scholarly journals Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 68 ◽  
Author(s):  
Abouzar Estebsari ◽  
Roozbeh Rajabi
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Distribution Networks ◽  
Wind Generation ◽  
Renewable Energy Resources ◽  
Image Encoding ◽  
Household Level ◽  
Intermittent Behavior ◽  
Forecasting Methods ◽  
Key Factor

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

scholarly journals Effect of Distributed Wind Generation on the Voltage Sag of Distribution Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 1884-1889
Keyword(s):  
Renewable Energy ◽  
Distribution Systems ◽  
Short Circuit ◽  
Distribution Network ◽  
Distribution Networks ◽  
Voltage Regulation ◽  
Energy Resources ◽  
Wind Generation ◽  
Voltage Sag ◽  
Renewable Energy Resources

As the issue of global warming is worsening, the shift towards using renewable energy resources is becoming more of an obligation rather than an option. With the continual decline in the cost of distributed small and medium-scale renewables and government sponsored programs, the outlook of growth of these converter-based resources remain high. Renewable energy resources are connected at the end-user terminals, in close proximity to the load at the distribution network. Such connection in the locale brings perceived benefits of transmission loss reduction, increased energy efficiency and improved voltage regulation. Yet, distributed renewable generation have noticeable effects on system’s power quality. This paper investigates the impacts of distributed wind generation on the voltage sag of distribution systems. A systematic approach is constructed to capture voltage sag occurrence incidents, due to wind generation connected at distribution nodes, and trigger the dynamic voltage restorer (DVR) into active operation mode to rectify the voltage sag problem. A test feeder system is represented using MATLAB/Simulink with wind turbines connected at several nodes of the system. A model for the DVR is developed in Simulink. It was then integrated with the test feeder system. Simulation results show that the incorporation of increased proportions of wind generation into the distribution network may give rise to negative operating conflicts as far as the voltage sag is concerned. Results manifest that the DVR is capable of effective correction of the voltage sag, caused by a three phase short-circuit fault, in presence of high penetration levels of variable wind generation connected at disparate locations in the distribution network.

scholarly journals Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 318
Author(s):  
Niyam Haque ◽  
Anuradha Tomar ◽  
Phuong Nguyen ◽  
Guus Pemen
Keyword(s):  
Demand Response ◽  
Low Voltage ◽  
Dynamic Network ◽  
Distribution Networks ◽  
Congestion Management ◽  
Renewable Energy Resources ◽  
Monthly Basis ◽  
Agent Based ◽  
Local Decision ◽  
Residential Distribution

Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

scholarly journals Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3458
Author(s):  
Santiago Bañales ◽  
Raquel Dormido ◽  
Natividad Duro
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Time Complexity ◽  
Energy Use ◽  
Series Data ◽  
Wind Generation ◽  
Smart Meters ◽  
Two Stage ◽  
Time Series Clustering ◽  
Load Shape

The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys.

scholarly journals Stochastic Expansion Planning of Various Energy Storage Technologies in Active Power Distribution Networks

2021 ◽  
Vol 13 (10) ◽  
pp. 5752
Author(s):  
Reza Sabzehgar ◽  
Diba Zia Amirhosseini ◽  
Saeed D. Manshadi ◽  
Poria Fajri
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Power Flow ◽  
Lithium Ion ◽  
Distribution Networks ◽  
Renewable Energy Resources ◽  
Flow Equations ◽  
Second Order Cone ◽  
Li Ion ◽  
The Cost

This work aims to minimize the cost of installing renewable energy resources (photovoltaic systems) as well as energy storage systems (batteries), in addition to the cost of operation over a period of 20 years, which will include the cost of operating the power grid and the charging and discharging of the batteries. To this end, we propose a long-term planning optimization and expansion framework for a smart distribution network. A second order cone programming (SOCP) algorithm is utilized in this work to model the power flow equations. The minimization is computed in accordance to the years (y), seasons (s), days of the week (d), time of the day (t), and different scenarios based on the usage of energy and its production (c). An IEEE 33-bus balanced distribution test bench is utilized to evaluate the performance, effectiveness, and reliability of the proposed optimization and forecasting model. The numerical studies are conducted on two of the highest performing batteries in the current market, i.e., Lithium-ion (Li-ion) and redox flow batteries (RFBs). In addition, the pros and cons of distributed Li-ion batteries are compared with centralized RFBs. The results are presented to showcase the economic profits of utilizing these battery technologies.

scholarly journals A Review on Thermal Energy Modelling for Optimal Microgrids Management

Thermo ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 63-76
Author(s):  
Mengxuan Yan ◽  
Dongxiao Wang ◽  
Chun Sing Lai ◽  
Loi Lei Lai
Keyword(s):  
Energy Management ◽  
Thermal Energy ◽  
Critical Role ◽  
Distribution Networks ◽  
Energy Resources ◽  
Future Research ◽  
Renewable Energy Resources ◽  
Energy Modelling ◽  
Microgrid Energy Management ◽  
Combined Cooling

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management.

Time Series Forecasting methods for the Baltic Dry Index (BDI)

2021 ◽  
Author(s):  
Christos Katris ◽  
Manolis G. Kavussanos
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Forecasting Methods ◽  
The Baltic ◽  
Baltic Dry Index

scholarly journals Health and Housing Energy Expenditures: A Two-Part Model Approach

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 943
Author(s):  
Fátima Lima ◽  
Paula Ferreira ◽  
Vítor Leal
Keyword(s):  
Low Income ◽  
Health Expenditures ◽  
Household Level ◽  
Energy Expenditures ◽  
Health And Wellbeing ◽  
Health Gains ◽  
Key Factor ◽  
Using Data ◽  
Part Model ◽  
Model Approach

Interest in the interaction between energy and health within the built environment has been increasing in recent years, in the context of sustainable development. However, in order to promote health and wellbeing across all ages it is necessary to have a better understanding of the association between health and energy at household level. This study contributes to this debate by addressing the case of Portugal using data from the Household Budget Survey (HBS) microdata database. A two-part model is applied to estimate health expenditures based on energy-related expenditures, as well as socioeconomic variables. Additional statistical methods are used to enhance the perception of relevant predictors for health expenditures. Our findings suggest that given the high significance and coefficient value, energy expenditure is a relevant explanatory variable for health expenditures. This result is further validated by a dominance analysis ranking. Moreover, the results show that health gains and medical cost reductions can be a key factor to consider on the assessment of the economic viability of energy efficiency projects in buildings. This is particularly relevant for the older and low-income segments of the population.

Deep Reinforcement Learning for Demand Response in Distribution Networks

2020 ◽  
pp. 1-1
Author(s):  
Shahab Bahrami ◽  
Yu Christine Chen ◽  
Vincent W.S. Wong
Keyword(s):  
Reinforcement Learning ◽  
Demand Response ◽  
Distribution Networks
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