [2].

2. The International Energy Agency (IEA) defines DG as generating plant serving a customer onsite or providing support to a distribution system, connected to the grid at distribution-level voltages [3].

3. The US Department of Energy (DOE) defines DG as modular electric generation or storage located near the point of use. The DOE considers distributed power systems to

typically range from less than a kilowatt (kW) to 10 MW in size as DG unit [4].

4. The Electric Power Research Institute (EPRI) treats small generation units from a few kW up to 50 MW and/or energy storage devices typically sited near customer loads or distribution and sub-transmission substations as distributed energy resources [5].

In addition, in regards to the rating of distributed generation power units, the following dissimilar definitions are currently used: The Electric Power Research Institute defines distributed generation as generation from „a few kilowatts up to 50 MW‟

[6]. As per the Gas Research Institute, distributed generation is

„typically [between] 25 and 25 MW‟ [7]. Preston and Rastler define the size as „ranging from a few kilowatts to over 100 MW‟ [8]. Cardell defines distributed generation as generation

„between 500 KW and 1 MW‟ [9] and the International Conference on Large High Voltage Electric Systems (CIGRE´) defines DG as „smaller than 50 –100 MW‟ [10].

II. DISTRIBUTED GENERATION

Distributed generation is known by various names like decentralized generation, dispersed generation, embedded generation, on-site generation, distributed energy or redistributed energy. Any distributed generation creates electricity from many small energy sources. Almost all the countries generate electricity in large centralized facilities, such as fossil fuel, nuclear, hydropower plants or large plants [11].

The classification of DG [12] has been illustrated in Fig. 1.

According to their ratings, DG may be classified as micro, small, medium or large.

Distributed Generation (Classes)

Micro ( ~1Watt < 5 kW)

Small (5 kW < 5 MW)

Medium (5 MW < 50 MW)

Large (50 MW < 300 MW) Fig 1. Distributed Generation classes

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To provide some circumstance on what distributed generations are, the following is a simplified overview of the major topology categories of the electric system, as shown in Figure 2. First, there is generation which uses some form of energy to produce electricity. Next, there is transmission which delivers electricity at high voltage from the large generators to the rest of the system. Then, there is distribution which is connected to transmission and supplies the electricity to the consumer. The amount of electricity required by the consumer is also referred to as the electric load or demand. To satisfy electric demand the electricity must be produced by the generators and delivered to the load when it is needed [13].

Y Y Y

Generation

Transmission

Load

Distribution

Load

Load Y

Fig 2 Diagram of electric system topology

Most of the energy generated in the system is provided by large units, (hundreds of MWs), connected to the transmission system. The concept of also having multiple small generation sources distributed along the distribution system has been referred to as distributed generation (DG). If energy storage devices are included with DG it has been known as dispersed storage and generation (DSG) [13].

III. TYPES AND TECHNOLOGIES OF DISTRIBUTED GENERATION

There are different types of DGs from the constructional and technological points of view as shown in Fig.3 [14]. DGs may be broadly categorized as conventional and non- conventional generators.

3.1 Conventional Generator

Micro turbines are a relatively new type of combustion turbine that produces both heat and Electricity on a small scale.

Micro turbines offer a clean and efficient solution to direct Mechanical drive markets such as compression and air- conditioning [15]. Micro turbines are small combustion turbines with outputs of 25 kW to 500 kW. They developed from automotive and truck turbochargers, auxiliary power units (APUs) for small jet engines, and airplanes [15].

Distributed Generation Types & Technologies

Conventional Generator

Non- Conventional Generator

Electrochemical Devices

Storage Devices

Renewable Devices Micro Turbine

Fuel Cells Batteries Flywheels

Photovoltaic Cells

Wind Turbine Natural Gas Turbine

Fig 3. Distributed generation types and technologies

Gas turbines produce pressure gas and high temperature.

Turbine shaft rotate with the help of this high pressure, which drives a compressor, an electric alternator and generator. Gas turbines are always used above 1MW, but nowadays we can generate electricity through small modular with a micro-gas turbine of 200kW size [16].

