Decreasing the randomness of renewable energy sources is the priority for the stability of novel power systems. Renewable energy prediction models have been studied extensively with higher precision. However, these models have become much more complicated and opaquer, meanwhile accuracy improvements almost reach the convergence. Major prediction deviations are still inevitable and how the deviations occurred is inexplicable in those black-box models. This prediction interpretability problem arises puzzling power system operators. Specifically, advanced solar power forecasting technologies have proposed multimodal prediction models that involve various input forms, such as remote-sensing cloud images, exacerbating the forecast opacity. Hence, this study focuses on the interpretability issue of deep-learning-based multimodal solar power predictions, and proposes a post-hoc local traceability method. Based on neural-backed decision trees, the method can decouple solar power forecast outputs into an inference hierarchy and weather transition probabilities. Effects of multimodal inputs can be also quantified with Shapley values in the method. By providing qualitative results of input effects and the prediction inference process, the proposed method increases local interpretability while maintaining forecast accuracy.

With the continuous integration of distributed generations and flexible loads, accurate real-time forecasting-aided state estimation (FASE) is increasingly crucial for the safe operation of distribution systems. However, compared to the static state estimator that utilizes information from only a single time instance, the Kalman filter (KF)-based FASE, while capable of leveraging predicted information to enhance state tracking performance, faces challenges in practical application due to heavy computational cost. This paper proposes a complex domain constant Jacobian matrix FASE method, which enables fast and robust state estimation for distribution systems. Moreover, a corresponding massively parallel processing scheme is constructed utilizing low-power field-programmable gate arrays (FPGAs) to further enhance computational efficiency, ensuring the reliability of faster-than-real-time (FTRT) hardware-in-the-loop (HIL) applications. Case studies conducted on single-phase IEEE 33-bus as well as three-phase unbalanced IEEE 13-and 123-bus distribution systems with real-world load and DG profiles validate the advantages of the proposed method.

The combination of Internet of Things technology and power grid technology greatly strengthens the situational awareness in integrated energy systems. However, present development fails to consider the internal dynamic characteristics of the natural gas system. This is addressed in the present study by proposing an asynchronous distributed dynamic state estimation (SE) method for integrated electricity–gas energy systems. First, static SE based on a weighted least-square method is applied to the electric power network, and an improved extended Kalman filter SE method based on a transient model is applied to the natural gas network. The asynchronous exchange of information between the two subsystems is addressed by applying an iterative update step correction to the dynamic SE of the natural gas network. Second, we propose an adaptive SE execution cycle adjustment method to track the state of the natural gas subsystem in real time under conditions of large disturbances in that subsystem. The application of the proposed algorithm to simulation case studies demonstrates that it can greatly improve the estimation accuracy and trajectory tracking performance of the SE process relative to conventional methods.

The growing penetration of electric vehicles results in interdependence between power and transportation networks, in which the coordination among drivers and network operators is highly desirable for raising the respective benefits. Considering the self-interest behavior of each entity, this article develops a Nash–Stackelberg–Nash game framework to model their noncooperative interactions. Specifically, the upper level power and transportation system operators determine monetary incentives independently to achieve the respective economic-emission dispatch, while each lower level driver reacts to the incentive and makes route/charging choice to minimize his own travel cost. To mitigate the upper bounding issue in the existing tariff-based incentive scheme (i.e., the upper bound of the tariff needs to be specified manually), we propose a subsidy-based method to evoke the self-discipline of the upper level operators and to facilitate public acceptance. The bilevel model is transformed into two single-level subproblems through the Karush–Kuhn–Tucker conditions, and a Gauss–Seidel iterative algorithm is developed to identify an operational equilibrium. Numerical experiments demonstrate the performance of the noncooperative operation and the efficacy of the subsidy-based scheme by comparing them with the cooperative operation and the tariff-based scheme, respectively.

This study intends to improve the accuracy, efficiency, and timeliness of state estimation (SE) for large-scale electric power systems by presenting a recursion correction hybrid linear state estimator that utilizes all the field measurements received from supervisory control and data acquisition (SCADA) systems and phasor measurement units (PMUs). We present a novel reformulation of the SE problem, where the SCADA and PMU measurements are processed asynchronously. Here, the PMU measurements along with the previous SE results are utilized during the time gap between two successive SCADA measurement sets to run a recursion correction, and thereby, provide real-time updates of the system state estimates. Moreover, the efficiency of this process is guaranteed through multithreaded stream processing. The effectiveness of the proposed method is demonstrated based on simulations involving IEEE 14-bus, 118-bus, and Polish 2383-bus systems.

The current efforts to address the expansion planning of power and heat production facilities can lead to suboptimal results from a market perspective because these efforts often implicitly assume that power and heat systems are planned by a single entity. The present study addresses this issue by establishing a Nash–Cournot equilibrium model, where the producers determine the quantity of power and heat that they produce independently and simultaneously, and thereby collectively affect the price. The model considers three types of producers, including power-only, combined power and heat, and heat-only producers, where each producer determines its own expansion investment strategies independently with the aim of maximizing its own profit. An investment equilibrium is obtained by reformulating the equilibrium model as a mixed complementarity problem. Numerical results carried out on two test systems illustrate the impact of transmission congestion and supply-side market power on the investment equilibria.

Due to the stochastic and non-stationary characteristics of wind speed, the wind power generation is highly uncertain and fluctuating, which significantly challenges the operation of the power system and the associated electricity market. In this article, a new spatial-temporal method is proposed for short-term wind power prediction based on image inputs and augmented convolutional network. First, the geographical locations of various wind farms and the relevant wind vectors are processed into a series of multiframe spatial-temporal wind images, which can be handled by the convolutional networks. Then, wind power conversion and prediction models are developed based on those networks, where recurrent paths and attention mechanism are introduced to enhance the model architecture. The testing results have validated the high performance of the proposed method within a forecast horizon of up to seven hours. In particular, even when the terrain information is not available, the implicit wind flow field within the original inputs can still be approximately learned by the proposed convolutional networks.

Summary It is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.