![REFERENCES
21
1. Gholami, R.; Shahabi, M.; Haghifam, M. An efficient optimal capacitor allocation in DG embedded distribution
networks with islanding operation capability of micro-grid using a new genetic based algorithm. Int. J. Electr.
Power Energy Syst. 2015, 71, 335–343. [CrossRef]
2. Wang, M.; Zhong, J. A novel method for distributed generation and capacitor optimal placement considering
voltage profiles. In Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA,
24–28 July 2011; pp. 1–6.
3. Yazdavar, A.H.; Shaaban, M.F.; El-Saadany, E.F.; Salama, M.M.A.; Zeineldin, H.H. Optimal planning of distributed
generators and shunt capacitors in isolated microgrids with nonlinear loads. IEEE Trans. Sustain. Energy 2020,
11, 2732–2744. [CrossRef]
4. Kirthiga, M.V.; Daniel, S.A.; Gurunathan, S. A methodology for transforming an existing distribution network into
a sustainable autonomous micro-grid. IEEE Trans. Sustain. Energy 2013, 4, 31–41. [CrossRef]
5. Anand, M.P.; Ongsakul,W.; Singh, J.G.; Sudhesh, K.M. Optimal allocation and sizing of distributed generators in
autonomous microgrids based on LSF and PSO. In Proceedings of the 2015 International Conference on Energy
Economics and Environment (ICEEE), Greater Noida, India, 27–28 March 2015; pp. 1–6.
6. Jamian, J.J.; Mustafa, M.W.; Mokhlis, H.; Baharudin, M.A.; Abdilahi, A.M. Gravitational search algorithm for
optimal distributed generation operation in autonomous network. Arab. J. Sci. Eng. 2014, 39, 7183–7188.
[CrossRef]
7. Farag, H.E.Z.; El-Saadany, E.F. Optimum shunt capacitor placement in multimicrogrid systems with consideration
of islanded mode of operation. IEEE Trans. Sustain. Energy 2015, 6, 1435–1446. [CrossRef]
8. Leghari, Z.H.; Kumar, M.; Shaikh, P.H.; Kumar, L.; Tran, Q.T. A Critical Review of Optimization Strategies for
Simultaneous Integration of Distributed Generation and Capacitor Banks in Power Distribution Networks.
Energies. 2022, 15, 8258. [CrossRef]
9. Vita, V.; Fotis, G.; Pavlatos, C.; Mladenov, V. A New Restoration Strategy in Microgrids after a Blackout with
Priority in Critical Loads. Sustainability 2023, 15, 1974. [CrossRef]](https://image.slidesharecdn.com/presentationeffectiveutilizationofdistributed-241126084953-a8a8ff98/85/Presentation-Effective-Utilization-of-distributed-pptx-21-320.jpg)
Presentation Effective Utilization of distributed.pptx
- 1. 1 Effective Utilization of Distributed Power Sources under Power Mismatch Conditions in Islanded Distribution Networks Zohaib Hussain Leghari et. al Presented by : Shehzada Taimur D11310214 National Yunlin University of Science and Technology
- 2. TITLE PAGE OF THE ARTICLE 2
- 3. 3 OUTLINE • ABSTRACT • INTRODUCTION • AIM OF STUDY • PROBLEM FORMULATION • SYSTEM MODEL • JAYA ALGORITHM • CONSTRAINTS • WORKFLOW ELABORATION • CASE STUDY • RESULTS AND DISCUSSION • CONCLUSION • REFERENCES
- 4. ABSTRACT This study presents a strategy for the effective utilization of deployed active and reactive power sources under power mismatch conditions in the islanded distribution networks. Initially, the DGs’ and capacitors’ optimal placement and capacity were identified using the Jaya algorithm (JA) with the aim to reduce power losses in the grid-connected mode. Later, the DG and capacitor combination’s optimal power factor was determined to withstand the islanded distribution network’s highest possible power demand in the event of a power mismatch. To assess the optimal value of the DG–capacitor pair’s operating power factor for the islanded operation, an analytical approach has been proposed that determines the best trade-off between power losses and the under-utilization of accessible generation. The test results on 33-bus and 69-bus IEEE distribution networks demonstrate that holding the islanded network’s load power factor (p f load) equal to p f source during the power imbalance conditions allows the installed distributed sources to effectively operate at full capacity 4
