A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration

Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability

The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.

Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

A Feasibility Study of Simulating the Micro-Scale Wind Field for Wind Energy Applications by NWP/CFD Model with Improved Coupling Method and Data Assimilation

Understanding the details of micro-scale wind fields is important in the development of wind energy. Research has proven that coupling Numerical Weather Prediction (NWP) and Computational Fluid Dynamics (CFD) models is a better approach for micro-scale wind field simulation. The main purpose of this work is to improve the NWP/CFD model performance in two parts: (i) developing a new coupling method that is more suitable for complex terrain between the NWP and CFD models, and (ii) applying a data assimilation system in the CFD model. Regarding part (i), in order to solve the problem of great topographical difference at the domain boundaries between the two models, Cressman interpolation is utilized to impose the NWP model wind on the CFD model boundaries. In part (ii), an assimilation method, nudging, to apply assimilation of observations into the CFD model is explored. Based on the Cressman interpolation coupling method, a preliminary implementation of data assimilation is performed. The results show that the NWP/CFD model with the improved coupling method may capture the details of micro-scale wind fields more accurately. Using data assimilation, the NWP/CFD model performance may be further improved by cooperating observation data.

FPGA Based Real-Time Emulation System for Power Electronics Converters

This paper deals with an emulation system for Power Electronics Converters (PEC). The emulation of PECs is performed on a Field-Programmable Gate Array (FPGA) capable of hard real-time operation. To obtain such a system, the converter operation is described using a differential equations-based model designed with the graph theory. Differential equation coefficients are changed according to the type of converter and pulse-width modulation (PWM) signals. The tie-set and incidence matrix approach for the converter modelling is performed to describe the converter operation in a general way. Such approach enables that any type of PECs can be described appropriately. The emulator was verified experimentally by synchronous operation with a real DC-AC converter built for this purposes.

A Survey of Real-Time Optimal Power Flow

There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.