A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response

Yang, Shenbo; Tan, Zhongfu; Liu, ZhiXiong; Lin, Hui; Ju, Liu; Zhou, Feng’ao; Li, Jiayu

doi:10.1016/j.jclepro.2020.124017

Cited by 47 publications

(18 citation statements)

References 13 publications

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“…With these results, PUC can find optimal pricing policies according to their preference for profit desire and load fluctuation reduction. In actual implementation, a weighting method can also be applied to find a certain solution to this optimization, when the PUC has specific preferences of the objectives or combined with the non-dimension operation and entropy-weighting method, like [42].…”

Section: Electricity Price Optimization Resultsmentioning

confidence: 99%

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

Lin

Zhang

2022

Journal of Modern Power Systems and Clean Energy

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In response to the imbalance between power generation and demand, demand response (DR) projects are vigorously promoted. However, customers' DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers' DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers' DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers' timevarying DR patterns and trace the changes dynamically, we define customers' DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers' current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

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Section: Electricity Price Optimization Resultsmentioning

confidence: 99%

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

Lin

Zhang

2022

Journal of Modern Power Systems and Clean Energy

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“…Regarding research on the uncertainty of renewable energy, most of the works used probabilistic decision-making methods [18][19][20] and fuzzy decision-making methods [21,22]. The probability decision-making method needs numerous data to fit the accurate probability distribution, which has certain requirements for the fitting accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-participant operation optimization for charging systems with orderly charging and cooperative game strategies considering carbon capture and uncertainties

Lin

Han

et al. 2023

Journal of Energy Storage

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“…There are various studies that contemplate the matter at hand. There are those that include battery energy storage systems (BESS) that are managed by aggregators with the consent of the end users [14]. The use of BESS has the added benefit of not interfering with the end user's consumption and therefore not requiring any consideration on their comforts.…”

Section: Problem Statementmentioning

confidence: 99%

A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

et al. 2021

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Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

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A multi-objective stochastic optimization model for electricity retailers with energy storage system considering uncertainty and demand response

Cited by 47 publications

References 13 publications

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

Multi-participant operation optimization for charging systems with orderly charging and cooperative game strategies considering carbon capture and uncertainties

A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

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