Total Transfer Capability Evaluation Research Articles

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.

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.

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.

Decentralized Total Transfer Capability Evaluation Using Domain Decomposition Methods

In order to ensure the reliable operation of the electric power grid, infrastructure with increasing levels of distributed generation deployment, various entities and subsystems (e.g., ISOs, distribution utilities, micro-grids) needs a higher level coordination. Such coordination requires a formal decentralized control and management architecture that creates mechanisms for the operation and planning of numerous entities. A key function in power system operations and planning is the determination of total transfer capability (TTC), or the maximum amount of power that can be transferred from a set of source points to a set of sink points. This paper develops an algorithm for the decentralized computation of TTC through the application of the domain decomposition method (DDM). DDM decouples the correlation among interconnected control areas by taking advantage of the Schur complement matrix to form the power transfer distribution factor matrix. The decentralized TTC evaluation requires sparse information exchange among control areas. It is efficient and fully parallelizable. We demonstrate the decentralized TTC evaluation method on the IEEE 118-bus test case.

Implementation of a Web-Based Grid Computing Model for Transient Stability Constrained TTC Evaluation

In this paper, a web-based grid computing model for total transfer capability (TTC) with transient stability (TS) constrains is proposed. The grid computing model is designed in a heterogeneous web environment to implement the cross-platform application. A kind of power system analysis software package is integrated as the computing engine during computation. Finally, application example with a real-sized Chinese power system demonstrates the validity of the proposed model.

A Deterministic Approach for Probabilistic TTC Evaluation of Power Systems Including Wind Farm Based on Data Clustering

Transfer capability of an electric transmission network indicates the maximum real power that can be exchanged between two areas in a reliable manner. This index is used in power system planning, operation and marketing. Transfer capability depends on the system state and is not a fixed value. Utilization of energy resources with stochastic nature adds new probabilistic dimension to the power system and makes total transfer capability (TTC) study more complex. Wind is one of the important alternative renewable energy resources for electric energy production but its speed is stochastic in nature. Increasing penetration of these resources is a motivation for research to study different aspects of this issue. This paper proposes a method for probabilistic evaluation of TTC in the presence of wind farms. It proposes a hybrid method based on data clustering and contingency enumeration. Contingency enumeration is done using two contingency lists (CLs). Single and double contingencies are established the first CL and the second CL considers a number of contingencies from the first CL. Monte Carlo simulation (MCS) is also used to assess probabilistic variation of TTC and to verify the proposed method. The obtained probabilistic TTC results have acceptable relative error compered to MCS results. Especially when the probabilities of contingencies are low the second approach works very well. The efficiency of the proposed method is investigated by conducting numerical studies on the IEEE Reliability Test System.

Implementation of an Intelligent Grid Computing Architecture for Transient Stability Constrained TTC Evaluation

An intelligent grid computing architecture is proposed and developed for transient stability constrained total transfer capability evaluation of future smart grid. In the proposed intelligent grid computing architecture, a model of generalized compute nodes with 'able person should do more work' feature is presented and implemented to make full use of each node. A timeout handling strategy called conditional resource preemption is designed to improve the whole system computing performance further. The architecture can intelligently and effectively integrate heterogeneous distributed computing resources around Intranet/Internet and implement the dynamic load balancing. Furthermore, the robustness of the architecture is analyzed and developed as well. The case studies have been carried out on the IEEE New England 39-bus system and a real-sized Chinese power system, and results demonstrate the practicability and effectiveness of the intelligent grid computing architecture.

Improved risk-based TTC evaluation with system case partitioning

Managing the security of a system has always been one of the challenging tasks for a system operator, especially within a competitive environment where a system is commonly operated close to its limits. Therefore, the Total Transfer Capability (TTC) index was introduced by NERC in 1996 mainly to provide the system operator with information necessary for the system security management. In addition, the presence of variable generation from renewable energy further complicates the system operation and planning. When evaluating the TTC, it is important to consider the uncertainty associated with the system parameters and the system security related to the transient stability. This paper presents a risk-based TTC evaluation by means of Monte Carlo simulation together with transient stability consideration. However, this method requires excessive computing time in view of a large sample size; as a result, the TTC may not be available to the system operators in time. Since the heavy computational burden is a major impediment to the widespread use of this method; hence, a system case partitioning using two filters, i.e. performance indices and decision tree, is proposed as a means to reduce the sample size and enhance the computational speed. The validation of the proposed method is conducted through a numerical simulation on the 90-bus system integrated with wind power and photovoltaic (PV).

