A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system

Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact on the HEMS is assessed. The influence of changing the battery’s capacity and the PV system size on the energy costs and the lost energy are also evaluated. A significant reduction in energy costs and energy lost to the utility can be achieved by combining a suitable overnight charging level, an appropriate sample time, and an accurate forecasting tool. The HEMS has been implemented on an experimental house emulation system to demonstrate it can operate in real-time

Microgrid energy management system for smart home using multi-agent system

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation

A biased load manager home energy management system for low-cost residential building low-income occupants

© 2018 Elsevier Ltd This research paper presents the development of a biased load manager home energy management system for low-cost residential building occupants. As a smart grid framework, the proposed load manager coordinates the operation of the inverter system of a low cost residential apartment consisting of rooftop solar photovoltaic panels, converter and battery, and provides a platform for discriminating residential loads into on-grid and off-grid supply classes while maximizing solar irradiance for optimum battery charging and improving consumer comfort from base levels. Modelled in a Matlab simulation environment, the system incorporates a converter system for maximum power point tracking using a hopping algorithm, with a dedicated mechanism for smart dispatch of specified loads to meet the users' comfort based on the priority ranking of the loads. Results obtained indicate a 34% reduction in electricity cost, 26% reduction in carbon emissions and a 4% increase in comfort level for the photovoltaic/battery/utility option compared to the utility only option. The results further show that cost is a major factor affecting the users' comfort and not necessarily dispatch of appliances to meet energy needs. The research can be useful for encouraging the adoption of the photovoltaic/battery/utility option by low/middle income energy users in developing countries

Improving the Power Outage Resilience of Buildings with Solar PV through the Use of Battery Systems and EV Energy Storage

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%. Keywords: HEMS; fuzzy logic; automatic tuning; embedded system; wireless communication; grid connected P

Implementation of solar panels and photovoltaic systems as an alternative for efficient energy saving at Universidad Nacional Abierta y a Distancia-UNAD

The Universidad Nacional Abierta y a Distancia- UNAD, is a public, educational organization of the National Order that through the conception and practice of Distance Education was not affected in the development of activities in the time of pandemic, according to its solid technological infrastructure to promote the virtuality.One of the principal changes implied by the preventive isolation because of COVID-19 was to reassess the context of the management system and the expectations and demands of the stakeholders, generating new opportunities and risks as a result of the sanitary situation. This situation, strengthening the commitment of the Environmental Management System with climate change based on the sustainable development objectives and the 2019-2023 internal development plan, which refers within its main goals, to the installation of 8 solar tables and 2 photovoltaic systems for outdoor lighting in different locations, in addition to operational control activities that contribute to mitigating the impacts  of the activities associated with the work at home and on-site modality, giving environmental legal compliance and expanding the scope to ISO 14001:2015 certification in new centers, thus promoting new challenges that have allowed the positioning in good environmental practices of the University at the national level. Keyword: Sustainable Development Goals, Energy, photovoltaic lighting systems, carbon footprin

A hybrid renewable energy production system using a smart controller based on fuzzy logic

Introduction. This article proposes an improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology with multiple inputs and outputs (I/O). It is used to control hybrid electric energy sources built around photovoltaic solar panels, wind turbine and   electric energy storage system assisted by the electric grid. The novelty in this work that solar photovoltaic, wind turbine and storage system energy sources are prioritized over the grid network which is solicited only during adverse weather conditions, in order to supply a typical household using up to 4,000 Wh per day. In addition of that, the surplus of renewable energy produced during favorable climatic condition is used to produce hydrogen suitable for household heating and cooking using eletrolyzer system. Purpose. Development of improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology. This system is embedded on Arduino 2560 mega microcontroller, on which the fundamental program of fuzzy logic and the distribution of events with all possible scenarios have been implemented according to a flowchart allowing the management of the hybrid system. Methods as well as a parametric search and a simulation to characterize the system, are carried out in order to put on the proposed techniques to ensure continuous accommodation at home. Results. The proposed system results confirm their effectiveness by visualizing the output control signals from the electronic switches. Practical value of which transmits power through a single-phase DC/AC converter to power the AC load for the accommodation.Вступ. У статті пропонується вдосконалена система керування та оптимізації енергоспоживання з інтелектуальною економічною стратегією, заснованою на методі нечіткої логіки з декількома входами та виходами. Вона використовується для керування гібридними джерелами електричної енергії, побудованими на основі фотоелектричних сонячних панелей, вітрових турбін та системи зберігання електричної енергії за допомогою електричної мережі. Новизна роботи полягає в тому, що сонячні фотоелектричні, вітряні турбіни та джерела енергії системи зберігання енергії мають пріоритет над електромережею, яка запитується лише за несприятливих погодних умов, щоб забезпечувати типове домашнє господарство до 4000 Вт×год на день. Крім того, надлишки відновлюваної енергії, що виробляється у сприятливих кліматичних умовах, використовуються для виробництва водню, придатного для опалення та приготування їжі за допомогою електролізера. Мета. Розробка вдосконаленої системи керування та оптимізації енергоспоживання з інтелектуальною економічною стратегією, що заснована на методі нечіткої логіки. Ця система вбудована в мегамікроконтролер Arduino 2560, на якому реалізована головна програма нечіткої логіки та розподілу подій з усіма можливими сценаріями за блок-схемою, що дозволяє керувати гібридною системою. Зазначені методи, а також параметричний пошук та моделювання для характеристики системи реалізуються для того, щоб застосувати запропоновані методи для забезпечення безперервного проживання у будинку. Результати. Результати реалізації запропонованої системи підтверджують їх ефективність візуалізацією вихідних сигналів керування від електронних перемикачів. Практичне значення полягає у передачі потужності через однофазний перетворювач постійного струму у  змінний для живлення навантаження змінного струму для житлових приміщень