Distributed optimization algorithm for heat pump scheduling

Diekerhof, Michael; Vorkampf, Stefanie; Monti, Antonello

doi:10.1109/isgteurope.2014.7028943

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…However, given the long-term scope of this study, it is considered that the average value used deliver sensible results. Other works with similar COP range can be found in [49], [54] and [55].…”

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confidence: 81%

Optimal planning and operation of aggregated distributed energy resources with market participation

et al. 2016

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This paper analyzes the optimal planning and operation of aggregated distributed energy resources (DER) with participation in the electricity market. Aggregators manage their portfolio of resources in order to obtain the maximum benefit from the grid, while participating in the day-ahead wholesale electricity market. The goal of this paper is to propose a model for aggregated DER systems planning, considering its participation in the electricity market and its impact on the market price. The results are the optimal planning and management of DER systems, and the appropriate energy transactions for the aggregator in the wholesale day-ahead market according to the size of its aggregated resources. A price-maker approach based on representing the market competitors with residual demand curves is followed, and the impact on the price is assessed to help in the decision of using price-maker or price-taker approaches depending on the size of the aggregated resources. A deterministic programming problem with two case studies (the average scenario and the most likely scenario from the stochastic ones), and a stochastic one with a case

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confidence: 81%

Optimal planning and operation of aggregated distributed energy resources with market participation

et al. 2016

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“…Eq. (56) can be split into two single objective functions using Lagrange Multipliers, λ, which is also the virtual price signal [27], [28]. The Lagrangian function of (55) with coupling constraint (56) can then be formulated in the following.…”

Section: G Distributed Algorithm For Te Realization In the Distribution Systemmentioning

confidence: 99%

“…In order to solve the above problem, a distributed optimization algorithm [26], [27] is applied to decompose the problem so that the complexity will be greatly reduced. The EVA and DSO will optimize their own schedules but with mutual interests which are linked by the λ.…”

Section: G Distributed Algorithm For Te Realization In the Distribution Systemmentioning

confidence: 99%

A Network-Constrained Rolling Transactive Energy Model for EV Aggregators Participating in Balancing Market

Hou

Yang

et al. 2020

IEEE Access

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“…However, given the long-term scope of this study, it is considered that Energy Management of Smart Cities 39 Christian F. Calvillo Muñoz using an average value can deliver sensible results. Other works using a similar COP simplification can be found in [160], [164] and [165].…”

Section: Heat Pump (Hp) Modelmentioning

confidence: 99%

5.12 Energy Management in Smart Cities

Fokaides

Apanavičienė

Klumbytė

2018

Comprehensive Energy Systems

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The Degree Certificates are giving reference to the joint programme. The doctoral candidates are jointly supervised, and must pass a joint examination procedure set up by the three institutions issuing the degrees. This Thesis is a part of the examination for the doctoral degree. The invested degrees are official in Spain, the Netherlands and Sweden, respectively. SETS Joint Doctorate was awarded the Erasmus Mundus excellence label by the European Commission in year 2010, and the European Commission's Education, Audiovisual and Culture Executive Agency, EACEA, has supported the funding of this programme. The EACEA is not to be held responsible for contents of the Thesis. vi Energy Management of Smart Cities Christian F. Calvillo Muñoz Energy Management of Smart Cities 1 Christian F. Calvillo Muñoz Chapter 7 Complete smart city energy model: aggregated DER and transport systems with active market participation Chapter 8 Conclusions, contributions and future work Chapter 1 Energy Management of Smart Cities 9 Christian F. Calvillo Muñoz 2 2 LITERATURE REVIEW This chapter presents the identification of energy systems task, corresponding to the smart city energy model methodology described in section 1.3. This includes the evaluation of energy systems available in a smart city, assessing their importance and possible synergies, in addition to the review of the existing models and tools, to select which ones could be used. The first part of the chapter introduces an overview of energy related work on planning and operation models within the smart city by classifying their scope into five main intervention areas. These areas are generation, storage, infrastructure, facilities, and transport. Then, more complex urban energy models integrating more than one intervention area are reviewed, outlining their advantages and limitations, existing trends and challenges, as well as some relevant smart cities applications. With this revision, important elements and possible synergies in the smart city are identified, and a framework for developing a comprehensive smart city energy model is proposed, along with some additional recommendations. Therefore, the main contributions of this chapter are, first, a comprehensive literature review of smart city energy systems and related modelling tools, classifying them in five intervention areas, and second, a framework with guidelines and recommendations for developing a comprehensive smart city energy model.

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Distributed optimization algorithm for heat pump scheduling

Cited by 12 publications

References 6 publications

Optimal planning and operation of aggregated distributed energy resources with market participation

Optimal planning and operation of aggregated distributed energy resources with market participation

A Network-Constrained Rolling Transactive Energy Model for EV Aggregators Participating in Balancing Market

5.12 Energy Management in Smart Cities

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