3.2 Non-Conventional Generators

The non-conventional generators include Electrochemical Devices, Storage devices and Renewable devices.

3.3 Electrochemical Devices

The fuel cell (FC) is a device used to generate electric power and provide thermal energy from chemical energy through electrochemical processes. It can be weigh as a battery supplying electric energy as long as its fuels are continued to supply. Unlike fuel cell, batteries do not need to be charged for the consumed materials during the electrochemical process since these materials are continuously supplied [17]. FC capacities vary from kW to MW for portable and static units, respectively.

It provides clean power and heat for several applications by using gaseous and liquid fuels [18]. FCs can use a variety of hydrogen-rich fuels such as natural gas, gasoline, biogas or propane [19].

3.3.1 Storage Devices

Storage device are consists of flywheels, batteries, and other devices, they are charged during low load demand and used when required. They are usually combined with other kinds of DG types to supply the required peak load demand [20]. These batteries have a charging controller for protection from overcharge and over discharge as it disconnects the charging process when the batteries have full charge.

3.3.2 Renewable Devices

Renewable devices commonly used for DG are Photovoltaic and Wind turbines. Photovoltaic (PV) is a method of generating electrical power by converting solar radiation into

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direct current electricity using semiconductors that exhibit the outcomes. PV power generation employs solar panels collected of a number of solar cells containing a photovoltaic material.

Currently material used for photovoltaic include, mono crystalline silicon, amorphous silicon, copper indium gallium selenide/sulphide, polycrystalline silicon and cadmium telluride.

Practically, each cell supply 2–4A according to its size with a 0.5V output voltage. Usually an array, cells connected in series, provides 12V to charge batteries PVs consist of modular which can be connected to provide a variety of power ranges but on the other hand there are many restrictions. Photovoltaic devices provide low output power, the cost of land is expensive where PVs installed and it is restricted to certain weather and geographic features [21].

Modern wind energy systems consist of three basic components: a tower on which the wind turbine is mounted; a rotor (with blades) that is turned by the wind; and the nacelle.

Their shape was capsule-shaped component which houses the instrument, including the generator that converts the mechanical energy in the spinning rotor into electricity. Rotor blades need to be strong and light in order to be aerodynamically efficient and to withstand prolonged use in high winds. The rotor, which spins when driven by the wind, supports blades that are designed to capture kinetic energy from the wind. The advantages of WT does not cause green house gases or other pollutants, great resources to generate energy in remote areas, take up less space in compare to power station, no fossil fuels are used to generate electricity.

Fig 4. shows the capacity of DGtechnologies in years [22].

One form of DG is combined heat and power (CHP), which reuses waste heat from on-site generation for purposes such as space heating and water heating. Such technologies are typically used in both commercial and industrial sectors. The subsequent information focuses on how EIA models DG, as well as CHP, in the residential and commercial sectors. The DG is characterized as residential or non-residential. For modeling purposes all non-residential, non-CHP installations are attributed to the commercial sector. DG in buildings includes

conventional and non-conventional energy generators as outlined in Table 1 [22].

TABLE1.DISTRIBUTED GENERATION TECHNOLOGIES BY SECTOR

Residential Commercial Conventional

Solar photovoltaic Solar photovoltaic Wind

Wind Hydroelectric Wood

Municipal solid waste

Non- conventional

Natural gas-fired fuel cells

Natural gas-fired reciprocating engines Natural gas-fired turbines

Natural gas-fired micro turbines Diesel reciprocating engines

Coal

IV. DG ASASINGLEOBJECTIVEOPTIMIZATION PROBLEM

To solve DG placement problem as a single objective optimization problem, one of the objectives from has been considered by the researchers for loss minimization, improvement voltage profile, voltage stability.