Probabilistic Assessment of TTC in Power Systems Including Wind Power Generation

Wind farms (WFs) are now being used for electric power generation in some parts of the world and their penetration is expected to increase significantly. With increasing penetration of WFs in electric power systems, modification of current tools for evaluating and managing the system is an important issue. Determination of total transfer capability (TTC) is one such tool and is of considerable importance in the restructured power systems. TTC is used to calculate maximum power that can be transferred between areas in a reliable manner and schedule future transactions and commercial activities. In this paper, a method is proposed for TTC evaluation in the presence of WFs. In the proposed approach, Monte Carlo method is used to simulate a system state and optimal power flow is used to calculate the TTC level for each state. Risk analysis is used as a decision-making tool to determine the appropriate TTC level. IEEE reliability test system is used to demonstrate the effectiveness of the proposed approach.

Differential evolution based method for total transfer capability evaluation

The application of Differential Evolution (DE) to compute the Total Transfer Capability (TTC) in deregulated market is proposed in this paper. The objective is to maximize a specific point-to-point power transaction without violating system constraints using DE. This algorithm is based on full ac optimal power flow solution to account for the effects of active and reactive power flow, voltage limits, and line flow limits. The real power output of generators in source area and the real power of the loads in the sink area were adjusted to obtain the maximum transfer capability. In order to calculate TTC more accurately, Transmission Reliability Margin is incorporated in the calculation of TTC by considering single and multiline outages and also a three phase to ground fault near a bus and clearing it by isolating the faulty line after certain time interval. The performance of the proposed method is tested on the modified IEEE-30 bus system and results are compared with that of Particle Swarm Optimization method (PSO). Further, the results are compared with other published results using CPF and EP. It is found that DE provides more reliable results than other methods. Keywords: Total transfer capability, available transfer capability, transmission reliability margin, differential evolution, particle swarm optimization.

Determination of available transfer capability (ATC) considering real-time weather conditions

Total Transfer Capability (TTC) should be pre-determined in order to estimate Available Transfer Capability (ATC). Typically, TTC is determined by considering three categories; voltage, stability, and thermal limits. Among these, thermal limits are treated mainly in this paper on the evaluation of TTC due to the relatively short transmission line length of Korea Electric Power Corporation (KEPCO) system. This paper presents a new approach to evaluate the TTC using the Dynamic Line Rating (DLR) for the thermal limit. Since the approach includes not only traditional electrical constraints but also real-time environmental constraints, this paper obtains more cost-effective and exact results. A case study using KEPCO system confirms that the proposed method is useful for real-time operation and the planning of the electricity market. Copyright © 2010 John Wiley & Sons, Ltd.

Assessment of Available Transfer Capability (ATC) considering Real-time Weather Conditions

Total Transfer Capability (TTC) should be pre-determined in order to estimate Available Transfer Capability (ATC). Typically, TTC is determined by considering three categories; voltage, stability and thermal limits. Among these, thermal limits are treated mainly in this paper on the evaluation of TTC due to the relatively short transmission line length of Korea Electric Power Corporation (KEPCO) system. This paper presents a new approach to evaluate the TTC using the Dynamic Line Rating (DLR) for the thermal limit. Since the approach includes not only traditional electrical constraints but also real-time environmental constraints, this paper obtains more cost-effective and exact results. A case study using KEPCO system confirms that the proposed method is useful for real-time operation and the planning of the electricity market.

Application of Interval Arithmetic in the Evaluation of Transfer Capabilities by Considering the Sources of Uncertainty

Total transfer capability (TTC) is an important index in a power system with large volume of inter-area power exchanges. This paper proposes a novel technique to determine the TTC and its confidence intervals in the system by considering the uncertainties in the load and line parameters. The optimal power flow (OPF) method is used to obtain the TTC. Variations in the load and line parameters are incorporated using the interval arithmetic (IA) method. The IEEE 30 bus test system is used to illustrate the proposed methodology. Various uncertainties in the line, load and both line and load are incorporated in the evaluation of total transfer capability. From the results, it is observed that the solutions obtained through the proposed method provide much wider information in terms of closed interval form which is more useful in ensuring secured operation of the interconnected system in the presence of uncertainties in load and line parameters.