PVM-based intelligent predictive control of HVAC systems

This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in terms of economical savings and thermal comfort, are compared with standard, temperature regulated control.(1) (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved

기후 변동성을 고려한 재생 에너지 기반 마이크로 그리드의 기술 경제성 분석 및 에너지 관리 기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2023. 2. 이종민.Micro-grids based on renewable energy resources have become a pivotal technology to address the growth of global climate crisis. While renewable energy is essential for the micro-grids, it has an intermittent nature and strong uncertainty, thus the climatic variability is a key issue for the micro-grids. Nevertheless, previous micro-grid's techno-economic analyses have rarely taken account of climatic variability, and there have been few studies related to sizing and energy management of a multi-stack micro-grid. We exploit big data driven analysis and mixed-integer stochastic energy management to resolve these issues. Utilizing climate data from 13,488 regions in 218 countries, climatic variability in techno-economic analysis is investigated. After reprocessing the data via uniform manifold approximation and projection, the dimensionally reduced data are clustered using hierarchical density-based spatial clustering of applications with noise algorithm, and optimal sizes of clusters micro-grids are compared to each other clusters according to climate patterns. The effects of climate on the sizes and costs of micro-grids are revealed based on the climate sensitivity analyses, which emphasizes the need to take climatic fluctuations into account when designing micro-grids. To decide structures and sizes of stacks, we propose mixed-integer stochastic programming that is appropriate for energy management of a multi-stack micro-grid under climate uncertainty. Validation of the proposed method's performance is followed by verification of the climatic influences on design of a multi-stack micro-grid through each illustrative example. In conclusion, it is indicated that climatic variability takes a significant role in micro-grids based on renewable energy. The contributions of this thesis can be written as follows: First, the correlation analysis through unsupervised clustering is carried out to verify that climatic variability is a factor that determine the design of techno-economical micro-grids. Mitigating their noise and clustering them via UMAP and HDBSCAN algorithm, climate data from 13,844 cities in 218 nations are used to the correlation analysis. Second, the strategies to install and operate a micro-grid during long project's lifespan are suggested according to regional climatic features. In third, a mixed-integer stochastic programming is developed to control a multi-stack micro-grid's energy distributions. Finally, it is verified that the climatic effects are noticeable in design of a multi-stack micro-grid.