Optimal Offering Strategies for Wind Power Producers Considering Uncertainty and Risk

Catalào, João P. S.; Pousinho, Hugo Miguel Inácio; Mendes, V.M.F.

doi:10.1109/jsyst.2011.2163009

Cited by 40 publications

(22 citation statements)

References 40 publications

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“…Power pricing uses the time of use (TOU) schedule and the typical load characteristics of the load in terms of time are used. The EH model with random wind, electricity, and power tariffs is also presented in [21,22] with a minimal objective function of daytime running costs as well as technical constraints of the EH.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation of energy hub in competitive electricity market considering uncertainties

Thang

Zhang

et al. 2018

Int J Energy Environ Eng

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This paper proposes a novel energy hub model for areas using both heat and cold demands that arise due to the major changes in environmental temperature in different periods of the year. The energy demand and the electrical price in a competitive electricity market are uncertain with stochastic values which are usually performed by a probability distribution function. Therefore, a stochastic mathematical model representing an optimal operation of energy hub is based on the objective function of minimization of energy costs (including electricity and gas). Several constraints such as energy balance, limited capacity of the transformer, air conditioners, gas boilers, absorption chillers, combined heat, and power and battery energy storage system are also incorporated into the model to guarantee the required specifications. The high-level algebraic modeling software, general algebraic modeling system has been employed to undertake calculations. Finally, numerical results have illustrated the efficiency and capability of the proposed models.

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Section: Introductionmentioning

confidence: 99%

Optimal operation of energy hub in competitive electricity market considering uncertainties

Thang

Zhang

et al. 2018

Int J Energy Environ Eng

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“…Thus, how to effectively quantify the risk of ESI is still an urgent problem to be solved. Value-at-risk (VaR) and conditional value-at-risk (CVaR), as well-known risk measures, have been widely used in various energy management problems for different entities in the electricity market such as retailers [9], producers [10][11][12], distribution companies [13], and in generation operation planning [14], optimal power flow [15] as well as coordinated energy trading problems [16,17]. improve the forecasting accuracy, uncertain energy consumption fashion in local-area still poses an enormous dilemma regarding the appropriate implementation of prediction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment

Shi

Wang²,

et al. 2017

Sustainability

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Abstract:To alleviate environmental pollution and improve the energy usage efficiency of terminals, energy systems integration (ESI) has become an important paradigm in the energy structure evolution. Power, gas and heat systems are becoming tightly interlinked with each other in ESI. The dispatching strategy of local-area ESI has significant impact on its operation. In this paper, a local-area ESI operational scheduling model based on conditional value-at-risk (CVaR) is proposed to minimize expected operational cost, which considers the uncertainty of energy supply-side and demand-side as well as multi-energy network constraints, including electrical network, thermal network and gas network. The risk cost is analyzed comprehensively under the condition of under-or overestimated cost. On this basis, a hybrid method combining particle swarm optimization with interior point algorithm is executed to compute the optimal solutions of two-stage multi-period mixed-integer convex model. Finally, a case study is performed on ESI to demonstrate the effectiveness of the proposed method.

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“…Optimal trading strategies for wind generation that consider the risk to power producers have been extensively researched [10][11][12][13][14][15][16][17][18][19][20]. In [10], different risk management approaches for wind trading in the electricity market were discussed, and the utility function method was considered to be more effective than the mean variance model.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], a CVaR constrained for the bid that maximized the expected revenue was proposed as a way of reducing the risk. Catalao et al [18] provided a two-stage stochastic programming approach for WPPs considering uncertainties related to electricity market and wind power generation, and also incorporated the risk averion by limiting the volatility of the expected profit through the CVaR methodology. Reference [19] presented new analytical expressions for determining the optimal forward market strategy for wind generators under the risk-neutral and risk-averse cases, and the methods required spcification only of the expected real-time prices given the forward price and wind out-turn.…”

Section: Introductionmentioning

confidence: 99%

Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

Zhang

Gao

et al. 2012

Energies

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Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS), the chance-constrained programming-based strategy (CPS) and the multi-objective bidding strategy (ECPS). Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.Keywords: wind power; bidding; day-ahead electricity market; risk; chance-constrained programming; multi-objective optimization

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Optimal Offering Strategies for Wind Power Producers Considering Uncertainty and Risk

Cited by 40 publications

References 40 publications

Optimal operation of energy hub in competitive electricity market considering uncertainties

Optimal operation of energy hub in competitive electricity market considering uncertainties

Operating Strategy for Local-Area Energy Systems Integration Considering Uncertainty of Supply-Side and Demand-Side under Conditional Value-At-Risk Assessment

Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

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