IEEE Transactions on Power Systems

This paper analyzes the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings, and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the redispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modeling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of the DLR in supporting cost-effective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyzes the impact of different operational strategies, the coordination among multiple flexible technologies, and the installed capacity of wind generation on the benefits of DLR.

The generation expansion-planning problem (GEP) is a large-scale stochastic nonlinear optimization problem. To handle the problem complexity, decomposition schemes have been used. Usually, such schemes divide the expansion problem into two subproblems: one related to the construction of new plants (investment subproblem) and another dealing with the task of operating the system (operation subproblem). This paper proposes an iterative genetic algorithm (IGA) to solve the investment subproblem. The basic idea is to use a special type of chromosome, christened pointer-based chromosome (PBC), and the particular structure of that subproblem, to transform an integer constrained problem into an unconstrained one. IGA's results were compared to those of a branch and bound (B&B) algorithm-provided by a commercial package-in three different case studies of growing complexity, respectively, containing 144, 462, and 1845 decision variables. These results indicate that the IGA is an effective alternative to the solution of the investment subproblem.

Having established feasibility and developed a method to decompose control area's A~C~E~/sub 1/ into components attributable to load changes in subareas within the control area, this paper addresses some of the practical issues with an on-line implementation of the A~C~E~/sub 1/ model and the decomposition method. Issues examined here include time-varying system conditions, nonlinearities in the governors' dead bands and AGC loop, and the impact of load disturbance external to the control area. An on-line recursive parameter estimation technique with adjustable forgetting factor to update the A~C~E~/sub 1/ model parameters is proposed.

The daily electrical peak load forecasting (PLF) has been done using the feed forward neural network (FFNN)-based upon the conjugate gradient (CG) back-propagation methods, by incorporating the effect of 11 weather parameters, the previous day peak load information, and the type of day. To avoid the trapping of the network into a state of local minima, the optimization of user-defined parameters, namely, learning rate and error goal, has been performed. The training dataset has been selected using a growing window concept and is reduced as per the nature of the day and the season for which the forecast is made. For redundancy removal in the input variables, reduction of the number of input variables has been done by the principal component analysis (PCA) method of factor extraction. The resultant dataset is used for the training of a 3-layered NN. To increase the learning speed, the weights and biases are initialized according to the Nguyen and Widrow method. To avoid over fitting, an early stopping of training is done at the minimum validation error.

The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency’s nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel constraint from the swing equation to contain the frequency nadir using all of these services. The expected cost of UFLS is found probabilistically to facilitate its comparison to the other frequency services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone (SOC), resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to $\pounds $559 m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.

Power system investment planning problems become intractable due to the vast variability that characterizes system operation and the increasing complexity of the optimization model to capture the characteristics of renewable energy sources. In this context, making optimal investment decisions by considering every operating period is unrealistic and inefficient. The conventional solution to address this computational issue is to select a limited number of representative operating periods by clustering the input demand-generation patterns while preserving the key statistical features of the original population. However, for an investment model that contains highly complex non-linear relationship between input data and optimal investment decisions, selecting representative periods by relying on only input data becomes inefficient. This paper proposes a novel investment cost-oriented representative day selection framework for large scale multi-spacial investment problems, which performs clustering directly based on the investment decisions for each generation technology at each location associated with each individual day. Additionally, dimensionality reduction is performed to ensure that the proposed method is feasible for large-scale power systems and high-resolution input data. The superior performance of the proposed method is demonstrated through a series of case studies with different levels of modeling complexity.

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variability introduced by renewable sources start to drive the system stability consideration from deterministic to probabilistic, which further exaggerates the complexity. In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem. First, a graph neural network-based transient simulator is trained by supervised learning to efficiently generate post-contingency rotor angle curves with the steady-state and contingency as inputs, which serves as a feature extractor for operating states and a surrogate time-domain simulator during the environment interaction for reinforcement learning. Distributional deep reinforcement learning with explicit uncertainty distribution of system operational conditions is then applied to generate the redispatch strategy to balance the user-specified probabilistic stability performance and economy preferences. The full distribution of the post-redispatch transient stability index is directly provided as the output. Case studies on the modified New England 39-bus system validate the proposed method.

This paper reports a study on determining steady. state voltage stability limit of interface flow on a set of interface lines between the metropolitan region and the neighboring regions in the Korea Electric Power Corporation (KEPCO). The interface flow limit considering severe contingencies is determined with a concept of interface flow margin, which is the active power flow margin of the key transmission lines between one region and others under a fixed load demand condition. The paper proposes a procedure to determine secure limit of active power flow on a set of specified interface lines. The procedure uses the modified continuation power flow (MCPF) tracing a path of power flow solutions with respect to generation shift to vary interface flow. In simulation, voltage stability limits of the metropolitan interface flow of the KEPCO'98 system are determined.

Real-time current-carrying capacity of overhead conductors is extremely variable due to its dependence on weather conditions, resulting in the use of traditionally conservative static ratings. This paper proposes a methodology for exploiting the latent current-carrying capacity of overhead transmission lines taking into account line ampacity forecasts, power flow simulations, and the network operator's risk aversion. The procedure can be described as follows: First, probabilistic forecasts for the current rating of transmission lines are generated, paying particular attention to the reliability of the lower part of the distribution. Second, a cost benefit analysis is carried out by solving a bilevel stochastic problem that takes into account the reduction in generation costs, resulting from a higher power transfer capacity and the increased use of reserves caused by forecast errors. The risk appetite of the network operator is considered in order to accept or penalize high-risk situations, depending on whether the network operator can be described as risk neutral or risk averse.

In this paper, we discuss issues and methods for attaining equilibrium in electric power auction markets with unit commitment. We consider a generation-side competition whereby producers are profit maximizing agents subject to prices only. For expository purposes, we consider the single-period unit commitment problem, which is still quite rich for this presentation. We show that it is possible to eliminate the duality gap or cycling that occurs in a decentralized decision-making environment that encompasses discontinuous nonconvex programs. This result extends previous work on coordination of locally constrained self-interested agents, and it has a broad scope of applications that may be of interest to power systems engineers, market designers, economists and mathematicians.