Fig 3 - uploaded by Maura Musio
Content may be subject to copyright.
Source publication
The exploitation of distributed generation based on intermittent renewable energy sources (RES) has increased the load and generation profile variability. The resort to distributed energy storage systems (DESSs) is usually proposed to compensate the volatility introduced by RES. In particular, plug-in electric vehicles (EVs) are considered one of t...
Context in source publication
Context 1
... order to determine the EVs plug-in availability, the data from the survey “Mobility in Germany” (MID 2008) [12] have been considered. These data have been filtered out according to several criteria: number of trips traveled each day, mean of transportation, travel distance and time per day, and purpose of trips. Six different modes of transport have been examined: passenger car driver, car passenger, bike, on foot and public transport. Moreover the travel motives have been classified in seven groups: leisure, work, education, private business, shopping (daily needs) and accompanying. Afterwards the data of [12] have been employed for calculating the hourly percentage of cars en-route. The Fig.1 shows the statistical time distribution of cars on the road in Germany for an average week. In addition for each trip purpose the percentage of trips traveled by car has been evaluated and used as input for the proposed modeling. The data are reported in Table I. A deeper analysis has highlighted the main destinations for leisure and private business journeys. The results are depicted in Fig. 2. It can be observed that the prevalent destinations are places where a charging station could not be available. As a consequence the V2G mobility model has to take into account both the mobility behavior (vehicle on the road or parked) and the statistical time distribution of destinations with the aim of evaluating the available access points. In fact this data is fundamental for the real time estimation of EVs capacity usable for applying V2G services. Basis on these considerations a novel mobility model devoted to calculate hour by hour plug-in EVs connectivity is proposed. In order to carry out the distribution of parked EVs according to motives and destinations of trips, a hourly model for an average week is proposed. The calculations have been performed with the following equations: where t is the time step, V p (t) indicates the EVs parked at time t and V r (t) represents the EVs on the road at the same time. The EVs connected to the grid V c have been determined as follows: where D i (t-1) is the share of EVs en-route in the hour t-1 , differentiated by travel destinations i ; p represents the share of parked EVs that are connected (plug-in ratio) for each destination i ; C i (t) is the share of EVs on the road in the hour t which are coming back home, starting their homeward journey from the position i . In Germany, as stated in [12], the trip distance driven by about 87% of vehicles is less than 25 km and the driven time does not exceed 30 minutes in about 77% of car trips. For these reasons in the simulation it has been assumed that trips starting in hour t last less than one hour. As a consequence in the hour t+1 such EVs will be parked and available for V2G, according to their destinations and charging structures accessibility. The simulation of the EV battery storage system is based on the model proposed in [13]. Several tests were carried out on a LiFePO 4 battery and charging and discharging models were developed. The obtained results confirmed a good matching between the simulations and the experimental tests. The model has been used to develop the charging and discharging process simulations of a LiFePO 4 battery used in a commercial EV, characterized by the technical parameters reported in Table II. The developed battery model allows to simulate the time evolution of energy, voltage and SoC for an EV LiFePO 4 battery. The results obtained have then been used to perform the V2G simulations in the VPP model. More details about the LiFePO 4 battery model and simulations can be found in [13]. III. V IRTUAL P OWER P LANT M ODEL The proposed VPP is schematically reported in Fig. 3. It is supposed to be geographically sited in Germany and characterized by a weakly interconnection to the main grid. The VPP is basically composed of three power plants (a CHP plant, a wind park and a PV farm), an industrial zone and a city district, characterized by the power capacities and demands reported in Table III. A gas turbine, which burns natural gas, is considered as the prime mover of the CHP plant. Additionally, it is supposed that the CHP is heat driven and the heat to power ratio is set to be one. An external boiler is assigned to the CHP plant, in order to avoid excess electrical production during heat demand peak. The city is considered to have 20,000 habitants. The car fleet of the overall energy district is characterized by 11,000 vehicles, equal to about 550 