As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.

The clustering of electricity customers might have an effective meaning if, and only if, it is verified by domain experts. Most of the previous studies on customer clustering, however, do not consider real applications, but only the structure of clusters. Therefore, there is no guarantee that the clustering results are applicable to real domains. In other words, the results might not coincide with those of domain experts. In this paper, we focus on formulating clusters that are applicable to real applications based on domain expert knowledge. More specifically, we try to define a distance between customers that generates clusters that are applicable to demand response applications. First, the k-sliding distance, which is a new distance between two electricity customers, is proposed for customer clustering. The effect of k-sliding distance is verified by expert knowledge. Second, a genetic programming framework is proposed to automatically determine a more improved distance measure. The distance measure generated by our framework can be considered as a reflection of the clustering principles of domain experts. The results of the genetic programming demonstrate the possibility of deriving clustering principles.

Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to reduce his energy consumption or not in the current liberalized energy market. Because this uncertainty can cause serious problems in a demand response system, it is clear that the amount of energy reduction should be predicted and managed. In this paper, a data-driven prediction method of load curtailment is proposed, considering two difficulties in the prediction. The first problem is that the data is very sparse. Each customer receives a request for load curtailment only a few times a year. Therefore, the k-nearest neighbor method, which requires a relatively small amount of data, is mainly used in our proposed method. The second difficulty is that the characteristic of each customer is so different that a single prediction method cannot cover all the customers. A prediction method that provides remarkable prediction performance for one customer may provide a poor performance for other customers. As a result, the proposed prediction method adopts a weighted ensemble model to apply different models for different customers. The confidence of each sub-model is defined and used as a weight in the ensemble. The prediction is fully based on the electricity consumption data and the history of demand response events without demanding any other additional internal information from each customer. In the experiment, real data obtained from demand response service providers verifies that the proposed framework is suitable for the prediction of each customer’s load curtailment.

IEC 61850, which drives to digitalize information in the entire system, is being increasingly adopted in the power utility industry, since the last decade. It is necessary for all devices communicating with distributed digital data to have a common understanding of time. As protection and control devices are directly related to the safety of the power system, they are strictly required to have time accuracies. This paper describes the design of a hybrid slave clock with the transparent clock (TC) functionality defined in the IEEE 1588 precision time protocol. The design objective includes not only the synchronization of the time accuracy but also the transient time, as specified in the IEC/IEEE 61850-9-3 precision time profile for power utility automation. To comply with these objectives, we adopt a hardware-software co-design approach. The timestamp of each port is implemented by hardware. Also, they are connected by hardware to calculate the bridge time on the fly, enabling the design to have a one-step TC functionality with low latency. On the other hand, the synchronizing controller is implemented by software for adapting to the varying external conditions. The controller uses the linear quadratic Gaussian (LQG) to achieve stable steady-state performance. Simultaneously, the least-squares estimator is utilized to estimate the initial conditions of the LQG controller for reducing the transition time. Experimental results show the three-sigma time inaccuracies are 15.6 ns for the TC and 149 ns for the slave with an average transition time of 13.5 s, satisfying the IEEE/IEC 61850-9-3 criteria.

With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.

Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective.