Ville Tikka

Methodology to Analyze the Economic Effects of Electric Cars as Energy Storages

The nature of transport and energy use is radically changing along with the upward trend of electric vehicles. The rapid technological development of electrical vehicles opens new opportunities from the electricity distribution point of view. Efficiency can be improved by implementing energy storages to the grid and cutting the load peaks by feeding power on peak hours from the energy storages to the grid. Electric vehicles with vehicle-to-grid (V2G) properties provide an opportunity to meet this challenge. In this paper, the challenge is approached from the economic perspective of an electricity distribution company. The key target of the paper is to determine whether there is economic potential for energy storages in networks in general. To this end, a generic model is introduced to analyze the feasibility of electric vehicles as energy storages in distribution networks. The methodological framework presented in the paper provides an opportunity for distribution system planners to estimate the preliminary feasibility of energy storages. The focus is on the discharging (vehicle to grid) perspective. The paper answers, for instance, the question of how to define the feasible level of energy storages (batteries) in the distribution system. In the paper, for background information, an extensive literature review is provided on electric vehicles.

Case study of the effects of electric vehicle charging on grid loads in an urban area

The number of electric vehicles (EVs) is rapidly increasing, and the upward trend seems to be continuing also in the future. The increasing number of electric vehicles causes a need to develop the charging infrastructure, and moreover, it is necessary to analyze the network effects of the simultaneous charging of numerous electric vehicles. A further interesting question is how all this affects the distribution fee paid by the electricity end-user. In this paper, the challenge is approached by an actual case example. The data used in the simulation are collected by measuring the traffic flow of the road leading to the case area. The aim of this paper is to demonstrate how the grid effects of large-scale electrification of transportation can be assessed and to define the needed reinforcements and effects on the distribution fee paid by the end customers. The data are processed by applying the Monte-Carlo method. The network effects and the change in the distribution fee are evaluated. The key result is that EV charging causes a substantial amount of additional load to the grid. Hence, the distribution fee may increase if the charging system is not intelligent.

Case study of the load demand of electric vehicle charging and optimal charging schemes in an urban area

The interest in electric vehicles (EVs) is rapidly increasing, and the upward trend seems to continue also in the future. As a result of the increasing energy consumption, the grid infrastructure has to be developed further, and moreover, it is necessary to analyze the network effects of the electric vehicle charging. In this paper, the challenge is approached by an actual case area in Finland and simulated EV traffic. The data used in the simulation are collected by measuring the traffic flow on the road leading to the case area. The objective of the paper is to demonstrate how the grid effects of large-scale electrification of transportation can be addressed, and what data are required to assess the additional charging load in a feasible manner. The data are processed by applying the Monte-Carlo method, and also a sensitivity analysis is performed.

Analyzing the effects of the customer-side BESS from the perspective of electricity distribution networks

This paper presents an analysis methodology to consider the effects of customer-side battery energy storage systems (BESS) on electricity distribution networks motivated by power-based tariffs. The paper describes the methodology by which the network effects can be defined. The methodology is verified by applying actual distribution network and load data. The analysis shows that transition from traditional energy-based distribution tariff to power-based tariff can lead to an accelerating increase in the application of customer-side energy storages. The results indicate that the network effects of the BESS are significant. The effect of energy storages depends partly on the voltage level of the network component. The results show that peak loads decrease in the case electricity distribution network. The most significant decrease, over 10% on average, is found in the low-voltage network, which is not a minor improvement.

Opportunities of bioenergy-based CHP production in balancing renewable power production

This paper considers opportunities of bioenergy-based combined heat and power (CHP) to balance fluctuation in solar and wind power. The paper studies participation of CHP production in an electrical power reserve, which provides opportunities to integrate more renewable energy production into the electrical system and also makes it possible to raise the profit of the CHP operator. Moreover, the paper discusses certain aspects of integrating heat pumps (HPs) with a CHP plant and participation of their combination CHP in electricity reserve power markets.

Case study: Smart charging plug-in hybrid vehicle test environment with vehicle-to-grid ability

The aim of the paper is to describe and introduce smart charging test environment and plug-in hybrid vehicle capable of smart charging and vehicle to grid functionality. Furthermore, the paper aims at demonstrating simple smart charging strategy in operation on smart charging test bed. The demonstration utilizes commercially available components and open source programming solutions. Charging strategy demonstration is a combination of actual hardware operations and stochastic sampling to synthetize driving cycles of the electric vehicle. Driving behavior synthetizing is based on national travel survey data to ensure reasonable driving behavior in testing of the smart charging strategy. The main outcome of the paper is the description of an actual smart charging test environment. The results also suggest that the charging strategy targeting to minimization of the charging costs may not be feasible for a single customer or single end user. However it must borne in mind that the electricity retailer (or market aggregator) may see some feasible incentives in smart charging strategies based on market price control.

Demonstration of smart charging interface in Green Campus

In this paper, a description of a smart charging interface in an actual smart grid environment is presented. In addition, some results of controlled smart charging events are shown. The smart charging interface in Green Campus Smart Grid allows handling and controlling multiple electric vehicles (EVs) within the campus area based on information from the EVs and other sources. Furthermore, the interface is uniformed to allow connection of different types of loads and generation units. With the information gathered by the interfaces, the smart charging system is able to form a charging window for the connected EVs and is also capable to perform power and energy balancing with the mobile energy storages which the EVs provide. In addition, the connected EVs can be used as a backup power if the EVs are suitable for V2G (vehicle to grid). The data connection between the EVs and the charging pole is implemented with power line communication (PLC) and the charging poles are connected to the smart charging system with Ethernet.

Electric vehicle smart charging aims for CO 2 emission reduction

In this paper, a methodology for electric vehicle charging by minimizing CO2 emissions and minimizing charging electricity costs is presented in an actual Nordic electricity market environment. The target of the paper is to illustrate the difference of smart charging and dumb charging schemes from the perspectives of CO2 emissions and electricity end-user electricity costs. The study takes advantage of a national transportation survey and actual electricity market data including generation-type-specific CO2 information.