Optimal Transmission Switching Research Articles

Learning-assisted optimization for transmission switching

AbstractThe design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

Resilience-oriented Transmission Expansion Planning with Optimal Transmission Switching Under Typhoon Weather

Resilience-oriented Transmission Expansion Planning with Optimal Transmission Switching Under Typhoon Weather

Optimal scheduling of BESS for congestion management considering reliability and OTS

The present research explores transmission congestion control in Battery based Energy Storage Systems (BESS) integrated grids using Optimal Transmission Switching (OTS). The oversimplification of underlying assumptions in prior OTS-based congestion management strategies leads to excessive line switching which is undesirable. Hence, a novel reliability constrained alternating current optimal power flow (AC-OPF) based OTS is presented to mitigate the line congestion in the BESS integrated grids. The suggested approach is solved to acquire the OTS solution for 24 h time frame with the primary goal of minimising generating costs subjected to ac power flow equality, BESS, reliability, and transmission congestion constraints. The proposed approach makes use of the integer variable to represent the transmission switching operations and is solved by using a mixed-integer non-linear programming (MINLP) solver. The inclusion of reliability constraints ensures the system reliability by limiting the value of load failure probability to a pre-specified limit. The feasibility of this approach is validated by testing it on a standard IEEE test system for various loading scenarios. The results exhibit that coordinated charging/discharging of BESS supports reducing the number of lines switchings required for congestion management compared to the existing approaches, substantially.

Specialized scatter search algorithm for optimal transmission switching with voltage control

This article presents a specialized scatter search (SS) algorithm to solve the optimal transmission switching (OTS) problem considering voltage control, with the objective of reducing operational costs. The nonlinear ac operation of the network is considered, and discrete variables are used to represent the states of the lines, positions of the taps of the transformers, and the states of shunt reactive compensators. The resulting formulation is a mixed-integer nonlinear programming problem, which is nonconvex and hard to solve. The proposed SS algorithm manages the discrete variables of the problem, which becomes a nonlinear programming (NLP) problem when the discrete variables are fixed. The NLP problem is then solved by an optimization solver, which returns all other continuous variables of the problem to the SS algorithm. Tests were conducted using eleven benchmark systems. The results indicated that the SS algorithm is efficient for solving the problem.

Optimal transmission switching strategy for multi‐infeed DC receiving‐end power network to prevent over‐limit of short‐circuit current

AbstractA large‐scale AC/DC hybrid power network has recently been constructed in China because of the fast development of high voltage direct current (HVDC) technology. The receiving AC system must have sufficient system strength to guarantee the safe operation of the DC side. The paradox is that raising the AC side system's strength will increase the short‐circuit current (SCC) level, endangering the safety of the system. In order to resolve the contradiction between the system strength of multi‐infeed DC receiving‐end power networks and SCC, this paper proposes an optimal transmission switching strategy. Firstly, the system strength is characterised by the multi‐infeed short‐circuit ratio (MISCR), followed by constructing a comprehensive constraint set that considers the MISCR and SCC simultaneously. Secondly, to optimize SCC with MISCR and N−1 safety constraints, a mixed integer linear programming model is developed. Finally, to enhance the computing performance for large power systems, a new connectivity constraint set and decision space reduction techniques are provided. The proposed method is tested for effectiveness using the IEEE 30‐bus and IEEE 118‐bus systems.

Optimal Transmission Switching With Uncertainties From Both Renewable Energy and N-K Contingencies

Optimal Transmission Switching With Uncertainties From Both Renewable Energy and N-K Contingencies

A data mining transmission switching heuristic for post-contingency AC power flow violation reduction in real-world, large-scale systems

Transmission switching has proven to be a highly useful post-contingency recovery technique by allowing power system operators increased levels of control through leveraging the topology of the power system. However, transmission switching remains only implemented in limited capacity because of concerns over computational complexity, uncertainty of performance in AC systems, and scalability to real-world, large-scale systems. We propose a heuristic which uses a sophisticated guided undersampling procedure combined with logistic regression to accurately identify transmission switching actions to reduce post-contingency AC power flow violations. The proposed heuristic was tested on real-world, large-scale AC power system data and consistently identified optimal or near optimal transmission switching actions. Because the proposed heuristic is computationally inexpensive, addresses an AC system, and is validated on real-world large-scale data, it directly addresses the aforementioned issues regarding transmission switching implementation.

