Modular energy cost optimization for buildings with integrated microgrid

Lešić, Vinko; Martinčević, Anita; Vašak, Mario

doi:10.1016/j.apenergy.2017.03.087

Cited by 53 publications

(16 citation statements)

References 35 publications

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“…Note that N is the finite horizon that can range from hours, days, or months [73]. Depending on the nature of the cost functions, system dynamics and involved variables, strategies like Stochastic MPC, Deterministic MPC, and even Hierarchical MPC have been implemented and verified on various platforms achieving economical operations of the microgrids [74][75][76][77][78].…”

Section: Microgrid Connected Building Comfort Management Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

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Section: Microgrid Connected Building Comfort Management Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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“…A modular coordination of building comfort and microgrid energy flows using an MPC scheme is presented [33]. The developed smart building scheme enables cost savings under microgrid energy price profiles.…”

Section: Mpc-based Energy Managementmentioning

confidence: 99%

“…Through Eq. (26), the vectors that represent the state constraints can be described as follows: (23) and (33) demonstrate that it is observed that some additional constraints on the system can be rewritten. These are constituted by the constraint from the utility grid, demand side constraint, restrictions of the PV and battery storage.…”

Section: System Constraintsmentioning

confidence: 99%

An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Mbungu

Bansal

Naidoo

et al. 2018

Sustainable Cities and Society

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Highlights• Develops a real-time electricity pricing structure in conjunction with TOU electricity tariff. • Develops an adaptive strategy in the framework of the smart grid using MPC design. • Develops a new approach to optimization of the cost of energy in real-time electricity pricing environment. • Designs an optimal strategy for integrating PV with battery storage to the grid in realtime electricity pricing scheme. • Gives the consumer an opportunity to deal with the cost of electricity usage.Abstract-This paper presents an approach to the energy management and control of the effective cost of energy in real-time electricity pricing environment. The strategy aims to optimise the overall energy flow in the electrical system that minimises the cost of power consumption from the grid. To substantiate these claims different cases of time-of-use (TOU) and renewable energy electricity tariff, i.e. in summer and winter seasons, and the robustness of system is analysed. A given energy demand for commercial usage in the city of Tshwane (South Africa) is used to investigate the behaviour of the designed method during low and high demand periods. As grid integrated renewable energy resources, photovoltaic (PV) is an important consideration in assuring excellent power supply and environmental issues in the commercial building. An adaptive optimal approach in the framework of model predictive control (MPC) is designed to coordinate the energy flow on the electrical system. The results show that the proposed adaptive MPC strategy can promote the new approach of an optimal electrical system design, which reduces the energy cost to pay the utility grid by about 46 % or more depending on the set target. Index Terms-Battery bank, Energy management, Model predictive control, Photovoltaic, Smart grid, Time-of-use tariff. NOMENCLATURE state matrix (-) input matrix (-) cost of electricity to pay or output vector (Rand or R) 1 cost of electricity to pay the utility grid (Rand or R) 2 cost of electricity consumption from the renewable energy (Rand or R) 3 cost of electricity consumption by the load (Rand or R) output matrix (-) 1 input vector (kWh) 1 energy flow of the utility grid (kWh) 2 energy flow of the PV (kWh) 3 energy flow of the charging of battery (kWh) 4 energy flow of the discharging of battery (kWh) energy consumption (kWh) charging energy on battery (kWh) discharging energy from battery (kWh) The deployment of buildings' energy management systems started in the 1970s when the development of direct digital control signal from the advent of a microprocessor was introduced.It offers the ability to control and manage the energy system by providing the users with a betterquality environment. Since its inception, it has been observed that the development of energy Page 3 of 38 energy demand (kWh) energy drawn from battery (kWh) energy consumption at sample (kWh) energy supply from grid (kWh) maximum energy from battery (kWh) energy nominal on battery (kWh) energy supply from photovoltaic array (kWh) PV energy consumption at...

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“…These methods allow the development of a system that operates autonomously but depends on collected data and the training of a model that must be retrained as parameters change over time. Moreover, if the prediction period is too long, the approach is not suitable for microgrid systems that support different types of energy storage [9][10][11] based on dynamic adaptation and parameters of the system based on capacity, current load, and speed of energy storage. Compared to the method described in this paper, machine learning-based systems require a large set of historical data and time to train the models to make the prediction.…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

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Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

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Modular energy cost optimization for buildings with integrated microgrid

Cited by 53 publications

References 35 publications

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Solar Irradiance Forecast Based on Cloud Movement Prediction

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