A Binary Archimedes Optimization Algorithm and Weighted Sum Method for UFLS in Islanded Distribution Systems Considering the Stability Index and Load Priority

Question

1. What is Malaysia's renewable energy target for 2025?

2. How does Binary AOA aid in load-shedding?

3. What are the steps in Binary AOA load-shedding scheme?

4. What factors are considered in the proposed fitness function for BAOA?

Accepted Answer

According to the provided information, Malaysia has set a target of a 31% renewable energy contribution to the national installed capacity mix by 2025. This target reflects the government's commitment to reducing carbon emissions and promoting clean, low-impact energy sources. In 2019, the installed capacities for renewable energy in Malaysia were 2.9%, 0.4%, and 1.2%, respectively. The government's focus on renewable energy aligns with global efforts to combat climate change and transition towards sustainable energy systems. Achieving this target will require significant investments in renewable energy infrastructure, research, and development, as well as policy support and public awareness campaigns. By increasing the share of renewable energy in the national energy mix, Malaysia can reduce its reliance on fossil fuels, lower carbon emissions, and enhance energy security. The proposed UFLS based on the binary Archimedes Optimization Algorithm (BAOA) and Weighted Sum Method (WSM) aims to facilitate the integration of distributed generation and improve grid resilience, further supporting the country's renewable energy goals.

Accepted Answer

Binary AOA, or Archimedes Optimization Algorithm, aids in load-shedding by using binary variables to represent each load in the distribution system. These variables indicate whether a load should be shed (1) or not (0) during frequency deviations. The algorithm considers load priority through the stability index, ensuring that critical loads are shed first to maintain system reliability. The fitness function evaluates the quality of a load-shedding scheme based on the stability index and load priorities, quantifying deviations from the desired stability index and penalizing the shedding of critical loads. This approach helps in determining the best and minimum combination of loads to be shed, predicting the effects of load shedding on the system, and minimizing the impact on critical loads.

Accepted Answer

The steps in Binary AOA load-shedding scheme are: 1. Initialization: Generate a random set of particles with buses of connected load, density, volume, and acceleration. 2. Updating density and volume: Update the density and volume of each particle based on the best particle. 3. Updating acceleration: Update acceleration using transfer and density operators. Normalize acceleration. 4. Updating position: Update particle positions using constants, transfer function, and flag parameter. A sigmoidal transfer function is implemented for discrete search space. The updated position is within the range of [0, 1].

Accepted Answer

The proposed fitness function for BAOA considers the minimum stability index (SI), optimal size of load shedding, power imbalance, and power reserve from mini hydro. It aims to select the minimum SI and optimal load size while ensuring power balance and minimizing system frequency overshooting. The function introduces penalties for load selection violations and considers the difference in power generated by mini hydro before and after islanding. Equations (13), (14), (15), and (16) are used to calculate the fitness function, taking into account voltage, load, line resistance, reactance, impedance, number of loads, and a constant for penalty. SI values range from 1.0 (stable) to 0.0 (critical stability).

Accepted Answer

The proposed UFLS system maintains stability by combining optimization and multi-criteria decision-making methods. The Load Selection Module (LSM) uses the BAOA to identify the minimum number of loads with the least power imbalance and the lowest stability index. The Load Ranking Module (LRM) utilizes the Weighted Sum Method (WSM) to determine the shedding sequence of loads. The ranked load list is passed to the Load-Shed Controller Module (LSCM), which activates the corresponding remote circuit breaker (RCB) for load disconnection. The system assumes the presence of reliable monitoring devices for measurement units and RCBs. Modeling and simulation were conducted using PSCAD/EMTC and MATLAB to validate the effectiveness of the proposed scheme in the distribution system.

Accepted Answer

WSM evaluates load alternatives based on four criteria: load priority, load category, stability index, and load size. Load priority is determined by power mismatch obtained from the BAOA. Load category assigns numerical values to represent nonvital, semi-vital, and vital loads. Stability index is calculated using (14) for each bus. Load size is based on the load demand for each bus. The weightage of each criterion is determined using criteria weights and alternative weights calculated in Table 1. The rank of the load to be shed is acquired based on the WSM weightage considering these four criteria.

Accepted Answer

The entropy method calculates criteria weightings by normalizing the arrays of the decision matrix for each load criteria. This process involves obtaining common scale values for the elements of different criteria, as shown in equation (17). The performance value of the decision matrix according to the jth criteria for load priority, stability index, load size, and load category for the ith alternative load is denoted by x. The total number of buses with connected loads is represented by nbus. The entropy method assigns weights to each criterion based on their importance, allowing for a more objective evaluation of alternatives.

Accepted Answer

In the entropy method, the weighting of each criterion is determined based on the entropy of the normalized elements in the decision matrix. This is computed using Equation 3, which involves calculating the entropy of the normalized elements. The weighting is then used to convert the decision matrix into a weighted normalized decision matrix. This is achieved by multiplying both beneficial and nonbeneficial elements of the decision matrix by each criterion's weight. The weighted normalized decision matrix is then obtained by summing the elements of all criteria for each alternative. The rank of each load to be shed is determined from the score for each alternative, as shown in Equations (22) and (23). The summary of the Weighted Sum Model (WSM), which includes the criteria and alternative weightings, is presented in Figure 2.