passenger cars per 1,000 inhabitants [14]. The total share of annual wind and photovoltaic energy production is equal to 20% of the annual district electrical energy demand, respecting the EU goal and reducing the energy dependence from fossil fuel plants. To evaluate the wind and PV annual production, generation profiles of German wind and PV farms have been used. The hour profile of the city electricity demand has been modeled referring to yearly measured data of German power networks. Contrariwise, the thermal and electrical behavior of the industrial zone has been evaluated employing data found in the scientific literature. An EMS coordinates the energy production depending on weather conditions, storage capacity and availability, and load demand. The EMS is modeled in Matlab environment as a real time management system, which works on time hour base. Moreover the developed algorithm allows to carry out an annual energetic analysis for the proposed VPP. The EMS task is the achievement of energy balance conditions between the hourly local energy generation and the hourly consumption. To make this possible, the EMS: • controls the CHP and external boiler energy production with an active regulation; • manages the RES production, reducing the power coming from wind plant and PV farm in case of high production; • regulates the industrial load demand with a Demand Side Management (DSM) control which allows to avoid or decrease peak load; • coordinates the charge and discharge cycles of the battery storage systems in order to exploit the RES electrical production and to support the system during peak load. In the developed model an EVs fleet is introduced. In order to be able to charge and discharge according to EMS signals, providing V2G services and exploiting their batteries as DESSs, the vehicles have been supposed to be equipped with bidirectional power connection and adequate meter and communication systems [15]. IV. A NNUAL VPP C OST A NALYSIS This work aims to analyze the impact of accessibility of EVs charging points on the economies of the proposed VPP by the evaluation of the Total Cost (TC) function. The VPP TC calculation has been integrated into the EMS algorithm and determined on an annual basis, according to ...
Similar publications
![Figure 1. Proposed architecture [7]](profile/Wai-Lok-Woo/publication/298311487/figure/fig1/AS:906635396538378@1593170131965/Proposed-architecture-7_Q320.jpg)
The coordinated control and management of distributed generators and renewable energy resources together with controllable loads and storage systems are the most important and challenging tasks in micro grid operation. Micro grid can be operated in grid-connected mode or in islanded mode. Typically, energy storage systems are repeatedly proposed to...
Citations
... On the other hand, PHEV can act as a distributed storage system, an effective solution for LFC in interconnected systems. In the study by Musio and Damiano (2012), the effect of PHEV on the cost of a virtual power plant is well analyzed. ...
The direct coal liquefaction process usually produces a liquefaction residue of about 30% of its coal feed. The effective utilization of the coal liquefaction residue (CLR), which contains about 80% organic matter, is of great significancefor improving the oil yield of the direct liquefaction process and reducing the amount of pollution emitted from this oil-containing organic solid waste. In this study, the CLR fluidized bed pyrolysis process was studied through a fluidized bed reactor pyrolysis experiment and steady-state thermal analysis. The characteristics of CLR were first analyzed, and then the pyrolysis experiment was conducted in a fluidized bed reactor system. The experiment results show that the oil yield is 34.81% at 540°C for an ash-free feedstock using a fluidized bed pyrolysis reactor. Based on the pyrolysis experimental data and the Aspen Plus software platform, the fluidized bed pyrolysis reactor, fractionation column, coke burner, gasifier, and other equipment were modeled to compare four different process schemes. CLR pyrolysis is an endothermic reaction, and its heat is usually supplied by coke combustion, which produces significant CO2 emissions. Case studies of the pyrolysis process were performed in detail in order to reduce CO2 emissions. Thermal efficiency, carbon efficiency, solid waste discharge, and CO2 emissions of the different schemes were compared, and a flexible fluidized pyrolysis (FFP) process coupled with water electrolysis was proposed. The introduction of green hydrogen and green oxygen to the process can realize near-complete utilization of carbon and hydrogen elements in the CLR and produce high-quality liquid fuels and syngas (for further chemical synthesis), and this new process can achieve almost near-zero CO2 emission in the entire unit. This process principle can also be applied to the CO2 emission reduction of organic solid waste pyrolysis, catalytical fluid cracking, fluid coking, and so on.
