Yufan Zhang

• Doctor of Philosophy
• PostDoc Position at Cornell University

Day-ahead unit commitment (UC) is a fundamental task for power system operators, where generator statuses and power dispatch are determined based on the forecasted nodal net demands. The uncertainty inherent in renewables and load forecasting requires the use of techniques in optimization under uncertainty to find more resilient and reliable UC sol...

This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs. At the training phase, a bilevel program for parameter estimation is formulated, where the upper level optimizes over the forecast model parameter to minimize the conditional value-at-risk (a risk metric) of...

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 approac...

Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and use them to schedule energy dispatch in advance. However, forecasting models are typically developed in a way that overlooks the decision value of forecasts. To bridge the gap, we design a value-oriented point forecasting approach for sequentia...

Renewable energy forecasting is the workhorse for efficient energy dispatch. However, forecasts with small mean squared errors (MSE) may not necessarily lead to low operation costs. Here, we propose a forecasting approach specifically tailored for operational purposes, by incorporating operational problems into the estimation of forecast models via...

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the "predict, then optimize" paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts i...

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power net...

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assum...

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power net...

p>Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of...

p>Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of...

Unit commitment (UC) are essential tools to transmission system operators for finding the most economical and feasible generation schedules and dispatch signals. Constraint screening has been receiving attention as it holds the promise for reducing a number of inactive or redundant constraints in the UC problem, so that the solution process of larg...

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of n...

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assum...

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is o...

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is o...

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is o...

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is o...

Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation fo...

Multi-microgrids (MMGs) interconnect microgirds (MGs) with geographical adjacency and improve the overall efficiency, stability, and reliability of a regional energy system. The uncertainty brought by renewable energy resources (RESs) is one of the challenges facing the schedule of MMGs. To cope with it, a multi-timescale schedule strategy is propo...

Abstract Under the digitalization trend in the energy sector, utilities are devoted to providing better service to their customers by mining knowledge in fine‐grained electricity consumption data. Understanding the group behaviour of customers by clustering method is essential to achieving this end. Different from shape‐based clustering methods, an...

With the wide deployment of smart meters in the end‐user side, demand response (DR) is gaining prominence. Estimating the potential response is a preliminary step to the DR implementation. However, how to select proper features, how to protect privacy, and how to capture the response uncertainty remains three challenges to the customer response pot...

With the liberalization of the retail market, new parties such as load aggregators are participating in the demand response (DR). Aggregated baseline load (ABL) estimation provides a basis for aggregators to quantify the total responsiveness. This paper aims to improve the ABL estimation accuracy by using Gaussian mixture model (GMM). Modeling the...

Interaction between microgrid (MG) and active distribution network (ADN) can effectively improve the operating economics and reliability of power system. A bi-level interactive optimization model for ADN with MGs is studied in this paper. The upper level is a multi-objective problem that minimizes both the operation cost and voltage deviation of ea...

With the fast development of industrial Internet of Things (IoT) for smart energy, data processing and storing are closer to the end used side. Edge data center, an intermediate platform between end data source and centralized data center, can reduce the data transmission pressure and processing time. To provide dependable data source for decision...

Distributed energy management of multi-microgrids (MMGs) system is essential to achieving energy coordination in a large area while protecting the privacy. Compared with existing work, we consider the detailed modeling of demand-side resources and aim to reduce carbon emission. Concretely, we propose a bi-level distributed day-ahead schedule model...

In this paper, a novel two-stage robust Stackelberg game is proposed to solve the problem of day-ahead energy management for aggregate prosumers considering the uncertainty of intermittent renewable energy output and market price. The aggregate prosumers operate in the form of virtual power plant (VPP) and participate in day-ahead (DA) and real-tim...

Because of environmental benefits, wind power is taking an increasing role meeting electricity demand. However, wind power tends to exhibit large uncertainty and is largely influenced by meteorological conditions. Apart from the variability, when multiple wind farms have geographical adjacency, their power generation also displays strong correlatio...

Short‐term load forecasting at the distribution transformer level provides a basis for demand‐side aggregators to take part in the power market. However, under the competitive market environment, certain parties might be discriminated and do not have access to enough data and thus the challenge of load forecasting under limited dataset arises. To t...