MATLAB | Multi-Objective Differential Evolution Algorithm Based Reactive Power Optimization Model for DG-Integrated Distribution Networks [IEEE 33 Nodes]

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💥1 Overview

📚2 Results

🎉3 References

🌈4 MATLAB Code, Data, Article Explanation

1 Overview

The multi-objective reactive power optimization can balance both economic efficiency and voltage stability in the objective function, attracting widespread attention from researchers. The essential difference between multi-objective reactive power optimization and single-objective reactive power optimization is that the solutions for multi-objective optimization are not unique; that is, there is no solution that simultaneously optimizes both economic efficiency and voltage stability. Instead, there exists a set of non-dominated solutions known as the Pareto optimal set, where the elements in this set are incomparable in terms of all objectives. Current methods for solving multi-objective reactive power optimization problems can be roughly divided into two categories: 1) A priori methods. These methods transform the multi-objective optimization model into a single-objective optimization problem by setting parameters that reflect the preference levels of each objective in advance. Common methods include linear weighting methods [11-12] and fuzzy set theory [13-14]. Although these methods are computationally convenient, they have significant drawbacks: the weight vector or membership function is difficult to determine; each calculation can only yield one control scheme, and multiple calculations are required to obtain a set of approximate Pareto optimal solutions; they are sensitive to the shape of the Pareto front, making it difficult to search for a complete Pareto optimal set if the Pareto front is a non-convex set. 2) A posteriori methods. This method does not require prior specification of the priority relationships between objective functions; operators only need to select control schemes from the Pareto optimal set that meet the requirements. Therefore, quickly obtaining a well-distributed and broad range of Pareto fronts becomes crucial. References [15, 16] applied the Strength Pareto Evolutionary Algorithm (SPEA) and its improved version SPEA2 to obtain the Pareto optimal set; literature proposed using NSGA-II to obtain the Pareto front; additionally, multi-objective optimization techniques based on PSO have also been used to solve multi-objective reactive power optimization problems. However, the aforementioned algorithms often suffer from issues such as getting trapped in local optima, uneven distribution of non-dominated solutions, and difficulty in selecting control parameters. The multi-objective differential optimization algorithm is discussed in Section 4.

2 Results

Figure 1 shows the improved IEEE 33-node distribution system, where a group of parallel compensation capacitors and two distributed power sources are added while keeping the line parameters unchanged.

Assuming each distributed power source can generate 1 MW of active power, and the reactive output of these two distributed power sources is adjustable within the range of -100 to 500 kvar; the compensation capacity of the parallel compensation capacitors is set to 150 kvar x 4 and 150 kvar x 7.

3References

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[1] Qiu Wei, Zhang Jianhua, Liu Nian. Application of Adaptive Multi-Objective Differential Evolution Algorithm in Reactive Power Optimization Considering Voltage Stability [J]. Power System Technology, 2011, 35(08): 81-87. DOI:10.13335/j.1000-3673.pst.2011.08.021.

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