Total Transfer Capability Research Articles

Temporal assessment of operational resilience of transmission network and adaptation measures for a high-impact long duration cyclonic windstorm

The exposure to High-Impact Low-Probability (HILP) events can have significant impact on the performance of transmission networks. Under such conditions, the power system components must be resilient and robust to meet the uninterrupted load demand. This paper proposes a quantitative framework for temporal assessment of operational resilience of transmission network and suggests suitable adaptation measures when the system is subjected to high impact cyclonic windstorm lasting for a long duration. The fragility curves of transmission lines are correlated with wind profiles during cyclone and failure probability each transmission line is determined using the Monte Carlo Simulation (MCS) The operational resilience of transmission networks is quantified by computing Total Transfer Capability (TTC), Available Transfer Capability (ATC), Existing Transmission Uses (ETU), Total Reliability Margin (TRM) and unserved load. To improve the operational resilience, adaptation measures with modifications in the robustness of the structural strength is proposed and investigated on standard IEEE 57 bus system and IEEE 118 bus system. Sensitivity analysis is performed to understand how changes in the percentage increase of robustness affect the overall system performance. The findings give valuable insights for evaluating the operational resilience of transmission line infrastructure during such extreme weather events.

The Total Transfer Capability Assessment of Transmission Interfaces Combining Causal Inference and Multi-Task Learning

Power system security assessment has many tasks for different evaluation topics or objects, such as the Total Transfer Capability (TTC) assessment of different transmission interfaces. Some different tasks are related with each other, which is an important characteristic to improve the assessment performance if used properly but easily to be neglected. In this study, a novel architecture of the Causality-based Multi-Task Neural Network (CMTNN) is proposed, which can take the causal relevance between tasks into account while avoiding the introduction of too much extraneous information. First, the mask layer is applied after the input layer to filter irrelevant information task-specifically. Then, the causal analysis based on the Peter-Clark algorithm is performed to find those causally-related tasks. Finally, the causality-based information-sharing mechanism between related tasks is designed in the neural network to share deep information, so as to improve the model assessment performance on those tasks. In this paper, the proposed method is applied on the total transfer capability assessment problem of multiple transmission interfaces, and the case study on a real-world regional system with more than 8,000 nodes in China validates its effectiveness.

A Novel Ensemble Learning-Based Method for Online Total Transfer Capability Assessment of New Power System with Increased Stochastics

A Novel Ensemble Learning-Based Method for Online Total Transfer Capability Assessment of New Power System with Increased Stochastics

State of the art approach for comprehensive power system security assessment—Real case study

Power system security assessment plays a critical role in ensuring the safety and stability of the electric grid, evaluated by the operating state’s proximity to the loadability limits. However, with the increasing integration of intermittent renewable energy sources and rising electricity demands, there is a growing need for fast and reliable algorithms to perform power system security assessments effectively. This work reviews state-of-the-art approaches for steady-state security assessment and demonstrates the advantages of the recently proposed Transversality Enforced Newton Raphson (TENR) algorithm. The algorithm provides better computational tractability, scalability, and the capability to incorporate various technological constraints like limits on voltage setpoints, transmission line thermal limits, generator power outputs, etc. This work presents a generalization of the TENR algorithm, proof of convergence, and an adaptive step-size strategy with improved computational performance. The proposed algorithm was used to study maximum loadability, maximum boundness of the nodal power injections, and total transfer capability on a realistic case study of the Russian Far-East power system model, which expects substantial development in the nearest future.

Linearized AC power flow model based interval total transfer capability evaluation with uncertain renewable energy integration

Total transfer capability (TTC) evaluation plays an essential role in guaranteeing reliable power transactions in a deregulated electricity market. However, the large-scale integration of wind power brings significant challenges to TTC calculations. The conventional probabilistic TTC evaluation methods are highly dependent on the pre-defined probabilistic distributions of the input random variables, which could not be accurately predicted in practical situations. This paper proposes an interval TTC evaluation method based on the linearized AC power flow (LACPF) model, which can provide intuitive TTC boundary information without any prior distributions. The adoption of LACPF also contributes to yielding more accurate TTC values than those DC power flow (DCPF) based methods. The proposed interval optimization model is equivalently translated into an optimistic model and a pessimistic model. The optimistic model is formulated as a max-max problem and can be directly converted to a single-level linear programming model. The pessimistic model is formulated as a min-max problem that cannot be solved directly. Hence, the strong duality theory and the big-M method are introduced to transfer the original bilevel model into a single-level mixed integer linear programming (MILP) model for an efficient solution. The proposed method is validated on the IEEE 118-bus system and a modified actual power grid in Northwest China. Numerical results demonstrate its computational efficiency.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.

Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method

Total transfer capability evaluation is an effective method to analyze the backbone transmission capability among regions. To address renewable energy uncertainties, an interval total transfer capability model based on the multi-dimensional holomorphic embedding method and sum of squares relaxation technique is proposed to solve the regional total transfer capability in meshed high voltage direct current systems. First, the multi-dimensional holomorphic embedding method is used to derive the analytical expressions of regional tie lines. Second, the interval total transfer capability model can be reformulated by two bi-level optimization models. Third, sum of squares relaxation is employed to solve the two optimization problems. Numerical results on a 40-bus European Synthetic System and the Chinese meshed high voltage direct current system validate the effectiveness of the proposed model and method.

Modern Techniques for the Optimal Power Flow Problem: State of the Art

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.).

Learning-Aided Optimal Power Flow Based Fast Total Transfer Capability Calculation

Total transfer capability (TTC) is a vital security indicator for power exchange among areas. It characterizes time-variants and transient stability dynamics, and thus is challenging to evaluate efficiently, which can jeopardize operational safety. A leaning-aided optimal power flow method is proposed to handle the above challenges. At the outset, deep learning (DL) is utilized to globally establish real-time transient stability estimators in parametric space, such that the dimensionality of dynamic simulators can be reduced. The computationally intensive transient stability constraints in TTC calculation and their sensitivities are therewith converted into fast forward and backward processes. The DL-aided constrained model is finally solved by nonlinear programming. The numerical results on the modified IEEE 39-bus system demonstrate that the proposed method outperforms several model-based methods in accuracy and efficiency.

Interpretable Neighborhood Deep Models for Online Total Transfer Capability Evaluation of Power Systems

Deep learning is considered to be a promising method for total transfer capability (TTC) evaluation due to its high speed and accuracy for nonlinear and complicated problems. However, the operating scenarios of power systems are complex and ever changing. It is difficult to train a single deep model using an overly complex dataset containing operating scenarios with high variability. In addition, making the deep model transparent to validate for operators is another challenge to its application in the power system. To enable the practical application of deep models in TTC evaluation, the operating sample space is clustered into simpler spaces via a two-stage clustering method, and the idea of neighborhood model for the deep TTC evaluation network (DTEN) is proposed. Then, the local post hoc interpretability for the DTEN based on first-order control variable sensitivity is presented. During the interpretability analysis, the quasi-steady state is introduced to assess the coupling effects among different inputs. Case studies on the IEEE 39-bus system and a real-world regional system in China validate the effectiveness of the proposed method.

Individual Incremental loading factor based maximum loadability limit prediction using modern optimization tools

Infrastructure innovation in the power system industry encourages more partakers to participate in the electricity market which improvises the load utilization level. So, the maintenance of power system’s agility with respect to any dynamic update in terms of load level is necessary. Precise prediction of maximum allowable loading point helps to enhance the power system agility and also improvises the total allowable power transfer capability which in turn helps to supply continuous eminent power supply at the minimal cost to the customers by means of encouraging more contracts. Considering the above potential benefits, in this papear by using individual incremental loading factor (IILF) the precise prediction of total loadability limit (TLL) of the system is manipulated with the help of newly evolved meta-heuristic optimization algorithm such as Grey Wolf (GRW) optimizer and Flower Pollination Algorithm (FPA). The allowable single line contingency scenario is considering along with base case scenario to extract the more realistic TLL which helps to maintain the power system balance with respect to the dynamic nature of the load. The proposed maximum loading point extraction manipulation solution problem is tested with the help of three standard IEEE systems such as 30 Bus, 57 Bus and 118 bus systems. The extracted test results show that the predicted maximum allowable loading point enhances the load utilization level without affecting the system securities. The statistical performance measures of GRW and FPA confirmed the better balance of exploration and exploitation in extracting the optimal results.

