Christos S. Ioakimidis

Implementation of an e-bike sharing system: The effect on low voltage network using pv and smart charging stations

Nowadays distribution systems are becoming more and more complicated mainly due to the new methods of producing and storing electricity (PV, fuel cells, battery storage systems) as well as due to the new tensions of consuming electric energy (smart appliances, e-vehicles, e-bikes, etc.). Uncertainty in load, generation, and cost requires modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem can also be addressed. This work presents the design of an e-bike sharing system, in terms of system components and user mobility patterns. The integration of the designed system in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the proposed e-bikes sharing system. PV generation produced on the parking roof of the e-bikes smart charging stations (SCS) along with the energy stored in a local battery is also studied.

Simulation and design of a fast charging battery station in a parking lot of an e-carsharing system

Carsharing has the potential to reduce the total number of cars on the road, with significant benefits to the society and the environment, while at the same time relevant studies show that university communities are often more receptive to alternative transportation services compared to the general population. With the growing interest in electromobility, as a means of decarbonizing the transportation sector, this paper considers the case of combining carsharing with electric vehicles (EVs) to serve the commuting needs of students, employees and faculty of a university in Bilbao, Spain. The aim of the present work is to conceptualize the design of the charging infrastructure of the e-carsharing system under a fast charging scheme and define its components, their attributes and interactions. To this end, a MATLAB/Simulink based simulator is developed incorporating the dynamics of a real-world scenario based on arrival and departure data from the university parking lot.

Short-term wind speed forecasting model based on ANN with statistical feature parameters

The intermittent and unstable nature of wind raises significant challenges for the operation of wind power systems, either residential installations or utility-scale implementations, necessitating the development of reliable and accurate wind power forecasting techniques. Given that wind speed forecasting is typically considered the intermediate step for wind power forecasting, the present work proposes a novel short-term wind speed forecasting model based on an artificial neural network (ANN), with the key characteristic that statistical feature parameters of wind speed, wind direction and ambient temperature are employed in order to reduce the input vector and thus the complexity of the model. The results obtained indicate that the proposed model strikes a reasonable balance between accuracy and computational requirements for a forecasting time horizon of 24 hours, providing a light-weight solution that can be integrated as part of energy management systems for small scale applications.

Introduction of plug-in hybrid electric vehicles in an isolated island system

ABSTRACT 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 largely dependent on fossil fuels incurring high import costs, in order to assess and analyse the potential impact of the plug-in hybrid electric vehicle (PHEV) technology on the local power supply system. To this end, the present work employs The Integrated MARKAL-EFOM System (TIMES) to examine a number of scenarios with different levels of PHEVs penetration under the grid-to-vehicle (G2V) approach, taking into account the established Government policies, regarding the increase in renewable energy production quotas, for the evolution of demand and supply over time. The results obtained indicate that the PHEVs integration into the local grid system under the G2V energy transferring paradigm can be realized without immediate technical barriers and bears the potential to yield significant benefits to the energy mix, reducing thus the environmental impact.

Utilizing plug-in electric vehicles for peak shaving and valley filling in non-residential buildings with solar photovoltaic systems

This paper examines the concept of utilizing plug-in electric vehicles (PEVs) and solar photovoltaic (PV) systems in large non-residential buildings for peak shaving and valley filling the power consumption profile, given that the energy cost of commercial electricity customers typically depends on both actual consumption and peak power demand within the billing period. Specifically, it describes a hybrid approach that combines an artificial neural network (ANN) for solar irradiance forecasting with a MATLAB/Simulink model to simulate the power output of solar PV systems, as well as the development of a mathematical model to control the charging/discharging process of the PEVs. The results obtained from simulating the case of the power consumption of a university building, along with experimental parking occupancy data from a university parking lot, demonstrate the applicability and effectiveness of the proposed approach.

