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Efficient Trading in the Short-term Electricity Markets for Integration of Renewable Energy Sources

Multistage Stochastic and Agent-based Modeling Approaches for Continuous Intraday Electricity Market

Time: Thu 2023-02-02 13.00

Location: Kollegiesalen, Brinellvägen 8, Stockholm

Video link: zoom link for online defense

Language: English

Subject area: Electrical Engineering

Doctoral student: Priyanka Shinde , Elkraftteknik

Opponent: Assistant Professor Nikolaos Paterakis, Eindhoven University of Technology

Supervisor: Mikael Amelin, Elkraftteknik; Lennart Söder, Elkraftteknik

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QC 20221223


This thesis investigates the role of different short-term electricity market design aspects that can facilitate better coordination of resources within the power system. The work also emphasizes on better cross-border integration of the short-term markets to improve the market liquidity, competition, social welfare, and flexibility in the system, which is essential for facilitating the integration of renewable sources. Apart from the policy design issues, the thesis also focuses on developing several mathematical models to support algorithmic trading decisions in short-term markets with various sources of stochasticities. The models proposed in this thesis enable improved trading decisions closer to real-time both for the production and consumption portfolio. 

The work presented in this thesis encompasses the short-term electricity markets with a primary focus on intraday electricity markets. The contributions of this work are in two directions, one is from the perspective of the trader in the intraday market. In this direction of the work, the aim was to develop optimization models to support algorithmic decision-making for generation and consumption portfolios. To this end, multistage stochastic programming problems have been developed to model cross-border continuous intraday (CID) trading for a price-taking virtual power plant with hydropower, wind power, and thermal power assets. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing and the Partial Order Clearing models. Further advancements in the multistage model has been achieved by proposing a bilevel model to tackle the problem of unit commitment of thermal power plant in the continuous intraday market. The lower level of the model accounts for the continuous market clearing considering for the minimum generation level of the thermal power plant. In this model, the virtual power plant is able to post its own prices in the intraday market. The resulting multistage stochastic programming problem with integer variables is tackled by Stochastic Dual Dynamic integer Programming algorithm.

For the consumption portfolio, the participation of an electric vehicle aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. Intraday markets allow the electric vehicle aggregator to trade furtherbased on their updated forecasts of consumption and price development in the market. The response of the electric vehicle aggregator to the prices in the market helps the power system tobetter manage the imbalances that might be injected by the intermittent generation sources. The algorithmic contribution in this work includes the deployment of randomized progressive hedging which was found to be faster than the conventional progressive hedging algorithm. Furthermore, the performance was accelerated by a parallel randomized progressive hedging algorithm and an asynchronous version of the randomized progressive hedging algorithm is leveraged to speed up the multistage model of electric vehicle aggregator trading.

The other direction of the work is from the point-of-view of policymakers where an open-source agent-based model is proposed to model the cross-border continuous intraday electricity market. In this model, the trading behavior of different agents, including renewable, thermal power producers, storage, and consumers, in a cross-border  Continuous Intraday market is simulated. The continuous market clearing is performed by a market operator agent. Two capacity calculation methods, Available Transfer Capacity, and Flow-based Market Coupling are availed to compute the cross-border transmission capacities. The agents are enabled to trade for multiple delivery products simultaneously in the CID market. Furthermore, the agents can choose between two different trading strategies, naive and modified trader adaptive aggressiveness, to decide the price-volume curves they post in the CID market. To simulate a realistic trading behavior, a user-defined parameter, switch, is introduced for the storage and thermal agents to allow them to choose the time-instant in the trading timeline when the traders can switch from trading towards increasing their profits to considering their ramping constraints in the CID market. The role of better forecasts of imbalance prices in deciding order prices and thereby the transaction prices in the CID market is also discussed.

Ultimately, a forward-looking mechanism is proposed for the system operator to dispatch generators to activate their up- and down-regulation bids on the basis of having lowest expected costs, taking into account both their production costs and potential deviations from nominated outputs. The proposed method has been mathematically applied to compare three imbalance pricing models.