An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids

Romero-Quete, David; Rosero, J.

doi:10.1016/j.apenergy.2019.03.159

Cited by 28 publications

(12 citation statements)

References 27 publications

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“…The management algorithm presented in [1] use all these data to solve the optimal economic dispatch problem. The data presented in this brief-data article can assist researchers and companies in the energy sector to simulate and compare various energy management and conversion systems that combine heat and power [4] , intelligent buildings [5] , virtual power plants [6] , microgrids [7] , among others. These meteorological data can validate energy conversion models and the management algorithm of renewable energy based on forecasts [8] .…”

Section: Value Of the Datamentioning

confidence: 99%

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid

et al. 2021

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Section: Value Of the Datamentioning

confidence: 99%

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid

et al. 2021

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“…The interface block represents the flow of information between the microgrid sensors and the MPC. One of the main features of MPCs is the prediction of the output of the process that wants to be controlled,ŷ(t + tk|t) during a prediction horizon N, for k = 1 to N, as a function of the control sequence that is optimized, u(t + tk − 1|t) [32]. The MPC includes a receding horizon strategy; that is, each time the horizon is displaced towards the future, once the optimal control sequence has been calculated, the first control signal for k = 1, u(t), is applied and the remainder is discarded so the control sequence is recalculated at the next time instant, k = 2, with new and updated information [33] (Figure 5).…”

Section: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

et al. 2021

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An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

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“…Xiong and Jirutitijaroen [46] pioneered the combination of AA and ARO for the solution of the energy and reserve dispatch problem. The effectiveness of AA stimulates the use of this approach to tackle the uncertainty in optimal power flow problems [47][48][49][50][51][52][53], and UCs in power systems [54] and isolated micro-grids [55].…”

Section: Introductionmentioning

confidence: 99%

A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty

et al. 2021

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Currently, optimization models for the safe and reliable operation of power systems deal with two major challenges: the first one is the reduction of the computational load when considering N−1 contingencies; the second one is the adequate modeling of the uncertainty of intermittent generation and demand. This paper proposes a new affinely adjustable robust model to solve the security constrained unit commitment problem considering these sources of uncertainty. Linear decision rules, which take into account the forecasts and forecast errors of the different sources of uncertainty, are used for the affine formulation of the dispatch variables, thus allowing the tractability of the model. Another major novelty is that the evaluation of the N−1 security constraints is performed by incorporating a novel method, proposed in the literature, based on the user-cuts concept. This method efficiently and dynamically adds only the binding N−1 security constraints, increasing the computational efficiency of the model when transmission line contingencies are considered. Finally, Monte Carlo simulations on the post-optimization results were run to demonstrate the effectiveness, feasibility and robustness of the solutions provided by the proposed model.

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An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids

Cited by 28 publications

References 27 publications

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty

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