Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant

Kong, Xiangyu; Xiao, Jie; Wang, Chengshan; Cui, Kai; Jin, Qiang; Kong, De‐Xing

doi:10.1016/j.apenergy.2019.04.130

Cited by 93 publications

(36 citation statements)

References 19 publications

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“…In the case of stable market conditions, according to historical bidding information corresponding to a total reduction amount, the reduction capacity held by each retailer corresponded to an equilibrium price, so that each of the retailers achieved their own profit. AI technology was used to select the appropriate function as the bidding decision function [36]. The independent variable of the function was the total load reduction and the shifting capacity held by each retailer.…”

Section: Control Process and Retailer's Peak Bidding Decisionmentioning

confidence: 99%

Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response

et al. 2020

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Thermostatically controlled loads (TCLs) have become a major tool for the demand response (DR) program when air conditioners cause peak loads in a day during the winter or summer. To solve the problem of a direct load control with TCL usually affecting user comfort and hardly considering responsiveness, a power retailer airconditioning load aggregation operation control and demand response method was proposed in this research. From the perspective of a power retailer, a compensation mechanism for TCL was constructed, which was composed of a basic incentive program and an additional incentive program. The basic incentive program aimed to encourage users with a low response degree to increase the response capacity in order to participate in DR. An auxiliary service market control strategy based on a new compensation mechanism of the electricity retailer was detailed, which fully considered the enthusiasm of the user in mobilizing the response and reducing the load reduction fluctuation when using the state-queuing (SQ) model. Case studies were provided to verify the effectiveness of the proposed method. Compared with other compensation schemes, the simulation results showed that the compensation mechanism provided in this research was more reasonable, and it could smooth the load and reduce fluctuations. The compensation distribution among the user groups could effectively control the uniform distribution in the user groups in the temperature range, and it could mobilize users at different temperatures to participate in DR.

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Section: Control Process and Retailer's Peak Bidding Decisionmentioning

confidence: 99%

Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response

et al. 2020

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“…where Equation (16) expresses that the power output of the CGT in the operation status should be smaller than the maximum allowed output and greater than the minimum required output. Equation 17means that the output change at adjacent moments cannot exceed the maximum climbing and downhill output.…”

Section: Cgt Operation Constraintsmentioning

confidence: 99%

“…The phenomenon of 'peak-valley inversion' caused by users' overresponse can be avoided by limiting the maximum load fluctuation and load reducible proportion provided by the PBDR. Similarly, the generation capacity provided by the PBDR needs to satisfy the start-stop time in Equations (16) and (17).…”

Section: Pbdr Operation Constraintsmentioning

confidence: 99%

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Three‐level interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

Tan

Lin

et al. 2020

Int J Energy Res

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Virtual power plants (VPPs) integrate distributed energy effectively and overcome geographical restrictions. This could improve the broad benefits of distributed energy generation. Owing to the intermittency of distributed wind and photovoltaic power at different time scales, when multiple VPPs exist, the interactive energy management strategy should be addressed in terms of two dimensions, namely multi-operator and multi-timescale (day-ahead (24 hours), intraday (4 hours), and real-time (1 hour)). In this study, an interactive energy management framework for multiple VPPs was designed. Furthermore, a three-level energy-coordinated management model was employed for multi-VPP optimal operation including day-ahead cooperative scheduling, intraday noncooperative bidding, and real-time cooperative reserve. To solve this model, a chaotic search algorithm was applied to improve the ant-colony optimization algorithm. The resulting improved chaotic ant-colony optimization algorithm is proposed to increase the optimization velocity and solution efficiency. Finally, a demonstration project, namely the Sanliduo's VPP in Guangxi Province in China, was taken as an example. The following points can be stressed according to the obtained results: (a) the proposed model implements dispatching-balance-reserve interactive optimization such that the output of wind and photovoltaic power increased by 3.42% and 1.85%, respectively, and the revenue increased by 2.53%; (b) the proposed solution algorithm develops the optimal global strategy: compared with the ant-colony algorithm, the average convergence time increased by 44 seconds, and the supply cost decreased by 1.2%; (c) the robust coefficient provides effective risk decisionmaking tools for different types of decision-makers considering the operational characteristics of VPPs; (d) the openness of power markets is positively correlated to the balance revenue, and higher levels are beneficial for VPPs.

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“…• Generator Constraints The output power of the thermal power unit is limited by its minimum and maximum output and the upward and downward climbing rate of the generator, and the start-stop state of units is restricted by the maximum and minimum start-stop time [26].…”

Section: Day-ahead Dispatching Model Constraintsmentioning

confidence: 99%

Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

et al. 2020

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With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent.

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Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant

Cited by 93 publications

References 19 publications

Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response

Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response

Three‐level interactive energy management strategy for optimal operation of multiple virtual power plants considering different time scales

Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

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