Addressing the stochastic nature of energy management in smart homes

Keerthisinghe, Chanaka; Verbič, Gregor; Chapman, Archie C.

doi:10.1109/pscc.2014.7038377

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…However, the underlying energy management problem is stochastic and non-linear. Therefore, approximations have to be made with MILP, which will result in lower quality solutions [14].…”

Section: B Stochastic Variablesmentioning

confidence: 99%

“…In [14], we presented a stochastic MILP approach for solving the stochastic optimisation problem formulated in Section II. First the device i's operational constraints are linearised.…”

Section: B Stochastic Variablesmentioning

confidence: 99%

“…In [14], we identified the drawbacks associated with these existing solution techniques by comparing an EMS using stochastic MILP to one using a dynamic programming approach. The paper showed that the dynamic programming approach results in a better quality schedule, because the non-linear characteristics of the devices can be incorporated with dynamic programming while stochastic MILP requires linear models.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems

Keerthisinghe

Verbič

Chapman

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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In Australia, the penetration of rooftop photovoltaic (PV) systems with storage is expected to increase in the future because of rising electricity costs, decreasing capital costs and growing concerns about climate change. Residential energy users can seize the full financial benefits of these systems by using an automated energy management system (EMS) to schedule and coordinate their energy use. An important aspect of an effective EMS is to control the battery state of charge, taking into consideration of the intermittent nature of PV generation and variability of electrical demand over a decision horizon of several days. However, this is difficult because of the computational burden associated with the currently proposed solution techniques. Given these existing shortcomings, this paper evaluates a two-stage stochastic optimisation framework for energy management of residential PV-storage systems to identify the benefits of having a longer decision horizon. That is: a simplified longer-horizon solver that uses stochastic mixed-integer linear programming (MILP) and a more detail shorter horizon solver using dynamic programming. In doing so, this paper discusses the general benefits of residential PV-storage systems coupled with an EMS.Index Terms-future grid, renewable energy sources, demand response, PV-storage systems, home energy management, stochastic mixed-integer linear programming, dynamic programming.

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“…However, the underlying energy management problem is stochastic and non-linear. Therefore, approximations have to be made with MILP, which will result in lower quality solutions [14].…”

Section: B Stochastic Variablesmentioning

confidence: 99%

“…In [14], we presented a stochastic MILP approach for solving the stochastic optimisation problem formulated in Section II. First the device i's operational constraints are linearised.…”

Section: B Stochastic Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems

Keerthisinghe

Verbič

Chapman

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

Self Cite

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“…In this paper we consider energy cost minimisation. A general SHEMS problem is given by [5] min f o (X, S), s.t. x i k and s i k satisfy device constraints, ∀k ∈ {1 .…”

Section: A Optimization Problemmentioning

confidence: 99%

“…A comparison of these methods is provided in [5]. Typically, the optimisation horizon is limited to one day, with the resolution usually between 15 minutes and one hour.…”

Section: B Solution Algorithmsmentioning

confidence: 99%

Investigating thermal inertia in lightweight buildings for demand response

Zhao

Verbič

Fiorito

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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This paper presents the use of buildings' thermal inertia as virtual thermal energy storage (TES) in smart home energy management systems (SHEMS). TES provides residential users with an additional degree of flexibility and helps them scheduling the energy usage in the off-peak periods to reduce the energy expenditure. Phase change materials (PCM) are added in the insulation of a lightweight building to increase its thermal inertia. We show through simulations that the addition of the PCM makes it possible to significantly shift the operation of the Heating, Ventilation and Air Conditioning (HVAC) system while still meeting the residents' comfort requirements. In the SHEMS, TES is treated an additional distributed energy resource. We develop the mathematical model of the underlying thermodynamical system and analyse the impact of the PCM layer. Due to the inherent properties of the PCM, the resulting optimisation problem becomes non-convex, which requires the use of heuristic optimisation tools. In this paper, we used genetic algorithm to minimise the energy expenditure. The results show that the use of PCMS effectively sheds and shifts the demand to off-peak periods thus reducing the cost for the user.Keywords-Home energy management, distributed energy resources, thermal inertia, phase change materials, thermodynamic process, load shifting, demand response, smart grid, future grids, genetic algorithms. NOMENCLATURE rIndoor (room) w Wall a Ambient ac Air-conditioner C r Specific heat capacity of indoor air[J/kg·K] C w Specific heat capacity of wall [J/kg·K] C el Dynamic electricity price [$/kWh] m r Mass of air inside the house [kg] m w Mass of the wall [kg] N Number of time steps P ac Rated power of air-conditioner [kW] m ac Air mass flow rate through air conditioning system [kg/s] Q ac Heat flow from the air conditioner [J/s] Q wr Heat flow between the wall and the indoor air [J/s] Q wa Heat flow between the wall and the ambient [J/s] Q w Heat gain through windows [J/s] R eq Equivalent thermal resistance of the wall [Km/W] R wa Thermal resistance between the wall's outer surface and the ambient [Km/W] R wr Thermal resistance between the wall's inner surface and the indoor air [Km/W] R w Thermal resistance of the window [Km/W] s ac Operational status of air-conditioner T ac Air temperature coming from air-conditioner [K] T r Indoor temperature of the smart house [K] T 0 r Initial room temperature [K] T min r Minimum limit of room air temperature [K] T max r

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Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

Schminke

2021

Intl J of Energy Research

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The decentralization of energy supply and the fluctuating feed-in from renewable energies lead to an increasing need to generate electricity and heat locally and efficiently. Therefore, different energy systems are used together at building level. Consequently, energy management systems (EMSs) in the building sector and households are becoming increasingly complex and are facing new challenges. To face these challenges, EMSs are required to enable efficient and combined operation of multiple energy systems and components. The increasing research into EMS is leading to a growing field of research with different characteristics, objectives, and algorithms. However, the resulting diversification of the research topic is accompanied by a lack of transparency. Therefore, this article is the first to provide a comprehensive quantitative evaluation of the state of research. For this purpose, 98 relevant publications are quantitatively evaluated. This article focuses on methods, model characteristics, optimization objectives, price structures, observation horizon, and components and devices used of EMS. The detailed evaluation of these topics creates a high level of transparency in EMS research to provide precise insights into current research priorities and potential weaknesses, to allow researchers to classify their own work and to identify potential research topics. Highlights• For the first time, a quantitative study on EMS research is presented • The evaluation of 98 relevant publications provides detailed insights • Insights about methods, characteristics, objectives, prices, horizons, and components • Detailed graphical evaluations for each sub-area of the study K E Y W O R D S energy price structures, energy management systems in buildings, optimization goals, overview

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Addressing the stochastic nature of energy management in smart homes

Cited by 15 publications

References 12 publications

Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems

Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems

Investigating thermal inertia in lightweight buildings for demand response

Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

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