... On the other hand, PHEV can act as a distributed storage system, an effective solution for LFC in interconnected systems. In the study by Musio and Damiano (2012), the effect of PHEV on the cost of a virtual power plant is well analyzed. ...
This is an edited book, which is the collection of research works published in a Research Topic titled "Sustainable planning and life-cycle thinking of energy infrastructure" in Frontiers in Energy Research. The aim of this collection is to enable the sustainable transformation of energy infrastructure during its implementation, modernization, expansion, decommissioning, and end-of-life from a science, technology, engineering, and policy perspective. A total of 22 research works were included in this edited book and they briefly fall under energy infrastructure dedicated to the power sector and other interdependent and dependent sectors.
... Several studies have explored various control methods for integrating EVs into VPPs. Musio and Damiano (2012) achieved control and dispatch of EVs by directly communicating with them using a VPP control center. Similarly, a centralized approach was used by Jansen et al. (2010) to optimize EV charging. ...
... On the other hand, PHEV can act as a distributed storage system, an effective solution for LFC in interconnected systems. In the study by Musio and Damiano (2012), the effect of PHEV on the cost of a virtual power plant is well analyzed. ...
With the growing dominance of inverter-based resources (IBRs), the synchronous inertia of the interconnected power systems (IPSs) is affected. The increase in IBRs results in a decrease in the system inertia. The decrease in inertia impacts the initial rate of decline of the frequency. Thus, there is a need for faster frequency response requirements to enhance the dynamic performance of the IPS. With the massive penetration of the plug-in hybrid electric vehicles (PHEVs) into expanding smart cities, PHEVs can act as controllable loads which support the inertial response of the system in a rapid manner. This gives a scope to monitor a large amount of EV operational data to ensure reliable operation considering extensive penetration of EVs. This study proposes a stochastic and iterative based optimization for a two-area interconnected power system (IPS) coupled with a hybrid energy storage system (HESS). The HESS uses 10,000 plug-in hybrid electric vehicles (PHEVs) in each area and a superconducting magnetic energy storage (SMES) device to aid load frequency control (LFC). The 10,000 PHEVs would contribute to massive operational data, which needs to be considered while studying the IPS dynamic performance. Here, we investigated two discrete tie lines: HVDC links parallel to the alternating current (AC) tie line and a virtual synchronous power-based (VSP)-HVDC link parallel to the AC tie line. The controller’s optimal parameters are recorded using two meta-heuristic algorithms, that is, particle swarm optimization (PSO) and biogeography-based optimization (BBO) along with simultaneous coordinated tuning of secondary controller and storage units. Results are taken both in the presence and absence of a HESS with two types of tie links. The analysis is performed with typical load changes and sensitivity analysis scenarios for an accurate record of variations in the outcomes. Thus, the proposed BBO-based LFC tracks the supply and demand variations, ensuring precision and accuracy, indicating improved IPS dynamic performance in smart cities.
... The general structure of VPP is depicted in Fig. 12.1. As it is shown, VPP aggregates diverse DERs including EVs and ESSs to fulfill its objectives through improving its operation and participating in electricity markets, different aspects of which are addressed in the literature [2,4,8,10,[20][21][22][30][31][32]. In this chapter, at first an EVs' model, including EVs' operation model, V2G operation, EV parking lots' (EVPLs) model, and associated uncertainties of EVs, is discussed. ...
... Two typical battery types that are mostly used are presented in Table 12.3 [39]. In addition, based on the survey "Mobility in Germany," the EVs are classified based on Table 12.4 [30]. ...
... The μ and σ associated with the arrival and departure of EVs can be obtained from historical data like in [30,43]. Moreover, random variables are calculated by (12.30), in which U 1 and U 2 are uniform random variables: ...
