Generation expansion planning optimisation with renewable energy integration: A review

Abstract

Generation expansion planning consists of finding the optimal long-term plan for the 5 construction of new generation capacity subject to various economic and technical constraints. It 6 usually involves solving a large-scale, non-linear discrete and dynamic optimisation problem in a 7 highly constrained and uncertain environment. Traditional approaches to capacity planning have 8 focused on achieving a least-cost plan. During the last two decades however, new paradigms for 9 expansion planning have emerged that are driven by environmental and political factors. This has 10 resulted in the formulation of multi-criteria approaches that enable power system planners to 11 simultaneously consider multiple and conflicting objectives in the decision-making process. 12 More recently, the increasing integration of intermittent renewable energy sources in the grid to 13 sustain power system decarbonisation and energy security has introduced new challenges. Such a 14 transition spawns new dynamics pertaining to the variability and uncertainty of these generation 15 resources in determining the best mix. In addition to ensuring adequacy of generation capacity, it 16 is essential to consider the operational characteristics of the generation sources in the planning 17 process. In this paper, we first review the evolution of generation expansion planning techniques 18 in the face of more stringent environmental policies and growing uncertainty. More importantly, 19 we highlight the emerging challenges presented by the intermittent nature of some renewable 20 energy sources. In particular, we discuss the power supply adequacy and operational flexibility 21 issues introduced by variable renewable sources as well as the attempts made to address them. 22 Finally, we identify important future research directions.

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

“…Uncertainty usually occurs due to lack of access to perfect information of key input factors involved in the planning. Hence, uncertainty cannot be neglected in power system expansion planning . It is therefore important to incorporate a reasonable level of uncertainty in GEP model, since they involve a long term planning so as to mitigate risks .…”

“…To overcome these shortcomings, the probabilistic approach assigns probability values to various uncertain variables. It further identifies the optimal output using tools such as stochastic programming and Monte Carlo simulation …”

“…The authors in another study presented a state‐of‐the‐art survey on TEP that focused not only on the issues discussed in Hemmati et al but also on integration of wind technology, security, and flexible AC transmission system (FACTS) devices . A review study conducted by Oree et al presented a survey on the evolution of GEP methods considering environmental policies and the presence of uncertainty. A survey of GEP problem based on key events in electrical power sector over the last 30 years has also been presented .…”

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

“…Environmental impact analysis environmental and ecological decision-making [84][85][86], life-cycle assessment [2, 87] C1.4. Energy evaluation and assessment investments [88][89][90][91], sustainability assessment of energy systems [38,[92][93][94][95], sources, technologies and options [12,13,15,28,35,39,94,[96][97][98], power expansion alternatives [99][100][101], plants [47,102], power generation scenarios [103,104] quantitative and qualitative factors. Based on [10], multi-attribute utility theory (MAUT), analytic hierarchy process (AHP), analytic network process (ANP), preference ranking organization method for enrichment evaluations (PROMETHEE), elimination and choice expressing reality (ELECTRE), and fuzzy models were the most commonly used techniques for renewable and sustainable energy planning.…”

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

“…(optimal physical location), and How much? (needed capacity in order to overcome the electrical demand) …”