Reactive Power Planning and Total Transfer Capability Enhancement using Facts and Capacitor Banks. (Dept. E)

In view of the continuous annual increase in demand, reactive power planning (RPP) is considered one of the most significant problems to address a major challenge of the secure power system operation. In this paper, a multi-objective genetic algorithm (MOGA) for RPP is proposed, with the goals of cost minimization of power losses, new reactive power (VAR) sources, and maximizing the Total Transfer Capacity (TTC). Different optimization factors are taken into account, including generator voltages, transformer tap changers, and various operating constraints. A fuzzy min-max approach is used to identify the optimum compromise option. Studies are being conducted to compare capacitor banks, flexible ac transmission systems (FACTS), or both as a new VAR support source to improve the system performance. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. The effectiveness of the proposed algorithm is examined on the IEEE 30-bus test system where felicitous results have been acquired. From the results, the total annual cost is decreased from 3.671×106 $ before adding new VAR sources to a range between 2.02×106 and 2.486×106 $ depending on the selected type of VAR source. While the transfer capacity is increased from 458.37MW to a range between 483.084 and 539.055 MW.

Total transfer capability assessment of HVDC tie‐lines in asynchronous grids

This paper discusses how frequency-stable operational decision-making of asynchronous grids with intensive high-capacity HVDC tie-lines, can be achieved leveraging a novel total transfer capability (TTC) evaluation approach. This problem is formulated as a hierarchical optimization model with multiple decision-makers, in which the master problem is solved by the coordinator to determine the base case maximum acceptable transmission power of each HVDC tie-line, while the sending- and receiving-end grid's local TSO each autonomously solves a frequency constrained optimal power flow sub-problem. Coordinated fast and slow frequency arrest strategies are fully integrated into the TTC assessment framework, which not only ensure that last-resort protections are not triggered following the HVDC bi-pole block contingency, but also take advantage of the potential control capability of the sending- and receiving-end grids and would therefore increase the TTC of HVDC tie-lines. A decentralized algorithm based on accelerated analytical target cascading is developed to solve the formulated model. Case studies on a modified two-area RTS-96 system and a practical large-scale system demonstrate the effectiveness of the proposed approach.

Dynamic transmission capability calculation using bioinspired optimization

This paper proposes a straightforward approach to determine the Total Transfer Capability (TTC) between two power system areas taking into account security constraints. The main goal is to adjust the generator’s control variables (active generated power and terminal voltage) to maximize the TTC considering contingency scenarios and small-signal stability constraints (minimum damping ratio requirement). It is a challenging nonconvex and nonlinear optimization problem that is generally solved in the literature by methods based on approximations in which the computational complexity involved outweighs the accuracy. Here, conventional power flow and modal analysis tools are easily integrated into an optimization approach solved by six bioinspired optimization techniques. Simulations are conducted for the well-known New-England test system, and transient stability studies validate the obtained results.

Probabilistic Available Transfer Capability Evaluation Considering Dynamic Line Rating Based on a Sequential Game-Theoretic Approach

Available transfer capability (ATC) plays an essential role in deregulated power network scheduling. This value determines the power that can be transferred between deregulated regions. The ATC value is limited by transmission line constraints and reliability marginal terms. In this article, the impact of dynamic line rating (DLR) instruments, which are generally used to enhance the capacity of transmission lines, on the ATC is investigated. In addition, a robust ATC evaluation model is proposed to find worst cases for determining the reliability marginal terms and considering them in the ATC evaluation. The robust ATC evaluation model includes a master problem, formulated to determine ATC and total transfer capability, as well as two bilevel subproblems, formulated to find the reliability marginal terms. The Benders decomposition algorithm is derived to solve the proposed robust ATC evaluation. An uncertain environment, including wind farm powers, dynamic line capacities, and load demand values, is also considered to cover the stochastic nature of such uncertainty sources. A probabilistic approach is proposed to calculate the expected value of ATC and depict the cumulative distribution function of each variable. The proposed probabilistic model is based on dividing the stochastic set into smaller groups, similar to clustering techniques. However, the significant differences between the proposed probabilistic and cluster-based models are due to detecting the optimal value of a reduced number of stochastic sets and finding the members of each cluster set. In other words, a sequential game-theoretic approach is applied to divide the data into smaller sets. The impact of DLR on the proposed robust ATC evaluation problem and the efficiency of the proposed probabilistic model are evaluated by comprehensive studies based on the IEEE 118-bus test system.

