Hybrid Dandelion Optimizer-based Multi-Objective Photovoltaic Power Penetration Maximisation in Reconfigurable Distribution Networks

Question

1. What are the operators and mathematical model of the Dandelion Optimizer (DO)?

2. How can Loss sensitivity factors (LSFs) influence distribution losses?

3. How do simulation results compare with literature for HDO case studies?

4. How does HDO optimize PV unit allocation?

Accepted Answer

The Dandelion Optimizer (DO) includes three operators: normal sowing, mutation sowing, and a selection strategy. In normal sowing, the Core Dandelion (CD) produces more seeds than the Assistance Dandelion (AD) based on the fitness scores of the dandelion population. The sowing radius of ADs is calculated differently from CDs, with the AD's sowing radius being adjusted dynamically to improve global search efficiency. Mutation sowing is proposed for the CD to avoid local optima and maintain population diversity, using a Levy distribution-based random number. The selection strategy in the Assistance Dandelion (AD) ensures that the optimal location from the last iteration is always kept, while the remaining locations are chosen using a disruptive selection operator to preserve population variety and improve global searches. The mathematical model of the DO includes equations for calculating the sowing radius, withering and growth factors, and selection probabilities. These equations take into account factors such as the fitness scores, iteration number, and total function evaluations to optimize the DO's performance.

Accepted Answer

Loss sensitivity factors (LSFs) can be used to identify potential candidate locations to influence the distribution losses. By knowing such candidate locations, the computational effectors in finding optimal locations can be reduced significantly, allowing for the attainment of global optima by avoiding local minima traps in the optimization process. Mathematically, LSFs can be defined by a specific equation, and locations with high LSFs are more suitable for PV penetrations in the network. By maximizing the PV capacity and considering peak loading conditions, the optimal allocation of PV-based DGs can be solved using the proposed HDO along with LSFs. This hybrid approach ensures maximum PV penetration without violating the operational constants of LV-RDNs effectively.

Accepted Answer

The simulation results for HDO case studies are compared with literature to evaluate the performance of the proposed HDO method. In Case 1, the optimal allocation of PV units considering multiple objectives is analyzed with constraints from Eqs. (12)-(17). Case 2 involves simultaneous allocation of PV units and ONR, considering the MOF defined in Eq. (11) and all constraints from Eqs. (12)-(19). The results obtained by HDO are compared with existing literature to determine its effectiveness and efficiency in optimizing power distribution and reducing greenhouse gas emissions. The comparison helps in understanding the strengths and limitations of the proposed HDO method and its potential for real-world applications.

Accepted Answer

HDO optimizes PV unit allocation by determining the best candidate locations using LSFs and searching within the top 20 locations. The optimal locations are buses 14, 24, and 29 with sizes of 800 kW, 1063 kW, and 1150 kW, respectively. The total PV capacity is 3013 kW, which is 81.1036% of the total load. HDO reduces total distribution losses to 72.2587 kW and 50.3921 kVAr, and the lowest voltage is 0.9686 p.u. at bus-33. The total GHG emission is estimated at 1.7639 E+6 lb/h. Compared to the base case, HDO reduces losses by 64.94% and GHG emissions by 78.01%. HDO outperforms other methods in terms of PV penetration and losses, but the PV total capacity may be oversized for the network load.

Accepted Answer

In the case study, simultaneous allocation of PV units and optimal network reconfiguration significantly reduces distribution losses and GHG emissions. The total distribution losses were reduced by 64.94% and GHG emissions were reduced by 81.57% compared to the base case. The best PV sizes at buses 7, 17, and 25 were 799.64 kW, 950 kW, and 1296.67 kW, respectively. The optimal branches and tie-lines opened under ONR were 10, 28, 31, 33, and 34, respectively. The lowest voltage was registered at bus-31 as 0.9777 p.u., and the AVDI was evaluated as 0.0022. The total GHG emission was estimated at 1.4786e+6 lb/h. The proposed HDO approach resulted in higher PV penetration compared to other methods, but losses were compromised due to the simultaneous priority for reduction in GHG emissions and higher PV penetration. The voltage profiles for the three cases showed a more smooth voltage profile in the network with the simultaneous approach.

Accepted Answer

Hourly Optimal Network Reconfiguration (HONR) aims to maximize overall network performance under variable loading conditions and PV penetration levels. It considers the best PV locations and sizes in the network, adjusting their generation levels according to hourly variation factors. The network performance HONR is given in Table 3 for before and after scenarios. By having optimal reconfiguration, the total energy losses are reduced, AVDI is decreased, and GHG emissions are minimized. This scenario indicates the need for HONR for variable loading conditions in modern electrical grids.

Accepted Answer

The proposed hybrid method significantly reduces energy losses by 31.19% and decreases daily GHG emissions from 111.936 to 110.231x10^6 lb/hr. The methodology achieves this by maximizing PV penetration while maintaining network operating conditions. The variance in GHG emissions is minimal, as it primarily depends on PV penetration, which remains unchanged in both scenarios. The reduction in energy losses and GHG emissions highlights the need for Hybrid Optimal Network Reconfiguration (HONR) in modern electrical grids under changing loading conditions.

Hybrid Dandelion Optimizer-based Multi-Objective Photovoltaic Power Penetration Maximisation in Reconfigurable Distribution Networks

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Most frequently asked questions

1. What are the operators and mathematical model of the Dandelion Optimizer (DO)?

2. How can Loss sensitivity factors (LSFs) influence distribution losses?

3. How do simulation results compare with literature for HDO case studies?

4. How does HDO optimize PV unit allocation?

5. How does simultaneous allocation of PV units and optimal network reconfiguration impact distribution losses and GHG emissions?

6. What is Hourly Optimal Network Reconfiguration?

7. How does the proposed hybrid method impact energy losses and GHG emissions?

Citations

Hybrid Dandelion Optimizer-based Multi-Objective Photovoltaic Power Penetration Maximisation in Reconfigurable Distribution Networks

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References

No free lunch theorems for optimization

Barriers to renewable energy penetration; a framework for analysis

Metaheuristic research: a comprehensive survey

Review of optimization techniques applied for the integration of distributed generation from renewable energy sources

Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques

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