4.1 Loss Minimization

Kashem et.al[23], proposed an efficient technique was used to determined the switching combinations, select the status of the switches, and find the best combination of switches for minimum loss. In the first stage of the proposed algorithm, a limited number of switching combinations was generated and the best switching combination was determined. In the second stage, an extensive search was employed to find out any other switching combination that may give rise to minimum loss compared to the loss obtained in the first stage. The proposed method has been tested on a 33-bus system, and the test results indicate that it was able to determined the appropriate switching- options for optimal (or near optimal) configuration with less computation. The results were compared with those of established methods reported earlier and a comparative study is presented. With the proposed method, for any input load conditions of the system, the optimum switching configuration could automatically be identified within a reasonable computer time and hence the method could be

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effectively employed for continuous reconfiguration for loss reduction. The method could be effectively used to plan and design power systems before actually implementing the distribution networks for locating the tie switches and providing the minimum number of sectional king switches in the branches to reduced installation and switching costs.

Moradi et.al [24], were proposed a Genetic Algorithms (GA) for solving optimal multi-distributed generation location and capacity. The main objective was to minimize the real power loss within security and operational constraints. They were considered four kinds of DG including distributed real power sources only, distributed real reactive sources only, DG supplying real power and consume reactive power, DG supplying real power and reactive power, in place of photovoltaic, synchronous condenser ,wind turbines, and hydro power, correspondingly. The completed performance analysis is carried out on 33 bus system to demonstrate the effectiveness of the proposed algorithm.

4.2 Voltage Profile Improvement

Vatankhah et.al [25] determined optimum size and location of distributed generators for maximizing voltage profile in distribution systems. In this purpose, Particle Swarm Optimization algorithm (PSO) approach was proposed. The important improvement of this study paper was using new coding in (PSO) which included both active and reactive powers of DGs to achieved better voltage profile enhancement.

Furthermore, four set of weighting factors are chosen based on the importance and criticality of the different loads. The effectiveness of the proposed method was tested on the 33 bus distribution systems.

Ela et.al [26], proposed an optimal proposed approach (OPA) to establish the optimal sitting and sizing of DG with multi-system constraints to achieved a single or multi-objectives by genetic algorithm (GA). The linear programming (LP) was used not only to confirmed the optimization results obtained by GA but also to examined the influences of varying ratings and locations of DG on the objective functions. A real section of the West Delta sub-transmission network, as a part of Egypt network, was used to test the capability of the OPA. The results show that the proper sitting and sizing of DG are important to improve the voltage profile, increased the spinning reserve, reduced the power flows in critical lines and reduced the system power losses.

4.3 Voltage Stability Improvement

Aman et.al [27], proposed an algorithm for Distributed Generator placement and sizing for distribution systems based on a novel index. The index was developed considering stable node voltages referred as power stability index (PSI). An analytical approach was adopted to visualized the impact of DG on system losses, voltage proﬁle and voltage stability. The proposed algorithm was tested on 12-bus, modiﬁed 12-bus and 69-bus radial distribution networks. The test results are also compared and found to be in close agreement with the existing Golden Section Search (GSS) algorithm.

Arya et.al [28], described a viewpoint for voltage stability measurement accounting uncertainties in line parameters and settings of reactive power control variables. Monte-Carlo simulation was used to estimate probabilities of voltage collapse for various operating conditions. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power ﬂow algorithm. Monte-Carlo simulation was a time-consuming process. Hence, a radial basis function (RBF) network was used to get probabilistic risk of voltage collapse. Training and testing instances were generated using Monte-Carlo simulation. The algorithm developed has been implemented on two standard test systems.

V. MULTI-OBJECTIVE FUNCTION

To solve the optimization problem of DG as the multi- objective function such as, reduce the power losses, improved voltage profile, voltage stability, reliability and power quality by the researchers as follows:

Renan et.al [29],proposed a multi-objective approach to a distribution network planning process that deals with the challenges derived from the integration of Distributed Generation. The proposal consists of a multi-objective version of the well-known Evolutionary Particle Swarm Optimization method (MEPSO). A broad performance comparison was made between the MEPSO and other multi-objective optimization meta-heuristics, the Non- dominated Sorting Genetic Algorithm II (NSGA-II) and a Multi-objective Tabu Search (MOTS), using two distribution networks in a given DG penetration scenario. Although the three methods proved to be applicable in distribution system planning, the MEPSO algorithm has presented promising attributes and a constant and high level performance when compared to other methods.

