Blockchain Based Trading Mechanism in Microgrid using CHIO Algorithm

Blockchain based microgrid mechanisms can be designed efficiently to provide uninterrupted power supply and to balance load demands dynamically. In this present work, a conceptual design of a microgrid system is proposed in power system modeling. A blockchain based trading mechanism has been implemented on this system. Various optimization algorithms have been used to maximize economic profit. Finally, the Coronavirus Herd Immunity Optimizer (CHIO) algorithm is described to accommodate the impression that arises for the optimal power flow (OPF) and energy capacity. A case study has been provided to authenticate the performance of this method. The result expresses that the present scheme can largely improve the power dispatch and trading system. © 2022 IEEE.

Effective multi-step ahead container throughput forecasting under the complex context

Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making. © 2023 John Wiley & Sons Ltd.

Research on machine learning-based epidemic impact on Social Electricity Consumption model

This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.

Solution of Solar Integrated Economic Load Dispatch Problems using COVID-19 Algorithm

Electrical power dispatch at a minimum cost of operation has been a challenging issue for thermal power stations and has research work has been carried out for decades. It has been observed that day by day resources of conventional energy are depleting so, the world is shifting towards renewable energy sources. This paper presents a novel technique COVID-19 Optimizer Algorithm (CVA) for solving the economic load dispatch problem of solar generation systems and thermal generating plants of a power system. The proposed method can be considered for solving the various types of economic load dispatch (ELD) problem considering numerous constraints viz. ramp rate limit & prohibited operating zones. Simulation results proved that the technique proposed performs way better than other modern optimization algorithms both in terms of quality of result obtained as well as computational efficiency. The robust nature of the CVA technique in solving solar integrated ELD problems can be inferred from the results. © 2022 IEEE.

MicroGrid Designer: user-friendly design, operation and control assist tools for resilient microgrid and autonomous community

During this decade, many countries have experienced natural and accidental disasters, such as typhoons, floods, earthquakes, and nuclear plant accidents, causing catastrophic damage to infrastructures. Since the end of 2019, all countries of the world are struggling with the COVID-19 and pursuing countermeasures, including inoculation of vaccine, and changes in our lifestyle and social structures. All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous, so that vital lifelines are secured during calamities. A paradigm shift has been taking place toward reorganizing the energy social service management in many countries, including Japan, by effective use of sustainable energy and new supply schemes. However, such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service. Therefore, new social infrastructures and novel management systems to supply energy and social service will be required. In this paper, user-friendly design, operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance. © 2022

Construction Plan for Backup Dispatch of Regional Power Grid

Emergencies such as the COVID-19 and natural disasters have brought severe ordeals to the current grid emergency dispatch system, and there is an urgent need to improve and consummate the existing backup dispatch system. This paper firstly analyzes the existing three kinds of backup dispatch systems and their advantages and disadvantages, and then compares in detail the construction of national dispatch, provincial dispatch, and prefectural dispatch, and points out several existing problems of backup dispatch at all levels under the current emergency system. In order to gradually solve these problems, a backup dispatch system combining emergency and disaster recovery has been proposed based on the two-place three-center mode, it gradually realizes the prevention of risks from social security incidents such as public health incidents and serious natural disasters. © 2022 IEEE.

Machine learning model to predict electricity demand and thermal generation during the pandemic

Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE

Predictive Analytics of Electrical Power Output of Coal-Fired Power Plant Using Machine Learning

The pandemics outbreak of Covid-19 in the world has made society and industrial activities very dynamic. The operating power plant must prepare to fulfil the fluctuating electricity demand from the load dispatcher. Hence, predicting the electrical power output is important to give the accuracy to maximize the profit and minimize losses. This paper discusses and predicts the half-hourly electrical output of Paiton Coal-Fired Power Station Unit 1 by develops many predictive models using five different machine learning regression methods. The five parameters that affect the electrical power output are used in the dataset, such as main steam flow, total coal flow, primary airflow, secondary airflow, and vacuum condenser pressure. These input and target variables as the dataset were collected over one year. The dataset is sorted and observed. Then, the best prediction model is sought for predicting electrical power output. Thus, the best performance of the best subset, which contains a complete set of input variables, has been analyzed using the most accurate machine learning algorithm, which is the random forest, with R-squared of 0.996. © 2021 IEEE.