Eduardo M. G. Rodrigues

Enhancing home appliances energy optimization with solar power integration

The theoretical potential for renewable energy resources (RES) to meet the global demands of energy is generally high and the ambitions for introducing RES into energy systems are growing worldwide, which also can contribute to global climate change mitigation if it is produced in a sustainable manner. To address these issues, more and more governments are implementing various programs and energy policies to accelerate the deployment of RES. The aforementioned two reasons lead to an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is to use RES which can inject electric energy to the grid while avoiding greenhouse gas emissions. However, the capacities of RES are not enough to supply all the required power from the side of the load. Facts that are leading to the proposal of original ways to reduce the use of energy in many sectors, namely in commercial, residential, and industrial sectors, in order to reduce the total energy costs of the consumer, to reduce the energy demand specially during on-peak hours and the greenhouse gas emissions while safeguarding end-user preferences. The aim of this paper is to determine the impact of model predictive control (MPC) on energy savings of residential households. Furthermore, the value and impact of generated power by local power sources, such as roof-top-solar, will be determined during off-peak, mid-peak, and on-peak, providing simulations during 24 hours in a house.

Optimal residential model predictive control energy management performance with PV microgeneration

Abstract The energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG) and other air pollutants emissions. Since home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper, a comprehensive comparison is made between the thermostat (ON/OFF), proportional-integral-derivative (PID) and Model Predictive Control (MPC) control models of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. A power interface that adjusts the MPC dynamic range of the output command signal into a discrete two level control signal is proposed, as a new contribution to earlier studies. The model of the house with local solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and 5 Time-of-Use (ToU) electricity rates applied on an entire week of August 2016. The purpose of the optimisation is to achieve the best compromise between temperature comfort levels and energy costs and also to assess which is the best electricity ToU rate option provided by the electricity retailer for the residential sector. Also, for each electrical load of the HVAC system, the energy and cost are calculated and the results are presented by varying the different MPC weight combination in order to obtain the best possible solution and increase the quality of the model. Finally, after the best tariff and controller are determined, the impact of the solar generation is assessed.

MPC weights tunning role on the energy optimization in residential appliances

Genuine concerns regarding air pollution, climate change, and dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to search for alternatives to conventional petroleum-fueled combustion power plants with the purpose to reduce greenhouse gas emission. This leads to an urgent need to substitute them with alternate generating capacity or reduce the consumption during peak periods, or both. One of the options for power generation is the use of renewable energy resources, which can inject power to the grid deprived of greenhouse gas emissions. But, from the load point of view, the renewable energy resources capacity is not sufficient to supply all the required power. These points to the necessity of innovative methods, able to diminish energy consumption in different sectors, but also with the aim of reducing the domestic customers total energy costs, greenhouse gas emissions and energy demand, especially during on-peak, while always considering the end user preferences. Hence, this paper analyses model predictive control (MPC) application in domestic appliances with the purpose of energy optimization. In this context, the research theme is focused on the relation between MPC weighting adjustment and the minimization of energy consumption. Three domestic loads are used for MPC tuning evaluation: water heater (WH), room temperature control by conditioner (AC) and refrigerator (RF).

Impact of EV charging-at-work on an industrial client distribution transformer in a Portuguese Island

This paper analyses the impact of the penetration of electric vehicles (EVs) charging loads on thermal ageing of a distribution transformer of a private industrial client that allows EVs to charge while their owners are at work and at three different working shifts during a day. Furthermore, the system is part of an isolated electric grid in a Portuguese Island. In this paper, a transformer thermal model is used to estimate the hot-spot temperature given the load ratio. Real data were used for the main inputs of the model, i.e. private industrial client load, transformer parameters, the characteristics of the factory and electric vehicle parameters.

