Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Wang, Xiaofei; Li, Fangxing; Dong, Jin; Olama, Mohammed M.; Zhang, Qiwei; Park, Byungkwon; Kuruganti, Teja

doi:10.1109/tsg.2021.3075386

Cited by 62 publications

(4 citation statements)

References 31 publications

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“…A similar principle has been applied to improve the voltage profile. In [84], it was demonstrated that the DLMP could also be very high when voltage constraints were binding. Thus, flexible HVAC and EV demands were shifted to low DLMP periods to alleviate the voltage violation.…”

Section: Improve System Reliabilitymentioning

confidence: 99%

See 1 more Smart Citation

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Wang

Bai

et al. 2023

Proc. IEEE

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| Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed.The state-of-the-art solution methods for distribution market Manuscript

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Section: Improve System Reliabilitymentioning

confidence: 99%

“…The DLMP is influenced by the substation LMP, nodal load demands, and power output of DGs [84]. These factors vary widely with time; thus, the DLMP also varies significantly within a day, days, weeks, months, seasons, and so on, making a general daily DLMP pattern hard to obtain.…”

Section: B Variability and Acceptance Of Dlmpmentioning

confidence: 99%

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Wang

Bai

et al. 2023

Proc. IEEE

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“…In terms of the dispatching of EVs by an aggregator, previous research [6] established a three‐level bidding dispatching framework based on a competitive allocation operation using the marginal price of the distribution location was established. A previous study [7] established a real‐time interaction model between EV aggregators and mobile and dynamic EVs.…”

Section: Introductionmentioning

confidence: 99%

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

Lin,

Zhou,

et al. 2023

IET Generation Trans & Dist

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With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis.

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“…Previous studies have shown that if nodes are not selected correctly, reducing demand at selected nodes may even lead to higher prices [16], [17]. Moreover, existing price-based control strategies often neglect the physical and operational constraints of the underlying electricity networks when deriving the price [18], [19]. Though reducing demand can act as an "action knob" to lower the electricity price, knowing the relationship between the price and demand under the network constraints is a preliminary step to formulate a targeting problem.…”

Section: Introductionmentioning

confidence: 99%

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

Zhang¹,

Wen²,

Feng³

et al. 2022

Preprint

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<p>Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes' demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.</p>

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Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Cited by 62 publications

References 31 publications

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

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