The growing utilization rate of electric vehicles (EVs) because of their advantages, like environmental pollutant reduction, may bring challenges to power systems for the reason of considerable charging load and random nature of their net load consumption. However, aggregation and coordination of EVs (especially EVs with vehicle-to-grid (V2G) capability) offer new opportunities to grid operators by providing the possibility of charging/discharging cycle control of their batteries. On the other hand, energy storage systems (ESSs) are one of the most applicable distributed energy resources (DERs) with growing penetration level in the last decades. The optimal coordination and schedule of ESSs, along with other DERs, can accommodate the uncertain and intermittent renewable energy sources (RESs) in the network, as well as improve service reliability and power quality. Virtual power plant (VPP) aggregates the capacity of DERs around the network and effectively manages them to obtain single operating profile in order to improve its operation and increase profit by participating in electricity markets. Thus, VPP is able to aggregate and coordinate EVs and ESSs around the network and effectively schedule them in order to fulfill its objectives. In this regard, VPP not only addresses the upcoming challenges of DERs but also optimally utilizes them to improve network operation and increase its profit through participating in electricity markets. By considering these explanations, in this chapter detailed models of EVs and ESSs are presented and then the optimal operation strategy of VPP by considering EVs and ESSs is investigated.
... Many modeling methodologies toward the EV operation in the smart grid have been studied within the global enterprises, colleges and research institutes. Musio and Damiano [4] models the EV routine drive and charging regularity based on the statistics of the driving objective, and Qian et al [5] proposes the EV charging load model based on the EV battery type, charging start time and state of charge. With the enhancement of the computing power, the heuristic modeling approaches have been adopted in the modeling of the EV. ...
The massive adoption of the electric vehicles in the near future with the rapid development of the smart grid shall significantly affair the resident transportation worldwide, while the modeling and simulation are the key approaches in analyzing the effect of the electrification of the vehicles. This paper firstly analyzed the procedures of the electric vehicles' refueling activities in the smart grid. Moreover, based on the complex adaptive system theory and agent-based modeling methodology, the paper further proposes the electric vehicle model through constructing the state-chart and rule base, and the agent-based electric vehicle model is derived. Furthermore, in order to compare the travelling ability between the electric vehicle and the original vehicle, the simulation platform is constructed and the corresponding interfaces, 3D module as well as the necessary functions are constituted. Based on the platform, this paper proposed two case scenes focus on the original internal combustion engine vehicle and electric vehicle respectively, and the simulation results denotes that the electrification of the vehicle shall reduce the travelling ability and confirms the effectiveness of the proposed model and simulation platform in studying the key phenomenon of the electric vehicles in the smart grid.
... The statistical time mobility behavior of vehicles users for an average week corresponding to the survey "Mobility in Germany" has been considered [13]. Technical features of PEV considered in this paper are described in Table II. ...
The energy consumption and supply requirements of a large number of plug-in electric vehicles (PEV) should be optimally attended in an economical and controlled form. In this manner, adequate economic remunerations to PEV owners that participate in energy selling/purchase transactions and normal operative conditions in a smart distribution grid (SDG) can be provided. The concept of “aggregators” represents an alternative to manage the new active role that the PEV will perform in the SDG. In this sense, this paper proposes a methodology to dispatch the PEV requirements in an optimal and economical manner. Aggregators located in a SDG are used to optimize the quantities of energy that each PEV can consume and supply. Mean-Variance Mapping Optimization (MVMO) metaheuristic algorithm is used in the optimization process and compared with an exhaustive analysis of the problem. The results show the robustness and the quickness of MVMO to solve this dispatch.
... Moreover, in presence of a V2G system the EMS requires the definition of the EVs mobility time evolution. On the basis of the mobility distribution deducted by [13] and analyzed in [14], the share of EVs plugged at each time step t (P plug (t)) can be evaluated as in (5): ...
... In this work, the VoLL value has been set equal to 1,000 €/MWh. More detailed information about economic assumptions used to determine the VPP total cost can be found in [14]. ...
... The proposed VPP has to supply both electrical and thermal energy to an industrial zone and a city with 20,000 habitants, characterized by the power demands reported in Table I. To emulate the yearly wind and PV electricity productions, the data of two German power networks have been employed [14]- [15]. Similarly, the profile of the city electricity demand has been modeled from real data of a German residential and commercial district [14]- [15]. ...
