The dial-a-ride problem with electric vehicles and battery swapping stations

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions

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

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

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