scholarly journals A Bilateral Tradeoff Decision Model for Wind Power Utilization with Extensive Load Scheduling

2019 ◽  
Vol 9 (9) ◽  
pp. 1777
Author(s):  
Qingshan Xu ◽  
Yajuan Lv ◽  
Dong Wang ◽  
Pengwei Du
Keyword(s):  
Wind Power ◽  
Decision Model ◽  
Sample Average Approximation ◽  
High Energy ◽  
Chance Constraint ◽  
Load Scheduling ◽  
Risk Levels ◽  
Sample Average ◽  
Mode Method ◽  
Average Approximation

In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation cost simultaneously by means of responsive loads, which are precisely divided into shiftable loads and high-energy loads. The chance constraint is applied to restrict the system imbalance with a small probability. Then, a revised sample average approximation (SAA) algorithm is developed to transform the chance constraint into sample average reformulations. Furthermore, the multi-objective differential evolution (MODE) method combined with SAA is proposed to solve the problem. Experiments enabling an effectiveness analysis of the two kinds of responsive loads are performed on the power system in Yancheng. The research of parameters of MODE, the sensitivity of different risk levels and the influence of iteration numbers are discussed. Finally, computational results prove that the combination of shiftable loads and high-energy loads have a better effect than adopting shiftable loads and high-energy loads separately, and the proposed method is convergent and valid in solving the problem.

scholarly journals Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 843 ◽  
Author(s):  
Nhung Nguyen Hong ◽  
Yosuke Nakanishi
Keyword(s):  
Energy Storage ◽  
Frequency Response ◽  
Wind Power ◽  
Forecast Error ◽  
Frequency Control ◽  
Operating Cost ◽  
Sample Average Approximation ◽  
Chance Constrained Programming ◽  
Sample Average ◽  
Average Approximation

Nowadays, the hybrid wind–diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm—a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

Sample Average Approximation

2017 ◽  
pp. 75-84
Author(s):  
Tingsong Wang ◽  
Shuaian Wang ◽  
Qiang Meng
Keyword(s):  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation

SAMPLE AVERAGE APPROXIMATION METHOD FOR SOLVING A DETERMINISTIC FORMULATION FOR BOX CONSTRAINED STOCHASTIC VARIATIONAL INEQUALITY PROBLEMS

2012 ◽  
Vol 29 (02) ◽  
pp. 1250014
Author(s):  
MEI-JU LUO ◽  
GUI-HUA LIN
Keyword(s):  
Variational Inequality ◽  
Approximation Method ◽  
Deterministic Model ◽  
Random Parameter ◽  
Sample Average Approximation ◽  
Variational Inequality Problems ◽  
Stochastic Variational Inequality ◽  
Sample Average ◽  
Sample Average Approximation Method ◽  
Average Approximation

In this paper, we discuss the Expected Residual Minimization (ERM) method, which is to minimize the expected residue of some merit function for box constrained stochastic variational inequality problems (BSVIPs). This method provides a deterministic model, which formulates BSVIPs as an optimization problem. We first study the conditions under which the level sets of the ERM problem are bounded. Then, we show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in BSVIPs. Since the integrality involved in the ERM problem is difficult to compute generally, we then employ sample average approximation method to solve it. Finally, we show that the global optimal solutions and generalized KKT points of the approximate problems converge to their counterparts of the ERM problem. On the other hand, as an application, we consider the model of European natural gas market under price uncertainty. Preliminary numerical experiments indicate that the proposed approach is applicable.

scholarly journals Sample average approximation of stochastic dominance constrained programs

2010 ◽  
Vol 133 (1-2) ◽  
pp. 171-201 ◽  
Author(s):  
Jian Hu ◽  
Tito Homem-de-Mello ◽  
Sanjay Mehrotra
Keyword(s):  
Stochastic Dominance ◽  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation
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