Demand Response in Future Power Networks: Panorama and State-of-the-art

Amini, M. Hadi; Talari, Saber; Arasteh, Hamidreza; Mahmoudi, Nadali; Kazemi, Mostafa; Abdollahi, Amir; Bhattacharjee, Vikram; Shafie‐khah, Miadreza; Siano, Pierluigi; Catalão, João P.S.

doi:10.1007/978-3-319-98923-5_10

Cited by 29 publications

(22 citation statements)

References 88 publications

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“…In [6,7], the price-based DR and incentivebased DR are detailed studied, respectively. A comprehensive survey of DR programs about model, approach and optimisation algorithms is introduce in [8][9][10]. It can be seen that these DR programs works usually put emphasis on aggregation level in retail electricity market, whereas the meaning of DR in wholesale energy market is neglected.…”

Section: Literature Reviewmentioning

confidence: 99%

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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%

“…Load curtailment quantity constraint of DRA is formulated in (9). Load recovery quantity constraints of DRA is represented in (10). Equations (11) and (12) are response logic constraints of DRA in RT…”

Section: Upper-level Model: Dramentioning

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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“…Other mechanisms include inclining block rate (IBR), direct load control (DLC), demand bidding, and capacity programs in the electricity market. A more recent review of state-of-the-art approaches to implement DR programs in a smart grid environment can be found in [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

et al. 2019

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Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

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“…High efficiency, low emissions and multiple energy complements are the development goals of the current energy system. Deploying the demand‐side loads to alleviate the peak of grid, as demand response (DR) programs play an active role at future power system [1]. Integrated energy system (IES) contains distributed generations in energy supply side and elastic loads.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation and cost–benefit allocation for multi‐participant cooperation of integrated energy system

Luo

Zhang

Yang

et al. 2019

IET Generation, Transmission & Distribution

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Demand Response in Future Power Networks: Panorama and State-of-the-art

Cited by 29 publications

References 88 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

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Optimal operation and cost–benefit allocation for multi‐participant cooperation of integrated energy system

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