A bi-level risk-constrained offering strategy of a wind power producer considering demand side resources

Rashidizadeh‐Kermani, Homa; Vahedipour‐Dahraie, Mostafa; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.ijepes.2018.07.038

Cited by 60 publications

(34 citation statements)

References 28 publications

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“…This is plausible by constructing fast communication infrastructure with bidirectional data transition among the LSEs and responsive loads and the PEV parking lots . It should be noted that responsive loads can take part in price‐based DR programs with common schemes comprising sheddable and shiftable loads . Moreover, PEV owners can reduce their payments by choosing proper LSE for charging and discharging process.…”

Section: Stochastic‐based Decision‐making Problem Of Lsementioning

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: Stochastic‐based Decision‐making Problem Of Lsementioning

confidence: 99%

“…23 It should be noted that responsive loads can take part in price-based DR programs with common schemes comprising sheddable and shiftable loads. 24 Moreover, PEV owners can reduce their payments by choosing proper LSE for charging and discharging process.…”

Section: Stochastic-based Decision-making Problem Of Lsementioning

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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“…The initial SOC of EVs at each scenario is randomly generated within its technical limitation. The forecasted charge/discharge prices offered by three rival aggregators are obtained from [27] and their associated scenarios are generated with a three-segment normal PDF [28]. The value of adopted is 0.95 based on [14] and the simulation time step is set to 1 h. The scheduling horizon is 24 h. Finally, the bi-level stochastic programming problem is formulated as an equivalent MILP program and solved with CPLEX in the GAMS software [ extracted from the Nordpool market [27].…”

Section: Test Case Studymentioning

confidence: 99%

Section: Test Case Studymentioning

confidence: 99%

Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets

Rashidizadeh‐Kermani

Anvari‐Moghaddam²,

Guerrero³

2018

Energies

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This paper proposes the problem of decision making of an electric vehicle (EV) aggregator in a competitive market in the presence of different uncertain resources. In the proposed model, a bi-level problem is formulated where, in the upper-level, the objective of the aggregator is to maximize its expected profit through its interactions and, in the lower-level, the EV owners minimize their payments. Therefore, the objectives of the upper and the lower-level are contrary. To solve the obtained nonlinear bi-level program, Karush-Kuhn-Tucker (KKT) optimality conditions and strong duality are applied to transform the initial problem into a linear single-level problem. Moreover, to deal with various uncertainties, including market prices, EVs charge/discharge demands and the prices offered by rivals, a risk measurement tool is incorporated into the problem. The proposed model is finally applied to a test system and its effectiveness is evaluated. Simulation results show that the proposed approach has the potential to offer significant benefits to the aggregator and EV owners for better decision-making in an uncertain environment. During different situations, it is observed that with increasing risk-aversion factor, as the aggregator tries to hedge against volatilities, its purchases from day-ahead and negative balancing markets decreases significantly. However, the participation of EV aggregator in the positive balancing market increases accordingly to make more profit.

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“…Recently, the penetration level of renewable energy sources (RESs) such as wind and photovoltaic generation has increased at a rapid rate in smart distribution networks. Among others, wind power generation has been one of the fastest developing clean technologies, reaching a considerable penetration level in the energy mix which in turn imposes new challenges in operation management of power systems due to its intermittent nature [1,2]. These challenges are critical in microgrids, where uncertainties are higher due to minimal aggregation and smoothing effects [3].…”

Section: Introductionmentioning

confidence: 99%

Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation

2019

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This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.

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A bi-level risk-constrained offering strategy of a wind power producer considering demand side resources

Cited by 60 publications

References 28 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

Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets

Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation

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