![Map of Denmark. The West Denmark DK1 gird region is highlighted in orange while East Denmark or DK2 is colored in green. The main transmission lines and power plants are included as well [3]](https://www.researchgate.net/profile/Javier-Saez-Gallego/publication/317645589/figure/fig1/AS:668914241507334@1536492991777/Map-of-Denmark-The-West-Denmark-DK1-gird-region-is-highlighted-in-orange-while-East_Q320.jpg)
![Aggregate supply and demand curves, taken from [2].](https://www.researchgate.net/profile/Javier-Saez-Gallego/publication/317645589/figure/fig2/AS:668914241527835@1536492991919/Aggregate-supply-and-demand-curves-taken-from-2_Q320.jpg)
![The share of wind power offshore and onshore capacity is shown in blue and green bars, respectively. The share of domestic electricity demand supplied by wind power production is shown in red. Figure taken from [31].](https://www.researchgate.net/profile/Javier-Saez-Gallego/publication/317645589/figure/fig5/AS:668914245722118@1536492992286/The-share-of-wind-power-offshore-and-onshore-capacity-is-shown-in-blue-and-green-bars_Q320.jpg)
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Inverse Optimization and Forecasting Techniques Applied to Decision-making in Electricity Markets
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This thesis deals with the development of new mathematical models that support the decision-making processes of market players. It addresses the problems of demand-side bidding, price-responsive load forecasting and reserve determination. From a methodological point of view, we investigate a novel approach to model the response of aggregate price-responsive load as a constrained optimization model, whose parameters are estimated from data by using inverse optimization techniques.
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This article aims to investigate whether a statistical model known as Autoregressive Integrated Moving Average with Explanatory Variables can aid better predictability of volume-weighted average electricity prices compared to a commonly used forecasting method. This analysis was conducted for a specific bidding area, the Denmark-West bidding area (DK1). Autoregressive integrated moving average model with exogenous variable's performance was tested on the DK1 intraday market over a two-year period starting from 1 January 2019 until 31 December 2020. An explanatory variable used to support better the accuracy of the forecast is the day-ahead price for a corresponding intraday delivery hour. To ensure the validity of the paper, a well-known forecasting methodology was applied, and the results of the analysis show superior performance over the benchmark forecasting method. The autoregressive integrated moving average model with exogenous variables model developed was found to significantly outperform other commonly used forecasting methods, with an average mean absolute percentage error of 1.5%. The model was able to accurately predict intraday volume-weighted average prices up to 24 h in advance, using only publicly available data on day-ahead prices and historical intraday prices. Energy traders and other market players may find the developed autoregressive integrated moving average model with exogenous variables model to be a useful resource when looking to make more informed decisions in the intraday market.
We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator’s aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation (HPSOM) algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies – a flat-rate scheme and a time-of-use (TOU) scheme.
This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. 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.
Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial review of probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process.
The utility company has many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company-consumer interaction as a reverse Stackelberg game and present an iterative algorithm to design incentives for consumers while estimating their utility functions. Incentives are designed using the aggregated as well as the disaggregated (device level) consumption data. We simulate the iterative control (incentive design) and estimation (utility learning and disaggregation) process for examples.