A Bilevel Model for Optimal Bidding and Offering of Flexible Load Aggregator in Day-Ahead Energy and Reserve Markets

Jia, Yang; Mi, Zengqiang; Yu, Yang; Song, Zhuoliang; Sun, Chenjun

doi:10.1109/access.2018.2879058

Cited by 45 publications

(28 citation statements)

References 27 publications

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“…In addition, there are some other types of aggregators that have been extensively studied. In [26], a bilevel model for bidding strategy of flexible load aggregator in DA is presented, considering the operational constraints of multiple flexible loads. In [27,28], two profit maximisation models for microgrid aggregator in retail market are proposed, in which the microgrid aggregators are considered to be price-maker and price-taker, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Tri‐level decision‐making framework for strategic trading of demand response aggregator

Jia

et al. 2019

IET Renewable Power Generation

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This article presents a new decision-making framework for strategic trading of demand response aggregator (DRA). The proposed framework consists of three layers. At the first layer, the wholesale energy market operated by independent system operator (ISO), which seeks to maximise the social welfare in day-ahead (DA) market and to minimise the cost of minimise the cost for maintaining energy balance in real-time (RT) markets. At the second layer, the DRA act as an intermediate operator between ISO and flexible customers. Profit-maximising DRA affects the clearing results of market prices by strategic trading in DA energy market, providing reserve capacity in DA reserve market and offsetting energy deviation in RT. At the third layer, customers seek to optimise the trading strategy between earnings incentive reward from the DRA and considering uncomfortable cost of customers. The tri-layer framework is modelled as two bilevel optimisation models, which capture interaction between different entities. The bilevel model can be transformed to single level linear model through Karush-Kuhn-Tucker conditions and dual theory to solve the couple non-linear problem. The numerical studies using the IEEE 9-bus and 118bus test systems are presented to illustrate the application of the proposed tri-layer decision-making framework.

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Section: Literature Reviewmentioning

confidence: 99%

Tri‐level decision‐making framework for strategic trading of demand response aggregator

Jia

et al. 2019

IET Renewable Power Generation

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“…The authors in [12] consider a framework in which an aggregator of distributed storage energy systems, electric vehicles, and temperature control loads is present and bids in day-ahead energy and reserve markets. Even in this case, a bi-level optimization model is proposed.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Optimal Control of Multiple Microgrids and Buildings by an Aggregator

et al. 2020

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The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

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“…The concept of Demand Response (DR) introduced a new role for consumers in the grid. Becoming more active agents, end-users can reduce demand according to technical or economic problems or even in response to price signals and incentives, having more information about what is happening in the network infrastructure [1][2][3][4]. DR programs focus mostly on large-size resources despite recent efforts to highlight small-size ones as well.…”

Section: Introductionmentioning

confidence: 99%

Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response

2019

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Distributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present paper, a methodology for distributed resources management by an aggregator is proposed, which includes the resources scheduling, aggregation and remuneration. The aggregation, made using a k-means algorithm, is applied to different approaches concerning the definition of tariffs for the period of a week. Different consumer types are remunerated according to time-of-use tariffs existing in Portugal. Resources aggregation and remuneration profiles are obtained for over 20.000 consumers and 500 distributed generation units. The main goal of this paper is to understand how the aggregation phase, or the way that is performed, influences the final remuneration of the resources associated with Virtual Power Player (VPP). In order to fulfill the proposed objective, the authors carried out studies for different time frames (week days, week-end, whole week) and also analyzed the effect of the formation of the remuneration tariff by considering a mix of fixed and indexed tariff. The optimum number of clusters is calculated in order to determine the best number of DR programs to be implemented by the VPP.

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A Bilevel Model for Optimal Bidding and Offering of Flexible Load Aggregator in Day-Ahead Energy and Reserve Markets

Cited by 45 publications

References 27 publications

Tri‐level decision‐making framework for strategic trading of demand response aggregator

Tri‐level decision‐making framework for strategic trading of demand response aggregator

Optimal Control of Multiple Microgrids and Buildings by an Aggregator

Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response

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