Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load,this paper proposes the concept of power source and grid coordination uniformity planning.In this approach,the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning.A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle.In this study,the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model,thus improving the efficiency and accuracy of the solution.The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible.Moreover,they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.

Sorting of the Structure of Corrugated Yarn Production in Cluster Enterprises

In the article, the methods of processing and obtaining multicomponent raw materials were investigated with the properties of multicomponent threads obtained from fiber waste cluster enterprises in textile production, the interrelation of the components sorting composition. To solve the problem, we used the cause-effect relationship of information theory from the grid planning Matrix, solving the dependence of raw materials, physical and mechanical parameters and the established characteristics of the product. Empirical correlations were obtained on the nature and mechanism of the relationship between factors, which allowed the system to establish a theory of control and prediction of behavior. The methods of evaluation used in this study make it possible to expand the information base with respect to these indicators and to use them in the production of fabrics and threads with established characteristics.

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.