Optimal transmission switching for power system integrating renewable energy based on analytical target cascading

The penetration rate of intermittent renewable energy in power system is gradually increasing, which brings challenges to the optimal dispatch of power system. To solve these problems, a multi-regional interconnected transmission network optimization method based on analytical target cascading is proposed. Firstly, the reactive power regulation characteristics of renewable energy is investigated, and the models of wind turbine and photovoltaic generation are established. Secondly, the power system is decomposed into multiple sub-systems using bus tearing method, and an optimal transmission switching (OTS) model with renewable energies is established. Then, the analytical target cascading (ATC) approach is employed to decompose the model into the main problem and several sub-problems for parallel computation to achieve coordinated optimization of the complex transmission network. Finally, the IEEE 14-bus system and the IEEE 118-bus system are used to verify the proposed model. The results show that the proposed method can deal with the coupling nonlinear problem well and promote the consumption of renewable energy.

An Electrical Power System Reconfiguration Model Based on Optimal Transmission Switching under Scenarios of Intentional Attacks

Currently, operating electrical power systems (EPS) is a complex task that relies on the experience of the operators or the strength of algorithms developed for autonomous operation. The continuous operation of EPS is vulnerable to intentional cybernetic and physical attacks. With the most significant extension and distribution in the EPS, the transmission lines are most exposed to potential attacks. Before this, the entire behavior of the EPS changes, and, on occasions, a blackout can even be generated. The present investigation focused on developing a methodology for reconfiguring the power system against intentional attacks, considering the topology change through optimal switching of transmission lines (OTS) based on optimal DC flows and quantifying the contingency index, which allows for the identification of the weaknesses of the EPS. The methodology was applied to the IEEE 30−bus system, and contingencies were randomly generated, as is typical with intentional attacks. The study successfully identified the reconfiguration strategy of EPS based on OTS-DC, mitigating potential problems such as line loadability and voltage angle deviation in the nodes.

Learning-Based Topology Optimization of Power Networks

Transitions of power networks demand more powerful solution approaches for optimal transmission switching (OTS) and distribution network reconfiguration (DNR) to tackle complicated cases with high computational efficiency. In this paper, we propose a learning-based solution approach for OTS and DNR, which is established based on an identified common pattern behind existing heuristic algorithms. The original hand-crafted and short-sighted evaluation function is substituted by a parameterized function whose architecture is well-designed by mainly exploiting the gated graph neural network and multilayer perceptrons. For learning the parameterized function that lacks training labels, we further design a learning algorithm that combines the double deep Q-network algorithm and multi-step learning. Then, with the learned parameterized function, the learning-based heuristic algorithm is developed by embedding a feasibility recovery procedure. Finally, numerical studies demonstrate the superiority of the learning-based heuristic algorithm in both solution optimality and computational efficiency.

The Role of Optimal Transmission Switching in Enhancing Grid Flexibility: A Review

