Wind power investment within a market environment

Baringo, Luis; Conejo, Antonio J.

doi:10.1016/j.apenergy.2011.03.023

Cited by 83 publications

(53 citation statements)

References 33 publications

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“…Hence, when the average price increases, the wind power penetration is expected to decrease. This is in line with the previous literature [10,25,29]. Note that we show the data on average price, demand, and wind penetration.…”

Section: Analysis Under Different Prices and Demandssupporting

confidence: 77%

“…In this paper, we discretize the electricity price in a known and fixed sample range. In addition, we assume that the probabilities associated with given levels of electricity prices are not input parameters but are dependent on the investment decisions, as evidenced in the previous literature [7,9,10,28,29]. For example, researchers found that the average electricity price would decrease as the share of wind power in the generation portfolio increases.…”

Section: B Modeling the Decision-dependent Probabilitymentioning

confidence: 95%

“…The long-term wind power investment study from [10] indicates that the average expected value of price decreases as wind farms are added. Similarly, the study from [29] also shows a decrease of average price due to the increasing wind power. Therefore, as mentioned in many studies, the price dependency on wind capacity is important for investors in evaluating the economic effects of power generation investments.…”

Section: B Modeling the Decision-dependent Probabilitymentioning

confidence: 96%

See 2 more Smart Citations

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Zhan

Zheng

Wang

et al. 2017

IEEE Trans. Power Syst.

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Abstract-Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage, decisiondependent stochastic optimization model for long-term, largescale generation expansion planning where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming (MILP) model. The wind penetration, investment decisions, and the optimality of the decisiondependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

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Section: Analysis Under Different Prices and Demandssupporting

confidence: 77%

Section: B Modeling the Decision-dependent Probabilitymentioning

confidence: 95%

Section: B Modeling the Decision-dependent Probabilitymentioning

confidence: 96%

See 1 more Smart Citation

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Zhan

Zheng

Wang

et al. 2017

IEEE Trans. Power Syst.

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“…For example, the uncertainties in wind power production and real-time demand can be modeled as random variables. In [18] and [19], the stochastic programming approach has been combined with the MPEC approach to solve the long-term wind power investment problem. The medium-term decision-making problem of a retailer in the future market and the pool could also be modeled using the stochastic MPEC approach [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal bidding strategy of a strategic wind power producer in the short-term market

Dai

Wang

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-Wind energy is a clean and renewable energy source which is rapidly growing globally. As the penetration level of wind power grows, the system operators need to consider wind power producers as strategic producers whose bidding behaviors will have an impact on the locational marginal prices. This paper proposes a bilevel stochastic optimization model to obtain the optimal bidding strategy for a strategic wind power producer in the short-term electricity market. The upper level problem of the model maximizes the profit of the wind power producer, while the lower level problem represents the market clearing processes of both day-ahead and real-time markets. The uncertainties in the demand, the wind power production, and the bidding strategies of the strategic conventional power producers are represented by scenarios in the model. The conditional value at risk of the selected worst scenarios is included in the objective function for managing the risk due to uncertainties. Using the duality theory and Karush-Kuhn-Tucker condition, the bilevel model is transferred into a mixed-integer linear problem. Case studies are performed to show the effectiveness of the proposed model. Decision Variables: λ W D bjtOffer price of block b of the wind generating unit j in a period t in the day-ahead market. λ W R jtOffer price of the wind generating unit j in a period t in the real-time market. p W D bjtProduced power of block b of the wind generating unit j in a period t in the day-ahead market. P W R jtωRescheduled power of the wind generating unit j in a period t in the real-time market. p CD bitPower of block b produced by the conventional power producer i in a period t in the day-ahead market. P CR+ it /P CR− itIncreased/decreased power of the conventional power producer i in a period t in the real-time market. Voltage angle of bus m in a period t in the day-ahead/real-time market. ζ Auxiliary variable used to compute CVaR. η ω Auxiliary variable used to compute CVaR in a scenario. Random Variables: λ CD bitOffer price of block b of the conventional power producer i in a period t in the dayahead market

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“…Leuthold et al (2012) develop a large-scale perfect competition model of the European electricity market that covers transmission, variable demand, wind power, and pumped storage, for example. In bi-level models, a group of players in the lead role make optimal decisions anticipating the reaction of a group of follower players, e.g., see Baringo and Conejo (2011). Kunz (2013) presents a sequential model for Germany with a high level of wind generation in which the production schedules determined by a day-ahead market model ignoring the physical transmission network are fed into a congestion management model, which minimizes the re-dispatch costs, i.e., the costs of relieving congestion.…”

Section: Literature Reviewmentioning

confidence: 99%

How much is enough? Optimal support payments in a renewable-rich power system

Rintamäki

Siddiqui

Salo

2016

Energy

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The large-scale deployment of intermittent renewable energy sources may cause substantial power imbalance. Together with the transmission grid congestion caused by the remoteness of these sources from load centers, this creates a need for fast-adjusting conventional capacity such as gas-fired plants. However, these plants have become unprofitable because of lower power prices due to the zero marginal costs of renewables. Consequently, policymakers are proposing new measures for mitigating balancing costs and securing supply. In this paper, we take the perspective of the regulator to assess the effectiveness of support payments to flexible generators. Using data on the German power system, we implement a bi-level programming model, which shows that such payments for gas-fired plants in southern Germany reduce balancing costs and can be used as part of policy to integrate renewable energy.

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Wind power investment within a market environment

Cited by 83 publications

References 33 publications

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Optimal bidding strategy of a strategic wind power producer in the short-term market

How much is enough? Optimal support payments in a renewable-rich power system

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