A Case Study of Load Scheduling For Home Energy Management with Integrated Renewable Energy

Smart grids are very comprehensive systems where each sub-unit from generation to consumption should be considered separately. Home energy management systems and demand-side load management applications are also among the most important issues of smart grid systems. In this study, a smart home energy management model was developed in this concept, and energy management solutions were presented through an exemplary model. For this purpose, a home energy management algorithm was developed and simulated in the MATLAB Simulink environment by taking an apartment in Esenler, Istanbul as a reference. The smart home model discussed in this simulation study can generate its own electricity with renewable energy sources, store excess electrical energy in battery groups and also sell the surplus to the grid. This home model also enables end-users to control peak loads and schedule home appliances, especially during peak hours, following a demand-response program to consume energy more efficiently. Then, in order to see the electricity consumption results, electricity bill calculations were made according to both single and triple tariff pricing. The benefits of this model to the consumer and the grid were investigated, and also its effects on efficiency were examined. The results are given comparatively and the consumer’s saving is depicted in figures

Demand Side Management Techniques for Home Energy Management Systems for Smart Cities

In this paper, three distinct distributed energy resources (DERs) modules have been built based on demand side management (DSM), and their use in power management of dwelling in future smart cities has been investigated. The investigated modules for DERs system are: incorporation of load shedding, reduction of grid penetration with renewable energy systems (RES), and implementation of home energy management systems (HEMS). The suggested approaches offer new potential for improving demand side efficiency and helping to minimize energy demand during peak hours. The main aim of this work was to investigate and explore how a specific DSM strategy for DER may assist in reducing energy usage while increasing efficiency by utilizing new developing technology. The Electrical Power System Analysis (ETAP) software was used to model and assess the integration of distributed generation, such as RES, in order to use local power storage. An energy management system has been used to evaluate a PV system with an individual household load, which proved beneficial when evaluating its potential to generate about 20–25% of the total domestic load. In this study, we have investigated how smart home appliances’ energy consumption may be minimized and explained why a management system is required to optimally utilize a PV system. Furthermore, the effect of integration of wind turbines to power networks to reduce the load on the main power grid has also been studied. The study revealed that smart grids improve energy efficiency, security, and management whilst creating environmental awareness for consumers with regards to power usage

Methodological Proposal: First Steps for the Implementation of Demand Management Programs in Colombia

The electrification of isolated or rural areas brings with it technical, economic, and social challenges that differentiate the operation of these networks compared to the traditional operation of large electrical power systems, such as the dependence of air or river transport on fuel for the supply of generation plants, the absence of individual measurement and the obsolete infrastructure of the distribution networks. Therefore, this paper proposes a hybrid methodology for studying a Colombian case, analyzing the development of programs for rational electricity use and energy efficiency in isolated areas. These first steps are related to the diagnosis of the current conditions of the power network, the identification of actors that can influence the regulation of the electricity service in the area, and the proposal of mechanisms that allow promoting the rational and efficient use of the electricity

Short-term forecast techniques for energy management systems in microgrid applications

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency