Power Management of Nanogrid Cluster with P2P Electricity Trading Based on Future Trends of Load Demand and PV Power Production

This paper presents the power management of the nanogrid clusters assisted by a novel peer-to-peer(P2P) electricity trading. In our work, unbalance of power consumption among clusters is mitigated by the proposed P2P trading method. For power management of individual clusters, multi-objective optimization simultaneously minimizing total power consumption, portion of grid power consumption, and total delay incurred by scheduling is attempted. A renewable power source photovoltaic(PV) system is adopted for each cluster as a secondary source. The temporal surplus of self-supply PV power of a cluster can be sold through P2P trading to another cluster (s) experiencing temporal power shortage. The cluster in temporal shortage of electric power buys the PV power to reduce peak load and total delay. In P2P trading, a cooperative game model is used for buyers and sellers to maximize their welfare. To increase P2P trading efficiency, future trends of load demand and PV power production are considered for power management of each cluster to resolve instantaneous unbalance between load demand and PV power production. To this end, a gated recurrent unit network is used to forecast future load demand and future PV power production. Simulations verify the effectiveness of the proposed P2P trading for nanogrid clusters.Comment: This article is submitted for publication in Sustainable Cities and Societ

Deep learning based short-term total cloud cover forecasting.

In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images. The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction. Specifically, a region-based Otsu's method is used to segment clouds from satellite images. A time-series dataset is generated using the TCC information extracted from each image in image sequences using a new feature extraction method. The generated time series data are subsequently used to train several LSTM variant models, i.e. LSTM, bi-directional LSTM and Convolutional Neural Network (CNN)-LSTM, for future TCC forecasting. Our approach achieves impressive average RMSE scores with multi-step forecasting, i.e. 0.0543 and 0.0823, with respect to both the first half of daytime and full daytime TCC forecasting on a given day, using the generated dataset

Short-Term Wind Speed and Temperature Forecasting Model Based on Gated Recurrent Unit Neural Networks

Wind energy generation fluctuations and intermittency issues create inefficiency and instability in power management. The recurrent neural networks (RNNs) prediction approaches are an essential technology that can improve wind power generation and assist in energy management and power systems’ performance. In this paper, a prediction model based on Gated Recurrent Unit (GRU) neural networks is proposed to predict wind speed and temperature values one week ahead in the future at hourly intervals. The GRU prediction model automatically learnt the features, used fewer training parameters, and required a shorter time to train compared to other types of RNNs. The GRU model was designed to predict 169 hours ahead as a short-term period of wind speed and temperature values based on 36 years of hourly historical data (1 January 1985 to 6 June 2021) collected from Dumat al-Jandal city. The findings notably indicate that the GRU model has promising performance with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalizable processes. The GRU model is characterized by its good performance and influential evaluation error metrics for wind speed and temperature values

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks

Gulf Cooperation Council Countries’ Electricity Sector Forecasting : Consumption Growth Issue and Renewable Energy Penetration Progress Challenges

The Gulf Cooperation Council (GCC) countries depend on substantial fossil fuel consumption to generate electricity which has resulted in significant environmental harm. Fossil fuels also represent the principal source of economic income in the region. Climate change is closely associated with the use of fossil fuels and has thus become the main motivation to search for alternative solutions, including solar and wind energy technologies, to eliminate their reliance on fossil fuels and the associated impacts upon climate. This research provides a comprehensive investigation of the consumption growth issue, together with an exploration of the potential of solar and wind energy resources, a strict follow-up to shed light on the renewable energy projects, as currently implemented in the GCC region, and a critical discussion of their prospects. The projects foreshadow the GCC countries’ ability to comply with future requirements and spearhead the renewable energy transition toward a more sustainable and equitable future. In addition, four forecasting models were developed to analyse the future performance of GCC power sectors, including solar and wind energy resources along with the ambient temperatures, based on 40 years of historical data. These were Monte Carlo Simulation (MCS), Brownian Motion (BM), and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model model-based time series, and bidirectional long short-term memory (BI-LSTM) and gated recurrent unit (GRU) model-based neural networks. The MCS and BM prediction models apply a regression analysis (which describes the behaviour of an instrument) to a large set of random trials so as to construct a credible set of probable future outcomes. The MCS and BM prediction models have proven to be an exceptional investigative solution for long-term prediction for different types of historical data, including: (i) four types of fossil fuel data; (ii) three types of solar irradiance data, (iii) wind speed data; and, (iv) temperature data. In addition, the prediction model is able to cope with large volumes of historical data and different intervals, including yearly, quarterly, and daily. The simplicity of implementation is a strength of MCS and BM techniques. The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, an approach that helps to reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. This iii research proposes a forecasting framework that applies the SARIMAX model to forecast the long-term performance of the electricity sector (including electricity consumption, generation, peak load, and installed capacity). The SARIMAX model was used to forecast the aforementioned factors in the GCC region for a forecasted period of 30 years from 2021 to 2050. The experimental findings indicate that the SARIMAX model has potential performance in terms of categorisation and consideration, as it has significantly improved forecasting accuracy when compared with simpler, autoregressive, integrated, moving average-based techniques.The BI-LSTM model has the advantage of manipulating information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BI-LSTM’s output layer concurrently receives information from both the backward and forward layers. The BI-LSTM prediction model was designed to predict solar irradiance which includes global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) for the next 169 hours. The findings demonstrate that the BI-LSTM model has an encouraging performance in terms of evaluation, with considerable accuracy for all three types of solar irradiance data from the six GCC countries. The model can handle different sizes of sequential data and generates low error metrics. The GRU prediction model automatically learned the features, used fewer training parameters, and required a shorter time to train as compared to other types of RNNs. The GRU model was designed to forecast 169 hours ahead in terms of forecasted wind speeds and temperature values based on 36 years of hourly interval historical data (1st January 1985 to 26th June 2021) collected from the GCC region. The findings notably indicate that the GRU model offers a promising performance, with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalisable processes. The GRU model is characterised by its superior performance and influential evaluation error metrics for wind speed and temperature fluctuations. Finally, the models aim to help address the issue of a lack of future planning and accurate analyses of the energy sector's forecasted performance and intermittency, providing a reliable forecasting technique which is a prerequisite for modern energy systems

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Can We `Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Just like everything in the nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could `feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition