Comprehensive smart home energy management system using mixed-integer quadratic-programming

Killian, Michaela; Zauner, Michael; Kozek, Martin

doi:10.1016/j.apenergy.2018.03.179

Cited by 100 publications

(43 citation statements)

References 22 publications

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“…This can be observed in Figure 6. The method of choosing weights in such fashion was inspired from [40] in which, mixed-integer quadratic programming is introduced with multi-objective optimization. The simulation is performed for the duration of 48 hours with prediction and control horizon of 24 hours.…”

Section: Simulation Resultsmentioning

confidence: 99%

Phase Balancing Home Energy Management System Using Model Predictive Control

2018

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Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

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Section: Simulation Resultsmentioning

confidence: 99%

Phase Balancing Home Energy Management System Using Model Predictive Control

2018

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“…It is also used in the context of energy management in SG. For instance, in the aim to reduce energy consumption and cost, the authors of Reference 67 proposed MILP‐based energy optimization algorithms. The electricity bills are presented by a cost function.…”

Section: Optimization Techniques For Energy Tradingmentioning

confidence: 99%

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

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Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

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“…• dynamic pricing- 2,[7][8][9][10][11][12][13][14] • time of use- 10,[15][16][17] • maximum demand- 18 The time of use tariff sets the prices of energy for different times in a day well in advance. It does not represent the actual conditions in the energy market at a given time.…”

Section: Introductionmentioning

confidence: 99%

Economic analysis and control strategy of residential prosumer

Badar

Sanjari

2020

Int Trans Electr Energ Syst

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Summary A smart home requires energy management systems to operate optimally. It has an integrated renewable energy source along with a storage system. The output of the renewable energy source cannot be controlled. The ESS is optimally scheduled for charging and discharging in order to derive the maximum utilization of it. Optimal scheduling of battery integrated smart home is proposed in this paper to minimize the energy costs for a given set of daily electricity tariffs scheme. The sizing of the battery installed in a prosumer system is investigated. An SHEMS is modelled, and an appropriate objective function is defined to reflect the operational costs of a prosumer. The battery efficiency is related to the applied tariff for deciding the operational characteristics of the storage system. The optimal control of the battery operation is then proposed considering its constraints. The sensitivity analysis is performed on the battery to observe its performance in different conditions. The results show the effectiveness of the proposed algorithm to reduce the operational costs of the smart home.

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Comprehensive smart home energy management system using mixed-integer quadratic-programming

Cited by 100 publications

References 22 publications

Phase Balancing Home Energy Management System Using Model Predictive Control

Phase Balancing Home Energy Management System Using Model Predictive Control

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Economic analysis and control strategy of residential prosumer

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