Considering recent development of deregulated energy markets and the intermittent nature of renewable energy generation, it is important for power system operators to ensure cost effectiveness while ...maintaining the system reliability. To achieve this goal, significant research progress has recently been made to develop stochastic optimization models and solution methods to improve reliability unit commitment run practice, which is used in the day-ahead market for ISOs/RTOs to ensure sufficient generation capacity available in real time to accommodate uncertainties. Most stochastic optimization approaches assume the renewable energy generation amounts follow certain distributions. However, in practice, the distributions are unknown and instead, a certain amount of historical data are available. In this research, we propose a data-driven risk-averse stochastic unit commitment model, where risk aversion stems from the worst-case probability distribution of the renewable energy generation amount, and develop the corresponding solution methods to solve the problem. Given a set of historical data, our proposed approach first constructs a confidence set for the distributions of the uncertain parameters using statistical inference and solves the corresponding risk-averse stochastic unit commitment problem. Then, we show that the conservativeness of the proposed stochastic program vanishes as the number of historical data increases to infinity. Finally, the computational results numerically show how the risk-averse stochastic unit commitment problem converges to the risk-neutral one, which indicates the value of data.
Due to increasing penetration of intermittent renewable energy and introduction of demand response programs, uncertainties occur in both supply and demand sides in real time for the current power ...grid system. To address these uncertainties, most ISOs/RTOs perform reliability unit commitment runs after the day-ahead financial market to ensure sufficient generation capacity available in real time to accommodate uncertainties. Two-stage stochastic unit commitment and robust unit commitment formulations have been introduced and studied recently to provide day-ahead unit commitment decisions. However, both approaches have limitations: 1) computational challenges due to the large scenario size for the stochastic optimization approach and 2) conservativeness for the robust optimization approach. In this paper, we propose a novel unified stochastic and robust unit commitment model that takes advantage of both stochastic and robust optimization approaches, that is, this innovative model can achieve a low expected total cost while ensuring the system robustness. By introducing weights for the components for the stochastic and robust parts in the objective function, system operators can adjust the weights based on their preferences. Finally, a Benders' decomposition algorithm is developed to solve the model efficiently. The computational results indicate that this approach provides a more robust and computationally trackable framework as compared with the stochastic optimization approach and a more cost-effective unit commitment decision as compared with the robust optimization approach.
This paper proposes a distributionally robust optimization approach for the contingency-constrained unit commitment problem. In our approach, we consider a case where the true probability ...distribution of contingencies is ambiguous, i.e., difficult to accurately estimate. Instead of assigning a (fixed) probability estimate for each contingency scenario, we consider a set of contingency probability distributions (termed the ambiguity set) based on the N-k security criterion and moment information. Our approach considers all possible distributions in the ambiguity set, and is hence distributionally robust. Meanwhile, as this approach utilizes moment information, it can benefit from available data and become less conservative than the robust optimization approaches. We derive an equivalent reformulation and study a Benders' decomposition algorithm for solving the model. Furthermore, we extend the model to incorporate wind power uncertainty. The case studies on a 6-Bus system and the IEEE 118-Bus system demonstrate that the proposed approach provides less conservative unit commitment decisions as compared with the robust optimization approach.
Due to the significant improvements of power generation technologies and the trend of replacing traditional power plants with renewable generation resources, the generation portfolio will experience ...dramatic changes in the near future. The uncertainty and variability of renewable energy and their sitting call for strategic and economic plans for expanding the transmission capacities. In this study, we develop a data-driven two-stage stochastic transmission expansion planning with uncertainties. In the proposed approach, purely by learning from the historical data, we first construct a confidence set for the unknown distribution of the uncertain parameters. Then, we develop a two-stage data-driven transmission expansion framework, by considering the worst-case distribution within the constructed confidence set, so as to provide a reliable while economic transmission planning decision. Furthermore, to tackle the model complexity, we propose a decomposition framework embedded with Benders' and Column-and-Constraint generation methods. We implement our approach on 6-bus and 118-bus systems to test its effectiveness. Finally, we show as the amount of historical data grows, the conservativeness of the model decreases.
With the increasing penetration of wind power into the power grid, maintaining system reliability has been a challenging issue for ISOs/RTOs, due to the intermittent nature of wind power. In addition ...to the traditional reserves provided by thermal, hydro, and gas generators, demand response (DR) programs have gained much attention recently as another reserve resource to mitigate wind power output uncertainty. However, the price-elastic demand curve is not exactly known in advance, which provides another dimension of uncertainty. To accommodate the combined uncertainties from wind power and DR, we allow the wind power output to vary within a given interval with the price-elastic demand curve also varying in this paper. We develop a robust optimization approach to derive an optimal unit commitment decision for the reliability unit commitment runs by ISOs/RTOs, with the objective of maximizing total social welfare under the joint worst-case wind power output and demand response scenario. The problem is formulated as a multi-stage robust mixed-integer programming problem. An exact solution approach leveraging Benders' decomposition is developed to obtain the optimal robust unit commitment schedule for the problem. Additional variables are introduced to parameterize the conservatism of our model and avoid over-protection. Finally, we test the performance of the proposed approach using a case study based on the IEEE 118-bus system. The results verify that our proposed approach can accommodate both wind power and demand response uncertainties, and demand response can help accommodate wind power output uncertainty by lowering the unit load cost.
