Incremental incentive mechanism design for diversified consumers in demand response

Liu, Di; Qin, Zhaoming; Hu, Hua; Ding, Yi; Cao, Junwei

doi:10.1016/j.apenergy.2022.120240

Cited by 29 publications

(5 citation statements)

References 34 publications

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“…Considering the reliability of power systems, the capacity demand curve based on the result of load forecasting is determined by the system operators [30]. At the same time, the available installed capacity and the price of capacity suppliers are quoted to the electricity market.…”

Section: Capacity Market Mechanismmentioning

confidence: 99%

Key problems of gas‐fired power plants participating in peak load regulation: A review

Wang

Fan

et al. 2022

IET Cyber-Phy Sys Theory & Ap

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The peak regulation capacity of gas‐fired power plants has always been an important flexibility resource of the power grid. Under the guidance of carbon emission reduction, the coal power units are gradually shut down, making the role of gas‐fired power plants more important. However, in practice, gas‐fired power plants often fail to show satisfactory flexibility. The main reasons are as follows: (1) Part of the capacity mechanism fails to effectively encourage gas‐fired power plants to provide reliable flexibility and (2) the unreliability of fuel supply for gas‐fired power plants. Aiming at these problems, the current capacity mechanism in different countries is first summarised and the applicability of the capacity mechanism for gas‐fired power plants under the government regulation and market‐oriented environment is analysed, respectively. Then, the characteristics of power dispatching and gas dispatching are analysed to explore the internal reasons for the unreliable fuel supply in gas‐fired power plants. Based on the above analysis, the gas‐electric coordination mechanism adapted to different development stages is proposed to solve the problem that the flexibility of gas‐fired power plants cannot be guaranteed. In summary, through the research of this study, it is found that the main reason for the limited flexibility of gas‐fired power plants is the lack of coordination among multiple entities belonging to different energy systems, such as electricity and gas. The cooperation mechanism proposed is an attempt to realise the cooperation between the electric system and the gas system, which provides the reference for closer collaboration among multiple energy systems in the future.

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Section: Capacity Market Mechanismmentioning

confidence: 99%

Key problems of gas‐fired power plants participating in peak load regulation: A review

Wang

Fan

et al. 2022

IET Cyber-Phy Sys Theory & Ap

Self Cite

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“…24 So, DRL has a powerful capability of solving problems with uncertainties offline. 25 Using DRL to train the DNN can ensure that one state of the reusable rocket corresponds to one optimal value of ignition height.…”

Section: Introductionmentioning

confidence: 99%

Calculate the ignition height of the vertical landing phase online for the reusable rocket

Cheng,

Jing,

Gao

2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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For the vertical landing phase of the reusable rocket, in order to improve the landing accuracy with consideration of multiple uncertainties, a novel strategy to calculate the ignition height online is proposed based on polynomial guidance law (PGL), particle swarm optimization (PSO), and deep reinforcement learning (DRL). Firstly, a deep neural network (DNN) is designed to describe the relationship between the state of the reusable rocket and the ignition height. To accomplish the guidance task of the vertical landing phase, PGL is modified by introducing the estimated aerodynamic acceleration. Through simulation, the output range of the DNN is estimated by the modified PSO. Then, the reward function is shaped and the parameters of the DNN are trained on a training set of simulation scenarios by the DRL algorithm. Finally, to demonstrate the effectiveness of the proposed strategy, the trained DNN is used to calculate the ignition height of 1500 unlearned simulation scenarios online. The numerical simulation results show that the proposed strategy has higher landing accuracy and lower fuel consumption than the offline strategy of fixed ignition height based on the modified PSO.

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“…DSR plays a crucial role in power systems. Research suggests using price incentives and deep learning algorithms to improve load profiles and consumer response depths [13,14]. Techniques like recurrent neural networks and resilient distributed controllers mitigate risks like fake data injection attacks [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

Wang,

2024

Energies

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With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

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Incremental incentive mechanism design for diversified consumers in demand response

Cited by 29 publications

References 34 publications

Key problems of gas‐fired power plants participating in peak load regulation: A review

Key problems of gas‐fired power plants participating in peak load regulation: A review

Calculate the ignition height of the vertical landing phase online for the reusable rocket

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

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