The integration of variable renewable energy sources (RES) into power grids has resulted in a more complex operating environment for power system operators (SO), necessitating the need for increased grid flexibility. This is crucial for utilities, as the cost-effective utilization of existing infrastructure is necessary, given the prolonged construction period and high investment costs associated with building new power delivery facilities. Under the umbrella of smart grid, optimal transmission switching (OTS) is a cost-effective transmission technology that can alleviate concerns related to network congestion, limited transmission capacity, and high penetration of renewables. OTS is a concept that is integrated into the optimal power flow (OPF) problem such that it provides SO the choice to temporarily switch one or more lines out of service from the network. OTS is shown in the literature to have economic benefits, reduce operational cost, relieve network congestion, serve as a corrective mechanism for reducing voltage violations, improve system reliability, and minimize system loss. Despite the significant attention given to OTS by researchers over the past decade, no extensive review paper exists on the topic in the literature. Therefore, this paper provides a state-of-the-art overview of the OTS problem. The concept of OTS is explained by approximating the alternating current OPF problem into a linear direct current OPF problem. Various alternative models of the OTS problem proposed in the literature are discussed. The paper also analyzes the interaction between OTS and other flexibility options such as Dynamic thermal rating (DTR), Energy storage systems (ESS), and RES. Furthermore, the paper presents the general framework and impact of the short-term OTS problem on long-term expansion planning problems. Finally, an extensive literature review on the impact of OTS on system reliability is provided.

Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations.

Optimal Transmission Switching for Short-Circuit Current Limitation Based on Deep Reinforcement Learning

The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies.

Resilience Maximization in Electrical Power Systems through Switching of Power Transmission Lines

This research aims to maximize the resilience of an electrical power system after an N−1 contingency, and this objective is achieved by switching the transmission lines connection using a heuristic that integrates optimal dc power flows (DCOPF), optimal transmission switching (OTS) and contingencies analysis. This paper’s methodology proposes to identify the order of re-entry of the elements that go out of the operation of an electrical power system after a contingency, for which DCOPF is used to determine the operating conditions accompanied by OTS that seeks to identify the maximum number of lines that can be disconnected seeking the most negligible impact on the contingency index J. The model allows each possible line-switching scenario to be analyzed and the one with the lowest value of J is chosen as the option to reconnect, this process is repeated until the entire power system is fully operational. As study cases, the IEEE 14, 30 and 39 bus bars were selected, in which the proposed methodology was applied and when the OTS was executed, the systems improved after the contingency; furthermore, when an adequate connection order of the disconnected lines is determined, the systems are significantly improved, therefore, the resilience of power systems is maximized, guaranteeing stable, reliable and safe behavior within operating parameters.

Formulating Connectedness in Security-Constrained Optimal Transmission Switching Problems

This paper focuses on the issue of network connectedness (NC) in security-constrained optimal transmission switching problems, which is complicated by branch contingencies and corrective line switching. Two criteria are firstly proposed with the principle of preserving NC as much as possible within reasonable limits. By extending the electrical flow based NC constraints, a proposition is derived to associate different cases of NC with the optimum of a linear program, yielding the mathematical formulation of the NC criteria. By Karush-Kuhn-Tucker conditions, this formulation is further transformed into a tractable version which can be incorporated with existing SCOTS models without affecting the applicability of original solution approaches. Finally, case studies on various networks and SCOTS models demonstrate the efficacy of the proposed approach.

Solving Combined Optimal Transmission Switching and Optimal Power Flow sequentially as convexificated Quadratically Constrained Quadratic Program

A novel approach to efficiently solve a combined optimal transmission switching and optimal power flow problem is derived and evaluated. The approach is based on a sequentially solved quadratically constrained quadratic program with a new way of convexification. This convexification requires adapted modeling and uses two procedural steps: First, nonlinear equality constraints are eliminated by quadratically approximating their inverse system of functions and inserting it into the objective function and into the inequality constraints. The resulting objective function and inequality constraints are approximated quadratically. Second, the remaining nonconvex parts of the objective function and the inequality constraints are identified by eigenvalue analysis of their Hessian matrices and eliminated by using piecewise linear approximations. Issues of accuracy and convergence are examined and countermeasures are presented. Additionally, the avoidance of islanding and the sequentiality of parallel circuits are considered. Case studies show fast und stable convergence of the approach.

