Dynamic bidding strategy for a demand response aggregator in the frequency regulation market

Liu, Xin; Li, Yang; Lin, Xueshan; Guo, Jiqun; Shi, Yunpeng; Shen, Yunwei

doi:10.1016/j.apenergy.2022.118998

Cited by 21 publications

(2 citation statements)

References 69 publications

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“…The authors of [47] presented an approach related to demand side resource bidding by using an aggregator to enter the market for frequency regulation. This addresses the uncertainty in markets for electricity and take advantage of the potential to earn revenue for demand response.…”

Section: Optimization Schemes Based On Demand Response With Frequency...mentioning

confidence: 99%

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Colucci,

Mahgoub,

Yousefizadeh

et al. 2024

IEEE Access

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There is a rapid increase in the utilization of renewable sources such as solar and wind to provide power and electricity. The reason for this trend is to reduce costs and preserve the environment. However, the challenge is to efficiently use and store the energy from these sources. One approach is to optimize the decision when to charge or discharge a battery. The objective is to generate the greatest monetary gain. More charge/discharge cycles will reduce the life of a battery, thereby increasing the cost. A strategy to reduce the number of charge cycles while maintaining the effectiveness of electricity distribution from battery storage will improve battery life. With the inevitable proliferation of electric vehicles (EVs) in the market, strategies specific to electric vehicle battery profitability will be explored. An additional concern which threatens the financial feasibility of battery energy storage systems (BESSs) is the requirement of secure operation. There is currently little research study on strategies to detect cyberattacks on such systems. In this paper, we have presented a novel taxonomy for battery optimization, survey representative BESS utilization strategies, and classify these schemes within the taxonomy. Within our classification, we outline the battery optimization methods that have been discussed, analyze their ability to address issues that arise when implementing a BESS, and describe alternative research that could be explored. Future research could refer to this information to create unique battery optimization schemes to provide more efficiency and optimal revenue for a BESS when compared to current strategies.

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Section: Optimization Schemes Based On Demand Response With Frequency...mentioning

confidence: 99%

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Colucci,

Mahgoub,

Yousefizadeh

et al. 2024

IEEE Access

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show abstract

“…This employed an economically responsive load model based on the customer benefit function and price elasticity combined with the DR approach. In [21], a dynamic bidding strategy for demand-side resources was proposed. This allowed DR aggregators to participate in the frequency regulation market considering the risks of various uncertain factors in the electricity market.…”

Section: B Literature Reviewmentioning

confidence: 99%

Two-Level Optimal Bidding Strategy for Load Aggregator Based on a Data-Driven Approach Combined With LSTM-Based Forecasting and Agent-Based Models

Ryu,

Kim,

Kim

2023

IEEE Access

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Demand response (DR) is an economical way of addressing the challenges faced by the massive penetration of distributed energy resources, such as renewable energy. Residential consumers account for a significant proportion of electricity consumption. However, their behavior is highly random and uncertain, meaning it is difficult to quantify the impact of DR programs in which they participate. This paper presents a two-level optimal bidding strategy framework for load aggregators that combines a data-driven forecasting model and a data-driven agent-based model (D-ABM) to provide a realistic estimate of the impact of DR. First, the aggregated load of all consumers and market prices are predicted via a long short-term memory (LSTM) autoencoder forecasting model. Then, the proposed D-ABM estimates and quantifies the difference rate in terms of total load due to DR. Since D-ABM is a bottom-up approach, each consumer can be treated as a heterogeneous agent and changes in individual electricity usage patterns due to DR can be estimated. Changes in collective electricity consumption patterns can also be quantified by considering the estimated individual behavior and the interactions defined by the basic rules. In addition, assumptions about biases and preferences that explain the irrationality of individual decision-making are given to agents, and the uncertainty of DR participation is considered more realistically. Finally, based on these uncertainties addressed at each level, various bidding strategies for load aggregators can be obtained. The numerical simulation results indicate that our framework provides a more realistic estimation of the impact of total load under DR, minimizes any deviations from bidding strategies, and ensures maximum profits for load aggregators.INDEX TERMS bidding strategy, load aggregator, demand response, long short-term memory autoencoder, data-driven agent-based model.

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Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

et al. 2023

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Dynamic bidding strategy for a demand response aggregator in the frequency regulation market

Cited by 21 publications

References 69 publications

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Survey of Strategies to Optimize Battery Operation to Minimize the Electricity Cost in a Microgrid With Renewable Energy Sources and Electric Vehicles

Two-Level Optimal Bidding Strategy for Load Aggregator Based on a Data-Driven Approach Combined With LSTM-Based Forecasting and Agent-Based Models

Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

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