(116c) Beyond Price-Taker: Multiscale Optimization of a Wind-Battery Integrated Energy System within the Wholesale Electricity Market

Increasing penetration of renewable power is essential to meet the ambitious decarbonization goals. However, the intermittent nature of renewable energy systems limits their availability, reliability, and profitability in the wholesale electricity market. Integrated Energy Systems (IES) [1] exploit synergies between different technologies. For example, retrofitting a wind farm with a battery storage system increases the overall flexibility, reduces curtailment, and increases revenue opportunities [2,3] within the multiscale electricity market. However, optimally sizing and operating an IES remains challenging because of the complex interactions with electricity markets across seconds to decades timescales. The “price-taker” approach is widely used to investigate the economic performance of an IES in the electricity market. It assumes that the electricity market can take any amount of electricity from the IES without affecting the locational marginal price (LMP). Although the price-taker approach is easy to implement, it ignores the interaction between the IES and the market, thereby overestimating the IES's profitability [4,5,6].

This work presents the optimization of a wind-battery IES using the multiscale optimization framework [7] proposed in our previous work to quantify errors from the price-taker assumption. The framework, built over Prescient [8] (an open-source package for solving production cost models) is applied to the RTS-GMLC [9] dataset, an open-source dataset which is a representative of the southwest U.S. wholesale electricity market. The framework provides detailed bidding, market clearing, and control processes of an IES, and it can quantify how the IES interacts with the market.

Prior works have performed the optimization of a wind-battery IES using the price-taker approach. For example, Moghaddam [10] showed that battery storage does well in mitigating the uncertainty associated with renewable resources. Loukatou [11] showed that a wind-battery IES can compensate for the temporal energy imbalance and building a battery storage is more profitable than wind farms with a Power Purchase Agreement in the UK. In this work, we use the retrofit of a wind farm with a battery storage system as an example to show the difference in the market outcomes and revenues obtained from both price-taker and multiscale optimization approaches. In the multiscale optimization, we use a time-variant bidding strategy from [9], which is based on solving a stochastic optimization problem that bids the IES at different marginal costs according to LMP forecasts and compares its outcomes with default time-invariant bids. We observe that, with an increase in the size of the battery, the price-taker approach can overestimate the revenue by up to 290% compared to the multiscale optimization approach. The main reason for the overestimation of the price-taker is its infinity capacity assumption and perfect information on price signals. In contrast, in the multiscale optimization approach, the perfect forecast is impossible, and with more electricity that IES offers to the market, the LMP will change. We also observe the change in the LMP distribution under different bidding strategies, indicating that the operation of IES will impact the market and thus reduce the accuracy of the price-taker assumption. In multiscale optimization, the market price is decided by solving an optimization problem constrained by the operation decisions of energy systems. However, in price-taker, the operation decision is made after knowing market prices.

Our work goes beyond price-taker and deep dives into quantifying IES-market interaction in optimizing IES. This framework enables users to explore how different design and operation decisions of energy systems interact with the market and provides a more accurate evaluation than the price-taker assumption. In addition to the case study we showed in this abstract, the multiscale optimization framework can be applied to other integrated energy systems and bidding strategies. In our future work, we would like to use this framework to explore how different bidding strategies impact the IES operation and its economic performance.

Acknowledgements

This work was conducted as part of the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) project through the Grid Modernization Lab Consortium with funding from the U.S. Department of Energy's Office of Fossil Energy and Carbon Management, Office of Nuclear Energy, and Hydrogen and Fuel Cell Technologies Office. Additional work was conducted as part of the Institute for the Design of Advanced Energy Systems (IDAES) with support from the U.S. Department of Energy's Office of Fossil Energy and Carbon Management (FECM) through the Simulation-based Engineering Research Program.

Disclaimer

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, expressor implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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