Retail Pricing: A Bilevel Program for PEV Aggregator Decisions Using Indirect Load Control

Momber, Ilan; Wogrin, Sonja; Román, Tomás Gómez San

doi:10.1109/tpwrs.2014.2379637

Cited by 89 publications

(70 citation statements)

References 29 publications

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“…Figure illustrates the forecasted demand of both customers and PEVs. The pattern of PEVs demand is obtained on the basis of Momber et al, which represents how demand of PEVs changes during a day. It should be noted that in each time period of the scheduling horizon, only a number of PEVs are connected to the network and can participate in the DR program.…”

Section: Simulations and Numerical Resultsmentioning

confidence: 99%

“…A bi‐level complementarity model for a price‐maker energy storage system to determine the most beneficial trading actions in pool‐based markets, including day‐ahead (DA) and balancing settlements is represented in Nasrolahpour et al Also, the uncertainties of the problem are incorporated into the model using a set of scenarios generated. A mathematical program with equilibrium constraints has been provided by Momber et al to maximize the profit of PEV aggregator and to minimize the PEV owners' costs. In Xu et al, joint bidding and pricing problem of an LSE as a bi‐level framework is modeled such that the optimal energy bids and reserves offers that the LSE submit to the wholesale electricity markets, as well as, its optimal energy and reserve prices in the retail electricity markets are determined simultaneously so as to maximize the LSE's profit.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Simulations and Numerical Resultsmentioning

confidence: 99%

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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“…By using game theory, the results demonstrate that the market players can maximize their expected payoff/profit by undertaking strategies through the price bidding strategy. Reference [33] proposes a mathematical program with equilibrium constraints determining retail electricity price for plug-in electric vehicles (PEVs) coordinated by an aggregating agent. Simulated analysis indicates that adequate competition in the retail market is necessary to limit the aggregator's monopolistic profitability.…”

Section: Introductionmentioning

confidence: 99%

Stochastic optimization for retailers with distributed wind generation considering demand response

Golmohamadi

Keypour

2018

J. Mod. Power Syst. Clean Energy

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In this paper, a multi-stage stochastic model is presented for a renewable distributed generation (RDG)-owning retailer to determine the trading strategies existing in a competitive electricity market. Uncertainties associated with wholesale electricity market price, clients' consumption and power output of wind resources are considered through auto regressive integrated moving average (ARIMA) approach. In the proposed method, three trading floors are addressed for the retailer to hedge against the uncertainties. In the first stage, the retailer participates in day-ahead market to supply the clients and in the second stage, intraday market is addressed to allow the retailer to modify the schedule of its clients' consumption/RDG production. Due to unfavorable uncertainties, especially in renewable power production, real-time market is considered in the third stage to diminish the uncertainty at power delivery time. Cost function of wind resources considering capital, operation and maintenance (O&M) cost is incorporated in the objective function to increase the applicability of the mechanism. The proposed approach is formulated for risk-averse and risk-taker retailer through conditional value at risk (CVaR) approach. In order to study the impact of retail strategies on consumption pattern and consumers' electricity bills, time-of-use (TOU) demand response programs are discussed in this paper. Formulating the problem, the mixed integer non-linear programming (MILNP) problem is transformed into mixed integer linear programming (MILP) by jointly using decomposition and disjunctive constraints. Finally, a case study containing wind power resources, energy storage system and retailer is considered to analyze the proficiency of the proposed approach.

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“…Bilevel programming (BLP) with a structure of two levels can be introduced to solve this type of problem in scientific and engineering fields (Bacaken and Mcgill 1973;He et al 2011;Xu et al 2013;Feijoo and Das 2015;Momber et al 2016;Pousinho et al 2016). Although a limited number of BLP studies have been conducted for solving water resources allocations, they failed to consider health risk as a significant objective into the optimization groundwater remediation framework.…”

Section: Introductionmentioning

confidence: 99%

A bilevel groundwater management model with minimization of stochastic health risks at the leader level and remediation cost at the follower level

Chen

et al. 2016

Stoch Environ Res Risk Assess

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Traditional single-objective programs cannot deal with the tradeoffs between the decision makers who represent different perspectives and have inconsistent decision goals. Multi-objective ones can hardly represent a complex dominant-subordinate relationship between the leader and the follower. This study presents a new bilevel programming model with considering leader-follower-related health-risk and economic goals for optimal groundwater remediation management. The bilevel model is formulated by integrating health-risk assessment and environmental standards (the leader or the environmental concern) and remediation cost (the follower or the economic concern) into a general framework. In addition, stochastic uncertainty in health risk assessment is considered into the decision-making process. The developed bilevel model is then applied to a petroleum-contaminated aquifer in Canada. Results indicate that the performance of bilevel programming can not only meet the low remediation cost as the expectation from the follower but also simultaneously conform to the low contamination level as the expectation from the leader. Furthermore, comparative analyses show that the bilevel model with two-level concerns has the advantage of maximizing the interests and satisfaction degrees of decision makers, which can avoid the extreme results generated from the single-level models.

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Retail Pricing: A Bilevel Program for PEV Aggregator Decisions Using Indirect Load Control

Cited by 89 publications

References 29 publications

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

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

Stochastic optimization for retailers with distributed wind generation considering demand response

A bilevel groundwater management model with minimization of stochastic health risks at the leader level and remediation cost at the follower level

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