Loading margin calculation with line switching: A decomposition method

Corrective Security constrained optimal power flow (CSCOPF) is widely used in online and offline progress occurring in the assessment, planning, and operation of real power systems. This paper uses a generalized benders decomposition (GBD) method to decompose the large scale CSCOPF problem which considering generation re-dispatching and load transferring into a master problem and a series of N-l sub-problems related to transmission line or transformer fault in same form. The MP corresponds to the security constrained optimal power flow model with benders cuts in normal condition. The sub-problem corresponds to the normal re-dispatch strategy at generation side as well as the novel load transfer strategy on demand side, forming a bilateral corrective control in case of electrical elements fault. Moreover, an improved sub-problem model is proposed to increase the quality of the solution and convergence based on the existing models. The model is tested by using two standard systems.

Emergency transmission line switching to suppress power system inter-area oscillation

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.

The increasing penetration levels and stochastic characteristics of renewable energies and aging of transmission equipment bring potential negative impact on the economic and reliable operation of power grid. Therefore, the realistic and cost-effective choice for the utilities is to tap the potential of existing transmission infrastructures. Some smart grid technologies can leverage the potential transmission capacity and offer a flexible way for the power system operation. The dynamic thermal rating can evaluate the maximum transmission capacity of lines dynamically based on the environmental data. The optimal transmission switching can change the transmission topology and remove the transmission congestions. These technologies should be considered in the network-constrained unit commitment problem systematically to study their impact on the power system scheduling. However, the dynamic thermal rating incorporated network-constrained unit commitment studies are all based on the fixed transmission topology. So the overall influence of dynamic thermal rating and transmission line switching on network-constrained unit commitment has not been studied as yet. In this paper, the line switching technology and dynamic line rating according to multi-regional environmental information are incorporated in the network-constrained unit commitment framework. Case studies are implemented on a modified RTS-79 system. The simulation results verify the effectiveness of the coordination of two technologies in the reduction of generation cost and wind curtailment.

Purpose This paper aims to survey the need for full capacity utilisation of transmission lines in power systems network operations. It proposes a review of the N-1 security criterion that does not ensure reliable dispatch of optimum power flow during outage contingency. The survey aims to enlarge the network capacity utilisation to rely on the entire transmission lines network operation. Design/methodology/approach The paper suggests transmission line switching (TLS) approach as a viable corrective mechanism for power dispatch. The TLS process is incorporated into a constraint programming language extension optimisation solver that selects the switchable line candidates as integer variables in the mixed integer programming problem. Findings The paper provides a practical awareness of reserve capacity in the lines that provide network security in outage contingency. At optimum power flow dispatch, the TLS is extended to optimal transmission line switching (OTLS) that indicates optimal capacity utilisation (OCU) of the available reserve capacity (ARC) in the network lines. Practical implications Computational efficiency influenced the extension of the OTLS to optimal transmission switching of power flow (OTSPF). The application of OTSPF helps reduce the use of flexible AC transmission systems (FACTS) and construction of new transmission lines.. Originality/value The paper surveys TLS efforts in network capacity utilisation. The suggested ARC fulfils the need for an index with which the dispatchable lines may be identified for the optimal capacity utilisation of transmission lines network.

Power system utilities continue to strive for increased system resiliency. However, quantifying a baseline system resilience, and deciding the optimal investments to improve their resilience is challenging. This paper discusses a method to create scenarios, based on historical data, that represent the threats of severe weather events, their probability of occurrence, and the system wide consequences they generate. This paper also presents a mixed-integer stochastic nonlinear optimization model which uses the scenarios as an input to determine the optimal investments to reduce the system impacts from those scenarios. The optimization model utilizes a DC power flow to determine the loss of load during an event. Loss of load is the consequence that is minimized in this optimization model as the objective function. The results shown in this paper are from the IEEE RTS-96 three area reliability model. The scenario generation and optimization model have also been utilized on full utility models, but those results cannot be published.