- 5. 5 INTRODUCTION Distributed Generation Distributed generation refers to a variety of technologies that generate electricity at or near where it will be used, such as solar panels and combined heat and power. Distributed generation may serve a single structure, such as a home or business, or it may be part of a microgrid A microgrid is a group of interconnected loads and distributed energy resources that acts as a single controllable entity with respect to the grid. It can connect and disconnect from the grid to operate in grid-connected or island mode Micro Grid
- 6. Distributed Network The distribution network consists of substations, feeders, and step- down transformers to deliver electric power to the end user. A capacitor/Battery is a small-scale energy resource such as rooftop solar panel, a micro hydro or wind plant, in a Distributed Network usually situated near sites of electricity use. Capacitors / Batteries 6 INTRODUCTION
- 7. 7 AIM OF STUDY The primary goal of this study is to efficiently utilize the installed DGs and capacitors to their full capacity so that the mounted devices can carry the highest potential share of the networks’ entire load during the autonomous operation. In this context, a multi-criterion function is formulated considering the objectives of power loss minimization and reduced under-utilized capacity of the accessible active–reactive power generation. It proposes a dual-stage strategy for optimally placing DGs and capacitors during the grid-integrated mode of distribution networks and efficiently operating them for islanded mode as well.
- 8. Problem formulation 8 Obtaining favorable results by introducing distributed generation (DG) and capacitors into distribution networks necessitates a well-thought-out design strategy. The optimal positioning and sizing of capacitors and DGs potentially result in improved voltage stability, reliability, power quality, reduced power losses, and eliminating or deferring the upgrades of the electrical power networks. Although heuristic and meta-heuristic techniques have been commonly used to optimize the capacity and placement of DGs and capacitor units in the distribution networks. However, the literature focuses on identifying the DGs’ and capacitors’ simultaneous allocation for grid- integrated distribution networks.
- 9. 9 SYSTEM MODEL Consider the single-line diagram of a simple two-bus radial distribution system depicted in Figure The power flow solution for such networks may be computed by using Equations The mathematical expression of the first objective of the considered optimization problem minimizing the power loss function is presented in Equation 1 𝑃𝑙𝑜𝑠𝑠𝑇 = ∑ 𝑏𝑟 =1 𝑛 𝑏−1 𝑃𝑙𝑜𝑠𝑠𝑏,𝑏+1 ………….1
- 10. 10 Cont.. Power-generating sources generally operate at high power factors to minimize power losses and optimize the distribution networks’ utilization capacity. Furthermore, the utilization factor of the DGs, which is the ratio of the device’s actual output to the maximum achievable output (or rated capacity), can alter significantly as the operational power factor varies. ………….2 the mathematical description of the proposed multi-criteria optimization problem is given as following Equation using a weighted sum approach ………….3
- 11. 11 Jaya Algorithm In order to determine the optimal location and sizing for the DG and capacitor units, the Jaya algorithm (JA) is used. It is a stochastic heuristic optimization algorithm that only involves a single recombination step. In addition, the JA is different from most population-based optimization approaches as it uses no parameters, chosen by the user, in its execution. Only the maximum number of iterations (Max Itr) and population size (n Pop) are to be specified for the JA, To mathematically describe the JA’s implementation cycle, let z be the real valued vector of decision variables composing one solution. The and indicate the best and worst so far solutions. The ith candidate’s jth decision variable in the kth iteration is represented as , Then, at the kth iteration, the numerical equation used to update the candidate solution is given as represented in …….4 where and are random numbers selected with uniform probability between 0 and 1 at the kth iteration. The objective function value decides whether the updated solution can be preferred or not over the current solution
