A Multi-Level Investment Demand Assessment Model for Renewable-Dominated Power System Planning

To achieve the goal of carbon peak and carbon neutrality, the integration of diversified renewable energy will be the principal feature of the planning framework of the smart grid, and the planning direction and focus of power systems would shift to the network transmittability and flexibility enhancement. This paper presents an infrastructure investment demand assessment model based on multi-level analysis method for the renewable-dominated power system planning. First, for the load side, the composite capacity ratio is used to assess the capacity demand of power transformation infrastructure for satisfying the load growth. Then, the renewable energy permeability is adopted as the basis to assess the extensional transmittability capacity for the integration of high renewables. Furthermore, the capacity demand of flexible transmission lines for power grid flexibility enhancement is also estimated. Finally, the amount of unit investment for source-network-load infrastructure capacities can be predicted based on the least square generation adjunctive network and support vector machine (LSGAN-SVM) algorithm. The performance of the proposed model has been tested and benchmarked on a practical-sized power system to verify its effectiveness and feasibility.

Comparison of Regression and Neural Network Model for Short Term Load Forecasting: A Case Study

Short term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors such as time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations used for prediction, later the model improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR observed as 47% and it reduced to 8.9% for CFBP.

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.