• Energy Research

Abstract In order to realize the large-scale and normal implementation of power-to-gas technology, as well as the large-capacity storage and long-distance transportation of gas in the later stage of electric hydrogen production, a power-to-gas technology with real-time energy conversion characteristics and large-scale energy storage characteristics is studied in this paper. Firstly, the water-electricity-gas coupling process between the electrolytic cell and the methanation reactor in the power-to-gas system is discussed in detail, and the model of the power-to-gas system based on the coordination of real-time energy conversion and large-scale energy storage is proposed. Then, based on a multi-energy coupling regional integrated energy system, a highly decoupled multi-step modelling method is adopted to construct its optimal operation model, and the equivalent variable substitution method is used to transform it into a linear programming problem. Finally, the calculation results show that the proposed power-to-gas technology can further improve the energy utilization efficiency due to the complementary characteristics of the two functions, so as to effectively promote the implementation of large-scale power-to-gas projects in the future.

We propose a planning method for offshore wind farm electrical collector system (OWF-ECS) with double-sided ring topology meeting the "N-1" criterion on cable faults, in which the submarine cables layout of OWF is optimized considering cable length and power losses. The proposed mixed-integer quadratic programming (MIQP) model is based on the Capacitated Vehicle Routing Problem (CVRP) formulation and power network expansion planning, which could approximate the power losses in OWF-ECS. In addition, cross-avoidance constraints are proposed to avoid crossing cables, and the minimum k-degree center tree model is included to improve the convergence. Case studies on OWFs with 30 and 62 WTs demonstrate the effectiveness of the proposed method. Considering the potential outage cost in the radial topology, the total cost of the planning result is reduced by up to 25.9% with reliability improvement. The cable investment is reduced by 4%~8% with the proposed method compared with conventional heuristic methods and Google OR-tools. The proposed method/model can also achieve acceptable computation efficiency and OWF-ECS planning results with good optimality. Moreover, it could be solved by modern commercial solvers/optimization software, thus it's easy to use even for large-scale OWF. 10 pages, 10 figures

With the increasing integration of distributed PV generation, the distribution network requires more and more flexibility to achieve the security-constrained optimal power dispatch. However, the conventional flexibility sources usually require additional investment cost for equipment. Moreover, involving the security constraints is very challenging due to the requirements of accurate network model that may be unavailable in practice. This paper addresses the aforementioned challenge by proposing a topology-free optimal power dispatch framework for distribution networks. It utilizes building thermal inertia to provide flexibility to avoid additional investment. To guarantee the operation safety, a multi-layer perception (MLP) is trained based on historical operational data and then reformulated as mixed-integer constraints to replace the inexplicit original security constraints. Numerical results confirm that the proposed framework can derive a feasible and optimal strategy without any topology information. 5 pages

Abstract The energy industry is at a crossroads. Digital technological developments have the potential to change our energy supply, trade, and consumption dramatically. The new digitalization model is powered by the artificial intelligence (AI) technology. The integration of energy supply, demand, and renewable sources into the power grid will be controlled autonomously by smart software that optimizes decision-making and operations. AI will play an integral role in achieving this goal. This study focuses on the use of AI techniques in the energy sector. This study aims to present a realistic baseline that allows researchers and readers to compare their AI efforts, ambitions, new state-of-the-art applications, challenges, and global roles in policymaking. We covered three major aspects, including: i) the use of AI in solar and hydrogen power generation; (ii) the use of AI in supply and demand management control; and (iii) recent advances in AI technology. This study explored how AI techniques outperform traditional models in controllability, big data handling, cyberattack prevention, smart grid, IoT, robotics, energy efficiency optimization, predictive maintenance control, and computational efficiency. Big data, the development of a machine learning model, and AI will play an important role in the future energy market. Our study’s findings show that AI is becoming a key enabler of a complex, new and data-related energy industry, providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. As a result, the energy industry, utilities, power system operators, and independent power producers may need to focus more on AI technologies if they want meaningful results to remain competitive. New competitors, new business strategies, and a more active approach to customers would require informed and flexible regulatory engagement with the associated complexities of customer safety, privacy, and information security. Given the pace of development in information technology, AI and data analysis, regulatory approvals for new services and products in the new Era of digital energy markets can be enforced as quickly and efficiently as possible.

Abstract Accurate energy analyses and forecasts not only impact a nation’s energy stability/security and environment but also provide policymakers with a reliable framework for decision-making. The load forecast of buildings and electricity companies for the arrangement of risk/low-cost demand and supply resources that fulfill future government commitments, plans consumer targets, and respond appropriately for stockholders. This study introduces two novels deep supervised machine learning models, including: (i) fit Gaussian Kernel regression model with random feature expansion (RFEM-GKR); and (ii) non-parametric based k-NN (NPK-NNM) models for buildings and the utility companies load demand forecasts with a higher predictive potential, speed, and accuracy. Five-fold cross-validation is used to reduce prediction errors and to improve network generalization. Real-load consumption data from two different locations (utility company and office building) are used to analyze and validate the proposed models. Each location data is further divided into six different feature selection (MFS) states. Each state is composed of various (16, 19, 17, 09, 16, and 13) types of real-time energy consumption and climatic feature variables. The energy consumption behaviors are then analyzed in terms of the feature significance applied with 5 min, 30 min, and 1-h of time-based on short-, and medium-term intervals. Eleven distance metrics used to measure the number of the neighboring object and the number of objective functions of the model network for accuracy. With less computational time, higher precision, and high penetration levels of multiple input feature variables, the method RFEM-GKR is proven superior. Therefore, because of its high accuracy and stability, the proposed model can be a successful tool to predict energy consumption.