Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities

Fang, Xiuzhong; Hu, Qinran; Li, Fangxing; Wang, Beibei; Li, Yang

doi:10.1109/tpwrs.2015.2431271

Cited by 166 publications

(83 citation statements)

References 28 publications

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“…Given that the lower level ED is a LP problem that has a unique optimal solution satisfying Karush-Kuhn-Tucher (KKT) optimality conditions, the bi-level models in Equations (7)- (14) can be transformed into a single level mathematical program with equilibrium constraints (MPEC) by recasting the lower level problem as its KKT optimality condition [25][26][27][28][29][30][31][32]. These conditions are later added into the upper-level problem as a set of additional complementarity constraints.…”

Section: Mpec Formulation Of Bi-level Optimization Modelmentioning

confidence: 99%

Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

et al. 2015

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Available transfer capability (ATC) is the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses which needs to be posted in the electricity market to facilitate competition. ATC evaluation is a complicated task including the determination of total transfer capability (TTC) and existing transfer capability (ETC). In the deregulated electricity market, ETC is decided by the independent system operator's (ISO's) economic dispatch (ED). TTC can then be obtained by a continuation power flow (CPF) method or by an optimal power flow (OPF) method, based on the given ED solutions as well as the ETC. In this paper, a bi-level optimization framework for the ATC evaluation is proposed in which ATC results can be obtained simultaneously with the ED and ETC results in the deregulated electricity market. In this bi-level optimization model, ATC evaluation is formulated as the upper level problem and the ISO's ED is the lower level problem. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality condition. Then, the MPEC is transformed into a mixed-integer linear programming (MILP) problem, which can be solved with the help of available optimization software. In addition, case studies on PJM 5-bus, IEEE 30-bus, and IEEE 118-bus systems are presented to demonstrate the proposed methodology.Keywords: available transfer capability (ATC); deregulated electricity market; bi-level optimization; economic dispatch (ED); mathematic program with equilibrium constraints (MPEC); mixed-integer linear programming (MILP)

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Section: Mpec Formulation Of Bi-level Optimization Modelmentioning

confidence: 99%

Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

et al. 2015

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“…A robust optimization approach is proposed in Nojavan et al to handle market price uncertainty, in which the retailer seeks to minimize the energy procurement costs with only considering DR programs. In Fang et al, a strategic bidding framework for LSE agent has been proposed in which the objective is to maximize LSE's profit by implementing DR programs. In the same work, energy management of PEVs as a significant part of responsive loads has not been considered in the LSE scheduling process.…”

Section: Introductionmentioning

confidence: 99%

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

2019

Int Trans Electr Energ Syst

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Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.

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“…According to [29][30][31], this paper also considers LSEs, who can participate in electricity market as strategic bidders, as the DR aggregators. Considering that integrating and consuming more RES can bring much welfare to the whole society, this paper considers a single LSE in a certain region and promoting RES consumption as the goal of large-scale DR.…”

Section: Introductionmentioning

confidence: 99%

A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

Fan

Jia

et al. 2018

Applied Sciences

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Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.

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Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities

Cited by 166 publications

References 28 publications

Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

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