Control of Electricity Loads in Future Electric Energy Systems

Madsen, Henrik; Parvizi, Jacopo; Halvgaard, Rasmus; Sokoler, Leo Emil; Jørgensen, John Bagterp; Hilger, Klaus Baggesen

doi:10.1002/9781118991978.hces033

Cited by 16 publications

(12 citation statements)

References 47 publications

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“…For the sake of the simplicity, natural ventilation, which was usually applied in TEK87 buildings, is modeled as balanced mechanical ventilation with a heat recovery effectiveness η HR of 0%. This study considers the U-values of the building walls, but it should be noted that the building insulation level [15] as well as the thermal capacity of a building [60][61][62] influences the flexibility potential. The climate of Trondheim is also relevant for Norway in general.…”

Section: Case Buildingmentioning

confidence: 99%

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Clauß

Stinner

Solli³

et al. 2019

Energies

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This work introduces a generic methodology to determine the hourly average CO2eq. intensity of the electricity mix of a bidding zone. The proposed method is based on the logic of input–output models and avails the balance between electricity generation and demand. The methodology also takes into account electricity trading between bidding zones and time-varying CO2eq. intensities of the electricity traded. The paper shows that it is essential to take into account electricity imports and their varying CO2eq. intensities for the evaluation of the CO2eq. intensity in Scandinavian bidding zones. Generally, the average CO2eq. intensity of the Norwegian electricity mix increases during times of electricity imports since the average CO2eq. intensity is normally low because electricity is mainly generated from hydropower. Among other applications, the CO2eq. intensity can be used as a penalty signal in predictive controls of building energy systems since ENTSO-E provides 72 h forecasts of electricity generation. Therefore, as a second contribution, the demand response potential for heating a single-family residential building based on the hourly average CO2eq. intensity of six Scandinavian bidding zones is investigated. Predictive rule-based controls are implemented into a building performance simulation tool (here IDA ICE) to study the influence that the daily fluctuations of the CO2eq. intensity signal have on the potential overall emission savings. The results show that control strategies based on the CO2eq. intensity can achieve emission reductions, if daily fluctuations of the CO2eq. intensity are large enough to compensate for the increased electricity use due to load shifting. Furthermore, the results reveal that price-based control strategies usually lead to increased overall emissions for the Scandinavian bidding zones as the operation is shifted to nighttime, when cheap carbon-intensive electricity is imported from the continental European power grid.

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Section: Case Buildingmentioning

confidence: 99%

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Clauß

Stinner

Solli³

et al. 2019

Energies

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“…Through the generation and submission of time-varying prices that depend on the actual conditions of the grid, each SO is able to exploit the flexibility of consumers that are located in its territory. Upon receiving time-varying prices by the EMSs [25], consumers react to minimise their electricity cost. A high-level discussion of AS4.0 setup is provided in [11] and [22].…”

Section: A Brief Description Of As40 Mechanismmentioning

confidence: 99%

A Control-Based Method to Meet TSO and DSO Ancillary Services Needs by Flexible End-Users

Zotti

Pourmousavi

Morales

et al. 2020

IEEE Trans. Power Syst.

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This paper presents a new methodology to exploit consumers' flexibility for the provision of ancillary services (AS) in the smart grid era. The proposed framework offers a controlbased approach that adopts price signalsas the economic driver to modulate consumers' response. In this framework, various system operators broadcast price signals independently to fulfil their AS requirements. Appropriate flexibility estimators are developed from the transmission system operator (TSO) and distribution system operator (DSO) perspectives for price generation. An artificial neural network (ANN) controller is used for the TSO to infer the price-consumption reaction from pools of consumers in its territory. A proportional-integral (PI) controller is preferred to represent the consumers' price-response and generate timevarying electricity prices at the DSO level for voltage management. A multi-timescale simulation model is built in MATLAB to assess the proposed methodology in different operational conditions. Numerical analyses show the applicability of the proposed method for the provision of AS from consumers at different levels of the grid and the interaction between TSO and DSOs through the proposed framework.

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“…The proposed market structures mainly use linear and deterministic bidding and clearing process [3]. Nevertheless, the increasing level of uncertainty in consumption and generation necessitates the adoption of nonlinear and stochastic operation methods [4].…”

Section: Introductionmentioning

confidence: 99%

Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs

Zotti

Binder

Hansen

et al. 2020

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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In power systems, electrical consumers can become a significant source of flexibility, by adjusting their consumption according to grid's needs while respecting their operational constraints. Consumers' flexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Specifically, the nonlinear autoregressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers' price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.

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Control of Electricity Loads in Future Electric Energy Systems

Cited by 16 publications

References 47 publications

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

A Control-Based Method to Meet TSO and DSO Ancillary Services Needs by Flexible End-Users

Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs

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