Ganguly et.al [30], presented a multi-objective planning approach for electrical distribution systems under uncertainty in load demand incorporated distributed generation. Radial and meshed systems both were considered. The two objectives in system planning were minimization of total installation and operational costs, and minimization of the risk factor. The risk factor was a function of the contingency load-loss index (CLLI), which measured load loss under contingencies, and the degree of network constraints violations. CLLI minimization improved network reliability. The network variables optimized were the network structure type (radial or meshed), the number of feeders and their routes, and the number and location of sectionalizing switches. The optimization tool was a multi-objective particle swarm optimization (MOPSO) variant that used heuristic selection and assignment of leaders or guides for capable identiﬁcation of non-dominated solutions. The optimal number, location, and size of the DG units were determined in another planning stage. Performance comparisons between the planning approaches with possibilistic and deterministic load models highlight the relative merits and demerits.

Kayal et.al [31], proposed a constrained multi-objective Particle Swarm Optimization (PSO) based Wind Turbine

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Generation Unit (WTGU) and photovoltaic (PV) array placement approach for power loss reduction and voltage stability enhancement of radial distribution system. That paper reﬂects the effectiveness of WTGU and PV array performance models in DG placement problem formulation. Wind and solar based DGs were operated in different active and reactive power mode and tested on 12-bus, 15-bus, 33-bus and 69-bus radial distribution system. A Voltage Stability Factor (VSF) was proposed in this paper which can measure voltage stability levels of buses in the system.

Moradi et.al [32], were proposed a combined genetic algorithm (GA)/particle swarm optimization (PSO) for optimal location and sizing of DG on distribution systems. The objective was to minimize network power losses, better voltage regulation and improved the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A comprehensive performance analysis was carried out on 33 and 69 bus systems to express the effectiveness of the proposed algorithm.

Taghikhani et.al [33], studied indicates that placing and application of DGs by modified shuffled frog leaping algorithm (MSFLA) would be reduced losses and improved voltage profile of power systems. Voltage Profile Improvement Index (VPII) and Line Loss Reduction Index (LLRI) were analyzed in that paper. The MSFLA was simulated with MATLAB software on IEEE-70 bus radial distribution system. Tested results indicated that MSFLA method could obtained better results than the SFLA method on the 70-bus radial distribution systems.

Rao et.al [34], proposed a method which applied an artificial bee colony algorithm (ABC) for capacitor placement in distribution systems with an objective of improving the voltage profile and reduction of power loss. The ABC algorithm was a new population based Meta heuristic approach inspired by intelligent foraging behaviour of honeybee swarm. The advantage of ABC algorithm was that it does not required external parameters such as cross over rate and mutation rate as in case of GA and differential evolution and it was hard to resolved these parameters in prior. The other advantage is that the global search ability in the algorithm was implemented by introducing neighbourhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 69-bus system and compared the results with the other approach available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.

Musa et.al [35], proposed an enhanced particle swarm optimization (PSO) algorithm for Distributed Generation placement and sizing using multi-objective optimization concept. It was based on the combination of Evolutionary Programming (EP) and PSO. The merits of EP and PSO were combined together so as to achieve faster convergence and accuracy of the DG sizes. The quality of the solution was improved by exploring the less crowded area in the existing solution space to obtain more non-dominated solutions. The

proposed approach was tested on standard IEEE 33 –Bus test system. Obtained result demonstrated the ability of the proposed algorithm towards production of well distributed Pareto optimal non-dominated solution of the multi-objective DG sizing problem.

VI. CONCLUSION

In this paper, a brief review of research work carried out by researchers for optimal placement and sizing of DG has been presented. The problem of finding optimal location and sizing of DG has been handled by various researchers in different ways by considering different objectives and optimization techniques and has been solved as single objective as well as multi objective optimization problem. To solve DG placement problem, one or more of the objectives loss minimization, improvement voltage stability, voltage profile has been considered by the researchers.

The sensitivity based as well as evolutionary computing techniques based methods have been reported in literature for optimal placement and sizing of DG. The sensitivity based methods are though fast and accurate, but complicated in nature.

On the other hand, recently developed evolutionary computing techniques based methods are easy to be implemented.

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