전세계적인 기후 위기의 증가를 대처하기 위해서 재생가능한 에너지원을 기반으로 하는 마이크로 그리드 (micro-grid) 는 중심 기술이 되고 있다. 재생 에너지는 마이크로 그리드에 필수적이지만 간헐적인 특성과 강한 불확실성을 가지고 있어 기후 변동성이 마이크로 그리드의 핵심 문제이다. 그럼에도 불구하고, 기존의 마이크로 그리드의 기술 경제성 분석들은 기후 변동성을 거의 고려하지 않았으며, 다중 스택 (multi-stack) 마이크로 그리드의 에너지 크기 조정 및 에너지 관리와 관련된 연구는 거의 없다. 우리는 이러한 문제를 해결하기 위해 빅 데이터 기반 분석과 혼합 정수 확률론적 기반의 (mixed-integer stochastic) 에너지 관리를 활용하였다. 218개국 13,488개 지역의 기후 데이터를 활용하여 기술 경제 분석의 기후 변동성을 조사하였다. 균일한 매니폴드 근사 및 투영 (uniform manifold approximation and projection) 을 통해 데이터를 전처리한 후 노이즈를 사용한 계층적 밀도 기반 공간 클러스터링 (hierarchical density-based spatial clustering of applications with noise) 알고리즘을 사용하여 차원 축소된 데이터를 클러스터링하고, 기후 패턴에 따라서 클러스터의 마이크로 그리드의 최적 크기를 서로 비교하였다. 기후 민감도 분석으로 마이크로 그리드의 규모와 비용에 기후가 미치는 영향을 밝혀냈으며, 이는 마이크로 그리드의 설계 시 기후 변동을 고려할 필요성을 강조한다. 다중 스택 마이크로 그리드의 구조와 스택의 크기를 결정하기 위해서 우리는 기후 불확정성의 존재하에서 다중 스택 마이크로 그리드의 에너지 관리에 적합한 혼합 정수 확률 프로그래밍 (mixed-integer stochastic programming ) 를 제안하였다. 각각의 예시 문제를 통해서 제안된 방법이 유효한 것을 확인한 이후에 다중 스택 마이크로 그리드의 설계에 기후가 영향을 미치는 것을 입증하였다. 결과적으로, 이는 재생 에너지 기반의 마이크로 그리드에서 기후 변동성이 중요한 역할을 하는 것을 시사한다. 본 학위논문이 제시하는 분석 및 방법의 특징은 다음과 같이 요약할 수 있다. 우선, 기후 변동성이 기술 경제적인 마이크로 그리드의 설계의 결정 요인 중 하나라는 것을 확인하기 위해서 비지도학습 클러스터링 (unsupervised clustering) 을 이용한 관계성 분석을 시행하였다. 균일한 매니폴드 근사 및 투영과 노이즈를 사용한 계층적 밀도 기반 공간 클러스터링 알고리즘을 사용하여 218개국가의 13,844개 지역의 기후 데이터의 노이즈를 완화시키고 클러스터링을 진행하였다. 다음으로, 지역적인 기후 특징을 바탕으로 마이크로 그리드의 설치와 장기적인 운영을 위한 전략을 제안하였다. 세번째로는, 다중 스택 마이크로 그리드의 에너지 분배를 제어하기 위해서 혼합 정수 확률 프로그래밍 방법론을 개발하였다. 마지막으로, 다중 스택 마이크로 그리드 설계에서 기후 영향이 두드러짐을 확인했다.1. Introduction 1 1.1 Motivation and previous work 1 1.2 Statement of contributions 5 1.3 Outline of the thesis 7 2. Background and preliminaries 8 2.1 Uniform manifold approximation and projection 8 2.2 Hierarchical density-based spatial clustering of applications with noise 9 2.3 Equipments models used in micro-grid 9 2.3.1 PV module 10 2.3.2 Wind turbine 11 2.3.3 Electrolyzer 11 2.3.4 Fuel cell 12 2.3.5 Energy storage - battery and hydrogen tank 12 2.4 Net present cost 13 2.5 Stochastic model predictive control 16 2.5.1 Stochastic tube model predictive control 17 3. Techno-economic analysis of micro-grid system design through climate region clustering 19 3.1 Introduction 19 3.2 Methods 22 3.2.1 Climatic feature extraction by UMAP 22 3.2.2 Clustering climate groups by HDBSCAN 24 3.2.3 Problem formulation 29 3.3 Result and discussion 33 3.3.1 Feature extraction and clustering of regional climate 33 3.3.2 Optimization of micro-grid considering climate variations 43 3.3.3 Sensitivity analysis on the optimal unit sizes to climate variability 59 4. Energy management and design of multi-stack micro-grid under climatic uncertainty 66 4.1 Introduction 66 4.2 Method 70 4.2.1 Objective function 70 4.2.2 Irradiance and load demands 71 4.2.3 Mixed-integer stochastic programming for a multi-stack micro-grid 74 4.3 Result and discussion 80 4.3.1 Size decision of a multi-stack micro-grid under climate uncertainty 80 5. Concluding remarks 86 5.1 Summary of the contributions 87 5.2 Future works 88 Bibliography 91박

