Active control of thermostatic loads for economic and technical support to distribution grids

Mendaza, Iker Diaz De Zerio; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Karki, Nava Raj; Gentle, Jake P.; Myers, Kurt S.

doi:10.1109/pesgm.2016.7741735

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Transactive agent [33] General framework [32,[34][35][36][37][38][39][40][41][42][43] HVAC (Consumer) [38] Refrigerator (Consumer) [35,[44][45][46][47] Water heater (Consumer) [48][49][50] EV (Prosumer) [51][52][53][54] Energy storage devices (Prosumer) [55,56] DG units (Producer)…”

Section: Refmentioning

confidence: 99%

“…The energy band in the hot water storage tank can be adjusted in accordance with the electricity price to utilise low-price periods and feeder loading conditions/voltages to take advantage of low-demand periods. The state of energy (SOE) and price-based adaptive SOE limits were modelled as follows [44]:…”

Section: 14mentioning

confidence: 99%

“…In (44), the term 'Water' is the amount of water extracted from the well. Consumption t wellwater is the amount of water consumed from well at time t .…”

Section: E Lossmentioning

confidence: 99%

“…The energy band in the hot water storage tank can be adjusted in accordance with the electricity price to utilise low‐price periods and feeder loading conditions/voltages to take advantage of low‐demand periods. The state of energy (SOE) and price‐based adaptive SOE limits were modelled as follows [44]:

\begin{equation}\text{SOE}_t = \frac{{{E}_{\rm initial} + {E}_{\rm IN} - {E}_{\rm OUT} - {E}_{\rm Loss}}}{{{E}_{\rm FC}}}\end{equation}

where E initial is the initial energy in the hot water storage tank, E IN is the input energy to the tank, E OUT is the hot water drawn from the tank, E LOSS is the energy loss due to the temperature difference, and E FC is the energy capacity of the tank.

$$\begin{equation}\left.

…”

Section: Level 1: Dermentioning

confidence: 99%

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Transactive energy management systems: Mathematical models and formulations

Thangavelu

2023

Energy Conversion and Econom

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Restructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF-related literature is divided into individual DER-, building-, microgrid-, and macrogrid-level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi-energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

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Section: Refmentioning

confidence: 99%

Section: 14mentioning

confidence: 99%

“…In (44), the term 'Water' is the amount of water extracted from the well. Consumption t wellwater is the amount of water consumed from well at time t .…”

Section: E Lossmentioning

confidence: 99%

\begin{equation}\text{SOE}_t = \frac{{{E}_{\rm initial} + {E}_{\rm IN} - {E}_{\rm OUT} - {E}_{\rm Loss}}}{{{E}_{\rm FC}}}\end{equation}

$$\begin{equation}\left.

…”

Section: Level 1: Dermentioning

confidence: 99%

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Transactive energy management systems: Mathematical models and formulations

Thangavelu

2023

Energy Conversion and Econom

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“…The solutions to (4)- (7) constrains the dissatisfaction of the consumer to its choice level of tolerance thereby offering scheduling with minimised dissatisfaction. Although this paper does not go into detail, thermal analysis of heating/cooling appliances [21,22], consumer satisfaction is still ensured using (5) whose second derivative is less than zero for x a,j,t < u a,j,t and x a,j,t < u a,j,t . This, therefore, ensures that the consumer is not infinitely satisfied by increasing appliance power consumption.…”

Section: Appliance Scheduling Dissatisfaction Modelmentioning

confidence: 99%

Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid

et al. 2017

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The contributions of Distributed Energy Generation (DEG) and Distributed Energy Storage (DES) for Demand Side Management (DSM) purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s). A grid-connected smart microgrid comprising heterogeneous (active and passive) smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA) installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction) and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR) demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.

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