Two-Stage Minimax Regret Robust Unit Commitment

Jiang, Ruiwei; Wang, Jianhui; Zhang, Muhong; Guan, Yongpei

doi:10.1109/tpwrs.2013.2250530

Cited by 211 publications

(125 citation statements)

References 45 publications

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“…where P ti , Q ti and V ti are the active, reactive power and voltage amplitude of bus i, respectively; N bus is the number of buses; q ij is the phase angle difference between bus i and j; G ij and B ij are conductance and susceptance between bus i and j. ; i2N g ; t2T (22) where P g i;min and P g i;max are the minimum and maximum active power of conventional generator i; Q g i;min and Q g i;max are the minimum and maximum reactive power of conventional generator i; P g ti and Q g ti are the active and reactive power of conventional generator i at time t. (24) where DP g i;max is the active power ramping rate of generator i. The AROD model is proposed for a 24-h dispatch horizon.…”

Section: ) Power Flow Equations [37]mentioning

confidence: 99%

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RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response

Wang

Jiang

2016

Renewable Energy

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a b s t r a c tDue to dramatically increased uncertainties caused by wind power (WP) integration and price responsive demand, economic dispatch (ED) in a day-ahead market faces new challenges. With the involvement of price responsive demand response (DR), independent system operator (ISO) has to adapt to the changes of both nodal prices and loads under different WP conditions. Moreover, here the changeable nodal prices are determined by both power generation and load not using a simple deterministic mathematical function. The robustness of dispatch strategies can be enhanced through proper demand side management. Motivated by these, we propose a novel active robust optimization dispatch (AROD) model in this paper. The proposed model amalgamates robust optimization (RO) with dynamic optimization (DO) to reveal the effects of price responsive DR, while considering all possible WP conditions. We present extensive numerical case studies on the modified IEEE 30-bus system and IEEE 118-bus system. Computational results demonstrate the effectiveness of the proposed AROD model for the secure and economic operation of the power system under various uncertainties in a day-ahead market.

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Section: ) Power Flow Equations [37]mentioning

confidence: 99%

“…[23]. To reduce the conservativeness of the traditional RO [24], proposes an innovative two-stage robust UC model with a minimax regret strategy. Moreover, uncertain demand response is involved in the multistage robust UC model proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response

Wang

Jiang

2016

Renewable Energy

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“…Here, the P is the orthogonal matrix used for orthogonal decomposition (15) and D is a diagonal matrix consists of eigenvalues of positive definite matrix Q. For the axial ellipsoid E ′ , the matrix which the column vectors consist of the 2 × n vertices coordinates can be expressed as:…”

Section: Vertices Of the Ellipsoidmentioning

confidence: 99%

“…To encounter the uncertainties brought by the DGs, stochastic optimisation methods such as chance-constrained optimisation [12,13] and robust optimisation techniques [14][15][16][17][18] can be used. Among the robust optimisation method, the uncertainty set is to describe the robust region which the optimal decision should be adaptive to.…”

Section: Introductionmentioning

confidence: 99%

Improved extreme‐scenario extraction method for the economic dispatch of active distribution networks

Zhang

Wen

2017

J. eng.

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Optimisation techniques with good characterisation of the uncertainties in modern power system enable the system operators well trade-off between security and sustainability. This study proposes the extreme-scenario extraction-based robust optimisation method for the economic dispatch of active distribution network with renewables. The extreme scenarios are selected from the historical data using the improved minimum volume enclosing ellipsoid (MVEE) algorithm to guarantee the security of system operation while avoid frequently switching the transformer tap. It is theoretically proved that if the decision can be adaptive to the selected extreme scenarios, it can be robust to all the possible scenarios. Simulation results demonstrate that the proposed improved MVEE algorithm significantly reduces the number of scenarios, so that the computational burden is dramatically cut down. Additionally, compared to the existing robust optimisation approach, the proposed method is less conservative for the smaller size of the uncertainty set.

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“…For example, regret minimization objective is introduced as a means to decrease the conservativeness of the robust UC solution [70], where the regret of a UC solution is defined as the maximum difference between the minimum total cost by adjusting the dispatch decision for the fixed UC solution and the total cost in the perfect-information case where the UC and dispatch solutions can be jointly optimized after knowing the uncertainty. A hybrid approach of combining the worst-case cost of a robust UC model with the average cost of a stochastic UC model is proposed in [71].…”

Section: Robust Optimizationmentioning

confidence: 99%

Advanced optimization methods for power systems

Panciatici¹,

Campi

Garatti

et al. 2014

2014 Power Systems Computation Conference

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Abstract-Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixedinteger programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.

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Two-Stage Minimax Regret Robust Unit Commitment

Cited by 211 publications

References 45 publications

RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response

RETRACTED: Active robust optimization for wind integrated power system economic dispatch considering hourly demand response

Improved extreme‐scenario extraction method for the economic dispatch of active distribution networks

Advanced optimization methods for power systems

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