402 research outputs found

    Demand side control for power system frequency regulation

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    The increasing penetration of renewable energy resources brings a number of uncertainties to modern power system operation. In particular, the frequent variation of wind or solar power output causes a short-term mismatch between generation and demand and system frequency fluctuation. The traditional approach to dealing with this problem is to increase the amount of system spinning reserve, which increases costs. In recent years, researchers have been actively exploring the utilization of residential and commercial loads in frequency regulation without affecting customers’ comfort level. This is called dynamic demand control (DDC). This dissertation describes an in-depth study of DDC for bulk power system frequency regulation, from both a technical and economic perspective

    Indirect control of flexible demand for power system applications.

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    Dynamic Load Models for Power Systems - Estimation of Time-Varying Parameters During Normal Operation

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    Economic and environmental concerns will slow down the expansion of the transmission system in many countries. The addition of new transmission lines will be few and far between. The de-regulation of the power supply will introduce new power flow patterns on the bulk transmission systems. The net result is that the power systems will operate much closer to their transfer limits and operate there much longer time than has been necessary. The risk for voltage collapse determines the transfer limits in many bulk transmission systems. The accurate determination of the transfer limits will be an increasingly important task to maintain the operational security and economic operation of the power system. Many studies have shown the importance of the load representation in voltage stability analysis. Static load models are not accurate enough for capturing the dynamics of the network. Therefore dynamic load models are needed even if voltage collapse, in many cases, is a slow phenomenon. iii Due to the large amount of electric heating loads in Sweden and its effect on voltage stability, Hill and Karlsson have proposed a load model with exponential recovery. The model is expressed as a set of nonlinear differential equations, where the real and reactive load powers have a nonlinear dependency with voltage. The standard dynamic active load model is characterized by three parameters, steady state load-voltage dependence, transient load-voltage dependence and a load-recovery time constant. The same applies to reactive load. As an extension of the mentioned work, the present author proposes an automatic method for the determination of parameters in standard dynamic load models. The dynamic set of nonlinear equations has been linearised and the problem has been reduced to a linear identification problem. The Least Squares criterion is used for minimizing the error function between measured and simulated data. Field measurements from continuous normal operation at the 20 kV and 50 kV-level from a substation in the South East of Sweden have provided over 1 GByte of data covering all seasons during the time period July 2001-June 2002. The determination of the load parameters based on this data has resulted in valuable information. The parameters’ time-varying characteristic and their dependency with weather and season of the year have been studied; there is correlation between the active and reactive recovery time constants, and between them and the corresponding steadystate characteristic of the load. Strong dependency of the transient active and reactive characteristic of the load with the temperature has been found. Furthermore, some unexpected deviations in the reactive load parameters have led to a new representation of the reactive load. The reactive power level, which was previously used as normalization factor, is inappropriate. If instead apparent power level is used, the variability in the parameters that describe the reactive load response is drastically reduced

    Managing power system congestion and residential demand response considering uncertainty

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    Electric power grids are becoming increasingly stressed due to political and environmental difficulties in upgrading transmission capacity. This challenge receives even more interest with the paradigm change of increasing renewable energy sources and demand response (DR) programs. Among DR technologies, existing DR programs are primarily designed for industrial and commercial customers. However, household energy consumption accounts for 38% of total electricity consumption in the U.S., suggesting a significant missed opportunity. This dissertation presents an in-depth study to investigate managing power system congestion and residential DR program under uncertainty.First, an interval optimization model is presented for available transfer capability (ATC) evaluation under uncertainties. The conventional approaches of ATC assessment include deterministic and probabilistic methods. However, the proposed interval optimization model can effectively reduce the accuracy requirements on the renewable forecasting, and lead to acceptable interval results by mitigating the impacts of wind forecasting and modeling errors. Second, a distributed and scalable residential DR program is proposed for reducing the peak load at the utility level. The proposed control approach has the following features: 1) it has a distributed control scheme with limited data exchange among agents to ensure scalability and data privacy, and 2) it reduces the utility peak load and customers’ electricity bills while considering household temperature dynamics and network flow.Third, the impacts of weather and customers’ behavior uncertainties on residential DR are also studied in this dissertation. A new stochastic programming-alternating direction method of multipliers (SP-ADMM) algorithm is proposed to solve problems related to weather and uncertain customer behavior. The case study suggests that the performance of residential DR programs can be further improved by considering these stochastic parameters.Finally, a deep deterministic policy gradient-based (DDPG-based) HVAC control strategy is presented for residential DR programs. Simulation results demonstrate that the DDPG-based approach can considerably reduce system peak load, and it requires much less input information than the model-based methods. Also, it only takes each agent less than 3 seconds to make HVAC control actions. Therefore, the proposed approach is applicable to online controls or the cases where accurate building models or weather forecast information are not available