From the ‘smart ground’ to the ‘smart city’: An analysis of ten European case-studies

During the last two centuries, the urban percentage of the worlds population, combined with the overall growth phenomenon, has deeply increased and it is projected to reach 60% by 2030. In this current context linked to environmental issues managing to plan sustainable cities appears a main policy target. The implementation of Zero Energy Buildings as a European target becomes a challenge for the energy savings with the significant commitment for larger urban scales. The aim of this paper is the development of a methodological systemic approach about energy management in a ‘district scale’ with zero energy context within the analysis of ten European case-studies to the potential of a ‘smart ground’ towards the development of a ‘smart city’. This work opens and addresses numerous future research perspectives that should be investigated widely to develop districts with an operational, sustainable and long-term context.

Effect of electric vehicles' optimal charging-discharging schedule on a building's electricity cost demand considering low voltage network constraints

Nowadays, one of the dominant reasons of excessive energy consumption is the high energy demand in corporate and/or public buildings. At the same time, electric vehicles (EVs) are becoming more and more popular worldwide being a considerable alternative power source when parked. In this work we initially propose an energy management framework which optimizes the control of the charging-discharging schedule of a fleet of EVs arriving at a university building for two typical load-days in February and May aiming at the minimization of the energy demand and, thus, the electricity cost of the building. To this end, a mixed integer linear programing (MILP) model containing binary and continuous variables was developed. Uncertainties in load, generation, and cost require modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem is also possible to be addressed. The integration of the EVs in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the corresponding EVs fleet.

A scheduling optimization model for minimizing the energy demand of a building using electric vehicles and a micro-turbine

High energy demand in corporate and/or public buildings is nowadays one of the main reasons of excessive energy consumption. At the same time, electric vehicles (EVs) have become very popular worldwide being a considerable alternative power source when parked. In this work we propose a scheduling mechanism which optimizes the control of the charging-discharging schedule of an altered but finite number of EVs arriving at a university building for a typical load-day in February aiming at the minimization of the energy demand and the electricity cost of the building. In the aforementioned framework, a parallel operation of a small sized gas turbine generator (GGT) is considered. To this end, a mixed integer linear programing (MILP) model containing binary and continuous variables has been developed to optimize the control process and minimize energy cost. Results showed that the use of the EVs as an alternative energy source can significantly contribute to the reduction of the buildings energy demand leading to important cost decrease. The exploitation of the energy produced by the GGT further contributed to the minimization of the total energy consumption of the building and the total electricity cost.

Coupling building thermal network and control system, the first step to smart buildings

Buildings are one of the main energy consumer and carbon emission sources in European countries. Population growth in cities may be effective for economic growth, but by considering overpopulated cities, making new efficient buildings and optimizing energy consumption in older ones may be a good solution for energy management and carbon reduction in them. For doing this, one needs to collect buildings data for months to get an idea about the energy consumption in a building. On the other hand, computer simulations may not provide very accurate results, but they can give a crude idea about energy consumption in buildings. There are many different tools to simulate energy consumption in buildings, among all of them simplified models provide fast and accurate results. Developing building model based on lumped capacitance method by means of resistance-capacitance (RC) circuits provides good results and a comprehensive schematic about the heat transfer in the building. In this paper, the application of thermal networks for building load calculation is introduced and it will be shown how effectively it can be used in control systems to make a smart building.

User characteristics of an e-carpooling system at UMONS as part of a smart district concept

Carpooling is a mobility concept that has the potential to effectively reduce the single occupancy trips with passenger cars, and thus energy consumption as well as traffic congestion, while coupled with electric vehicles (EVs) and intelligent transportation systems (ITS) can contribute to the smarter and more sustainable use of transportation networks as integrated part of smart cities. However, in practice, the success of carpooling systems has been limited by psychological barriers related to the level of trust for sharing a ride with strangers, and the necessity for convergence of schedules and trips for ride-matching. To this end, the present work advances the concept of a university-based carpooling system with EVs (e-carpooling), as a means of restricting the access to a closed community with a critical mass of users having the same origin/destination. In particular, this paper reports on the results of a preliminary survey conducted at University of Mons (UMONS), Belgium, in order to explore the characteristics of this user community with respect to the concepts of carpooling and electro-mobility. The results of the survey not only reveal the user preferences for the adoption of the proposed system, but also provide some useful insight for the implementation of the e-carpooling concept in the city of Mons.