Distribution system planning for active distribution network with DERs.ppt

Distribution system Planning

1. Combined Techno-Economic and Emission Assessment in Optimal
Allocation of Distributed Generation in Distribution Grid

2. CONTENTSCONTENTS
1.Introduction
2.Literature survey
3.Research Gap Findings
4.Problem Statement
5.Research Objectives
6.Mathematical Formulation and Schemes
7.Road Map
8.References
9.Course work Details
10. Syllabus

3. 1.INTRODUCTION1.INTRODUCTION
3
•Distribution system(DS) are transforming from passive to active.
•Penetration of renewable energy and advancements in energy storage systems and
increasing popularity of Plug-in electric vehicles at distribution level poses new
challenges in the planning and operation of DS
•For reliable operation and planning of distribution system, precise modeling of renewable
DGs(Distributed Generation) and Load considering its uncertainties is required
•Also system taken into consideration for distribution studies should accommodate all the
participants to reflect the real world functionality which give rise to challenges emerging
in the operation of distribution Grid.

4. 1.1 DISTRIBUTED GENERATION TECHNOLOGIES1.1 DISTRIBUTED GENERATION TECHNOLOGIES
Figure 1: Various Categories of DG technologies

5. 1.2 THE BENEFITS OF DG1.2 THE BENEFITS OF DG

6. •Variations in power losses,
•Issues of voltage fluctuations
• Overstepping the protection measures
• Generation of Harmonics and Transients
•Stochastic nature of PV and Wind
1.3 DG INTERCONNECTION ISSUES1.3 DG INTERCONNECTION ISSUES

7. 2.LITERATURE STUDY2.LITERATURE STUDY
S. No Citation Methodology Major findings
1 Das, S., Fosso, O.B. and Marafioti, G. (2023)
‘Probabilistic Planning of Distribution
Networks with Optimal DG Placement Under
Uncertainties’, IEEE Transactions on Industry
Applications,.https://doi.org/10.1109/tia.2023.32
34233.
•RES-based DGs and network load demands are
considered here for the optimal DG placement
and sizing problem by developing an efficient
planning methodology based on the (Point
Estimate Method) PEM technique
•The paper utilizes probabilistic modeling
techniques to capture the uncertainties associated
with load demand, DG availability, and other
relevant factors in distribution networks. By
incorporating probabilistic analysis, the study
provides a more realistic and robust framework
for planning DG integration, enhancing the
accuracy of the results..
•The optimal DG locations are determined by
combined loss sensitivity and voltage stability
factor-based approaches, while the DG sizes are
obtained by solving non-linear probabilistic AC
OPFs, which ensure the satisfaction of all
network operational constraints.
•The proposed methodology is implemented on
standard test systems like the IEEE 69 bus and
the Indian 85 bus networks
IEEE 69 Bus System
Base Case - P
loss
(KW):200.640
Number of DG : 1
DG location : 61
DG size(KW) : 1834.6
Ploss(KW) : 31.408
Number of DG : 2
DG location : 61 and 12;
DG size(KW) :1834.6 and 953.77
Ploss(KW) :13.737
Number of DG : 3
DG location : 61, 12 and 64;
DG size(KW) : 1834.6 ,953.77, 394.38
Ploss(KW) :7.841

Indian 85bus System
Base Case - Ploss(KW):530.923
Number of DG : 1
DG location :28
DG size(KW) :2503.5,
Ploss(KW) :195.200
Number of DG : 2
DG location :28 and 60;
DG size(KW) :2503.5 and1526.52
Ploss(KW) :70.588
Number of DG : 3
DG location : 28, 60 and 48;
DG size(KW) : 2503.5,1526.52, 455.496;
Ploss(KW) : 44.473
Microsoft Office
Excel 97-2003 Worksheet

8. S. No Citation Methodology Major findings
2Ramadan, A. et al. (2023) ‘Optimal allocation of
renewable DGs using artificial hummingbird
algorithm under uncertainty conditions’,
Ain Shams Engineering Journal,
https://doi.org/10.1016/j.asej.2022.101872.
•The proposed technique uses (AHA) which
effectively solves the problem of optimal DG
allocation in radial distribution systems.
•The expected costs, emissions, voltage deviation,
and voltage stability are reduced significantly with
the inclusion of RDGs

