Stochastic Security for Operations Planning With Significant Wind Power Generation

Bouffard, François; Galiana, F.D.

doi:10.1109/tpwrs.2008.919318

Cited by 620 publications

(328 citation statements)

References 27 publications

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“…In the constraints following objective (43), the value of all variables denoted with an upper dash, like the commitment status, has already been determined by the UC solution and is not re-optimized in RTD. In that respect, (44) describes the actual deployment of the uncertainty reserve by each unit in RTD as compared to its hourly day-ahead energy schedule, (45) and (46) impose the unit power output limits in every operating state, (47) and (48) enforce the ramping constraints, (49) and (50) determine the unit output based on the maximum offered quantity per step of the associated energy offer, while (51) describes the power balance equation. The latter constraints (52)- (55) are used only in the RTD mode RTD_SI_Var (explained below) to consider the variability reserve as an alternative way of "looking ahead" in RTD.…”

Section: Real-time Dispatch (Rtd)mentioning

confidence: 99%

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

2017

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Abstract:The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.

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Section: Real-time Dispatch (Rtd)mentioning

confidence: 99%

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

2017

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“…In a similar way, employing the dual optimization, S-lemma and Schur complement [20,21], the random wind power in the optimization model is eliminated [28], and Equations (17) and (18) are converted into linear matrix inequalities as shown in Equations (19) and (20). The detailed derivation is shown in Appendix B:…”

Section: Determination Model For Distributional Robust Chance Constraintmentioning

confidence: 99%

“…The branch power and node voltage constraints in Equations (10) and (11) can be converted into LMI form as shown in Equations (19) and (20). (2) Initialization: set the initial solution P 0 G and Q 0 G , calculate the fitness value, and let the iteration counter K = 0.…”

Section: Linear Matrix Inequality (Lmi)-based Particle Swarm Optimizamentioning

confidence: 99%

“…Instead, only partial wind power information is available, such as the first and second order moment information [19]. In the prior scenario-based simulation methods and probabilistic analysis methods, wind speeds are assumed to follow a normal distribution [20,21], beta distribution [22,23], Laplace distribution [24,25], Cauchy distribution [26] or other probability distribution functions [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Available Transfer Capability Assessment Strategy for Wind Integrated Transmission Systems Considering Uncertainty of Wind Power Probability Distribution

et al. 2016

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Abstract:Wind power prediction research shows that it is difficult to accurately and effectively estimate the probability distribution (PD) of wind power. When only partial information of the wind power probability distribution function is available, an optimal available transfer capability (ATC) assessment strategy considering the uncertainty on the wind power probability distribution is proposed in this paper. As wind power probability distribution is not accurately given, the proposed strategy can efficiently maximize ATC with the security operation constraints satisfied under any wind power PD function case in the uncertainty set. A distributional robust chance constrained (DRCC) model is developed to describe an optimal ATC assessment problem. To achieve tractability of the DRCC model, the dual optimization, S-lemma and Schur complement are adopted to eliminate the uncertain wind power vector in the DRCC model. According to the characteristics of the problem, the linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the DRCC model which contains first and second-order moment information of the wind power. The modified IEEE 30-bus system simulation results show the feasibility and effectiveness of the proposed ATC assessment strategy.

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“…Random output of wind speed lowers the schedulability of active power in a wind farm, which in turn limits the availability of wind energy [2]. With increasing permeability of wind power in the state grid, lower schedulability would increase operational risks, especially wind power curtailment [3,4].…”

Section: Introductionmentioning

confidence: 99%

Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control

et al. 2016

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Abstract:The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.

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Stochastic Security for Operations Planning With Significant Wind Power Generation

Cited by 620 publications

References 27 publications

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

Optimal Available Transfer Capability Assessment Strategy for Wind Integrated Transmission Systems Considering Uncertainty of Wind Power Probability Distribution

Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control

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