Generation expansion planning (GEP) – A long-term approach using system dynamics and genetic algorithms (GAs)

Pereira, Adelino J. C.; Saraiva, João Tomé

doi:10.1016/j.energy.2011.06.021

Cited by 100 publications

(65 citation statements)

References 17 publications

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“…The cash flow data generated by simulation model was subjected to a decision tree. Unlike Dimitrovski et al [54] and Periera and Saraiva [55], no interface was mentioned to have been developed between SD and decision tree model. However, the study successfully showed flexibility of SD model's output being channelized into sequential characteristic of a decision tree.…”

Section: Mixing-methods Modelsmentioning

confidence: 99%

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Application of system dynamics approach in electricity sector modelling: A review

Ahmad

Tahar

Muhammad‐Sukki

et al. 2016

Renewable and Sustainable Energy Reviews

104

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Section: Mixing-methods Modelsmentioning

confidence: 99%

“…Periera and Saraiva [55] reported a novel approach of combining SD with an artificial intelligence technique of genetic algorithm (GA). Like Dimitrovski et al [54], the model attempted for optimization.…”

Section: Mixing-methods Modelsmentioning

confidence: 99%

Application of system dynamics approach in electricity sector modelling: A review

Ahmad

Tahar

Muhammad‐Sukki

et al. 2016

Renewable and Sustainable Energy Reviews

104

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“…The optimization object (7) need subject to not only power flow equation constraints ( (8) and (9)), but also the branch thermal constraint (10) and network voltage limits (11). The amount of active generation curtailed will be limited by the capacity of generation connected (12).…”

Section: ) Production Simulationmentioning

confidence: 99%

“…Recently, people typically conclude that the heuristic approaches can provide "high-quality" solutions in an acceptable computational time, even for large-scale problems. Several meta-heuristic methods have already been introduced in literatures [11], e.g. Genetic Algorithms (GAs), Simulated Annealing, Ant Colonies, Particle Swarm Optimal Algorithms (PSOs), Expert Systems and Fuzzy Logic and combinations of GAs and Simulated Annealing.…”

Section: Introductionmentioning

confidence: 99%

Generation expansion planning considering integrating large-scale wind generation

Zhang

Ding

Østergaard

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Abstract-Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation while satisfying technical and economical constraints. In the deregulated and competitive environment, large-scale integration of wind generation (WG) in power system has necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. A bi-level generation expansion planning approach considering large-scale wind generation was proposed in this paper. The first phase is investment decision, while the second phase is production optimization decision. A multi-objective PSO (MOPSO) algorithm was introduced to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of the proposed bi-level planning approach and the MOPSO algorithm have been verified by a numerical test system.

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“…Sensitivity analysis (SA) is also often applied to LTGI models in order to understand how model outputs react to changes in model inputs. SA in [6,9,11,[13][14][15] were carried out using a simple one-at-a-time method, where each uncertain parameter is varied independently across a range of possible values while all others are held constant. The one-at-a-time method fails to treat the analysis with sufficient care (i.e., no formal weight or probability is attached to each outcome), and is incapable of taking into account interactions among different inputs.…”

Section: Introductionmentioning

confidence: 99%

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Wilson

Dent

2016

Sustainable Energy, Grids and Networks

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. (2016) 'Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation.', Sustainable energy, grids and networks., 5 . pp. 58-69. Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractInvestments in generation are high risk, and the introduction of renewable technologies exacerbated concern over capacity adequacy in future power systems. Long-term generation investment (LTGI) models are often used by policymakers to provide future projections given different input configurations. To understand both uncertainty around these projections and the ways they relate to the realworld, LTGI models can be calibrated and then used to make predictions or perform a sensitivity analysis (SA). However, LTGI models are generally computationally intensive and so only a limited number of simulations can be carried out. This paper demonstrates that the techniques of Bayesian emulation can be applied to efficiently perform calibration, prediction and SA for such complex LTGI models. A case study relating to GB power system generation planning is presented. Calibration reduces the uncertainty over a subset of model inputs and estimates the discrepancy between the model and the real power system. A plausible range of future projections that is consistent with the available knowledge (both historical observations and expert knowledge) can be predicted. The most important uncertain inputs are identified through a comprehensive SA. The results show that the use of calibration and SA approaches enables better decision making for both investors and policymakers. Keywords: Generation investments, calibration, uncertainty analysis, sensitivity analysis, Bayesian emulation Nomenclature Sets and Functions TSet of planning years of interest, indexed by t. P Set of past planning years. Set of fuel types including uranium, coal, gas, and carbon.Functions of the simulator and the emulator, respectively.Functions of the main modules within the simulator. GP(·, ·)Gaussian process function. p(·)Probability distribution function. Parameters and Variables

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Generation expansion planning (GEP) – A long-term approach using system dynamics and genetic algorithms (GAs)

Cited by 100 publications

References 17 publications

Application of system dynamics approach in electricity sector modelling: A review

Application of system dynamics approach in electricity sector modelling: A review

Generation expansion planning considering integrating large-scale wind generation

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

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