From demand response to transactive energy: state of the art

Chen, Sijie; Liu, Chen Ching

doi:10.1007/s40565-016-0256-x

Cited by 247 publications

(108 citation statements)

References 31 publications

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“…Previous literature has addressed the coordinated dispatch of a fleet of distributed devices in comparable problem frameworks. Prior approaches can be broadly clustered into three categories: optimal control, mean-field control [4]- [6] and transactive energy [7]- [10]. Our approach lies in the first category and we directly optimise power flows between storage and the grid via explicit feedback policies.…”

Section: A Backgroundmentioning

confidence: 99%

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

Evans

Tindemans

Angeli

2020

IEEE Trans. Smart Grid

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We consider the problem of dispatching a fleet of heterogeneous energy storage units to provide grid support. Under the restriction that recharging is not possible during the time frame of interest, we develop an aggregate measure of fleet flexibility with an intuitive graphical interpretation. This analytical expression summarises the full set of demand traces that the fleet can satisfy, and can be used for immediate and straightforward determination of the feasibility of any service request. This representation therefore facilitates a wide range of capability assessments, such as flexibility comparisons between fleets or the determination of a fleet's ability to deliver ancillary services. Examples are shown of applications to fleet flexibility comparisons, signal feasibility assessment and the optimisation of ancillary service provision. NOMENCLATURE nTotal number of devices D i ith device N Set of all devices e i (t)Energy of the ith device at time t u i (t)Power output of the ith device at time t u(t) Vector of power outputs at time t, across all devices p i Maximum power rating of the ith devicē pVector of maximum power ratings, across all devices P Diagonal matrix of maximum power ratings, across all devices UpProduct set of power constraints, with vector of maximum powersp x i (t), z i (t) Time-to-go of the ith device at time t x(t), z(t) Vector of time-to-go values at time t, across all devices X State-space X(t) ). This work was supported by EPSRC studentship 1688672. P r (t)Reference at time t P r [t0,t)

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Section: A Backgroundmentioning

confidence: 99%

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

Evans

Tindemans

Angeli

2020

IEEE Trans. Smart Grid

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“…According to [21], DR programs are categorized into incentive-based DR (IBDR) (e.g. emergence demand response program, direct load control, interruptible/curtailable service, ancillary service market, capacity market, and demand bidding) and price-based DR (e.g., time-of-use, real time pricing and critical peak pricing).…”

Section: Introductionmentioning

confidence: 99%

Coordinated expansion co-planning of integrated gas and power systems

Wang

Qiu

Meng

et al. 2017

J. Mod. Power Syst. Clean Energy

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As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way to plan natural gas and power systems. In this paper, the co-planning process is formulated as a mixed integer nonlinear programming problem to address emerging challenges, such as system reliability evaluation, market time line mismatch, market uncertainties, demand response effect, etc. An innovative expansion co-planning (ECP) framework is established in this paper to find the best augmentation plan which comes with the minimum cost. Specifically, to cope with uncertainties in market share, decision analysis is introduced. Meanwhile, the energy conversion efficiency between gas and electricity in the coupled load center is considered in the ECP constraints. Comprehensive case studies are applied to validate the performance of proposed approach.

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“…Each prosumer makes intelligent decisions on when and how much to purchase, consume, store, or sell energy, thereby opening the door to competitive energy transactions among peer prosumers as they opt to trade energy directly and freely [65]. Accordingly, prosumers' enhanced market role lays the foundation for implementing a fully decentralized energy trading system [66].…”

Section: Application Scenario: Cyber-secure Transactive Energy Managementioning

confidence: 99%

Cyber-secure decentralized energy management for IoT-enabled active distribution networks

Shahidehpour

2018

J. Mod. Power Syst. Clean Energy

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This paper provides a strategic solution for enhancing the cybersecurity of power distribution system operations when information and operation technologies converge in active distribution network (ADN). The paper first investigates the significance of Internet of Things (IoT) in enabling fine-grained observability and controllability of ADN in networked microgrids. Given severe cybersecurity vulnerabilities embedded in conventionally centralized energy management schemes, the paper then proposes a cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation. In particular, the proposed framework takes advantage of software-defined networking technologies that can secure communications among IoT devices in individual microgrids, and exploits potentials for introducing blockchain technologies that can preserve the integrity of communications among networked microgrids in ADN. Furthermore, the paper presents the details of application scenarios where the proposed framework is employed to secure peer-to-peer transactive energy management based on a set of interoperable blockchains. It is finally concluded that the proposed framework can play a significant role in enhancing the efficiency, reliability, resilience, and sustainability of electricity services in ADN.The development toward ADN has been driven by grid modernization requests for sustainable, flexible, secure, efficient, reliable, resilient electricity services at the power distribution level. As shown in Fig. 1, technological and Cyber-secure decentralized energy management for IoT-enabled active distribution networks 901

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From demand response to transactive energy: state of the art

Cited by 247 publications

References 31 publications

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

Coordinated expansion co-planning of integrated gas and power systems

Cyber-secure decentralized energy management for IoT-enabled active distribution networks

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