Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

Abstract

In generation expansion planning, sustainable generation expansion planning is gaining more and more attention. Based on the comprehensive consideration of generation expansion planning economics, technology, environment, and other fields, this paper analyzes the sustainable development of power supply planning evaluation indicators and builds a multi-objective generation expansion planning decision model considering sustainable development. According to the target variables in the model, the variables such as attribute variables are divided into different subsets, and the logical relationship analysis method between different nodes is obtained based on Dynamic Bayesian network theory, which reduces the complexity of the planning model problem. The application examples show the feasibility and effectiveness of the proposed model and the solution method.

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

“…This key point is coordination and interaction between the transmission and distribution systems that in [36] GEP problem is regarded with this criterion. Additionally, in [37] is used a new way which is called Bayesian network for the dynamic behavior of the system with considering uncertainties of renewable power plants in a multi-objective generation expansion planning decision model regarding permanent development. Because of the changeable output of renewable energy resources, considering flexibility requirements with respect to the power system is an important issue.…”

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

“…In addition, there are various objective functions such as minimizing generation planning cost [2], maximizing profit in the market [3], maximizing reliability [4,5], minimizing environmental pollution [6], and combination of mentioned objective functions. The problem constraints include load supply, transmission lines, and generators capacity [7], and in some cases, greenhouse gas generation, and also some uncertainties [8,9] have been considered as constraint.…”