Home Energy Management System for Dynamic Loads using Mamdani Fuzzy Logic Approach

HEMS (home energy management systems) are controllers that manage and coordinate a home's generation, storage, and loads. These controllers are becoming increasingly important. To ensure that distributed energy penetration continues to grow resources are appropriately utilized and the process is not disrupted within the grid[1]. An approach to hems design based on behavioural control approaches is discussed in this paper which do not require accurate models or forecasts and are particularly responsive to changing situations, in this study. In this study, the role of the customer as well as the micro grid in intelligent demand management is demonstrated using MATLAB 2018 Fuzzy tool.[3]

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Smart Home Energy Management System

With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.

IoTivity Cloud-Enabled Platform for Energy Management Applications

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality, calling for sustainable measures to reduce overall consumption and peak load. The dawn of the smart grid concept, embedded systems, and ICTs have paved the way for novel Home Energy Management Systems (HEMS) design. In this regard, the Internet of Things (IoT), an enabler for intelligent and efficient energy management systems, is the subject of increasing attention for optimizing HEMS design and mitigating its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low cost, technology availability, and scalability. We addressed the backend complexities of IoT Home Area Networks (HAN) using the Open Consortium Foundation (OCF) IoTivity-Lite middleware. To augment the quality, servicing, reduce the cost, and the development complexities, this work leverages open-source cloud technologies from Back4App as Backend-as-a-Service (BaaS) to provide consumers and utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) under a peak shaving algorithm yielded peak load reduction around 15% of the based load, and appliance operation control using a HEM App via an Android smartphone.

Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage

This article presents a case study of a single-family house with several photovoltaic micro-installations oriented in different directions, in which the energy electricity storage systems have been operating for several months. In the house, the heat source is the air–water heat pump cooperating with heat buffers. The first photovoltaic installation was installed in 2016 and, in the subsequent five years, was expanded using microinverters. The final amount of energy from photovoltaics covers 50% of the energy demand of the building. The procedure for dealing with technical and economic aspects was presented, allowing us to determine whether it is profitable to install energy storage in the given conditions of energy prices, equipment efficiency, and prices, as well as government support. This paper presents the effects of the designed and built home energy management system that supervises energy storage in heat and batteries, mainly through its impact on the self-consumption of energy from the photovoltaic system and on final costs. Comparative calculations were performed with the demand-side management, which dictated the instantaneous energy costs. Attention was paid to the possibility of obtaining a high self-consumption, but the economic calculations showed that it was not always beneficial. An annual self-consumption increased by approximately one-sixth upon installation of the electrical energy storage system and by one-third from the start of use of the home energy management system. Concurrently, by utilising energy storage in heat and batteries, almost 95% of energy was consumed in the cheapest multi-zone tariff. The impact of inverters and battery charging systems on the power grid is also presented. Often, when the active energy was nearing zero, the capacitive reactive energy was significant.