Strategic Planning for Carbon Capture and Storage Implementation in the Electricity Sector of Greece: A TIMES Based Analysis

This paper presents a roadmap performed in 2010 as part of a European project for the modelling of carbon capture and storage technology, and various scenarios with different taxations and permit prices for the CO2 emissions considering the Greek national plans, then the gradual decommissioning of various lignite or other units of electricity power plants. In addition, this study presents a first check, 10 years after its writing, of the current situation of the Greek energy system, regarding the correspondence of the roadmap designed in 2010 to what has been finally executed during this period, including the possibility of other energy sources complimenting or substituting the national strategic energy plans. For this purpose, the integrated MARKAL-EFOM system (TIMES) was employed to model the Greek energy system and evaluate its development over time, until 2040, by analyzing three different scenarios with respect to taxation and permit prices for carbon emissions. The results obtained show that, if this study had been considered and executed by the different stakeholders during that period, then the implementation of CCS in the new licensed power plants from 2010 and onwards could reduce the use of lignite and imported hard coal power production in a much smoother and beneficial way in the next years, and until the present, without compromising any major power plants. This implementation would also make the transition to a lignite free economy in Greece much faster and better, while complimenting the EU regulations and also enhancing the possible greater use of alternative energy sources in the green energy mixture

A University E-Bike Sharing System used as a Real-Time Monitoring emissions tool under a smart city concept

This work intents to describe a new approach that would be able to combine the positive effects from the use of an E-Bike sharing system in a medium-large population urban city of Belgium demonstrated initially in case of the local University Campuses along with the use of the E-Bikes as environmental mobile sensing units

Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges

In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the complexity of Machine Learning to urban planning strategies

Integration of Seawater Pumped-Storage in the Energy System of the Island of São Miguel (Azores)

This paper considers the case of São Miguel in the Azores archipelago as a typical example of an isolated island with high renewable energy potential, but low baseload levels, lack of energy storage facilities, and dependence on fossil fuels that incurs high import costs. Using the Integrated MARKAL-EFOM System (TIMES), a number of scenarios are examined in order to analyze and assess the potential benefits from the implementation of a seawater pumped-storage (SPS) system, in the absence or presence of electric drive vehicles (EDVs) under a grid-to-vehicle (G2V) approach. The results obtained show that the proposed solution increases the penetration of renewable energy in the system, thus reducing the dependence on fossil fuel imports and allowing, at the same time, for the deployment of EDVs as a promising environmentally friendly alternative to conventional vehicles with internal combustion engines

On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings

This paper describes the evolution of the thermal network and its applications for making simplified thermal models of buildings by means of thermal resistances (R) and capacitances (C). In the literature, there are several modelling schemes for buildings. Here, we investigate the advantages, disadvantages, and improvements of thermal networks. The thermal network method has been used in different studies for calculating indoor air temperature and heating load, estimating model parameters, and studying building interactions with heating and cooling systems. This review paper conducts an investigation into the application, system identification, and structure of thermal networks compared to other tools. Within the framework of the thermal network method, we conclude with some new proposals for research in this field to expand the idea of the thermal network to other engineering and energy management fields

Implementation of System Identification Techniques and Optimal Control for RC Model Selection by Means of TRNSYS Simulation Results and Experimental Data

Simulating the thermal model of a district requires simultaneously retaining accuracy and simplicity, in order to avoid cumbersome calculations and unrealistic predictions. Within this scope, introducing a simple structure for modeling the energy consumption of a building that can be expanded to the district level becomes essential. In this paper, a hierarchy of thermal models with increasing complexity is developed to identify the simplest structure that can effectively represent the thermal behavior of a building, using a simulated building in TRNSYS and the measurements of a real building as two datasets to estimate the model parameters. Each model is placed in a closed loop system to track the constant indoor temperature by means of the linear quadratic regulator (LQR) technique. To select the best structure, the model outputs are compared to TRNSYS simulations and measurements. The main features of the selected models include the use of only a few parameters to predict the indoor temperature, peak power, total heat demand, and transient behavior of a building. It is shown that the proposed models are able to determine the indoor temperature with less than 1 °C of error, and the total heat demand and peak power are also determined with an error lower than 25%

Implementation of System Identification Techniques and Optimal Control for RC Model Selection by Means of TRNSYS Simulation Results and Experimental Data

Simulating the thermal model of a district requires simultaneously retaining accuracy and simplicity, in order to avoid cumbersome calculations and unrealistic predictions. Within this scope, introducing a simple structure for modeling the energy consumption of a building that can be expanded to the district level becomes essential. In this paper, a hierarchy of thermal models with increasing complexity is developed to identify the simplest structure that can effectively represent the thermal behavior of a building, using a simulated building in TRNSYS and the measurements of a real building as two datasets to estimate the model parameters. Each model is placed in a closed loop system to track the constant indoor temperature by means of the linear quadratic regulator (LQR) technique. To select the best structure, the model outputs are compared to TRNSYS simulations and measurements. The main features of the selected models include the use of only a few parameters to predict the indoor temperature, peak power, total heat demand, and transient behavior of a building. It is shown that the proposed models are able to determine the indoor temperature with less than 1 °C of error, and the total heat demand and peak power are also determined with an error lower than 25%

Spatio-Temporal Trends of E-Bike Sharing System Deployment: A Review in Europe, North America and Asia

Recent data on conventional bike and/or electric bike (e-bike) sharing systems reveal that more than 2900 systems are operating in cities worldwide, indicating the increased adoption of this alternative mode of transportation. Addressing the existing gap in the literature regarding the deployment of e-bike sharing systems (e-BSSs) in particular, this paper reviews their spatio-temporal characteristics, and attempts to (a) map the worldwide distribution of e-BSSs, (b) identify temporal trends in terms of annual growth/expansion of e-BSS deployment worldwide and (c) explore the spatial characteristics of the recorded growth, in terms of adoption on a country scale, population coverage and type of system/initial fleet sizes. To that end, it examines the patterns identified from the global to the country level, based on data collected from an online source of BSS information worldwide. A comparative analysis is performed with a focus on Europe, North America and Asia, providing insights on the growth rate of the specific bikesharing market segment. Although the dockless e-BSS has been only within three years of competition with station-based implementations, it shows a rapid integration to the overall technology diffusion trend, while it is more established in Asia and North America in comparison with Europe and launches with larger fleet sizes

Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings

Data-driven models, either simplified or detailed, have been extensively used in the literature for energy assessment in buildings and districts. However, the uncertainty of the estimated parameters, especially of thermal masses in resistance–capacitance (RC) models, still remains a significant challenge, given the wide variety of buildings functionalities, typologies, structures and geometries. Therefore, the sensitivity analysis of the estimated parameters in RC models with respect to different geometric characteristics is necessary to examine the accuracy of identified models. In this work, heavy- and light-structured buildings are simulated in Transient System Simulation Tool (TRNSYS) to analyze the effects of four main geometric characteristics on the total heat demand, maximum heat power and the estimated parameters of an RC model (4R3C), namely net-floor area, windows-to-floor ratio, aspect ratio, and orientation angle. Executing more than 700 simulations in TRNSYS and comparing the outcomes with their corresponding 4R3C model shows that the thermal resistances of 4-facade building structures are estimated with good accuracy regardless of their geometric features, while the insulation level has the highest impact on the estimated parameters. Importantly, the results obtained also indicate that the 4R3C model can estimate the indoor temperature with a mean square error of less than 0.5 °C for all cases

Solar thermal and wind energy applications: Case study of a small Spanish village

The present work examines the supply of heating and electricity to the Spanish village of Uruena, using biomass and other local renewable sources as a result of the growing interest worldwide towards the development of sustainable and energy independent small communities. Specifically, this case study considers the design of a district heating system consisting of a solar heating plant, a biomass plant using straw as a sustainable fuel for the base load and an oil boiler for the peak load, coupled with a hot water tank as a thermal energy storage option. Two alternative scenarios are analyzed for electricity generation purposes, namely a system consisting of three small wind turbines and a system with a single large wind turbine. The results show that the cost of large-scale electricity storage depends on the application and often involves significant capital investments