Tiago D. P. Mendes

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

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.

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).

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.

Influence of Large Renewable Energy Integration on Insular Grid Code Compliance

Large-scale deployment of renewables in island energy systems attracts local attention of grid operators as a way of reducing fuel fossil consumption. Planning a grid based on renewable power plants poses serious challenges to the normal operation of a power system, namely on frequency and voltage stability. In past grid code compliance, wind turbines did not require services for supporting grid operation. To shift to large renewable energy integration, the island grid code should incorporate a new set of requirements in order to regulate the inclusion of these services, which is the aim of this paper. The paper also discusses additional requirements such as “virtual” wind inertia.

Experimental Wireless Wattmeter for Home Energy Management Systems

This paper presents a novel prototype device for domestic load energy consumption monitoring. Zigbee-based wireless connectivity is included as a basic feature of the prototype. The proposed device allows individual tracking of major energy consumption loads. Real time energy data is acquired and transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Both voltage and current measurements are implemented using Hall Effect principle based transducers, while C code is developed on two 16-bit RISC MCU. The experimental setup is described and tests are conducted in order to assess its performance.

Impact of electric vehicle V2G operation and demand response strategies for smart households

The mature bulk power system requires to meet the needs of 21th century in terms of efficient and effective utilization of electric energy, together with the capability of accommodating recently growing renewable energy resources penetration. As a new idea of modernizing the current grid structure, the smart grid issue is a widely growing area of interest with investments from developed/developing country governments. As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest as such strategies provide consumers the chance for the real-time control of their consumption to reduce their bills, while utilities can lower the peak power value to be supplied to consumers. As a new type of consumer load in the electric market, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-grid (V2G) option instead of peak power procurement from utility. This study aims to discuss the impacts of different DR strategies and EV owner consumer preferences on the reduction of total electricity prices. Different case studies are conducted to better analyze the price reduction potential of different operating strategies.

Stochastic modeling of lead-acid battery parameters

Renewable energies are in constant growth and evolution, being a clean way to provide the energy required for the sustainable development of human society. In this context, energy storage systems are a key factor in the integration of renewable generation, because through them, the flexibility of the power system can be increased. Lead-acid batteries have been extensively used to provide electricity in isolated and rural locations, and could be integrated to the smart grid in order to improve its performance. However, this is a complex element due to its working principle, specifically during charging periods. In this paper, a general purpose model is formulated from a probabilistic point-of-view in order to determine the range of possible values of state-of-charge due to the uncertainty and to estimate the battery efficiency. A case study is analyzed and the results are compared with Monte Carlo Simulation approach in order to evaluate the proposed model.

Effect of plug-in electric vehicles traffic behavior on multi-energy demand's dependency

In this paper, a multi energy system (MES) model incorporating the traffic behavior of plug-in electric vehicles (PEVs) is proposed. It is assumed that in a micro MES two charging options are available for the PEVs: the home charging (HC) stations and the PEV parking lot (PL). The operation of these elements within the micro MES concept is studied. The matrix model of the micro MES is adapted to enable the integration of PL and HC. Moreover, the traffic flow of the PEVs is added to the model as an input to the micro MES. The model is tested for various case studies and possible traffic behavior between the PL and HC. The results show that the presence of these two elements leads to effective integration of reduced system operation costs.