- 12. Inputthe JA͛ s and Optimization problem͛ s parameters Generate the initial population and evaluate the value of cost function Identify the best and worst solutions Based on the best and worst solutions, modify the solutions as per update equation Is the new solution zk better than the previous solution zk? No Previous value= New Value Yes Previous value= New Value Termination criteria satisfied? Yes Optimum solution No 12 Jaya Algorithm Flow chart
- 13. 13 Constraints To evade undesired results in the proposed optimization problem, some constraints are imposed that must be satisfied in order to solve the problem successfully. the first constraint posed for the islanded distribution network is the maximum active and reactive power flows across the network (P island_max, Q island_max) that must be equal to or less than the installed generation capacity of the installed DG and capacitor 𝑃𝑖𝑠𝑙𝑎𝑛𝑑𝑚𝑎𝑥 ≤∑𝑃𝐷𝐺 And voltage (Vb) that should be within 5% of the rated voltage (V rated). The assumed V rated for this study is 1 p.u. 𝑄𝑖𝑠𝑙𝑎𝑛𝑑𝑚𝑎𝑥 ≤∑𝑄𝑐𝑎𝑝 0.95𝑝 .𝑢≤ 𝑉 𝑏 ≤1.05 𝑝.𝑢 0 .8 ≤ 𝑃 . 𝑓 𝑠𝑜𝑢𝑟𝑐𝑒 ≤ 0.93 And the upper and lower bounds set for P.f source are 0.8 and 0.93, respectively.
- 14. 14 Workflow elaboration detailed frame of work is presented stepwise as follows Step 1. Define the base power, base voltage, load data, and line data for the selected distribution network. Step 2. Calculate the starting values of the objective functions, the active power loss in this case, by running the base case load flow for all the solutions of the starting population. Step 3. Set the JA’s parameters, nPop and MaxItr, and the parameters of the optimization problem, n (number of design variables) and Ud and Ld (upper and lower bounds). Step 4. Initialize the starting population with random values of the design variables. Step 5. Execute the power flow to compute the value of the objective function for each search agent of the starting population. Step 6. Find out the cost function values to determine the best and worst solutions. Step 7. Update the solutions of the current population, based on known best and worst solutions, as per equation 4. Step 8. Carry out the power flow for each new solution vector and determine the cost function’s updated values. Step 9. Compare the new updated cost function values with the previous values for each solution. Adopt the new solution if it is superior to the old one; else, stick with the old solution. Create the new population replacing the old one. Step 10. Stop the optimization process if the maximum iteration count is completed. Otherwise, repeat steps 6 to 9. Finally, report the obtained final optimum solutions of DG–capacitor sizes and locations.
- 15. 15 Step 11.Disconnect the distribution network from the grid and identify the available maximum active and reactive power generations for the autonomous distribution network. Step 12. Specify a value for the DG–capacitor combination’s working power factor, i.e Step 13. Gradually increase the active and reactive power demands of the load while keeping the source power factor constant at of ,Raise the load’s active and reactive power demands gradually and , we get equation 6 and 6 Step 14. Stop adding to the load demand, if voltage deviation increases 5%. Note these output values of and Step 15. Compute the weighted sum of cost function value as in equation 3. Step 16. For the next value, repeat step 12 to 15. Step 17. Compare the values of the cost function acquired at each and display the best solution value of . Workflow elaboration
- 16. 16 16 Case study Widely used IEEE 33- and 69-bus networks are used to implement the proposed methodological framework in this study with for four specific cases, as follows: Case 1: DG–capacitor couple supplying power at a power factor of 0.93 (i.e., at the maximum bound). Case 2: DG–capacitor couple supplying power at (also termed as ). Case 3: DG–capacitor couple supplying power to the load at the load power factor (). Case 4: DG–capacitor couple supplying power at a power factor of 0.8 (i.e., at the minimum bound).