The effects of Sn content on strain hardening behavior of as-extruded Mg-xSn (x = 1.3, 2.4, 3.6 and 4.7wt%) binary alloys were investigated by uniaxial tensile tests at room temperature. Strain ...hardening rate, strain hardening exponent and hardening capacity were obtained from the true plastic stress-strain curves. After hot extrusion, the as-extruded Mg-Sn alloys are mainly composed of α-Mg matrix and second phase Mg2Sn, which only exists in Mg-3Sn and Mg-4Sn. Average grain size decreases from 15.6μm to 3.6µm with Sn content increases from 1.3 to 4.7wt%. The experimental results show that Sn content decreases strain hardening ability of as-extruded Mg-Sn alloys, but gives rise to an obvious elevation in tensile strength, yield strength and elongation of them. With increasing Sn content, strain hardening rate decreases from 3527MPa to 1211MPa at (σ-σ0.2) = 50MPa, strain hardening exponent decreases from 0.21 to 0.13 and hardening capacity decreases from 1.66 to 0.63. The variation in strain hardening behavior of Mg-Sn alloys with Sn content is discussed in terms of the influences of grain size and distribution of grain orientation.
Tumor-associated macrophages (TAMs) is a promising therapeutic target for cancer immunotherapy, while TAMs targeting therapy using nano-sized drug delivery system (NDDS) is a great challenge. To ...overcome these drawbacks, a novel erythrocyte-cancer cell hybrid membrane camouflaged pH-responsive copolymer micelle (dextran-grafted-poly (histidine) copolymer) was prepared to target deliver a selective CSF-1R inhibitor: BLZ-945 (shorten as DH@ECm) to TAMs for TAMs depletion. The prepared DH@ECm possessed favorable particle size (~190 nm) preferable immune camouflage and tumor homologies targeting characteristic when it was intravenously administrated into blood system. In tumor acidic microenvironment, DH@ECm possessed pH-responsive characteristic and unique “membrane escape effect” to facilitate recognition and internalization by TAMs via dextran-CD206 receptor specific interaction (about 3.9 fold than free drug), followed by TAMs depletion in vitro. For in vivo studies, DH@ECm could reverse tumor immune-microenvironment with the elevation of CD8+ T cells and possess sufficient tumor immunotherapy (inhibition rate: 64.5%). All the in vitro and in vivo studies demonstrated the therapeutical potential of DH@ECm for tumor immunotherapy.
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This paper proposes an expected value and chance constrained stochastic optimization approach for the unit commitment problem with uncertain wind power output. In the model, the utilization of wind ...power can be adjusted by changing the utilization rate in the proposed expected value constraint. Meanwhile, the chance constraint is used to restrict the probability of load imbalance. Then a Sample Average Approximation (SAA) method is used to transform the objective function, the expected value constraint, and the chance constraint into sample average reformulations. Furthermore, a combined SAA framework that considers both the expected value and the chance constraints is proposed to construct statistical upper and lower bounds for the optimization problem. Finally, the performance of the proposed algorithm with different utilization rates and different risk levels is tested for a six-bus system. A revised IEEE 118-bus system is also studied to show the scalability of the proposed model and algorithm.
To understand intuitively the residual stress state on the rail surface after abrasive belt rail grinding (ABRG), the influences of grinding process parameters on residual stress were experimentally ...investigated on the ABRG test bench. Tensile residual stress was found in the grinding direction, while the residual stress in the radial direction was maintained mainly in the form of compressive stress. To investigate the mechanisms of the influencing factors during the forming process of residual stress, a 3D finite element model (FEM) of grain scratching based on thermo-mechanical coupling method was developed. Effects of contact surface friction, grain’s tip radius, grain’s protrusion depth, and grain’s rake angle on residual stress distribution in rail sub-layer were revealed, respectively. In addition, the FEM simulation of residual stress involving adjacent grains scratching was carried out, in which the variation of the residual stress field between the on scratching and the subsequent scratching was observed and discussed. Finally, the suggestions for the design of the last grinding unit and grinding process parameter selection were given based on the findings from the experiment and simulation.
A new high-strength Mg-5Zn-1Ce-0.5Y-0.6Zr (wt%) magnesium alloy with low rare-earth content was developed by extrusion and direct aging. The peak-aged sample exhibited a yield strength of 407 MPa and ...an ultimate tensile strength of 421 MPa. The yield strength of this alloy was much higher than those of several Mg-Gd-Y alloys to which large amounts of rare-earth metals were added. The high strength of the alloy was closely related to the combined contributions of ultra-fine dynamically recrystallized grains (about 1 μm diameter), numerous nanoscale precipitates, and a strong basal texture.
•A high-strength Mg-5Zn-1Ce-0.5Y-0.6Zr alloy with low RE content was developed.•A high yield strength (407 MPa) and moderate elongation (7.1%) are achieved.•The yield strength is much higher than those of several Mg-Gd-Y series alloys.•The high strength is related to fine grains, precipitates and texture.
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