•Monte-Carlo simulation approach and backward
reduction algorithm are used to to model the
uncertainties of loading and RDG output power.
For IEEE 33 bus System(2 DG) (considering
100% loading with Pwind=1254.2kW and
Psolar=26.08kW )
Expected total Cost ($/h):0.617,
Emission(kg/MWh):10,600
VD(pu):0.0019;VSI(pu):0.0568;
DG location:7 and 14
The with and without DG conditions are
compared and the results are enhanced by
38.91%, 62.43%, 70.48% accounting all the 12
scenarios
3.Gümüş, T.E., Emiroglu, S. and Yalcin, M.A.
(2023) ‘Optimal DG allocation and sizing in
distribution systems with Thevenin based
impedance stability index’,International Journal
of Electrical Power and Energy Systems,
https://doi.org/10.1016/j.ijepes.2022.108555.
•Optimal sizing and location of DGs is computed
based on Thevenin impedance stability index which
is effective in reducing power losses, improving
voltage profile and system stability.
•The proposed approach is verified by using Genetic
Algorithm (GA) and Grey Wolf Optimization
For IEEE 33 Bus System(Base Case)
Ploss(kW):224.95
For IEEE 33 Bus System( 3DG-GA)
DG location:61,21,11
Ploss(kW):69.916
For IEEE 33 Bus System( 3DG-GWO)
DG location:18,61,66
Ploss(kW):70.662
For IEEE118 Bus System(Base Case)
Ploss(kW):1298.091
For IEEE 118 Bus System( 7DG-GA)
DG location:31,42,50,72,80,91,110
Ploss(kW):526.4298 kW
For IEEE 118 Bus System(7DG-GWO)
DGlocation:42,81,109,96,11,50,71
Ploss(kW):534.10kW
2.LITERATURE STUDY2.LITERATURE STUDY

9. S.
No
Citation Methodology Major findings
4.
Ahmed, A. et al. (2023) ‘An improved hybrid
approach for the simultaneous allocation of
distributed generators and time varying loads in
distribution
systems’,Energy Reports,
https://doi.org/10.1016/j.egyr.2022.11.171.
•The optimum placement and sizing of DG
units in distribution system with time
varying voltage dependent (TVVD) loads to
minimize active power losses, reactive
power losses, and voltage deviation.
•A suitable combination of different DG
types is determined that minimizes the
multi-objective function (MOI), while
considering TVVD loads.
•A novel hybrid approach based on Salp
Swarm Algorithm(SSA) and Particle
Swarm Optimization (PSO) is proposed for
optimal allocation of DG units.
For IEEE69 bus System:
DG Placement and Sizing are computed for
different Scenarios. The hybrid (SSA+PSO)
optimizes Bus No 17 and 61 as candidate
buses for placement of PV and wind with a
capacity of 1.03MW and 0.38 MW.AMOI was
computed as 0.4140
5.
Purlu, M. and Turkay, B.E. (2022) ‘Optimal
Allocation of Renewable Distributed Generations
Using Heuristic Methods to Minimize Annual
Energy Losses and Voltage Deviation Index’,
IEEE Access
https://doi.org/10.1109/ACCESS.2022.3153042.
•This paper proposes two metaheuristic
methods, genetic algorithm and particle
swarm optimization, to determine the
optimal locations, sizes, and power factors
of distributed generation units.
•The above methods are implemented in
IEEE 33-bus radial distribution network and
the results are compared with the literature.
For IEEE 33 bus System(1 DG):
PSO offers better solution than GA for all the
cases. PSO optimizes BusNo.6 as DG
location with 71.94 kW and 53.385 kVAr loss
with an opf of 0.83 and VDI of 0.01427
whereas GA optimizes BusNo.6 as DG
location with 72.29 kW and 53.25 kVAr loss
with an opf of 0.83 and VDI of 0.01673
against the base case of 201.99 kW and
134.74 kVAr loss and VDI of 0.11642
2.LITERATURE STUDY2.LITERATURE STUDY