Experimental low cost reflective type oximeter for wearable health systems

Abstract The advent of wearable technology is fundamental to the dissemination of wearable personal health monitoring devices. Recent developments of biomedical sensors have decreased the form factor and power consumption that can be worn on a permanent basis. This paper discusses a low cost reflective photoplethysmography (PPG) system using a dedicated integrated circuit (IC) solution as the core of a wearable health monitoring device. The measurement of two physiological indicators is performed, namely the pulse rate (HR) and the blood oxygen saturation (SpO 2 ). The paper analyses in depth the PPG signals sensing architecture, guaranteeing high resolution measurements due to a delta-sigma analog to digital conversion unit. Post-processing digital filter operations are implemented to enhance low noise PPGs acquisition for physiological signals extraction. A complete system design is presented and a detailed evaluation is made in a real-time processing scenario. The test platform is completed with a PC based graphics application for on-line and off-line data analysis. Minimizing power dissipation is the main challenge in a wearable design. However, it restrains PPG signal measurement sensitivity by lowering signal quality. Using the developed prototype power consumption, studies concerning the characterization of power consumption and signal quality over various working conditions are performed. Next, a performance merit figure is proposed as the main research contribution, which addresses the power consumption and signal quality trade-off subject. It aims to be used as an analysis for trade-offs between these two conflicting design criteria.

Model predictive control technique for energy optimization in residential appliances

Several governments are phasing out coal fired generation power plants to reduce greenhouse gas emission. At the same time, nuclear generating facilities are reaching the end of their life and in the wake of the Fukushima disaster, developed countries have chosen to phase out nuclear energy early in the 2020s, removing 15% of the most stable and reliable portion of their energy mix. These two reasons create an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is the use of renewable energy resources, which can inject power to the grid without greenhouse gas emissions. But, the capacities of renewable energy resources are not enough to supply all the required power from the load side. All of these facts are leading to the proposal of novel approaches to reduce the utilization of energy in different sectors i.e. in residential, commercial, agricultural and/or industrial sectors to reduce the customers total energy costs, energy demand, especially during on-peak, and greenhouse gas emissions, while taking into account the end-user preferences. The main objective of this paper is to demonstrate the impact of optimization technologies on energy savings of residential households. In this regard, a model-based predictive control approach is proposed for home cooling and heating systems. Its effectiveness is compared to thermostat conventional control by providing simulations upon 24 hours in a household.

NaS battery storage system modeling and sizing for extending wind farms performance in Crete

Crete Island has significant natural resources when it comes to wind and solar energy. Likewise other European territories, renewable sources already are being explored for power production. Currently, a large amount of wind energy on Crete is curtailed during certain daily periods as a result of reduced demand and minimum operating levels of thermal generators. Reducing curtailment losses requires additional sources of flexibility in the grid, and electric energy storage is one of them. This paper address wind generation losses minimization through the storage of wind energy surplus. Sodium Sulfur (NaS) battery modeling is used in this study and an energy time-shift storage scheme is implemented to assess the overall storage system performance. The obtained results are supported on real data of renewable resources (wind and solar), conventional power production and demand of Crete Island in 2011. Conclusions are duly drawn.

Home HVAC energy management and optimization with model predictive control

The general energy demand of the residential sector and the ensuing option for fossil fuels produce adverse results by both CO2, greenhouse gases (GHG) and extra air pollutant emissions. As domestic energy demand consists mostly of energy necessities for space and water heating alongside the energy dedicated for appliances, distinct strategies that target to foment a practical consumption of energy have to be reinforced at all levels of human activity. In this paper the aim is to make a comparison between proportional-integral-derivative (PID), thermostat (ON/OFF) control and Model Predictive Control (MPC) models of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. The model of the household with local solar microgeneration is implicit to be located in a Portuguese city. The house of the case study is at the mercy to the local solar temperature, irradiance and 5 Time-of-Use (ToU) electricity rates applied on a complete week of August, 2016. The second purpose of this study is to assess which is the best electricity ToU rate option provided by the local electricity retailer for the residential sector.

Residential MPC controller performance in a household with PV microgeneration

The energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG), CO2 and other air pollutants emissions. The home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances. Therefore, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper a comparison is made between a Model Predictive Control (MPC) model, the ON/OFF and proportional-integral-derivative (PID) control models of an air conditioning unit AC system controlling the temperature of a room. The model of the house with local Photovoltaic (PV) solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and electricity tariff of a summer day.

Multifunctional control of an NPC converter for the grid integration of renewable energy sources

This paper presents a control method based on dynamic model of three-level neutral-point-clamped (NPC) voltage source converter (VSC) for integration of renewable energy sources (RESs) into the power grid. The proposed control method can provide continuous injection of active power besides the compensation of all reactive power and harmonic current components of loads through integration of RESs into the grid. Simulation results confirm a reduced total harmonic distortion (THD), increased power factor of the grid, and injection of maximum power of RESs to the grid. The proposed model is developed in Matlab/Simulink environment and emphasis is given to the challenges met during the modeling.