Real-Time Bilevel Energy Management of Smart Residential Apartment Building

Paul, Subho; Padhy, Narayana Prasad

doi:10.1109/tii.2019.2941739

Cited by 28 publications

(9 citation statements)

References 26 publications

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Section: ) Optimal Ems Power Managementmentioning

confidence: 99%

“…EMSs or demand response systems are widely investigated in the literature [2], [3], [6]- [12], [44]. Among some of the common objectives is to optimize costs [3], [8], [9], [44], achieve net-zero energy [7], [10]- [12], or peak shaving [10]. Often using smart control of storages such as BES systems or EVs in combination with renewable energy resources to achieve these objectives [3]- [5], [7]- [10].…”

Section: ) Optimal Ems Power Managementmentioning

confidence: 99%

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Optimal Sizing and Control of a PV-EV-BES Charging System Including Primary Frequency Control and Component Degradation

Vermeer

Mouli

Bauer

2022

IEEE Open J. Ind. Electron. Soc.

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This paper proposes a method for optimally dimensioning the components of a prosumer energy management system that integrates photovoltaic (PV) panels, multiple bidirectional electric vehicle chargers, an inverter, and a battery energy storage charger. Besides optimally dimensioning the components, it also optimizes power management while integrating the frequency containment reserve market and Li-ion battery degradation. The results show that the integration of the frequency containment reserve (FCR) market can increase lifetime cost savings by 36%, compared to optimal power management alone and up to 460% compared to non-optimal power management. Furthermore, the effects of PV and battery energy storage (BES) degradation on reservable capacity are analyzed including the importance of battery second-life value on lifetime net present cost is investigated.

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Section: ) Optimal Ems Power Managementmentioning

confidence: 99%

Section: ) Optimal Ems Power Managementmentioning

confidence: 99%

Optimal Sizing and Control of a PV-EV-BES Charging System Including Primary Frequency Control and Component Degradation

Vermeer

Mouli

Bauer

2022

IEEE Open J. Ind. Electron. Soc.

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show abstract

“…In [89], reducing wasted energy in various buildings is ensured by optimizing the consumed energy taking into consideration the abrupt changes produced by the rooftop solar generation and the real-time price of energy. Accordingly, a novel parameter called the load criticality rate has been deployed, which represents the threshold value applied by each building occupants to their power consumption.…”

Section: Methodologies and Algorithms For Energy Efficiency Recommend...mentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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“…In [19] the authors have proposed an adaptive backstepping controller to stabilize voltage and frequency in a smart grid and also to control current to share power between renewable energy resources in a smart island. Reference [20] has proposed an energy management strategy based on a novel bi-level stochastic multiobjective optimization model to maximize the utility of the consumed power, simultaneously with the cost minimization. Authors in [21] have proposed a new approach for the optimal energy management of a hybrid electric vehicle considering traffic conditions based on a bi-level decomposition.…”

Section: 𝑔 𝑖mentioning

confidence: 99%

Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment

et al. 2022

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In this paper, a bi-level optimization model including the problem of transmission network market and energy management in the distribution substation is presented. In the proposed bi-level model, the lower-level includes the demand-side management (DSM) program and the optimal charge/discharge of large-scale energy storage system (LSESS) at distribution substations to increase grid profits and send decisions to the upper-level transmission market operator. The upper level of the proposed model is a security-constrained unit commitment (SCUC) to minimize production, no-load, startup, shutdown, and active power curtailment costs, and also the unavailability of the generation units. In this paper, to solve the bi-level optimization problem, the Karush-Kuhn-Tucker (KKT) equation modeling method will be used to turn the problem into a single-level problem. One of the advantages of converting a bi-level model to a single-level model compared to the methods of the decomposition algorithms is the lack of use of iterative algorithms, which leads to an increase in problem-solving time. The proposed model is tested on standard distribution substations and transmission networks, which shows that the proposed method is more effective than decomposition algorithms in terms of problem solving time. The simulation results showed that the proposed method can be more efficient in large optimization problems.

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Real-Time Bilevel Energy Management of Smart Residential Apartment Building

Cited by 28 publications

References 26 publications

Optimal Sizing and Control of a PV-EV-BES Charging System Including Primary Frequency Control and Component Degradation

Optimal Sizing and Control of a PV-EV-BES Charging System Including Primary Frequency Control and Component Degradation

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment

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