10. S.
No
Citation Methodology Major findings
6.
Shaheen, A.M. et al. (2022)‘A heap-based
algorithm with deeper exploitative feature for
optimal allocations of distributed generations with
feeder reconfiguration in power distribution
networks’,
Knowledge-Based Systems,.
https://doi.org/10.1016/j.knosys.2022.108269.
•The work proposes an improved Heap-based
algorithm, HODEI, for the optimal
combination of power distribution feeder
reconfiguration (PDFR) with distributed
generators (DGs).
•The proposed HODEI and the conventional
HO are employed for simultaneous DG
allocations and PDFR using two IEEE
standard distribution networks of 33 and 69-
bus at various loading conditions
For IEEE 33 Bus System (3 DG):
For the heavy loading, the proposed HODEI
minimizes the power losses from 575.31 to
145.41 kW whereas the conventional HO
achieves 152.46 kW. DGs are optimally located at
bus no 12,25,33
For IEEE 69 Bus System (3DG):
For the heavy loading, the proposed
HODEI minimizes the power losses from 652.24
to 99.06 kW whereas the conventional HO
achieves 106.54 kW. DGs are optimally located at
bus no 18,61,64
7.
Shaheen, A., Elsayed, A., Ginidi, A., El-Sehiemy, R.
and Elattar, E. (2022). 'Improved Heap-Based
Optimizer for DG Allocation in Reconfigured
Radial Feeder
Distribution systems', IEEE Systems Journal,
doi:https://doi.org/10.1109/jsyst.2021.3136778.
•This paper presents an improved heap-based
optimizer (IHBO) for dealing with the optimal
combination of power distribution system
reconfiguration and distributed generators
allocation in power distribution systems
(PDSs).
•The IHBO integrates an efficient exploitation
mechanism to increase the exploring around
the leadership position, with the goal of
increasing its global search skills and avoiding
being trapped in a local optimum..
For IEEE 33 bus System(3 DG):
IHBO optimizes BusNo.10,3,33
as DG location with 56.69 kW loss and VSI of
30.0761 against the base case of 202.66kW loss
and VSI of 25.77.
For 59 Bus Cairo(5 DG):
IHBO optimizes BusNo 29,7,46,18,35
as DG location with 46.8692 kW and VSI of
55.729 against the base case of 218.9kW and VSI
of 56.952.
For 84 Bus Taiwan Company(6 DG):
IHBO achieves lower power
losses of240.88 kW than the standard HBO which
obtains power losses of 247.25 kW.
2.LITERATURE STUDY2.LITERATURE STUDY

11. S.
No
Citation Methodology Major findings
8.
Werkie, Y.G. and Kefale, H.A. (2022).' ‘Optimal
allocation of multiple distributed generation units
in power distribution networks for voltage profile
improvement and power losses
minimization‘,Cogent Engineering,
https://doi.org/10.1080/23311916.2022.2091668.
•The aim of this research is therefore to
minimize power losses and improve
voltage profile of DS using different types
of DGs.
•An improved particle swarm
optimization (IPSO)-based methodology is
applied to optimally allocate and size the
required DG units.
For IEEE 33 bus System(1 DG) :

IPSO is used to find the optimal DG sizing and
sitting in the IEEE 33-bus system. Sensitivity
factor analysis is done, and the candidate buses
are determined to be 6, 8, 28, 29, 9, 13, 10 and
3.DG is placed at 6th bus so that the real power
losses are reduced from 206.9kW to
62.85kW.Furthermore, the minimum voltage is
improved by 5.220%. after the placement of
DG.
9.
Shaheen, A., Elsayed, A., Ginidi, A., Elattar,E.
(2022).'Reconfiguration of electrical distribution
network-based DG and capacitors allocations using
artificial ecosystem optimizer: Practical case
study',
Alexandria Engineering Journal,
https://doi.org/10.1016/j.aej.2021.11.035.
•This paper discusses a new implementation
of the Artificial Ecosystem Optimizer
(AEO) technique for allocating distributed
generators (DGs) and capacitors.
• The AEO is inspired by three energy
transfer mechanisms in an ecosystem. The
paper demonstrates the efficacy of the
AEO through a case study and comparison
with other optimization techniques.
For 59 Bus Cairo(5 DGs):
• AEO algorithm is superior to other
optimization techniques for optimal RPDS and
DGs placements, resulting in decreased power
losses by up to 78.4%, 77.84% and 71.4% at
low, nominal and high levels, respectively.
• Optimal
RPDS, DGs, and capacitors placements with
AEO result in decreased power losses by up to
68.8%, 85.87% and 89.91% at low, nominal and
high levels, respectively.
• AEO is an effective technique
for Reconfiguration of Electrical Distribution
Network-based DG and capacitors allocations.
2.LITERATURE STUDY2.LITERATURE STUDY