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

Model predictive control for building automation

© 2018. The AuthorsWe propose a building HVAC system, integrating local energy production and storage, together with a model based controller. The heating system integrates several local heat production and storage devices and multiple fluid circuits at different temperatures to minimize entropy production through mixing. The controller uses a model of the System and predictive knowledge of demand and weather information to minimize electrical energy import, while maintaining thermal comfort by solving mixed integer optimization problems online. Time-varying and unknown system parameters are estimated and adapted online, using an unscented Kalman filter. The adaptation greatly reduces modeling effort and maintenance cost. The proposed setup is tested in a co-simulation, using a physical (modelica-) model of the building and energy system as well as realistic weather and demand data. Our system delivers nearly seven times more energy in the form of heat, than it needs to import (electrical) energy from external sources

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: 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.info:eu-repo/semantics/publishedVersio

An artificial neural network approach for modelling the ward atmosphere in a medical unit

Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four-year-long experimental data set, with the aim of analyzing the performance and clinical outcome of an existing medical ward, and predicting the effects that possible readjustments and/or interventions on the structure may produce on it. Advantages of the ANN technique over more traditional mathematical models are twofold: on one hand, this approach deals quite naturally with a large number of parameters/variables, and also allows to identify those variables which do not play a crucial role in the system dynamics; on the other hand, the implemented ANN can be more easily used by a staff of non-mathematicians in the unit, as an on-site predictive tool. As such, the ANN model is particularly suitable for the case study. The predictions from the ANN technique are then compared and contrasted with those obtained from a generalized kinetic approach previously proposed and tested by the authors. The comparison on the two case periods shows the ANN predictions to be somewhat closer to the experimental values. However, the mean deviations and the analysis of the statistical coefficients over a span of multiple years suggest the kinetic model to be more reliable in the long run, i.e., its predictions can be considered as acceptable even on periods that are quite far away from the two case periods over which the many parameters of the model had been optimized. The approach under study, referring to paradigms and methods of physical and mathematical models integrated with psychosocial sciences, has good chances of gaining the attention of the scientific community in both areas, and hence of eventually obtaining wider diffusion and generalization.

Adaptive Variable Structure Observer for System States and Disturbances Estimation with Application to Building Climate Control System in a Smart Grid

In order to reach the ambitious net-zero emission target by 2050, various technological solutions need to be developed to ensure efficient utilisation of energy. Commercial and residential buildings are a big source of greenhouse gas emissions, where efficient utilisation of energy can play a major role towards decarbonisation of the buildings sector. Heat pumps have recently emerged as an effective solution for space heating applications in buildings. Energy-efficient operation of heat pumps will make a significant contribution toward making buildings energy-efficient. In this context, heat pump control systems have a major role. Some of the existing literature on the heat pump control systems assume that various system states are available to measure. This may not always be true and/or economical to measure all the states. Moreover, the system is subject to various disturbances which cannot be directly measured. To reduce the number of sensors in heat pump control systems, an adaptive observer is developed in this paper to estimate inaccessible system states and disturbances simultaneously. An advantage of the proposed approach is that it does not require any bound on the disturbance itself, however, only assumes that the rate of change of disturbance is bounded. This is always the case in practice. In the developed method, adaptive control techniques and variable structure control techniques are combined to implement the proposed observer. In order to estimate the unknown disturbance, an augmented systems model is considered. Globally uniformly ultimately bounded property of the error dynamical systems is established by suitably designing the adaptive laws. The developed method is applied to a model of the heat dynamics of a house floor heating system connected to a ground source-based heat pump. Different disturbance signals formats and amplitudes are considered to show the effectiveness of the proposed technique. Simulation results are given to demonstrate the suitability of the proposed method

Compressibility, laws of nature, initial conditions and complexity

We critically analyse the point of view for which laws of nature are just a mean to compress data. Discussing some basic notions of dynamical systems and information theory, we show that the idea that the analysis of large amount of data by means of an algorithm of compression is equivalent to the knowledge one can have from scientific laws, is rather naive. In particular we discuss the subtle conceptual topic of the initial conditions of phenomena which are generally incompressible. Starting from this point, we argue that laws of nature represent more than a pure compression of data, and that the availability of large amount of data, in general, is not particularly useful to understand the behaviour of complex phenomena.Comment: 19 Pages, No figures, published on Foundation of Physic