Power grid vulnerability is a major concern of our society, and its protection problem is often formulated as a tri-level defender-attacker-defender model. However, this tri-level problem is computationally challengin...
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Power grid vulnerability is a major concern of our society, and its protection problem is often formulated as a tri-level defender-attacker-defender model. However, this tri-level problem is computationally challenging. In this paper, we design and implement a column-and-constraintgeneration algorithm to derive its optimal solutions. Numerical results on an IEEE system show that: (i) the developed algorithm identifies optimal solutions in a reasonable time, which significantly outperforms the existing exact algorithm;(ii) the attack solution obtained through solving the attacker-defender model does not lead to the optimal protection plan in general;and (iii) protection using the optimal solution from the defender-attacker-defender model always improves the grid survivability under contingencies. The proposed model and algorithm can be easily modified to accommodate for other critical infrastructure network protection problems. (C) 2013 Elsevier Ltd. All rights reserved.
In order to address the wind power uncertainty in the security constrained economic dispatch (SCED), this study proposes a two-stage data-driven distributionally robust reserve and energy scheduling (DDRRES) model tha...
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In order to address the wind power uncertainty in the security constrained economic dispatch (SCED), this study proposes a two-stage data-driven distributionally robust reserve and energy scheduling (DDRRES) model that considers the loss of wind spillage and load shedding. This model aims to minimise the total cost while ensuring that the operating constraints are satisfied on the adjustable uncertainty set, of which the boundaries are decision variables. Unlike the previous approaches, which assumed that the underlying true probability distribution (PD) of uncertainty is known, the proposed model does not rely on the specified distribution but extracts the information from historical data directly. The ambiguity sets, i.e., Wasserstein balls are constructed to contain the possible PDs. Fixing the boundaries of adjustable uncertainty set, the operational risk is the expected loss under the worst-case PDs over Wasserstein balls. Thus the operational cost and operational risk can be balanced by adjusting the adjustable uncertainty set. After the tractable formulation of DDRRES is obtained, such a model is solved with the column-and-constraint generation method. The performance of the proposed approach is verified on a 6-bus test system and a 118-bus system.
To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reli...
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To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models.
Developing efficient strategies for defending electric power systems against attacks is a major concern, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to min...
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ISBN:
(纸本)9781538622124
Developing efficient strategies for defending electric power systems against attacks is a major concern, especially when uncertainties are involved. This paper addresses the allocation of the defensive resource to minimize the damage when there are uncertainties regarding the resource the attacker has. A Multiple-Attack-Scenario (MAS) defender attacker-defender model is proposed by extending the conventional tri-level defender-attacker-defender model. The proposed model considers the uncertainties related to the offensive resource and the interactions involving the security personnel at the top-level, the attacker at the middle-level and the power system operator at the bottom-level. The column and-constraintgeneration (C&CG) algorithm is implemented by decomposing the MAS defender-attacker-defender model into an upper-level problem for the security personnel, and a lower-level problem for the attacker involving the optimal power flow analysis-based corrective power re-dispatch implemented by the power system operator. Case studies are performed based on the IEEE RTS79 system, and the results validate that the proposed method is able to minimize the damage when uncertainties are involved in the offensive resource. This work can offer meaningful insights into power system protection involving uncertainties in a cyber-physical environment.
The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead biddi...
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The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model's goal is to minimize the total operational cost in the worstcase scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraintgeneration (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.
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