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Abstract
This thesis deals with the development of new mathematical models that support the decisionmaking processes of market players. It addresses the problems of demandside bidding, priceresponsive load forecasting and reserve determination. From a methodological point of view, we investigate a novel approach to model the response of aggregate priceresponsive load as a constrained optimization model, whose parameters are estimated from data by using inverse optimization techniques.
The problems tackled in this dissertation are motivated, on one hand, by the increasing penetration of renewable energy production and smart grid technologies in power systems, that is expected to continue growing in the coming years. Nondispatchable electricity generation cannot ensure a certain production at all times, since it depends on meteorological factors. Also, smart grid technologies are affecting the consumption patterns that the load traditionally exhibited. On the other hand, this thesis is motivated by the decisionmaking processes of market players. In response to these challenges, this thesis provides mathematical models for decisionmaking under uncertainty in electricity markets.
Demandside bidding refers to the participation of consumers, often through a retailer, in energy trading. Under the smartgrid paradigm, the demand bids must reflect the elasticity of the consumers to changes in electricity price. Traditional forecasting models are typically not able to reflect this elasticity, hence we propose two novel approaches to estimate market bids. Both approaches are datadriven and take into account the uncertainty of future factors, as, for example, price. In both cases, demandside bids that comprise a priceenergy term decrease the expected imbalances and also increase the profit of retailers participating in electricity markets.
In the field of load forecasting, this thesis provides a novel approach to model time series and forecast loads under the realtime pricing setup. The relationship between price and aggregate response of the load is characterized by an optimization problem, which is shaped by a set of unknown parameters. Such parameters are estimated from data by using an inverse optimization framework. The usability of the proposed method is studied and we conclude that inverseoptimizationbased modeling is a computationally attractive method that outperforms the forecasting capabilities of traditional time series models. Regarding the reserve determination, the special characteristics of the Danish power system do not allow for cooptimizing the unit commitment and reserve requirements. Hence, we propose a probabilistic framework, where the reserve requirements are computed based on scenarios of wind power and load forecast errors and power plant outages. The solution of the stochastic optimization models increases the safety of the overall system while decreases the associated reserve costs, with respect to the method currently used by the Danish TSO.
The problems tackled in this dissertation are motivated, on one hand, by the increasing penetration of renewable energy production and smart grid technologies in power systems, that is expected to continue growing in the coming years. Nondispatchable electricity generation cannot ensure a certain production at all times, since it depends on meteorological factors. Also, smart grid technologies are affecting the consumption patterns that the load traditionally exhibited. On the other hand, this thesis is motivated by the decisionmaking processes of market players. In response to these challenges, this thesis provides mathematical models for decisionmaking under uncertainty in electricity markets.
Demandside bidding refers to the participation of consumers, often through a retailer, in energy trading. Under the smartgrid paradigm, the demand bids must reflect the elasticity of the consumers to changes in electricity price. Traditional forecasting models are typically not able to reflect this elasticity, hence we propose two novel approaches to estimate market bids. Both approaches are datadriven and take into account the uncertainty of future factors, as, for example, price. In both cases, demandside bids that comprise a priceenergy term decrease the expected imbalances and also increase the profit of retailers participating in electricity markets.
In the field of load forecasting, this thesis provides a novel approach to model time series and forecast loads under the realtime pricing setup. The relationship between price and aggregate response of the load is characterized by an optimization problem, which is shaped by a set of unknown parameters. Such parameters are estimated from data by using an inverse optimization framework. The usability of the proposed method is studied and we conclude that inverseoptimizationbased modeling is a computationally attractive method that outperforms the forecasting capabilities of traditional time series models. Regarding the reserve determination, the special characteristics of the Danish power system do not allow for cooptimizing the unit commitment and reserve requirements. Hence, we propose a probabilistic framework, where the reserve requirements are computed based on scenarios of wind power and load forecast errors and power plant outages. The solution of the stochastic optimization models increases the safety of the overall system while decreases the associated reserve costs, with respect to the method currently used by the Danish TSO.
Original language  English 

Place of Publication  Kgs. Lyngby 

Publisher  Technical University of Denmark 
Number of pages  190 
Publication status  Published  2017 
Series  DTU Compute PHD2016 

Number  425 
ISSN  09093192 
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Dive into the research topics of 'Inverse Optimization and Forecasting Techniques Applied to Decisionmaking in Electricity Markets'. Together they form a unique fingerprint.Projects
 1 Finished

Stochastic energy systems
Saez Gallego, J., Madsen, H., Morales González, J. M., Hjorth, P. G., Fleten, S. & Lindström, E.
Technical University of Denmark
15/12/2012 → 12/12/2016
Project: PhD