Accepted Answer

Alternative weights using the WSM are computed by applying values from the entropy method to the WSM. The criteria are identified as beneficial or nonbeneficial, determining whether maximum or minimum values are required. The decision matrix is normalized based on these criteria, with beneficial criteria using equation (20) and nonbeneficial criteria using equation (21). The elements preferred to have higher values (b) and lower values (nb) are used in the equations to calculate the alternative weights and scores for each bus load.

Accepted Answer

The Load-Shed Controller Module (LSCM) is designed to oversee occurrences and subsequent instances of overloading and islanding events. It governs the location and number of breakers to be opened based on load-shedding priority determined by the Weighted Sum Method (WSM) in the Load Ranking Module (LRM). By monitoring system frequency and power imbalance, the LSCM assesses the necessary load-shedding location. In the event of an islanding, it identifies the absence of grid power supply and detects the power imbalance between supply and demand. The LSCM also examines the availability of spinning reserve through mini hydro DGs. If the spinning reserve is insufficient, it transmits a signal to the relevant remote circuit breaker (RCB) to disconnect the load. The LSCM sheds loads from the list provided by the LRM until the system frequency stabilizes and the power imbalance approaches zero. It diligently monitors the network for any irregular conditions, such as overload or excessive power generation, and initiates load-shedding procedures when the estimated power imbalance surpasses a predefined threshold.

Accepted Answer

The UFLS scheme was tested on an existing 11 kV Malaysian actual distribution network, consisting of 30 buses with two mini hydro synchronous generators and PV generators. The network was connected to the grid via two step-down 132 kV/11 kV transformers and two MVA transformers. Each mini hydro unit was rated at 2 MVA with a maximum power dispatch of 1.8 MW, and the PV units generated a maximum power of 0.2 MW. The load connected was categorized into 11 nonvital, 11 semi-vital, and 5 vital loads. Three case studies were considered to evaluate the performance of the proposed UFLS scheme.

Accepted Answer

Simulations were conducted by disconnecting the incoming substation at 3.5 s for islanding and connecting a sudden increase in load at 40.0 s during islanding to validate the LSCM's efficacy. The rankings of loads to be disconnected were shown in Appendix A, prioritizing nonvital loads first. The efficacy of the load rank was assessed based on frequency response in the next section.

Accepted Answer

Islanding significantly reduces the system's frequency response due to the sudden disruption of power supply from the main grid. In the provided scenario, at a 3.11 MW load demand, the frequency dropped to 47.73 Hz but recovered at 30.5 seconds. However, at a 3.74 MW load demand, the frequency dropped to 44.55 Hz but recovered at 40 seconds. The instability of the system becomes more apparent with increasing load, as seen at 40 seconds where the frequency response dropped to 47.96 Hz but recovered at 65 seconds. At a 3.74 MW load demand, the frequency did not recover, indicating the power supply from the DG system had reached its maximum reserve and was unable to meet the high load demand.

Accepted Answer

UFLS based on SI reached approximately a 50.0 Hz frequency at 28.5 s, while UFLS based on load size had a frequency overshoot of 50.12 Hz and recovered to a stable frequency at 34.57 s. The proposed UFLS, considering both stability index and load size, had no frequency overshoot and recovered at 16.47 s. In overloading events, UFLS based on load size had a frequency overshoot of 50.2 Hz and recovered at 73.9 s, while UFLS based on SI had no frequency overshoot and recovered at 77.93 s. The proposed UFLS had no frequency overshoot and recovered the fastest at 68.03 s. Overall, UFLS based on SI is more significant in islanding events, while UFLS based on load size is more significant in overloading events. The proposed UFLS considers both factors for load shedding.

Accepted Answer

The proposed UFLS scheme has proven to be effective in reducing frequency overshooting. The study's results show that the UFLS scheme successfully minimizes frequency overshooting, improving frequency undershooting, and recovering more quickly than other approaches. By optimizing load shedding based on the minimum stability index and power mismatch, the BAOA component of the UFLS scheme contributes to this effectiveness. Additionally, the WSM ranks loads to be shed based on various criteria, further enhancing the scheme's ability to maintain system stability. Overall, the proposed UFLS scheme demonstrates its effectiveness in ensuring the reliability and stability of the power system.

A Binary Archimedes Optimization Algorithm and Weighted Sum Method for UFLS in Islanded Distribution Systems Considering the Stability Index and Load Priority

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Most frequently asked questions

1. What is Malaysia's renewable energy target for 2025?

2. How does Binary AOA aid in load-shedding?

3. What are the steps in Binary AOA load-shedding scheme?

4. What factors are considered in the proposed fitness function for BAOA?

5. How does the proposed UFLS system maintain stability?

6. How does WSM evaluate load alternatives?

7. How does the entropy method calculate criteria weightings?

8. How is the weighting of each criterion defined in the entropy method?

9. How are alternative weights computed using the WSM?

10. What is the purpose of the Load-Shed Controller Module (LSCM) in managing overloading and islanding events?

11. How was the UFLS scheme tested?

12. How were simulations conducted for islanding and overloading events?

13. How does islanding impact frequency response?

14. How do UFLS based on SI and load size differ in frequency response?

15. How effective is the proposed UFLS scheme in reducing frequency overshooting?

Citations

Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System

References

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation

Adaptive Underfrequency Load Shedding Based on the Magnitude of the Disturbance Estimation

A review of the state of the art of UFLS schemes for isolated power systems

Underfrequency Load Shedding Using Locally Estimated RoCoF of the Center of Inertia

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