arXiv subjects

Zhongyang Zhao

Publications and source records attributed to Zhongyang Zhao.

7 recordsLinked to original sources

arXiv2024

Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market

Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. The proposed method underscores the importance of an accurate electricity price forecasting model and introduces a new method of evaluating the price forecast model from a virtual bidder's perspective, providing valuable insights for future research.

cs.LG↗

arXiv2021

Revenue Analysis of Stationary and Transportable Battery Storage for Power Systems with High Penetration of Renewable Sources: A Market Participant Perspective

The power system needs more transmission capacity to deal with the increasing integration of renewable energy resources. The battery energy storage systems (BESSs) are effective to enhance the grid capacity and relieve the transmission congestion. A comprehensive revenue analysis of BESSs is critical for market participants to install such systems in a market-based power system. Taking PJM as an example, this paper carries out a thorough revenue analysis for the entire system. Highly profitable nodes in the system are revealed and characterized for BESS participants. A comparison study of stationary and transportable BESSs shows the transportable energy storage can produce higher potential revenue in the energy and regulation markets. Based on the results of the revenue analysis and characterization of commercial pricing nodes, an optimal placement algorithm is proposed for finding the profitable sites for market participants to install BESSs in the system and the algorithm is validated with real PJM market data.

eess.SY↗

arXiv2020

A Two-Stage Optimal Bidding Algorithm for Incentive-based Aggregation of Electric Vehicles in Workplace Parking Lots

This paper proposes an incentive-based aggregator for electric vehicles (EVs) in workplace parking lots to participate in the energy and regulation markets. With the implementation of seasonal Autoregressive Integrated Moving Average (ARIMA) model to predict the market information, a Day-Ahead (DA) planning model coordinated with the EV's responses to the incentive is formulated to determine the days to activate the aggregation program and the optimal incentives that are broadcasted to the EV owners in the first stage. Given the determined incentives and EV's responses, an real-time (RT) optimal bidding algorithm complying with EVs' energy demand in the second stage is designed to maximize the aggregator's profits in the markets. The proposed models are tested using the data collected from PJM's energy and regulation markets. The results show the incentive-based aggregator can benefit both the EV owners and the aggregator from the credits obtained from the markets. Meanwhile, the results also indicate the proposed optimal bidding algorithm is capable of handling the uncertainty of regulation signals and following the signals with high precision.

eess.SY↗

arXiv2019

Data-driven Analysis of Regional Capacity Factors in a Large-Scale Power Market: A Perspective from Market Participants

A competitive wholesale electricity market consists of thousands of interacting market participants. Driven by the variations of fuel costs, system loads and weathers, these market participants compete actively and behave variously in the power market. Although electricity markets tend to become more transparent, a large amount of market information is still not publicly available to market participants. Hence, data-driven analysis based on public data is crucial for market participants to better understand and model large-scale power markets, and ultimately to perform better in power trading. While most of the previous researches related to the large-scale power markets are based on the synthetic networks, a data-driven approach utilizing the real power market data is proposed in this paper. First, the power plants' monthly net generation and capacity data are obtained from U.S. Energy Information Administration (EIA) and aggregated to figure out the monthly regional capacity factors which are used to characterize the market's regional behaviors for market participants. Then, the regional capacity factors are analyzed against the metered system loads and natural gas prices to study the generation behaviors in the power market. The analysis reveals the impacts of regional natural gas prices on capacity factors and the responses of generating behaviors to the system loads. The analysis results present the solid evidence and rational references for market participants to model and validate the large-scale power market in the future.

eess.SY↗

arXiv2018

Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment, especially for \revv{low-voltage} distribution grids. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

eess.SY↗

arXiv2018

Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution prediction. First, the data features of Form EIA-923 are explored and the natural gas fuel costs of Texas generating facilities are used to develop and validate the forecasting algorithm for the Texas example. Furthermore, the spot price associated with the natural gas hub in Texas is utilized to enhance the fuel cost prediction. The forecasted data is fit to a normal distribution and the Kullback-Leibler divergence is employed to evaluate the difference between the real fuel cost distributions and the estimated distributions. The comparative evaluation suggests the proposed forecasting algorithm is effective in general and is worth pursuing further.

stat.AP↗

arXiv2018

Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models

Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA models

eess.SP↗