Multi-objective matching method of bilateral transactions in medium and long-term power markets

2019 ◽  
Author(s):  
Yongqing Wang ◽  
Bo Kong ◽  
Lei Zhou ◽  
Meng Yang ◽  
Yufeng Jiang ◽  
...  
Keyword(s):  
Power Markets ◽  
Matching Method ◽  
Multi Objective

Modelling short and long-term risks in power markets: Empirical evidence from Nord Pool

Energy Policy ◽  
2010 ◽  
Vol 38 (10) ◽  
pp. 5671-5683 ◽  
Author(s):  
Nikos K. Nomikos ◽  
Orestes A. Soldatos
Keyword(s):  
Empirical Evidence ◽  
Power Markets ◽  
Short And Long Term

scholarly journals Multi-objective optimisation of a rock coast evolution model with cosmogenic <sup>10</sup>Be analysis for the quantification of long-term cliff retreat rates

2021 ◽  
Vol 9 (6) ◽  
pp. 1505-1529
Author(s):  
Jennifer R. Shadrick ◽  
Martin D. Hurst ◽  
Matthew D. Piggott ◽  
Bethany G. Hebditch ◽  
Alexander J. Seal ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Production Model ◽  
Relative Sea Level ◽  
Coastal Evolution ◽  
Radionuclide Production ◽  
Multi Objective ◽  
Cosmogenic Radionuclide ◽  
Cliff Retreat

Abstract. This paper presents a methodology that uses site-specific topographic and cosmogenic 10Be data to perform multi-objective model optimisation of a coupled coastal evolution and cosmogenic radionuclide production model. Optimal parameter estimation of the coupled model minimises discrepancies between model simulations and measured data to reveal the most likely history of rock coast development. This new capability allows a time series of cliff retreat rates to be quantified for rock coast sites over millennial timescales. Without such methods, long-term cliff retreat cannot be understood well, as historical records only cover the past ∼150 years. This is the first study that has (1) applied a process-based coastal evolution model to quantify long-term cliff retreat rates for real rock coast sites and (2) coupled cosmogenic radionuclide analysis with a process-based model. The Dakota optimisation software toolkit is used as an interface between the coupled coastal evolution and cosmogenic radionuclide production model and optimisation libraries. This framework enables future applications of datasets associated with a range of rock coast settings to be explored. Process-based coastal evolution models simplify erosional processes and, as a result, often have equifinality properties, for example that similar topography develops via different evolutionary trajectories. Our results show that coupling modelled topography with modelled 10Be concentrations can reduce equifinality in model outputs. Furthermore, our results reveal that multi-objective optimisation is essential in limiting model equifinality caused by parameter correlation to constrain best-fit model results for real-world sites. Results from two UK sites indicate that the rates of cliff retreat over millennial timescales are primarily driven by the rates of relative sea level rise. These findings provide strong motivation for further studies that investigate the effect of past and future relative sea level rise on cliff retreat at other rock coast sites globally.

scholarly journals Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

2011 ◽  
Vol 22 (4) ◽  
pp. 379-389
Author(s):  
Ricardo C. Silva ◽  
Edilson F. Arruda ◽  
Fabrício O. Ourique
Keyword(s):  
Probability Distributions ◽  
Phase Method ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Term Operation ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.

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