Thermal heat storage is becoming important in systems with renewable energy sources. Their largest benefit is smoothing the intermittent production and reduction in the site peak demand. The ...advantages of thermal energy storage with phase-change material are storing energy at a lower temperature for reduction in thermal losses, and enabling energy transfer at a constant temperature, which reduces the risk of equipment damage. In this paper, a low-order model of latent thermal energy storage, derived in a state-space form by using the mixed logical dynamical approach, is proposed. The model is compared to a stratified model and shows significant improvements of physical accuracy and execution time. Finally, a model predictive control algorithm suited for the real case study is designed, implemented and compared to classical rule-based control. The obtained results show significant energy savings of 8.43%, and improvements in user comfort and equipment duration.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Charging electric vehicles (EVs), whose number is increasing, is a great challenge for the power grid due to the charging load variability. Coordinated charging and schedule optimization with seized ...demand response opportunities are well-known conceptual solutions to that. Still, the main challenge is to adequately predict availability and parameters of electric vehicles which is crucial for determining the charging schedule and the demand response potential. We propose a method to represent a population of electric vehicles that on the one hand enables prediction via machine learning and on the other it enables an accurate optimization of the charging schedule and demand response ability. The method essence is to use five discrete-time signals spanned over a prediction horizon period which are related to envelopes of feasible charging power and charging states for the EV population on that horizon. We also introduce a robust conversion of any sequence of these signals into individual EVs data. It enables to pose and solve the optimization problem of charging scheduling with included demand response for a predicted population in the introduced representation. The proposed method is validated by schedule optimization using first the original data and then using reconstructed population data. The validation results show that the proposed EV population representation method preserves the valuable information needed for the charging schedule optimization and demand response.
This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. ...Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles.
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•Direct control of thermal energy per zone.•Enabled interaction with other building subsystems.•Integral part for upscaling towards smart grid and smart city concepts.•Deployment and verification on a scale of the whole skyscraper building.•Modular service built on top of the existing building automation infrastructure.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Building energy management hierarchical model predictive control is proposed.•Building thermal comfort and microgrid integration greatly reduces operation costs.•Possible savings of ...imminent or more distant technology utilization are illustrated.•Two-tariff and volatile pricing exploitation in energy and cost savings.•Realistic results with 2014 weather data and components datasheet specifications.
Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants. The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energy price profiles. This is a key element for technology separation, replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations, which are suitable for deployment as short- and long- term zero-energy building investments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•PV array static power production model is developed and verified.•Predictions are highly uncertain due to input weather prediction uncertainty.•A predictor-corrector method is developed to improve ...prediction quality.•Correctors are realized as neural networks which are trained on historical data.•Parallel operation of deterministic PV model and neural-network-based correctors.
In this paper we develop and verify a predictor-corrector method for a one-day-ahead photovoltaic array power production prediction. The most critical inputs to the prediction model are predictions of meteorological variables, such as solar irradiance components and the air temperature, which are the main sources of the power prediction uncertainty. Through a straightforward application of the weather forecast data sequence, photovoltaic array power production prediction is refreshed with the frequency of new forecasts generation by the meteorological service. We show that the prediction sequence quality can be significantly improved by using a neural-network-based corrector which takes into account near-history realizations of the prediction error. In this way it is possible to refresh the prediction sequence as soon as new local measurements become available. Except for predictions of meteorological variables, the prediction model itself is also a source of the prediction uncertainty, which is also taken into account by the proposed approach. The proposed predictor-corrector method is verified on real data over a 2-year time period. It is shown that the proposed approach can reduce the standard deviation of the power production prediction error up to 50%, but only for the first several instances of the prediction sequence (up to 6–8h ahead) which are in turn the most relevant for real-time operation of predictive control systems that use the photovoltaic array power production prediction, like microgrid energy flows control or distribution network regulation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper introduces a simple strategy for modular building energy management with explicit demand response based on a three-level hierarchical model predictive control that engages the entire ...building in optimal operation and financially viable flexibility provision. Financial viability of the assessed flexibility capacity is guaranteed regardless of the flexibility activation scenario, under the assumption that the entire building flexibility capacity is accepted. The strategy is verified on three pilot buildings. The selected pilot buildings have diverse configurations and are tested under different conditions to show the broad applicability of the developed approach. The analysis of the building flexibility is focused on particular characteristic days in the heating and cooling season and provides a comparison of the overall building operation costs for the following three control options applied to the building: hierarchical control with and without flexibility provision as well as conventional control. In this way, it is possible to quantify the benefits achievable exactly due to the advanced energy management system operation on the site. Flexibility sources and amounts within the building are analyzed in detail for each specific case.
