Design and Cosimulation of Hierarchical Architecture for Demand Response Control and Coordination

Bhattarai, Bishnu P.; Lévesque, Martin; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Maier, Martin; Tipper, David; Myers, Kurt S.

doi:10.1109/tii.2016.2634582

Cited by 50 publications

(36 citation statements)

References 35 publications

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“…In Reference [29], a hierarchical DR architecture is proposed in order to control and coordinate various DR categories. In Reference [30], the author refers to the way that incentivizing DR with flat incentives implies with the revenues of retailers.…”

Section: Related Literaturementioning

confidence: 99%

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

2018

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Demand response aggregators have been developed and implemented all through the world with more seen in Europe and the United States. The participation of aggregators in energy markets improves the access of small-size resources to these, which enables successful business cases for demand-side flexibility. The present paper proposes aggregator's assessment of the integration of distributed energy resources in energy markets, which provides an optimized reschedule. An aggregation and remuneration model is proposed by using the k-means and group tariff, respectively. The main objective is to identify the available options for the aggregator to define tariff groups for the implementation of demand response. After the first schedule, the distributed energy resources are aggregated into a given number of groups. For each of the new groups, a new tariff is computed and the resources are again scheduled according to the new group tariff. In this way, the impact of implementing the new tariffs is analyzed in order to support a more sustained decision to be taken by the aggregator. A 180-bus network in the case study accommodates 90 consumers, 116 distributed generators, and one supplier.

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Section: Related Literaturementioning

confidence: 99%

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

2018

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“…This ensures that the cost of energy for battery charging was minimised while the revenue from V2G was maximised. The use of PEVs in V2G/G2V operations encourage prosumers' participation in the energy value chain, but with different objectives from other actors [45,46]. This is desirable because it also ensures that the user contributes to the grid when electricity is needed the most, thereby maximizing the economic benefits while contributing to the power balance.…”

Section: Personal Vehiclesmentioning

confidence: 99%

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

et al. 2017

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Abstract:The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

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“…According to the above-mentioned classification of electrical loads in the industrial sector, we can conclude that some of these electrical loads (known as flexible demand) may respond well to DR programs [9] However, there are several technical/operational challenges and obstacles ahead of the application of such programs in the industrial sector. These mainly include [10][11][12] In the industrial sector, electrical loads can be categorized into several groups in terms of their response to DR programs. In [9], these loads are divided into three categories:…”

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confidence: 99%

“…According to the above-mentioned classification of electrical loads in the industrial sector, we can conclude that some of these electrical loads (known as flexible demand) may respond well to DR programs [9] However, there are several technical/operational challenges and obstacles ahead of the application of such programs in the industrial sector. These mainly include [10][11][12] In general, access to a flexible responsive load economic model (FRLEM) greatly contributes to our knowledge about the impact of consumer participation in DR programs and their influence on the load profile [13][14][15]. Therefore, many such models have been developed for DR programs of different types, such as time of use (TOU), critical peak pricing (CPP), and real-time pricing (RTP)-based programs [16][17][18][19][20].…”

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confidence: 99%

A Flexible Responsive Load Economic Model for Industrial Demands

et al. 2019

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The best pricing method for any company in a perfectly competitive market is the pricing scheme with regards to the marginal cost. In contrast to this environment, there is a market with imperfect competition. In this market, the price can be affected by some players in the generation/demand side (i.e., suppliers and/or buyers). In the economic literature, “market power” refers to a company that has the power to affect prices. In fact, market power is often defined as the ability to divert prices from competitive levels. In the electricity market, especially because of the integration of intermittent renewable energy resources (RESs) along with the inflexibility of demand, there are levels of market power on the supply side. Hence, implementation of demand response (DR) programs is necessary to increase the flexibility of the demand side to deal with the intermittency of renewable generations and at the same time tackle the market power of the supply side. This paper uses economic theories and mathematical formulations to develop a flexible responsive load economic model (FRLEM) based on real-time pricing (RTP) to show modification of the load profile and mitigation of the energy costs for an industrial zone. This model was developed based on constant elasticity of the substitution utility function, known as one of the most popular utility functions in microeconomics.

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Design and Cosimulation of Hierarchical Architecture for Demand Response Control and Coordination

Cited by 50 publications

References 35 publications

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

A Flexible Responsive Load Economic Model for Industrial Demands

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