12. S.
No
Citation Methodology Major findings
10
A. M. Shaheen, A. M. Elsayed, A. R. Ginidi, E. E.
Elattar and R. A. El-Sehiemy,( 2021) "Effective
Automation of Distribution Systems With Joint
Integration of DGs/ SVCs Considering
Reconfiguration Capability by Jellyfish Search
Algorithm”,IEEE Access,
doi: 10.1109/ACCESS.2021.3092337.
•This paper introduces an efficient and
robust technique based on Jellyfish
Search Algorithm (JFSA) for optimal
Volt/VAr coordination in distribution
systems to minimize losses and reduce
emission.
For IEEE 33 bus System(3 DG):

JFSA optimizes BusNo.14,24,30 as DG
location ;power losses are reduced to 12.572 kW
For IEEE69 bus System(3
DG):
JFSA optimizes BusNo.11,18,61 as DG location power
losses are reduced from 224.95kW to 4.6826 kW
11
Hussein Abdel-Mawgoud, Salah Kamel, Adel A. Abou
El-Ela & Francisco Jurado (2021) “Optimal
Allocation of DG and Capacitor in Distribution
Networks Using a Novel Hybrid MFO-SCA
Method”, Electric Power Components and Systems,
DOI:
10.1080/15325008.2021.1943066
•This paper proposes a novel hybrid
(MFO-SCA) method based on Moth
Flame Optimization (MFO) algorithm and
Sine Cosine Algorithm (SCA).
•The hybrid MFO-SCA method is used
with combined power loss sensitivity
(CPLS) to determine the optimal
allocation of single and multiple of
distributed generations (DGs) and
capacitors in distribution system
•The proposed method is validated using
the standard IEEE 69-bus and IEEE 33-
bus radial distribution system (RDS).
For IEEE 33 bus System(1 DG):
DG location:6 ;DG size(KW):
2590.21 ;Ploss(KW):111.027
For IEEE 33 bus System(2 DG):
DG location: 30 and
13;DG size(KW) :1157.5 and 851.566
Ploss(KW):87.1664
For IEEE 33 bus System(3 DG):
DG location: 30, 24
and 13;DG size(KW): 1054.5, 1090.7and 801.72;
Ploss(KW):72.7861
For IEEE69 bus System(1 DG):
DG location:61;DG size(KW):
1872.73,Ploss(KW):83.2224
For IEEE 69 bus System(2DG):
DG location: 61 and 17; DG size(KW) 1781.5 and
531.12 Ploss(KW): 71 .6745
For IEEE 69 bus
System(3DG):
DG location: 61, 17 and 11;DG size(KW): 1719.8,
380.27 and 526.44 ; Ploss(KW);69.4266
2.LITERATURE STUDY2.LITERATURE STUDY

13. 8.1 REFERENCES8.1 REFERENCES
7.Shaheen, A., Elsayed, A., Ginidi, A., El-Sehiemy, R. and Elattar, E. (2022). 'Improved Heap-Based
Optimizer for DG Allocation in Reconfigured Radial Feeder Distribution Systems', IEEE Systems
Journal, 16(4), pp.6371–6380. doi:https://doi.org/10.1109/jsyst.2021.3136778
8.Werkie, Y.G. and Kefale, H.A. (2022).' Optimal allocation of multiple distributed generation units in
power distribution networks for voltage profile improvement and power losses minimization', Cogent
Engineering, https://doi.org/10.1080/23311916.2022.2091668.
9.Shaheen, A., Elsayed, A., Ginidi, A., El-Sehiemy, R. and Elattar, E. (2022).'Reconfiguration of
electrical distribution network-based DG and capacitors allocations using artificial ecosystem
optimizer: Practical case study', Alexandria Engineering Journal, 61(8), pp.6105–6118.
https://doi.org/10.1016/j.aej.2021.11.035.
10.M. Shaheen, A. M. Elsayed, A. R. Ginidi, E. E. Elattar and R. A. El-Sehiemy,( 2021) "Effective
Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering
Reconfiguration Capability by Jellyfish Search Algorithm," in IEEE Access, vol. 9, pp. 92053-92069,,
doi: 10.1109/ACCESS.2021.3092337.
11.Hussein Abdel-Mawgoud, Salah Kamel, Adel A. Abou El-Ela & Francisco Jurado (2021) Optimal
Allocation of DG and Capacitor in Distribution Networks Using a Novel Hybrid MFO-SCA Method,
Electric Power Components and Systems, 49:3, 259-275, DOI: 10.1080/15325008.2021.1943066

