Abstract
In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.
Highlights
Using a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM)
Against standard feedforward neural networks, recurrent neural networks (RNN), which benefit from recurrent weights, can learn the temporal dependence among data
The robust version of RNNs known as LSTM networks have been introduced in [33]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Generation expansion planning (GEP) is an inevitable and important issue for power system planners due to energy consumption growth In this problem, technology type, installation time, and location of new power plants are determined to supply the predicted load with appropriate reliability [1]. In [14], a comprehensive and deep review has been performed about the GEP problems such as uncertainties, energy policies, low carbon economy requirements, renewable sources, electricity market, demand-side programs, distributed generation, and so on. Investigating the effects of power plants’ lifetime constrain on new and existing plants This constraint affects the GEP problem due to two practical reasons: 1-Some power plants have less lifetime than the planning horizon. Some power plants go out of operation every year due to reaching the end-of-life This issue is considered as a constraint in the model and a salvage value in the objective function.
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