Analysis Application of Controllable Load Appliances Management in a Smart Home

Osório, Gerardo J.; Shafie‐khah, Miadreza; Carvalho, Gonçalo C. R.; Catalão, João P.S.

doi:10.3390/en12193710

Cited by 16 publications

(8 citation statements)

References 43 publications

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“…The median usage temperature dropped to 44.5 °C, increasing the energy savings to [1.51, 2.19, 3.0] kWh/day ([27.37, 34.08, 47.56] %). However, the significant drop in usage temperature increased the number of cold events to [0, 3,9,24,137]. The PAPR decreased to 0 dB, meaning that the demand profile was flat throughout the day.…”

Section: Thermostat Control (Tc)mentioning

confidence: 99%

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Centrally adapted optimal control of multiple electric water heaters

Ritchie¹,

Engelbrecht²,

Booysen³

2021

Preprint

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Breakthroughs in smart grid technology make it possible to deliver electricity in controlled and intelligent ways to improve energy efficiency between the user and the utility. Demand-side management strategies can reduce overall energy usage and shift consumption to reduce peak loads. Electric water heaters account for 40% of residential energy consumption. Since they are thermal storage devices, advanced control strategies can improve their efficiency. But existing methods disregard the connection between the user and the grid. We propose a centrally adapted control model that allows for coordinated scheduling to adapt the optimal control schedule of each EWH, spreading the load into off-peak periods to ensure that the grid's generation capacity is not exceeded. We consider two strategies for the delivery of hot water, temperature matching, and energy matching with \textit{Legionella} sterilisation, and compare them to a baseline strategy where the thermostat is always switched on. Simulation results for a grid of 77 EWHs showed that an unconstrained peak load of 1.05 kW/EWH can be reduced as low as 0.4 kW/EWH and achieve a median energy saving per EWH of 0.38 kWh/day for the temperature matching strategy and 0.64 kWh/day for the energy matching strategy, without reducing the user's comfort.

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Section: Thermostat Control (Tc)mentioning

confidence: 99%

“…Techniques have been devised to smooth the demand profile of the grid and limit the power demand [1]- [3]. This is particularly important in the case of electric water heaters (EWHs), as they account for 40% of the residential sector's energy consumption, and this sector accounts for 20% of greenhouse gas emissions [4]- [9].…”

Section: Introductionmentioning

confidence: 99%

Centrally adapted optimal control of multiple electric water heaters

Ritchie¹,

Engelbrecht²,

Booysen³

2021

Preprint

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“…All in all, it becomes quite apparent that IoE is capable of helping in achieving and further increasing the sustainability for the smart grids through making the production, transmission, and consumption of electric energy more efficient and economically feasible. With all that benefits smart grids and IoE are offering, it is quite surprising that many agents at the contemporary energy market (e.g., car producers, energy suppliers, or utility companies), still have certain troubles in adjusting to the new technological advancements [20,21] (even though it is apparent that their deployment requires substantial capital investments). However, it quite clear that new types of energy demand and supply that would include more renewable energy sources and prosumers would inevitably lead to the profound changes in world's electrical networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Overall, there are two cases when EVs do not exhibit smart charging capability and when the EVs exhibit smart charging capability and are scheduled through dynamic pricing. In the former case, EVs are assumed to start charging immediately after returning home; given that most users return home during late afternoon and/or evening hours [17][18][19][20][21] when the non-EVs demand peak occurs, the system peak demand at these hours is significantly increased. In the latter case, the demand response of smart-charging EVs is concentrated and creates a new demand peak at the late-night hours of the day since the latter exhibit the lowest prices due to their low inflexible demand levels.…”

Section: Smart Network Technologiesmentioning

confidence: 99%

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

et al. 2019

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The growing importance of the Internet of Energy (IoE) brands the high-renewables electricity system a realistic scenario for the future electricity system market design. In general, the whole gist behind the IoE is developed upon a somewhat broader idea encompassing the so-called “Internet of Things” (IoT), which envisioned a plethora of household appliances, utensils, clothing, smart trackers, smart meters, and vehicles furnished with tiny devices. These devices would record all possible data from all those objects in real time and allow for a two-way exchange of information that makes it possible to optimize their use. IoT employs the Internet Protocol (IP) and the worldwide web (WWW) network for transferring information and data through various types of networks and gateways as well as sensor technologies. This paper presents an outline stemming from the implications of the high-renewables electric system that would employ the Internet of Energy (IoE). In doing so, it focuses on the implications that IoE brings into the high-renewables electricity market inhabited by smart homes, smart meters, electric vehicles, solar panels, and wind turbines, such as the peer-to-peer (P2P) energy exchange between prosumers, optimization of location of charging stations for electric vehicles (EVs), or the information and energy exchange in the smart grids. We show that such issues as compatibility, connection speed, and most notoriously, trust in IoE applications among households and consumers would play a decisive role in the transition to the high-renewables electricity systems of the 21st century. Our findings demonstrate that the decentralized approach to energy system effective control and operation that is offered by IoE is highly likely to become ubiquitous as early as 2030. Since it may be optimal that large-scale rollouts start in the early 2020s, some form of government incentives and funding (e.g. subsidies for installing wind turbines or solar panels or special feed-in-tariffs for buying renewable energy) may be needed for the energy market to make early progress in embracing more renewables and in reducing the costs of later investments. In addition, there might be some other alternative approaches aimed at facilitating this development. We show that the objective is to minimize the overall system cost, which consists of the system investment cost and the system operating cost, subject to CO2 emissions constraints and the operating constraints of generation units, network assets, and novel carbon-free technologies, which is quite cumbersome given the trend in consumption and the planned obsolescence. This can be done through increasing energy efficiency, developing demand side management strategies, and improving matching between supply and demand side, just to name a few possibilities.

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“…Li-Ion batteries emerge as the main Energy Storage System (ESS) technology in the automotive field and smart grids for the integration of renewable energy sources, electric mobility, Vehicle to Grid (V2G) systems and demand response programs into the electric grid [1][2][3][4]. With respect to other technologies, Lithium-Ion batteries are characterized by high energy densities, fast-response and competitive costs.…”

Section: Introductionmentioning

confidence: 99%

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

et al. 2021

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Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

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Analysis Application of Controllable Load Appliances Management in a Smart Home

Cited by 16 publications

References 43 publications

Centrally adapted optimal control of multiple electric water heaters

Centrally adapted optimal control of multiple electric water heaters

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

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