Multigroup differential evolutionary and multilayer Taylor dynamic network planning for zero-carbon grid extension model with user satisfaction

Grid planning is a long-term process with uncertainties at all stages, which requires a versatile design approach to address potential risks. From the perspectives of system operation and user participation, a zero-carbon grid extension model with user satisfaction for the cooperative operation of wind power and conventional energy is proposed in this study to obtain the equipment planning address with the objectives of minimizing the total cost of grid investment, minimizing the power loss and optimizing renewable energy capacity. Based on the consideration of long-term net load growth uncertainty, and real-time feedback on user experience, a multigroup differential evolutionary and multilayer Taylor dynamic network planning method is proposed to calculate the carrying capacity of new construction of power grids for loads and distributed power sources at multi-temporal scales. The planning scheme is sequentially specified by the current objective function value and expected future objective function value. In particular, the multi-objective function value is obtained by the strategy of adaptive mutation and elite selection. The best individuals are selected to guide the population variation, and the crossover rate is updated by evolutionary information, which improves algorithm convergence and obtains the optimal Pareto solution set. In the IEEE 14-bus systems in phase 3, compared to the best model which considers no user willingness, only a single objective, and without multi-stage, the proposed model in the study reduces the total investment cost by 27.33%, 36.99%, and −13.68%, and decreases the network loss by 26.11%, 34.62%, and 25.48%, respectively. Compared with the algorithm with the second best performing, the inverted generational distance of the proposed method in the study decreases by 4.81%. The results of the IEEE 118-bus systems in phase 5 validate the feasibility and effectiveness of the proposed method in obtaining an extended solution, which significantly reduces the investment risk, allocates suitable energy equipment, and promotes renewable energy efficient utilization.