A two-stage robust investment model for a risk-averse price-maker power producer

Aryani, Morteza; Ahmadian, Mohammad Taghi; Eslami, Mohammad H.

doi:10.1016/j.energy.2017.10.119

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…Also, a market-clearing design that makes a more efficient use of the system flexibility leads to larger amounts of installed wind power. A two-stage bi-level robust model for application in risk-averse price-making generation investment is presented in [18]. The investor owns dispatch-able and wind generation units.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bi-level optimisation models have been widely used in investment problems in competitive electricity markets. In this respect, among the researches involved in investing in wind power generation, [11][12][13][14][15][16][17][18][19] have used bi-level models to study investor interactions and various electricity market designs. However, to the best of our knowledge, the impact of ID market, along with the DA and balancing markets, on the investment behaviour of wind power plants has not been simultaneously studied.…”

Section: Contributionsmentioning

confidence: 99%

“…Constraints (14)- (17) represent the limitation of power flow of lines, production of conventional units, production of wind power and voltage angles of each node, respectively. Constraint (18) imposes the voltage angle at the reference node must be fixed to zero. All the description of LL 2 constraints is similar to LL 1 .…”

Section: Bi-level Modelmentioning

confidence: 99%

“…Constraints (12), (13), (18), (20), (21) and 26 ), and their ID equivalent in constraint (43). To obtain the MILP problem, these non-linear terms must be expressed by corresponding linear terms.…”

Section: Mathematical Program With Equilibrium Constraintsmentioning

confidence: 99%

See 3 more Smart Citations

Strategic wind power investment in competitive electricity markets considering the possibility of participation in intraday market

Mousavi

Barforoushi

2020

IET Generation, Transmission & Distribution

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Section: Literature Reviewmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Bi-level Modelmentioning

confidence: 99%

Section: Mathematical Program With Equilibrium Constraintsmentioning

confidence: 99%

See 2 more Smart Citations

Strategic wind power investment in competitive electricity markets considering the possibility of participation in intraday market

Mousavi

Barforoushi

2020

IET Generation, Transmission & Distribution

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“…Moreover, the subject of developing products for risk averse investor was also considered in the literature. As an example, [37] proposed a two-stage robust investment model regarding electricity market. In this model, hedging methodology is considered to minimize uncertainty so that financial products can become appropriate for risk averse investors.…”

Section: Literature Reviewmentioning

confidence: 99%

IT2-Based Fuzzy Hybrid Decision Making Approach to Soft Computing

Dınçer

Yüksel

2019

IEEE Access

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This aims to evaluate the risk appetite of the financial investors in emerging economies using an integrated interval type-2 fuzzy model. For this purpose, eight different criteria are identified with the supporting literature. The interval type-2 fuzzy DEMATEL approach is used to weight these criteria regarding the importance level. In addition, investors are classified into three different groups with respect to the risk appetite which are the aggressive/risk taker, moderate/risk neutral, and conservative/risk averse. Moreover, the interval type-2 fuzzy QUALIFLEX methodology is taken into consideration to rank these investor groups. The novelty of this paper is to propose a hybrid fuzzy decision-making approach to the investors' risk appetite based on the interval type-2 fuzzy sets. The findings show that aggressive investors play the most important role in emerging economies. Therefore, financial products, which offer high returns, should be developed to attract the attention of these aggressive investors. Owing to this aspect, it can be possible for emerging economies to improve their financial systems. INDEX TERMS Interval type-2 fuzzy DEMATEL, interval type-2 QUALIFLEX, financial investment, risk appetite.

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Operational optimization of industrial steam systems under uncertainty using data‐Driven adaptive robust optimization

2018

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This paper addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semi-empirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then employed to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multi-level optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability.

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A two-stage robust investment model for a risk-averse price-maker power producer

Cited by 9 publications

References 23 publications

Strategic wind power investment in competitive electricity markets considering the possibility of participation in intraday market

Strategic wind power investment in competitive electricity markets considering the possibility of participation in intraday market

IT2-Based Fuzzy Hybrid Decision Making Approach to Soft Computing

Operational optimization of industrial steam systems under uncertainty using data‐Driven adaptive robust optimization

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