•Hierarchical MPC-based BEMS for operation scheduling and flexibility provision.•Assessment of the entire building flexibility capacity.•Guaranteed financial viability of flexibility provision.•Verification with a detailed analysis on 3 diverse case-study buildings.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In recent years, application of advanced control, fault detection and diagnosis algorithms for building heating and cooling systems has been intensively investigated with the aim to improve their ...energy efficiency and bring the buildings sector into the smart city arena. Hindering the trend, hysteresis and proportional–integral–derivative controllers are still a common practice for temperature control in buildings with Fan Coil Units (FCUs). Introduction of more sophisticated controllers for additional savings requires a cost-effective approach for identification of an energy model which accurately resembles thermal and hydraulic performance of a system of FCUs. In the present work, the control-oriented energy model of a system of FCUs is developed and accompanied with replicable, robust and simple methodologies for its identification derived by consolidating the advantages of physical modelling, identification methods and manufacturer’s catalogue data. The validity of the developed approach is tested on the 248-office living-lab. The introduced simple and accurate dynamic characterization of energy transmitted from a FCU to zone air fills the gap between thermal and energy management for buildings. This enables implementation of predictive building controls and unleashes significant energy and cost-saving potentials of a smart building in a smart city.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A new model predictive controller for VRLA battery charging is developed.•Convexity of the battery charging optimization problem is proved.•Recursive feasibility and stability of the battery ...charging problem is proved.•The developed VRLA battery charging algorithm is experimentally verified.
In this paper an algorithm for optimal charging of a valve-regulated lead-acid (VRLA) battery stack based on model predictive control (MPC) is proposed. The main objective of the proposed algorithm is to charge the battery stack as fast as possible without violating the constraints on the charge current, the battery voltage and the battery temperature. In addition, a constraint on the maximum allowed voltage of every battery in the battery stack is added in order to minimize degradation of the individual batteries during charging. The convexity of the VRLA battery charging optimization problem is proven, which makes the control algorithm suitable for efficient on-line implementation via solving a quadratically constrained quadratic program (QCQP). The recursive feasibility and stability of the proposed control strategy is ensured. The proposed algorithm is validated both through simulation tests and on the experimental setup.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Described model predictive control algorithm for photovoltaic panel orientation.•Anticipated weather forecast with its uncertainty, panel model and constraints.•Evaluation via numerous scenarios in ...a year-scale simulations with benchmarks.•Mild action towards the positioning systems which improves the overall reliability.•Statistical properties of the future power profile facilitate smart grid integration.
In this paper we develop and verify a model predictive control algorithm for photovoltaic panel orientation with the aim to maximize the photovoltaic system netto power production. Thereby we take into account local weather forecast with its uncertainty, thermal behavior of the panel, and the positioning system energy consumption with its technical constraints. The model predictive control synthesis procedure comprises two basic steps: (i) identification of solar irradiance model and development of the photovoltaic system model and (ii) development of predictive control algorithm for the photovoltaic panel active surface orientation, based on the obtained models. Performance of the developed algorithm is verified through year-scale simulations based on a large number of solar irradiance and other weather data patterns. It turns out that the proposed algorithm is fully competitive with the mostly used sun tracking or maximum irradiance seeking controls, and that it outperforms them. The other advantages of the proposed algorithm are: (i) the positioning system is controlled smoothly and (ii) prediction of energy yield one day ahead is available together with its uncertainty for easier photovoltaic system integration into the electricity distribution network.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In this paper we develop and verify a static model for a photovoltaic array power production prediction by integrating manufacturers’ and on-line data. The static model is a fundamental part of ...dynamic models that are used to predict the photovoltaic array power production along a prediction horizon, and it is important to assess its limit performance to (i) reach maximum accuracy in the power production prediction, and to (ii) enable monitoring of the photovoltaic plant for alerting the owner in the case of unexpectedly low performance. The static model is developed in two subsequent steps: (i) power production model parameters are identified on manufacturers’ data, and (ii) a model corrector is identified on on-line solar irradiance and the photovoltaic array temperature data, which significantly improves accuracy of the static power production model compared to the model identified on manufacturers’ data only. Verification is performed on measurements of solar irradiance components, the PV array temperature and the output power during a 17-month time period. The proposed combination of the model initialization with manufacturers’ data and on-line data-based correction shows very high model accuracy, and enables adaptation to different system setups. It also incorporates robustness to systematic solar irradiance prediction errors.
•PV panel and array static power production models are developed and verified.•Models are developed by integration of manufacturers’ and on-line data.•Fast numerical algorithms are developed to solve a PV panel implicit function.•The model corrector is developed to improve the model overall performance.•Integration of manufacturers’ and on-line data reveals the model limit performance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
