An Incentive-Based Implementation of Demand Side Management in Power Systems

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES

Impact of demand side management methods on modern power systems

Efficient energy consumption remains an important factor in Europe's ambitious goals for sustainable development and activities related to air quality, global warming and climate change. However, especially during the summer months, southern Europe's electricity generation and distribution grid operates at extremely high loads. In order to meet the growing demand for electricity, a number of solutions for efficient electricity consumption, its production from Renewable Energy Sources (RES) and the implementation of new models for management and control have been taken into account, with some of them have been promoted through regulations at national and European level. This work is an attempt to study a field that forms new ideas about the efficient and economical use of electricity, which is called Demand Side Management. This term consists of advanced activities of planning, implementation and monitoring of power transmission and distribution activities in order to motivate consumers to modify the level and the way of use of the energy consumed. For this reason, an Incentive- Based Demand Response model is implemented, in order to present in practice the way in which consumers communicate with grid administrator how they modify their demand curve in response to grid signals. © 2021 IEEE

Stochastic Heuristic Optimization of Machine Learning Estimators for Short-Term Wind Power Forecasting

The continuous fluctuation of wind speed, wind direction and other climatic variables affects the power produced by wind turbines. Accurate short-term wind power prediction models are vital for the power industry to evaluate future energy extraction, increase wind energy penetration and develop cost-effective operations. This research examines short-term wind power forecasting and investigates the effect of sharp, smooth and slow temperature reduction functions on the Simulated Annealing (SA) optimization technique for several prominent prediction models. The regressors under investigation include a Support Vector Machine, a Multi-Layer Perceptron and a Long-Short Term Memory neural network. Their optimization is based on the SA, which is used to specify the hyperparameters of each model in order to enhance the prediction accuracy. The results for each model based on the data of the Greek island of Skyros denote the superiority of the slow temperature reduction function in terms of error metrics and observe that the optimized Multi-Layer Perceptron is the most suitable model for this forecasting task when slow temperature reduction is implemented. © 2022 IEEE

Enhanced short-term load forecasting using artificial neural networks

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Comparative Energy Information Analytics of Five European Economies

Real GDP per capita and energy consumption are often, but not always, correlated variables. Comparative analysis of their respective time series offers new insights and information and new decision-making metrics. During the Great Recession, Greece faced a sudden and dramatic decline in GDP and a prolonged debt crisis fueled by structural issues and severe austerity measures. While all other European economies rebound and experienced GDP growth Greece continued to decline. Energy consumption, however, declined across Europe regardless of GDP growth. Using comparative information analytics, trends are identified, underlying factors unfolded and conclusions are drawn on the energy behavior of Greece's economy as well as those of Germany, Italy, France and Bulgaria. © 2020 IEEE

An incentive-based implementation of demand side management in power systems

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Intelligent Data Analytics from the Energy Economics of the Greek Debt Crisis

Intelligent tools are brought to bear on the analysis of energy data from Greece in the period 2010-20. During this period a paradoxical phenomenon is observed namely, an increase in energy productivity in the midst of unprecedented economic decline as measured by Gross Domestic Product (GDP). Preliminary results identify improvements in the overall efficiency of capital due to the collapse in demand following draconian austerity measures. Background, directions and trends are analyzed, policy alternatives examined, and, a new measure of energy utilization with possible real-time applications is proposed. © 2020 IEEE