    The role of the energy performance modelling with a view to low energy buildings

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    Climate change requires courageous choices, the European Union has accepted this challenge. One of the 2050 European low-carbon targets is energy savings in the building sector which is responsible for around 40% of energy consumption and 36% of CO2 emissions in the EU. The role of Building Performance Simulation (BPS) is central in that it allows to improve the design, optimization, construction, operation and maintenance of new and existing buildings. In order to achieve the correct estimate of energy consumption of buildings, different models have been developed in the last decades. They can be grouped into three categories: black box models, gray box models and white box models. They are differentiated by the degree of detail with which they describe the physical phenomena that govern the calculation of energy performance instead of using statistical algorithms for the estimation of the same or some characteristics of the building. However, the most detailed models are still only a representation of reality and therefore with margins of error due to assumptions and approximations of calculation. These aspects could be critical in the estimation of energy performance of nearly Zero Energy Buildings (nZEBs) where low performance values could become comparable with errors in estimating energy performances themselves. nZEBs are currently not diffuse in the EU building stock, however are those on which Europe is pointing as a key to building renewal. This thesis aims to investigate the role of energy performance modelling of buildings with low energy consumption. For this reason, research fields of BPS are identified in which the energy performance modelling has been used. They are: climatic data versus energy performance, energy performance rating and ranking of buildings, definition of minimum building requirements and exploring of technologies and valuation methods of energy efficiency measures. For having a wider vision on which model can be used, with what simplifications and what expectations, a research was carried out for each application field. Numerical models are applied both to single buildings and to building stocks, but first ones are the main focus of the investigation. Concerning the first application field, in order to estimate the energy performance (EP) of buildings which have a very low amount of energy covered to a very significant extent by energy from renewable sources accurate and reliable climatic data are necessary. The analysis of EP estimated with different calculation methods shows that the sources of climate data currently available lead to results which can be very different from each other. An improved Typical Meteorological Year construction procedure is proposed to higher the reliability and representativeness of climatic data. Two data mining methods for selecting energy efficiency measures on an urban scale are tested and validated by saved energy of dynamic models. With reference to application field of definition of minimum building requirements the thesis analyses the process to define them. Moreover, it studies how the energy performance modelling influence the definition of minimum building requirements (about the fabric or the HVAC system) and as a fixed requirement could have an imbalance effect between different services. An improved procedure is shown to define the notional reference building and an analysis is led on a heating generator to show how the modelling of technology can affect minimum requirements. Finally concerning EP in valuation methods, case studies with Cost-Optimal Analysis (COA) and Multi-Criteria Analysis (MCA) are performed. The first one gives the possibility to compare results obtained with two calculation methods, the second one permits to investigate the role of energy performance in MCA

    Technical-Environmental-Economical Evaluation of the Implementation of a Highly Efficient District Heating System in China

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    Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

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    The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand

    Assessment of the nitritation and anammox processes for mainstream wastewater treatment

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    The implementation of autotrophic nitrogen removal processes such as the combined partial nitritation and anammox processes will contribute to maximise the wastewater energy recovery converting the wastewater treatment plants in net energy producers and enabling the wastewater reuse. Consequently, this thesis aims to assess the nitritation and anammox processes implementation at mainstream conditions characterised by its low temperature, low nitrogen concentration and high fluctuations of wastewater characteristics. Different reactor configurations, including one and two-stage systems, were employed. The treatment of different types of effluents, blackwater from a source-separation on-site system and municipal wastewater were studied, being specially focused on overcoming the nitrite oxidizing bacteria development that challenged the anammox based processes implementations. Special attention was paid to the production of effluents complying with the European discharge limits as well as the evaluation of the activities of the involved microbial populations
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