14. 10.3 DISTRIBUTED GENERATION AND MICRO-GRID10.3 DISTRIBUTED GENERATION AND MICRO-GRID
Distributed Renewable energy Technology: Solar Photovoltaic Technology- Solar Cell Working Principle- Solar Cell
Electrical Characteristics, Photovoltaic Systems for Distributed Generation. Wind Energy Conversion- Types of Wind
Turbine -Fixed-Speed Wind Turbine, Variable-Speed Wind Turbine-, Fully Rated Converter (FRC) Wind Turbine,
DFIG. Small Hydroelectric Power Plants for Distributed Generation- Components and System parameters. Fuel Cell-
Types and Applications, Fuel Cell system for Distributed Generation
Distributed Generation : Planning and Evaluation: Need for Distributed generation, renewable sources in distributed
generation, current scenario in Distributed Generation, Planning of DGs – Sitting and sizing of DGs – optimal
placement of DG sources in distribution systems.
Grid integration of DGs: Grid integration of DGs – Different types of interfaces - Inverter based DGs and rotating
machine based interfaces - Aggregation of multiple DG units. Energy storage elements: Batteries, ultra-capacitors,
flywheels
Technical Impacts of DG integration in Power Grid: Technical impacts of DGs – Transmission systems, Distribution
systems, De-regulation – Impact of DGs upon protective relaying – Impact of DGs upon transient and dynamic stability
of existing distribution systems
Economic and control aspects of DGs: DGs –Market facts, issues and challenges - Limitations of DGs. Voltage
control techniques, Reactive power control, Harmonics, Power quality issues. Reliability of DG based systems –
Steady-state and Dynamic analysis
Introduction to Micro-grids: Introduction to micro-grids – Types of micro-grids – autonomous and non-autonomous
grids – Sizing of micro-grids- modeling & analysis- Micro-grids with multiple DGs – Micro- grids with power
electronic interfacing units. Transients in micro-grids - Protection of micro-grids – Case studies.

15. 10.4 SWARM INTELLIGENT TECHNIQUES IN POWER SYSTEMS10.4 SWARM INTELLIGENT TECHNIQUES IN POWER SYSTEMS
1.Multi objective optimization: Multi-Objective optimization Introduction- Concept of Pareto optimality - Non-dominant sorting
technique-Pareto fronts-best compromise solution-min-max method-NSGA-II algorithm and applications to power systems
2.Fundamentals of Soft Computing Techniques: Definition-Classification of optimization problems- Unconstrained and
Constrained optimization Optimality conditions- Introduction to intelligent systems- Soft computing techniques- Conventional
Computing versus Swarm Computing - Classification of meta-heuristic techniques - Single solution based and population based
algorithms – Exploitation and exploration in population based algorithms - Properties of Swarm intelligent Systems - Application
domain - Discrete and continuous problems - Single objective and multi-objective problems
3.Introduction to Evolutionary Programming: Genetic algorithms, genetic programming and evolutionary programming;
Genetic Algorithm versus Conventional Optimization Techniques; Genetic representations and selection mechanisms; Genetic
operators- different types of crossover and mutation operators; Optimization problems using GA-discrete and continuous- single
objective and multi-objective problems; Procedures in evolutionary programming. PSO topologies-swarm types- control
parameters-constriction coefficient; PSO applications in electrical engineering application
4.Shuffled Frog-leaping Algorithm and Bat Optimization Algorithm: Bat Algorithm- Echolocation of bats- Behaviour of
microbats- Acoustics of Echolocation- Movement of Virtual Bats- Loudness and Pulse Emission- Shuffled frog algorithm-virtual
population of frogs comparison of memes and genes -memeplex formation- memeplex updation- BA and SFLA algorithms for
solving ELD and optimal placement and sizing of the DG problem.
5.Whale Optimization Algorithm and Salps Swarm Algorithm: Whale Optimization Algorithm- Mathematical model and
optimization algorithm- Encircling prey, Spiral Bubble-Net Feeding Maneuver, and Search for prey .Salps Swarm Algorithm-
Single objective SSA and Multi Objective SSA- WOA and SSA algorithms for solving power system Problem
6. Hybridization of Swarm Based Algorithm: Differential search algorithms, harmony Search algorithms, cuckoo search
algorithms, firefly algorithms,gravitational search Algorithms, Hybrid swarm intelligent systems; Applications in electrical
engineering

16. Thank YouThank You