In this paper the integration between the Vehicle to Grid technology and a stationary energy storage system in a Virtual Power Plant (VPP) has been investigated. In order to evaluate the effects of such energy storage systems in an autonomous VPP, an Energy Management System (EMS), oriented to improve the local matching between electrical energy demand and production, has been proposed. To achieve this target and optimize the local energy production exploitation, an energy system design procedure, based on the minimization of the VPP total cost through an annual emulation of the VPP’s EMS strategy, has been applied. The proposed methodology, tested on a benchmark VPP, considers the contribution of both electric vehicles and stationary battery in different possible configurations. The comparison among three different cases of
study has finally presented at the aim of pointing out the technical and economic benefits.
... This allows managing those processes without putting in risk the operational safety of the SDG. The PEV behavior can be described by different random percentages of connection and disconnection during the day [14], or by data bases related to the vehicle mobility in the study area [15]. ...
... Table VI describes the features of PEV considered in this paper, which were extracted from SAE J1772 standard [13]. The statistical time mobility behavior of vehicles users for an average week corresponding to the survey "Mobility in Germany" has been considered [15]. Moreover, analyzes include the daily supply from PV up to 100 kW randomly connected to the load nodes in the medium voltage distribution network, and from one PV up to 1 MW connected to the AT/MT transformer station (Node 650). ...
The normal operative conditions of a distribution network can be severely affected when a large number of plug-in electric vehicles (PEV) will be connected in an uncontrolled manner. Besides network energy consumption, these PEV can supply part of the energy stored in their batteries. To dispatch the PEV requirements in an economical and controlled form, it is fundamental to develop algorithms, calculation tools and integrated systems that facilitate this functionality. Such systems should interact between electric vehicles and distribution network operators (DNOs). An alternative is to use the concept of “aggregators”. In this sense, this paper proposes a methodology to solve the short-term economic dispatch (ED) problem of a smart distribution grid (SDG). This ED especially considers the active role that the PEV should play, their representation by aggregators and a hierarchical architecture of economic control required by this purpose.
... El comportamiento de los PEV puede ser descrito mediante diferentes porcentajes aleatorios de conexión y desconexión durante el día [6], [17], o mediante bases de datos correspondientes a la movilidad vehicular dentro del área de estudio. Por ejemplo, la referencia [18] utiliza los datos de movilidad vehicular en Alemania para determinar la cantidad de vehículos parqueados en cada periodo horario de tiempo; así como también clasifica los motivos que originan la movilidad de los vehículos. Estos motivos pueden ser ocio, trabajo, acompañamiento, educación, compra de artículos y negocios privados. ...
... En este contexto, cada PEV puede aportar un 75% de la energía almacenada en la batería cuando la misma esté completamente cargada. Esto considerando una profundidad máxima de descarga del 20% y un límite máximo de carga del 95% [18]. Junto a ello, la predisposición de los propietarios de los PEV a participar en transacciones de venta de energía juega un rol fundamental dentro del DE. ...
... Lo último involucra prever el estado de carga de las baterías y el tiempo de conexión de los vehículos a la RID. Las referencias [6], [17], [18], [25], [27] y [28] describen algunos métodos de pronóstico que pueden ser utilizados para este propósito. ...
La conexión no controlada de una gran cantidad de vehículos eléctricos puede afectar severamente el estado operativo de la red eléctrica de distribución. Estos vehículos tienen la capacidad de consumir, almacenar y suministrar energía. En este contexto, es fundamental desarrollar algoritmos y herramientas de cálculo, para despachar económicamente y de manera controlada los requerimientos impuestos por los mismos. Para facilitar esta funcionalidad, se requiere de sistemas que interactúen entre los vehículos eléctricos y los operadores de los centros de control de distribución. Una alternativa para ello es utilizar el concepto de los “agregadores”. En este sentido, el presente trabajo describe las características y funcionalidad de los agregadores. Además, propone una estructura jerárquica de control y analiza el rol de los agregadores en el despacho económico de una red de distribución que integra una gran cantidad de vehículos.