Surrogate‐assisted optimal re‐dispatch control for risk‐aware regulation of dynamic total transfer capability

To enable reliable power delivery through transmission tie-lines, total transfer capability (TTC) must be calculated and regulated to accommodate the transferred amount. However, the traditional optimal power flow (OPF)-based total transfer capability calculation is computationally expensive for efficient total transfer capability control due to the inclusion of a large set of differential-algebraic equations (DAEs) to verify transient stability constraints. In order to enable practicable total transfer capability regulation, a novel risk-aware deep learning-assisted paradigm is proposed here. First, a deep belief network (DBN) is employed to establish the total transfer capability predictor and surrogate the computation-intensive differential-algebraic equations in original optimal power flow formulas, simplifying the high-dimensional and intractable constraints deep belief networks without loss of nonlinearity. Particularly, in order to be aware of control risk from the predictive error of the deep belief networks, prediction intervals (PIs) are produced improved by using ensemble learning and used to disclose the probability of insufficient actions, further guaranteeing the sufficient and cost-effective control by compromising the tradeoff between cost and risk. Symbiotic organisms search (SOS) is then applied to solve the proposed risk-aware deep belief network-assisted total transfer capability control problem globally. The numerical studies testify that the proposed method enables economical, reliable, and full nonlinearity-retained dynamic total transfer capability regulation control within a risk-free surrogate-assisted and tractable physical model-driven hybrid framework.

Hybrid Deep Learning for Dynamic Total Transfer Capability Control

This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.

Analytic Deep Learning-Based Surrogate Model for Operational Planning With Dynamic TTC Constraints

The increased penetration of wind power introduces more operational changes of critical corridors and the traditional time-consuming transient stability constrained total transfer capability (TTC) operational planning is unable to meet the real-time monitoring need. This paper develops a more computationally efficient approach to address that challenge via the analytical deep learning-based surrogate model. The key idea is to resort to deep learning for developing a computationally cheap surrogate model to replace the original time-consuming differential-algebraic constraints related to TTC. However, the deep learning-based surrogate model introduces implicit rules that are difficult to handle in the optimization process. To this end, we derive the Jacobian and Hessian matrices of the implicit surrogate models and finally transfer them into an analytical formulation that can be easily solved by the interior point method. Surrogate modeling and problem reformulation allow us to achieve significantly improved computational efficiency and the yielded solutions can be used for operational planning. Numerical results carried out on the modified IEEE 39-bus and 68-bus systems demonstrate the effectiveness of the proposed method in dealing with complicated TTC constraints while balancing the computational efficiency and accuracy.

A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems With Renewables

The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this paper, a novel data-driven sparse polynomial chaos expansion (DDSPCE) method is proposed for estimating the probabilistic characteristics (e.g., mean, variance, probability distribution) of probabilistic TTC (PTTC). Specifically, the proposed method, requiring no pre-assumed probabilistic distributions of random inputs, exploits data sets directly in estimating the PTTC. Besides, a sparse scheme is integrated to improve the computational efficiency. Numerical studies on the modified IEEE 118-bus system demonstrate that the proposed DDSPCE method can achieve accurate estimation for the probabilistic characteristics of PTTC with a high efficiency. Moreover, numerical results reveal the great significance of incorporating discrete random inputs in PTTC and ATC assessment, which nevertheless was not given sufficient attention.

Reactive Power Control Method for Enhancing the Transient Stability Total Transfer Capability of Transmission Lines for a System with Large-Scale Renewable Energy Sources

With the increased proportion of intermittent renewable energy sources (RES) integrated into the sending-end, the total transfer capability of transmission lines is not sufficient during the peak periods of renewable primary energy (e.g., the wind force), causing severe RES power curtailment. The total transfer capability of transmission lines is generally restricted by the transient stability total transfer capability (TSTTC). This paper presents a reactive power control method to enhance the TSTTC of transmission lines. The key is to obtain the sensitivity between TSTTC and reactive power, while the Thevenin equivalent voltage is the link connecting TSTTC and reactive power. The Thevenin theorem states that an active circuit between two load terminals can be considered as an individual voltage source. The voltage of this source would be open-circuit voltage across the terminals, and the internal impedance of the source is the equivalent impedance of the circuit across the terminals. The Thevenin voltage used in Thevenin’s theorem is an ideal voltage source equal to the open-circuit voltage at the terminals. Thus, the sensitivities between TSTTC and the Thevenin equivalent voltages of the sending-end and receiving-end were firstly derived using the equal area criterion. Secondly, the sensitivity between the Thevenin equivalent voltage and reactive power was derived using the total differentiation method. By connecting the above sensitivities together with the relevant parameters calculated from Thevenin equivalent parameter identification and power flow equation, the sensitivity between TSTTC and reactive power was obtained, which was used as the control priority in the proposed reactive power control method. At last, the method was applied to the Gansu Province Power Grid in China to demonstrate its effectiveness, and the accuracy of the sensitivity between TSTTC and reactive power was verified.