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Interactive Dashboard With Python: Customer Retention Analysis
Learning Insights From Neural Networks: A Summary With Code
Generating Don Quijote sentences using a deep LSTM network
Shortest path across Spain: the squirrel problem
shinyFiddle R package: how to modify colors in tabs
Introduction to inverse optimization
My PhD presentation - and what does Donald Trump has to do with it
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Publications

Thumbnail of A bicriteria perspective on L-Penalty Approaches - A corrigendum to Siddiqui and Gabriel's L-Penalty Approach for Solving MPECs

A bicriteria perspective on L-Penalty Approaches - A corrigendum to Siddiqui and Gabriel's L-Penalty Approach for Solving MPECs

Kerstin Dächert, Sauleh Siddiqui, Javier Saez-Gallego, Steven Gabriel, and Juan Miguel Morales
Networks and Spatial Economics 2019

This paper presents a corrigendum to Theorems 2 and 3 in Siddiqui S, Gabriel S (2013). In brief, we revise the claim that their L-penalty approach yields a solution satisfying complementarity for any positive value of L, in general. We also elaborate further assumptions under which the L-penalty approach yields a solution satisfying complementarity.

BibTeX, PDF

Thumbnail of Power curve measurement uncertainty – follow up comparative exercise for IEA Task 32

Power curve measurement uncertainty – follow up comparative exercise for IEA Task 32

Luke Simmons, K. Franke, Christos Tsouknidas, Javier Saez-Gallego, E. Weyer, and Paula Gómez
TORQUE 2018

A comparative exercise for estimating the uncertainty associated with new methods for power performance measurements was coordinated by the International Energy Agency (IEA) Wind Task 32. The exercise showed significant variability among participants reflecting difficulty with the interpretation and application of the informative guidance.

BibTeX, PDF, DOI

Thumbnail of Short-term Forecasting of Price-responsive Loads Using Inverse Optimization

Short-term Forecasting of Price-responsive Loads Using Inverse Optimization

Javier Saez Gallego and Juan M. Morales
In IEEE Transactions on Smart Grid 2016

This paper presents a practical solution method for generalized inverse optimization problems, which is then applied to short-term load forecasting. The price-response of the aggregation is modeled by an optimization problem that is characterized by a set of marginal utility curves and minimum and maximum power consumption limits. A case study based on the simulation of the price-response behavior of a pool of buildings equipped with a heat pump is built and analyzed.

BibTeX, PDF, DOI

Thumbnail of Optimal Price-energy Demand Bids for Aggregate Price-responsive Loads

Optimal Price-energy Demand Bids for Aggregate Price-responsive Loads

Javier Saez Gallego, Mahdi Kohansal, Ashkan Sadeghi-Mobarakeh, and Juan M. Morales
In IEEE Transactions on Power Systems 2016

In this paper we seek to optimally operate a retailer that, on one side, aggregates a group of price-responsive loads and on the other, submits block-wise demand bids to the day-ahead and real-time markets. Such a retailer/aggregator needs to tackle uncertainty both in customer behavior and wholesale electricity markets. We derive closed-form solutions for the risk-neutral case and also provide a stochastic optimization framework to efficiently analyze the risk-averse case.

BibTeX, PDF, DOI

Thumbnail of A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity

A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity

Javier Saez Gallego, Juan M. Morales, Marco Zugno, and Henrik Madsen
In IEEE Transactions on Power Systems 2016

This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. An inverse optimization method is used for estimating the optimal complex market bid, relative to a pool of price-responsive consumers. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market.

BibTeX, PDF, DOI

Thumbnail of Determining reserve requirements in DK1 area of Nord Pool using a probabilistic approach

Determining reserve requirements in DK1 area of Nord Pool using a probabilistic approach

Javier Saez Gallego, Juan M. Morales, Henrik Madsen, and Tryggvi Jonsson
In Energy 2014

It consists of a stochastic optimization model to optimally schedule the electricity reserves in the western power system area of Denmark, based on the uncertainty related to the load, the wind power production, and to the outages of power plants.

BibTeX, PDF, DOI

Talks

July 12, 2015

A data-driven bidding model for a cluster of price-responsive consumers of electricity
EURO Glasgow. 27th European Conference on Operational Research & INFORMS Annual Meeting in Philadelphia

April 1, 2014

Optimal Spinning Reserve by taking advantage of probabilistic forecasting
Danish Wind Industry Annual Event, Denmark

November 20, 2013

Determining reserve requirements in DK1 area of Nord Pool using a probabilistic approach
International Conference on Computationa Management Science, Lisbon, Portugal

Contact

E-Mail:

javiersaezgallego@gmail.com

More:

LinkedIn, GitHub, Researchgate