10:00 - 11:00 ET, May 29th (Zoom link)

1. Liudong Chen (PhD Student, earth and environmental engineering, Columbia University)

1. • Talk title: Prudent Price-Responsive Demands

   • Abstract: We investigate a flexible demand with a risk-neutral cost-saving objective in response to volatile electricity prices. We introduce the concept of prudent demand, which states that future price uncertainties will affect immediate consumption patterns, despite the price expectations remaining unchanged. We develop a theoretical framework and prove that demand exhibits prudence when the third-order derivative of its utility cost function is positive, and show a prudent demand demonstrates risk-averse behaviors despite the objective being risk-neutral. Our analysis further reveals that, for a prudent demand, predictions of future price skewness significantly impact immediate energy consumption. Prudent demands exhibit skewness aversion, with increased price skewness elevating the cost associated with prudence. We validate our theoretical findings through numerical simulations and conclude their implications for demand response modeling and the future design of incentive-based demand response mechanisms.

   • Speaker contact: lc3671@columbia.edu

2. Seyedsoroush Karimi Madahi (PhD Student, Information Technology, Ghent University)

1. • Talk title: Learning for Energy Arbitrage in Imbalance Settlement Mechanism - Simulation to Real-world Deployment

   • Abstract: A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. This talk outlines our reinforcement learning-based control framework for battery energy storage systems (BESS), to perform energy arbitrage in the imbalance settlement mechanism. Our proposed framework takes a risk-sensitive perspective, while constraining the daily number of cycles for BESS. Moreover, we introduced a new policy correction step using a knowledge distillation process to ensure the correctness and safety of the final policy for real-world deployment. We will present the performance of the proposed framework on the Belgian imbalance price, in both simulation and real-world implementation.

   • Speaker contact: seyedsoroush.karimimadahi@ugent.be

10:00 - 11:00 ET, June 29th (Zoom link)

1. Attila Balint (PhD Student, electrical engineering, KU Leuven)

   • Talk title: EbFoBench: A community-driven energy forecast benchmark

   • Abstract: This talk outlines the design principles underpinning EnFoBench and details its evaluation process, which focuses on the entire forecasting pipeline rather than just the model itself. We also present the list of available metrics, allowing for the evaluation of model performance, computational efficiency, and relative performance compared to baseline models in greater detail than in previous works. Finally, this talk also present results from the first round of evaluated models, sourced from our previous work, popular forecasting frameworks, previous competitions and recent foundational models.

   • Speaker contact: attila.balint@kuleuven.be

2. Zhirui Liang (PhD Student, electrical engineering, Johns Hopkins University)

   • Talk title: Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

   • Abstract: Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

   • Speaker contact: zliang31@jhu.edu  

10:00 - 11:00 ET, July 31st (Zoom link)

1. Yufan Zhang (Postdoc., electrical and computer engineering, UC San Diego)

2. • Talk title: Improving Sequential Market Clearing via Value-oriented Renewable Energy Forecasting

   • Abstract: Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. While existing deterministic market clearing fails to accommodate the uncertainty, the recently proposed stochastic market clearing struggles to achieve desirable market properties. In this work, we propose a value-oriented forecasting approach, which tactically determines the RESs generation that enters the day-ahead market. With such a forecast, the existing deterministic market clearing framework can be maintained, and the day-ahead and real-time overall operation cost is reduced. At the training phase, the forecast model parameters are estimated to minimize expected day-ahead and real-time overall operation costs, instead of minimizing forecast errors in a statistical sense. Theoretically, we derive the exact form of the loss function for training the forecast model that aligns with such a goal. For market clearing modeled by linear programs, this loss function is a piecewise linear function. Additionally, we derive the analytical gradient of the loss function with respect to the forecast, which inspires an efficient training strategy. A numerical study shows our forecasts can bring significant benefits of the overall cost reduction to deterministic market clearing, compared to quality-oriented forecasting approach.

   • Speaker contact: Yufan's website

2. Zuguang Gao (Assistant Professor, School of Business, UC Irvine)

2. • Talk title: Aggregating Distributed Energy Resources: Efficiency and Market Power

   • Abstract: The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, electric vehicles/storage, thermal storage, combined heat and power plants, etc. Due to the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. In this talk, we discuss the question of how to integrate these DER supplies into the electricity market, with the objective of achieving full market efficiency. Specifically, we study three aggregation models, where there is an aggregator who procures electricity from DERs, and sells them in the wholesale market. In the first aggregation model, a profit-maximizing aggregator announces a differential two-part pricing policy to the DER owners. We show that this model preserves full market efficiency, i.e., the social welfare achieved by the aggregation model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, the profit-seeking aggregator is forced to impose a uniform two-part pricing policy to prosumers from the same location, and we numerically show the efficiency loss of this model. In the third aggregation model, a uniform two-part pricing policy is applied to DER owners, while the aggregator becomes fully regulated but is guaranteed positive profit. It is shown that this third model again achieves full market efficiency. Furthermore, we show that DER aggregation also leads to a reduction on the market power of conventional generators. DER aggregation via profit-seeking and/or regulated aggregators have been investigated by CAISO and NYISO, among others, and the recent FERC Order No. 2222 paved the way for aggregators to bid in the wholesale market. Our efficient aggregation models may settle the debate on how DERs should be included in the wholesale electricity market.

   • Speaker contact: Zuguang's website