To integrate the location, inventory, and routing (LIR) problems arising in designing a resilient sustainable perishable food supply network (RSPFSN), a bi-objective optimization model is developed. To improve the resiliency and sustainability of the RSPFSN, a dynamic pricing strategy is used to cope with the disrupting events, along with minimizing the total cost and CO2 emission of the whole network. One of the important features of the proposed model is taking into account the effects of route disruptions and traffic conditions on the deterioration of products. To solve the mixed-integer nonlinear bi-objective optimization model, a novel hybrid method is developed using the Heuristic Multi-Choice Goal Programming and Utility Function Genetics Algorithm (HMCGP-UFGA). To improve resiliency, the dynamic pricing strategy, considering the traffic condition, can lead to around a 20% improvement in both cost and CO2 emission, based on the results of our case study in a dairy supply chain. Besides, the results of sensitivity analysis display the high flexibility of the proposed approach for various problems.

Simultaneous process synthesis, heat, power, and water integration of thermochemical hybrid biomass, coal, and natural gas facilities

The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

AC filter group (ACF) circuit breaker is an important part of the AC filter group field of UHV DC engineering, which realizes the control, measurement, protection, and switching of transmission lines. In this paper, the arc extinguishing chamber electric field simulation of the AC filter group circuit breaker is carried out. The dynamic process simulation analysis of the zero zone arc is carried out according to the working condition requirements of the filter group circuit breaker. The research results show that the cause of arc reignition is the low velocity of the airflow field inside the arc extinguishing chamber, and the arc contacts of the arc extinguishing chamber are severely ablated under multiple operations, resulting in the distortion of the internal electric field distribution and multiple breakdown discharges.

The reliability of the electrical system is a fundamental study that is carried out to determine the possible deficiencies that an electrical system can have in case of failures, since a failure can cause disturbances, power cuts, and load disconnections. For this reason, Optimal Transmission Switching (OTS) with Optimal AC Power Flows (OPF-AC) is used to reduce disturbances when faults occur and minimize equipment load and disconnections, but OTS offers possible switches in order to make it possible to reduce the damage that can be done for a fault with operating limitations in voltage, power, and angular deviation. However, to have a complete study, it is proposed to use a reliability analysis through contingency ranking to know the risks that a switched system may have at the time of simultaneous or consecutive failures. In addition, a load capacity investigation is conducted to determine if the transmission lines are within their operating limits. The study presents an analysis of the behavior of the switched system and an adequate operation for the mitigation of failures in the system through the switching of transmission lines with analysis of load capacity and reliability. The results presented by the proposed methodology will be compared with Matlab’s Matpower simulation package.

The robust Mixed-Integer Linear Programming (MILP) model is an approach to address uncertainty in linear optimization involving integer and continuous variables, which can be solved using the Benders Decomposition method. One of its applications is facility location problems, which often face demand, costs, and capacity uncertainties. This article presents a systematic literature review (SLR) on solving robust MILP models using the Benders Decomposition method and its application to facility location problems. The objectives are to explore the state-of-the-art and research trends, identify issues modeled as robust MILP and solved using Benders Decomposition, and determine the most frequently used uncertainty sets. SLR was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method on the Scopus, Science Direct, and Dimensions databases for the last five years of publication, with bibliometric analysis using VOSviewer and RStudio. The results show that there are limited articles that discuss the solution of the robust MILP model on the problem of facility location with the ellipsoidal uncertainty set. In addition, the Benders Decomposition method is widely used to solve robust MILP problems in energy, logistics, supply chains, and scheduling, with interval uncertainty sets being the most common. This topic is an influential theme and has the potential to be explored further.

Preface. Part I: Motivation. 1. Linear and Integer Linear Optimization. Part II: Theory. 2. Linear Systems and Projection. 3. Linear Systems and Inverse Projection. 4. Integer Linear Systems: Projection and Inverse Projection. Part III: Algorithms. 5. The Simplex Algorithm. 6. More on Simplex. 7. Interior Point Algorithms: Polyhedral Transformations. 8. Interior Point Algorithms: Barrier Methods. 9. Integer Programming. Part IV: Solving Large Scale Problems: Decomposition Methods. 10. Projection: Benders's Decomposition Methods. 11. Inverse Projection: Dantzig-Wolfe Decomposition. 12. Lagrangian Methods. Part V: Solving Large Scale Problems: Using Special Structure. 13. Sparse Methods. 14. Network Flow Linear Programs. 15. Large Integer Programs: Preprocessing and Cutting Planes. 16. Large Integer Programs: Projection and Inverse Projection. Part VI: Appendix. A. Polyhedral Theory. B. Complexity Theory. C. Basic Graph Theory. D. Software and Test Problems. E. Notation. Bibliography. References. Author Index. Topic Index.

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems.

Cost minimization in allocation of reactive power sources