- 17. 17 Case study In the first case, the DG and capacitor combination’s operating power factor is set to 0.93>. In this situation, the total load handled by the DG– capacitor pair is just 2.677 MVA or 61.27 percent of the distribution system’s entire load demand. The aggregate active power losses of 0.047 MW are reported under this situation, with bus 18 having the lowest bus voltage of 0.975 p.u. The DG and capacitor collectively produce 2.731 MVA power, which is 62.3 percent of the total power demand of the 33-bus network. As a result, assuming the installed DG and capacitor run at a power factor of 0.93, they can produce enough energy to meet 62.3 percent of the network’s total load in autonomous mode. The accessible generation capacity of 3.99 percent remains unutilized in this situation, whereas the operating efficiency evaluated in this case is 98.35 percent.
- 18. Results and discussion 18 a b Performance comparison of the 33-bus autonomous distribution network at different (a) Installed power generation capacity’s % utilization and (b) network’s % load share delivered with accessible power.
- 19. Results and discussion 19 a b Voltage profiles of the 33-bus autonomous distribution network attained in each case
- 20. Conclusion The main contributions of this study are listed below: A methodology correlating the effective utilization of the DG and capacitor units under autonomous operation mode is proposed for the scenario where the power supply is less than the power demand A bi-objective minimization function, incorporating the accessible power generation’s under-utilization and active power loss reduction, is established to optimize the islanded distribution network’s operation during power supply and demand imbalance events. 20
- 21. REFERENCES 21 1. Gholami, R.; Shahabi, M.; Haghifam, M. An efficient optimal capacitor allocation in DG embedded distribution networks with islanding operation capability of micro-grid using a new genetic based algorithm. Int. J. Electr. Power Energy Syst. 2015, 71, 335–343. [CrossRef] 2. Wang, M.; Zhong, J. A novel method for distributed generation and capacitor optimal placement considering voltage profiles. In Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 24–28 July 2011; pp. 1–6. 3. Yazdavar, A.H.; Shaaban, M.F.; El-Saadany, E.F.; Salama, M.M.A.; Zeineldin, H.H. Optimal planning of distributed generators and shunt capacitors in isolated microgrids with nonlinear loads. IEEE Trans. Sustain. Energy 2020, 11, 2732–2744. [CrossRef] 4. Kirthiga, M.V.; Daniel, S.A.; Gurunathan, S. A methodology for transforming an existing distribution network into a sustainable autonomous micro-grid. IEEE Trans. Sustain. Energy 2013, 4, 31–41. [CrossRef] 5. Anand, M.P.; Ongsakul,W.; Singh, J.G.; Sudhesh, K.M. Optimal allocation and sizing of distributed generators in autonomous microgrids based on LSF and PSO. In Proceedings of the 2015 International Conference on Energy Economics and Environment (ICEEE), Greater Noida, India, 27–28 March 2015; pp. 1–6. 6. Jamian, J.J.; Mustafa, M.W.; Mokhlis, H.; Baharudin, M.A.; Abdilahi, A.M. Gravitational search algorithm for optimal distributed generation operation in autonomous network. Arab. J. Sci. Eng. 2014, 39, 7183–7188. [CrossRef] 7. Farag, H.E.Z.; El-Saadany, E.F. Optimum shunt capacitor placement in multimicrogrid systems with consideration of islanded mode of operation. IEEE Trans. Sustain. Energy 2015, 6, 1435–1446. [CrossRef] 8. Leghari, Z.H.; Kumar, M.; Shaikh, P.H.; Kumar, L.; Tran, Q.T. A Critical Review of Optimization Strategies for Simultaneous Integration of Distributed Generation and Capacitor Banks in Power Distribution Networks. Energies. 2022, 15, 8258. [CrossRef] 9. Vita, V.; Fotis, G.; Pavlatos, C.; Mladenov, V. A New Restoration Strategy in Microgrids after a Blackout with Priority in Critical Loads. Sustainability 2023, 15, 1974. [CrossRef]
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