Prevision and planning for residential agents in a transactive energy environment

Toquica, David; Agbossou, Kodjo; Henao, Nilson; Malhamé, Roland P.; Kélouwani, Sousso; Amara, Fatima

doi:10.1016/j.segy.2021.100019

Cited by 18 publications

(8 citation statements)

References 55 publications

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“…Albeit a large number of researches are benefited from TE in the reliable and sustainable operation of the system, its application is not limited only to the operation of the power grid. This fact is proved in reference [61] by proposing a TE model for planning of the residential automated agents aiming to keep the dynamic balance in the system. As future modern energy grids are targeted for full usage of RERs in the power generation sector, several researches are concentrated to use TE technology in developing practical models for handling systems’ interactions with 100% RERs.…”

Section: Transactive Energy Approachmentioning

confidence: 99%

Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review

Kabiri-Renani

Daneshvar

Mohammadi‐Ivatloo

2022

IET Renewable Power Gen

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The electric industry is developing towards a more efficient, reliable, and resilient electric power network. In this way, utilizing distributed energy resources (DERs), especially renewable DERs (RDERs) are a paradigm change. DERs offer many advantages in power systems, including transmission loss reduction, environmental benefits of RDERs, and enhancement of security, reliability, and resiliency of the network. However, high penetration of DERs increases uncertainty and challenges the efficient and reliable operation of the power system. This paper provides a thorough review of the transactive distribution platform which is essential to address the aforementioned challenges. This distribution platform includes a transactive distribution system operator providing a seamless and coordinated control to dynamically balance supply and demand and follow uncertain generations of RDERs. Indeed, this review paper highlights the capability of transactive energy (TE) by considering its key aspect in providing energy sharing opportunities for the integration of DERs into smart grids (SGs). TE is acombination of economic and control methods that enable optimum, reliable, sustainable, and efficient operation of SGs. Furthermore, a fully transactive framework offers more choices for DERs to control and manage the energy transactions in the retail market, as well as improves the inter operability among various market players.

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Section: Transactive Energy Approachmentioning

confidence: 99%

Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review

Kabiri-Renani

Daneshvar

Mohammadi‐Ivatloo

2022

IET Renewable Power Gen

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“…The use of data analytic brought about by digitalisation informs decision making, improves efficiency, improves system planning, reduces unplanned outages, extends asset lifetimes, improves system stability, reduces investment needs, reduces costs and ultimately, reduces carbon emissions. As the COVID-19 pandemic hastens application opportunities for technologies in the domestic sector [33], commercial buildings also present an opportunity for digitalisation for smart technologies. Such as the deployment of smart building controls, enabling demand-side response and offering a variety of new business models that could further reduce energy demand and use in the post-COVID-19 world.…”

Section: Emerging Opportunities Of Digitalisation and Iotmentioning

confidence: 99%

Energy Consumption in Commercial Buildings in a Post-COVID-19 World

Jogunola

Morley²,

Akpan

et al. 2022

IEEE Eng. Manag. Rev.

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The ripple effects of the pandemic have resulted in an unprecedented shift in sectoral energy consumption as the workforce predominantly stays and works from home. Quantifying the impact of these restrictions on energy consumption offers a new direction towards intelligent energy services in a post coronavirus (post-COVID-19) world, especially for commercial buildings. Thus, utilising actual power consumption data, the study evaluates how energy usage in commercial buildings can change in a post-COVID-19 world, whilst examining the impact of digitalisation to identifying potential new opportunities. The paper analyses the changes in energy demand with occupancy rate based on data from 126 commercial businesses with varied classes across Manchester, United Kingdom. The results show that the reduction in energy demand is not proportionate to the occupancy level, resulting in high energy costs. For instance, an average footfall for February 2021 is 10% of 2020, while the costs of electricity only fall to 80% of 2020. Although most of the energy demand is from appliances, the absence of energy efficiency increases energy consumption, highlighting the urgent need for optimised energy efficiency measures to include the time of use and scheduled use of energy across people and processes.

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“…The residential agent controls the thermal loads to minimize the cost of energy consumption considering occupants' desires. Thermal comfort desires are used to formulate the concave utility function through [44],…”

Section: Residential Environmentmentioning

confidence: 99%

A Discount-Based Time-of-Use Electricity Pricing Strategy for Demand Response With Minimum Information Using Reinforcement Learning

et al. 2022

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Demand Response (DR) programs show great promise for energy saving and load profile flattening. They bring about an opportunity for indirect control of end-users' demand based on different price policies. However, the difficulty in characterizing the price-responsive behavior of customers is a significant challenge towards an optimal selection of these policies. This paper proposes a Demand Response Aggregator (DRA) for transactive policy generation by combining a Reinforcement Learning (RL) technique on the aggregator side with a convex optimization problem on the customer side. The proposed DRA can maintain users' privacy by exploiting the DR as the only source of information. In addition, it can avoid mistakenly penalizing users by offering price discounts as an incentive to realize a satisfying multi-agent environment. With an ensured convergence, the resultant DRA is capable of learning adaptive Time-of-Use (ToU) tariffs and generating near-to-optimal price policies. Moreover, this study suggests an off-line training procedure that can deal with issues related to the convergence time of RL algorithms. The suggested process can notably expedite the DRA convergence and, in turn, enable online applications. The developed method is applied to a set of residential agents in order to benefit them by regulating their thermal loads according to generated price policies. The efficiency of the proposed approach is thoroughly evaluated from the standpoint of the aggregator and customers in terms of load shifting and comfort maintenance, respectively. Besides, the superior performance of the selected RL method is represented through a comparative study. An additional assessment is also conducted by use of a coordination algorithm to validate the competitiveness of the recommended DR program. The multifaceted evaluation demonstrates that the designed scheme can significantly improve the quality of the aggregated load profile with a low reduction in the aggregator's income. INDEX TERMS Demand response, demand response aggregator, time-of-use tariffs, reinforcement learning. NOMENCLATURE Indices t Iteration index. i House index. k Time-step index. Parameters ω Trade-off weighting factor of the reward function.

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Prevision and planning for residential agents in a transactive energy environment

Cited by 18 publications

References 55 publications

Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review

Transactive energy revolution: Innovative leverage for reliable operation of modern energy networks—A critical review

Energy Consumption in Commercial Buildings in a Post-COVID-19 World

A Discount-Based Time-of-Use Electricity Pricing Strategy for Demand Response With Minimum Information Using Reinforcement Learning

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