Coordination of Hydro Units With Wind Power Generation Using Interval Optimization

Liu, Yangyang; Jiang, Chuanwen; Shen, Jingshuang; Hu, Jiakai

doi:10.1109/tste.2014.2382122

Cited by 68 publications

(43 citation statements)

References 25 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%

“…[20]. Different from the SO-based approaches, the IO-based approaches, not requiring the presumed PDFs, can overcome the computation obstacles [21,22]. Nevertheless, the uncertainty intervals should to be carefully designed as they affect the optimal solution.…”

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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“…Assuming that a constant λ and two interval numbers (A I and B I ) are given, the operation rules for scalar multiplication, addition and subtraction are defined as follows [43,44]:…”

Section: Arithmetic Operations Between Intervalsmentioning

confidence: 99%

“…Assuming the possibility degree ξ eq is given for the interval equality constraints (12) and (13), we can convert (12) and (13) into deterministic constraints, i.e., (44) where: m(P i (t)) = P esu,i (t) + P ev,i (t) + P pcc,i (t) + m(P I gt,i (t)) + m(P I wt,i (t)) + m(P I pv,i (t)) − m(P I load,i (t)) w(P i (t)) = w(P I gt,i (t)) + w(P I wt,i (t)) + w(P I pv,i (t)) + w(P I load,i (t))…”

Section: Interval Constraint Transformationmentioning

confidence: 99%

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

et al. 2017

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Abstract:In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project "Microgrid", and the results demonstrate the effectiveness of the proposed method.

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“…Mathematically, an interval optimization problem can be formulated as Equations (9)- (11). Some specific introduction of the interval optimization algorithm is detailed in [26]:…”

Section: The Day-ahead Dispatch Modelmentioning

confidence: 99%

A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles

et al. 2016

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Coordinated dispatch of plug-in electric vehicles (PEVs) with renewable energies has been proposed in recent years. However, it is difficult to achieve effective PEV dispatch with a win-win result, which not only optimizes power system operation, but also satisfies the requirements of PEV owners. In this paper, a multi-period PEV dispatch framework, combining day-ahead dispatch with real-time dispatch, is proposed. On the one hand, the day-ahead dispatch is used to make full use of wind power and minimize the fluctuation of total power in the distribution system, and schedule the charging/discharging power of PEV stations for each period. On the other hand, the real-time dispatch arranges individual PEVs to meet the charging/discharging power demands of PEV stations given by the day-ahead dispatch. To reduce the dimensions of the resulting large-scale, non-convex problem, PEVs are clustered according to their travel information. An interval optimization model is introduced to obtain the problem solution of the day-ahead dispatch. For the real-time dispatch, a priority-ordering method is developed to satisfy the requirements of PEV owners with fast response. Numerical studies demonstrate the effectiveness of the presented framework.

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Coordination of Hydro Units With Wind Power Generation Using Interval Optimization

Cited by 68 publications

References 25 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

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

A Multi-Period Framework for Coordinated Dispatch of Plug-in Electric Vehicles

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