Feasible region-based heuristics for optimal transmission switching

In this paper, we develop a optimal transmission switching (OTS) heuristic based on DC optimal power flow (OPF) and assess the efficacy of the approach when implemented within AC OPF. Traditional formulations of the OTS problem can result in hundreds or thousands of binary variables for large networks, making the OTS problem challenging to solve on fast timescales even for relatively small networks. Here, we identify which constraints and therefore which variables are constraining the DC OPF feasible region, and rank them based on their impact on the cost function. We develop a heuristic algorithm which iteratively removes these constraints and solves a series of standard DC OPF problems. The heuristic is tested on a variety of PGlib networks and the results show that the algorithm can provide substantial cost decreases without having to solve any mixed integer programs. We provide additional insights about the OTS problem, including identifying scenarios outside congestion where OTS can prove useful. Lastly, the performance of the DC-based heuristic is shown when the line switching decisions are implemented within AC OPF.

Management Optimization of Electricity System with Sustainability Enhancement

Based on new policies and social changes, renewable energies have highly penetrated electrical systems, making the system more vulnerable than before. On the other hand, it leads to congestion and competition within the network. To this end, this paper developed a probabilistic multi-objective-based congestion management approach and applied it to the optimal transmission switching (OTS) strategies, to maximize system suitability and minimize total production costs. A point estimation economic method (PEM) has been applied, as one of the best management and economic tools to handle the uncertainties associated with a wind turbine’s power production and load demand (LD). Results demonstrate the effectiveness and merit of the proposed technique, compared to the existing one, which can lead to higher reliability and sustainability for the grids.

Optimal Transmission Switching Based on Analytical Target Cascading Algorithm

The penetration of a high proportion of renewable energy sources (RES) into the power grid intensifies the source–load imbalance, which greatly weakens the network transmission performance and power supply quality, and the effect of relying only on individual regulation within the region is negligible. To enhance the capacity of interconnection and coordination among different areas of power systems and improve the accommodation level of RES and low-carbon efficiency, an optimal transmission switching model based on the bus tearing method is proposed in this article. Firstly, the complex power system is decomposed based on the bus tearing method, and thus, the interconnected power grid structure of the multiarea system is constructed. Secondly, the optimal model of interconnected power grid decomposition and coordination structure considering renewable energy generation is constructed, based on exquisite modeling, to reduce the difficulty of unified analysis and decision-making of the multiarea interconnected power system, and the expression of the model is simplified in the form of the matrix. Then, the analytical target cascading (ATC) method is used to decouple the complex model from the main problem and subproblem and solve the distributed parallel problem, to understand the optimization of the decomposition and coordination structure of the interconnected power grid with source–load coordination. Finally, based on the case studies of the IEEE 14-bus system and IEEE 118-bus system, the effectiveness of the proposed model and method is verified, the coordinated operation of the interconnected power grid and the optimal allocation of network resources are achieved, and the economy of power system operation is improved.

Blocking cascading failures with optimal corrective transmission switching considering available correction time

Corrective transmission switching has been a promising technic for handling contingencies in power systems. The corrective solutions obtained from optimal formulations or heuristic methods are usually considered executable. However, when considering the contingency-induced cascading failures, where the temperature relay of lines may act quickly, the systems may have not enough time to take corrective actions. Thus some corrective solutions may be infeasible in practical applications. To further study the feasibility of corrective solutions, in this paper, considering the available correction time, which is derived from a continuous temperature evolution model, two methods are proposed to validate the corrective solutions obtained from a modified optimal corrective transmission switching formulation. Besides, based on the available correction time, a comprehensive method is proposed to block cascading failures and a critical index is proposed to evaluate critical cascading failures. Numeric studies on test systems show that there are considerable infeasible solutions in the solutions obtained from the optimal corrective formulation, which indicates that it is critical to consider the available correction time when utilizing the corrective solutions in real cases. Results also show the proposed comprehensive method and index are effective to handle cascading failures.