A new strategy based on power demand forecasting to the management of multi-energy district boilers equipped with hot water tanks

Labidi, Mouchira; Eynard, Julien; Faugeroux, Olivier; Grieu, Stéphane

doi:10.1016/j.applthermaleng.2016.11.151

Cited by 20 publications

(8 citation statements)

References 46 publications

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“…This highlights the importance of analysis of seasonality to obtain certain patterns in the LV that can enhance the forecast model performance [2,4]. As an example, the ARX model adopted day/year as an external variable [14] compared to an ANN model in [15] which used the seasonal input parameter, with daytime/day type as external variables. Moreover, in [13] the study does not include an external predictor (weather conditions or temperature) that might aid in diminishing forecast error and increasing energy savings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the traditional ANN forecast model, optimization techniques such as steepest descent and the Gauss Newton method have been used in the literature [12][13][14][15][16] to solve the learning algorithm and achieve the best performance in ANN. Furthermore, these traditional optimization techniques work in finding local optimal parameters for ANN which requires that the objective function needs to simultaneously satisfy the following criteria: smoothness, continuity and differentiability.…”

Section: Ann-grom Forecast Modelmentioning

confidence: 99%

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Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

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More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Ann-grom Forecast Modelmentioning

confidence: 99%

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

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“…A lot of optimisation techniques have been applied to renewable energies [7] and MES [8], such as linear programming [9], nonlinear programming [10], integer programming [11], heuristic [12], meta-heuristic [13], distributed computing [14], robust optimisation [15] and multi-objective optimisation [16]. Indeed, the cost function of optimisation process needs to be minimised [8,7].…”

Section: Introductionmentioning

confidence: 99%

“…Some optimisation techniques are model-free and systems can be considered as black boxes [20]. Some other optimisation techniques are model-based optimisation methods and a physical representation of the system is required [10]. Some papers deal with physical representation as state-space in order to control the system with an optimisation [21].…”

Section: Introductionmentioning

confidence: 99%

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Blaud

Haurant

Claveau

et al. 2020

International Journal of Electrical Power & Energy Systems

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The present study deals with Multi-Energy Systems (MES) modelling and advanced control with Economic Model Predictive Control (EMPC). MES provide energy flexibility, efficiency, and adaptability thanks to several energy carriers. MES are identified as a lever for integrating renewable energy. A MES novel formulation technique called Multi-Prosumer Node (MPN) is developed in this paper. MPN makes possible the modeling of MES, considering MES dynamics, several energy carriers, converters, on-grid, and off-grid. In addition, this MES modeling approach is compatible with predictive control strategies like the EMPC. In fact, EMPC is able to take into account loads, weather, renewable power and energy grid cost predictions to minimise economic costs. A real case study is implemented to examine MPN capabilities, which it is composed of renewable generators, loads, storages from two-energy carriers. Two real scenarios have been developed in order to represent realistic winter and summer cases. Simulation results, thanks to modelling with MPN and EMPC advanced control, demonstrate that the node is optimally controlled, devices dynamics are considered on a minute scale, and energy conversion from one carrier to another one is taken into account while economic cost minimisation is performed. The gained results indicate that the presented MPN modelling and optimisation approach reduces economic cost by 8.21% in winter case and 84.24% in summer case compared to the benchmarks which are composed of rule-based control.

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“…In fact, a considerable number of studies have applied MPC strategies to the HVAC systems of buildings to make them more energy-efficient [20]. The majority of this work has taken advantage of sensible thermal energy storage [21] to further improve energy savings.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control strategy applied to different types of building for space heating

et al. 2018

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In recent years, the concept of energy-efficient buildings has attracted widespread attention due to growing energy consumption in different types of buildings. The application of thermal energy storage (TES) systems, especially latent heat energy storage (LHES), has become a promising approach to improve thermal efficiency of buildings and hence reduces CO 2 emissions. One way to achieve this could be by implementing a model predictive control (MPC) strategy, using weather and electricity cost predictions. To this end, a heat exchanger unit containing a phase change material (PCM) as a LHES medium, thermally charged by 2 solar energy was incorporated into three versions of a standard building. This paper reports on the use of EnergyPlus software to simulate the heating demand profile of these buildings, with Solving Constraint Integer Programs (SCIP) as the optimization tool. After applying the MPC strategy, the energy costs of different building types were evaluated. Furthermore, the effect of the prediction horizon and decision time step of the MPC strategy, and PCM mass capacity on the performance of the MPC were all investigated in 1 and 7-day simulations. The results showed that by increasing the prediction horizon and PCM mass, more cost saving could be obtained. However, in terms of decision time step, although the study revealed that increasing it led to a higher energy saving, it made the system more sensitive to sharp changes as it failed to provide an accurate reading of the parameters and variables.

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A new strategy based on power demand forecasting to the management of multi-energy district boilers equipped with hot water tanks

Cited by 20 publications

References 46 publications

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Model predictive control strategy applied to different types of building for space heating

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