Cost-comfort balancing in a smart residential building with bidirectional energy trading

Hasib, Abdullah Al; Nikitin, Nikita; Natvig, Lasse

doi:10.1109/sustainit.2015.7101366

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Different pricing policies are assessed in [19] and [20] to explore the effect on user participation and grid performance. Declining block rates are analyzed in [21] to achieve a balance between electricity cost and user comfort. The role of electricity tariffs in solar panel penetration and the benefit for residential users are explored in [22].…”

Section: Introductionmentioning

confidence: 99%

Optimization-based estimation of power capacity profiles for activity-based residential loads

Gomez-Herrera

Anjos

2019

International Journal of Electrical Power & Energy Systems

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This paper proposes a framework to determine capacity profiles in smart buildings. In this scheme the users choose a level of power capacity to account for their stochastic demand while paying the corresponding electricity prices through a flexible time-and-level-of-use pricing policy. We formulate a two-stage stochastic optimization model that minimizes the total cost of booking a power capacity level and meeting the energy demand for the planning horizon. We present two approaches to select the scenarios for the stochastic optimization. In the first approach, we assume that the probability distributions of the start times of the loads are known, and the scenarios are generated using those distributions. In the second approach, we assume that only historical consumption data is available and we propose a new algorithm to build the scenarios using this data. Our simulation experiments validate the performance of both approaches and report cost savings of up to 16%.

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Section: Introductionmentioning

confidence: 99%

Optimization-based estimation of power capacity profiles for activity-based residential loads

Gomez-Herrera

Anjos

2019

International Journal of Electrical Power & Energy Systems

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“…An autonomous microgrid optimal operation ap-80 proach is presented in [12], considering the generation and consumption sides and the balance between the two in a real-time scenario. A function based on declining block rates achieves a balance between user comfort and electricity cost in [13]. It presents a microeconomic analysis 85 of this function, and the method is used for bidirectional energy trading.…”

Section: Introductionmentioning

confidence: 99%

Optimal collaborative demand-response planner for smart residential buildings

Gomez-Herrera

Anjos

2018

Energy

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This work presents a collaborative scheme for the end-users in a smart building with multiple housing units. This approach determines a day-ahead operational plan that provides demand-response services by taking into account the amount of energy consumed per household, the use of shared storage and solar panels, and the amount of shifted load. We use a biobjective optimization model to trade off total user satisfaction versus total cost of energy consumption. The optimization works in combination with a price structure based on time and level of use that encourages load shifting and benefits the participants. Computational experiments and an extensive sensitivity analysis validate the performance of the proposed approach and help to clarify its strengths, its limits, and the requirements for ensuring the desired outcome.

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“…In this context, the procurement of flexibility by system operators has been extended to end-users and the potential services that communities can provide to network and system operators is a viable alternative solution to traditional capital intensive investments. Various studies have investigated optimal strategies to manage household's energy consumption (and in whole neighbourhoods) [2]- [6]. The ability of ESS to provide flexible consumption strategies for households is studied in [5] and the results show effective reduction in consumption costs.…”

Section: Introductionmentioning

confidence: 99%

“…The ability of ESS to provide flexible consumption strategies for households is studied in [5] and the results show effective reduction in consumption costs. Likewise, the study carried out in [6] proposes a user-comfort demand function to balance maximum consumers' comfort levels with minimum electricity costs in smart residential buildings.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated operation of distributed energy storage in low carbon networks

Rodrigues

Moreira

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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Recent developments on climate change regulations are leading to significant changes in the electricity industry with volatility levels and uncertainty increasing significantly. As a result, system operators are extending the procurement of flexibility to end-users and encourage more pro-active behaviors, including provision of system balancing services. In this context, this paper will investigate the potential flexibility and benefits that a coordinated operation of multiple households' rooftop solar and energy storage can offer to the various sectors of the electricity market. The model will analyze the coordinated operation of multiple households and minimize the supply costs for the whole community while offering flexibility services to the system operator and provide peak demand reduction at the distribution network level. Fundamentally, the proposed model will consider the possibility of households to share energy resources in a peerto-peer arrangement and thus minimize energy supply costs. In addition, the model also considers the provision of frequency response and reserve services and their associated revenues. Our results have shown significant cost savings can be achieved through a coordinated energy trading and consequently reduce energy transactions with suppliers. Provision of flexibility through balancing services becomes the major benefit to support the business case for coordinated operation of storage in low carbon communities.

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Cost-comfort balancing in a smart residential building with bidirectional energy trading

Cited by 9 publications

References 14 publications

Optimization-based estimation of power capacity profiles for activity-based residential loads

Optimization-based estimation of power capacity profiles for activity-based residential loads

Optimal collaborative demand-response planner for smart residential buildings

Coordinated operation of distributed energy storage in low carbon networks

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