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Abstract
A smart integration of electric vehicles (EVs) in the future energy system will be crucial in decarbonizing the energy sector. Bidirectional EVs can provide flexibility for the system and generate revenues for the user through multiple use cases. We model both exclusive photovoltaic (PV) self-consumption optimization and the combined usage of PV self-consumption optimization and arbitrage trading for a household with an unmanaged, smart, and bidirectional charging EV in a linear (LP) and mixed-integer linear programming (MILP). Since power flows in a typical household are low, varying non-linear charging and discharging efficiencies of the bidirectional EV in the MILP result in more realistic revenues that are 30% lower than in the LP with fixed efficiencies. For a typical German household using a bidirectional EV for optimizing PV self-consumption, these revenues are about 310 €/a, mostly generated during the summer. Arbitrage trading well complements this vehicle-to-home use case in the winter months, resulting in revenues up to 530 €/a. These significant revenue potentials can lead to more profitable and interactive EVs incentivizing users to change from internal combustion vehicles to electric mobility.
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... By implementing charging coordination, prosumers self-consumption increases to 65% for vehicleto-grid (V2G)-enabled smart charging. Thus, this leads to higher revenues and autarky using PV energy rather than grid power [28]. ...
... The proposed method minimizes the total cost of the system in Equation (28). The optimization model considers two types of network constraints: voltage deviations and network capacity. ...
... c is a very small cost constant, and P P Vcur t,k is the amount of power curtailed. Additionally, the objective function in Equation (28) was utilized to minimize the system's total cost, irrespective of whether grid constraints were considered. However, the constraint in Equation (18) was excluded from the optimization model, which did not incorporate grid constraints. ...
Prosumer communities are integrating renewable energy sources to reduce energy costs and carbon emissions for sustainable and clean energy awareness. However, increasing solar photovoltaic penetration in low-voltage distribution networks leads to serious power quality challenges, such as overvoltage for grid operators and prosumers. Integrating electric vehicles (EVs) as deferable loads can reduce prosumer costs and maximize environmental benefits as EV charging is managed. Therefore, this paper proposes a novel EV charging management that maximizes prosumer communities' power quality and benefits PV-rich prosumers by applying a dynamic active power curtailment framework. The methodology calculates each prosumer's maximum power injection into the grid based on their voltage sensitivities. The performance of the developed charging management is examined on the European 906 bus low-voltage distribution networks under unmanaged, managed, and vehicle-to-grid (V2G)-empowered scenarios. The prosumers' individual and aggregated economic cost-benefit results are analyzed considering increasing EV penetration. The results show that the proposed method considering fair active power curtailment could increase self-consumption and renewable fraction for prosumers. It is observed that increasing EV penetration could reduce the curtailed energy by 14.6%. The V2G-empowered method also increased up to 20% more renewable energy for charging EVs, improved self-consumption and renewable fraction up to 11% and 19.4%. Moreover, the V2G option reduced total costs by up to 37.93%. This work can potentially promote renewable energy sources by modifying consumers' charging behaviors to be more sustainable and environmentally friendly. INDEX TERMS active power curtailment, vehicle charging management, self-consumption, over-voltage mitigation, sensitivity matrix.
... Compared to Vehicle-to-Grid (V2G), where batteries of EVs are used for grid-related services, V2H is easier to implement and might reach market maturity within the next years, according to [2]. In comparable studies, the annual revenue potential of V2H is estimated to be between 200€ and 500 € [2], [3], [4]. To do this, EVs must be plugged into the home wallbox most of the time. ...
... To do this, EVs must be plugged into the home wallbox most of the time. The availability rates of synthetically generated plug-in profiles are assumed to be 80 % or higher [2], [3], [4], [5]. This presupposes, for example, that users always charge their EVs after returning home. ...
... The cost optimization relies on a mixed integer linear programming (MILP) approach. It has been shown to perform well in comparable studies [3], [7], [8], [9]. The free module Pyomo and the solver Gurobi were used. ...
This paper provides a comparison to what extent the usage of vehicle-to-home (V2H) could replace battery energy storage systems (BESS) in private households with photovoltaic (PV) installation. A house energy management system (HEMS) is developed in Python to simulate and quantify cost savings due to a BESS or V2H implementation. The HEMS includes a MILP optimization, which minimizes the costs of operation for each house. For realistic modeling, real measured charging processes of electric vehicles (EVs), PV generation profiles and household load profiles from a variety of German households were used as input data. It can be seen that the plug-in frequency of EVs plays a major role in potential V2H utilization. EVs need to be charged during times of high PV generation, which would require users to adopt their plug-in frequency. By this, V2H could attractively replace a classic BESS in private houses.
... Dengan diberikannya otonomi daerah tersebut setiap daerah diberikan kewenangan dan kewajiban untuk melaksanakan berbagai kegiatan pemerintahan secara lebih mandiri. Pemerintah daerah mempuyai hak dan kewenangan yang luas untuk menggunakan sumber-sumber keuangan yang dimilikinya sesuai dengan kebutuhan dan aspirasi masyarakat yang berkembang di daerah (Auster & Kellner, 2022;Baskaran et al., 2016;Cai, 2017;Kern et al., 2022;Meriläinen et al., 2022). ...
... Dalam era otonomi daerah seperti saat ini tentu proses pendelegasian wewenang yang diberikan pemerintah pusat dibarengi dengan tugas dan tanggung jawab yang besar untuk bisa lebih memaksimalkan potensi-potensi yang dimiliki oleh daerah untuk dapat berkembang ke arah yang lebih baik (Kaltenborn et al., 2020;Lee et al., 2020). Pengalihan pembiayaan dari pusat ke daerah atau yang lebih dikenal sebagai desentralisasi fiscal, dapat pula diartikan sebagai suatu proses distribusi anggaran dari pemerintah yang lebih tinggi kepada pemerintah yang lebih rendah untuk kemudian dikelola guna mendukung fungsi atau tugas pemerintahan dan pelayanan public sesuai dengan banyaknya wewenang bidang pemerintahan yang diberikan atau dilimpahkan oleh pemerintah pusat (Auster & Kellner, 2022;Baskaran et al., 2016;Cai, 2017;Kern et al., 2022;Meriläinen et al., 2022). ...
This article aims to describe the Regional Apparatus Organization (OPD) of Palu City. The implementation of regional autonomy has been implemented effectively since January 1 2002. The implementation of regional autonomy is a policy that is considered very democratic and fulfills the aspects of true government decentralization. This research uses a descriptive approach by explaining in detail the conditions of OPD in Palu City. For regional governments, DAU receipts through transfers from the central government are a source of funding that can be used to carry out their authority, while funding shortfalls are expected to be recovered through their own funding sources, namely through receipts from PAD sources. However, in reality, transfers from the central government are the main source of funds for regional governments to finance their main day-to-day operations or regional expenditure, which regional governments reportedly take into account in the APBD.
... Both degradation models are integrated into the linear optimization model eFlame which is described in detail in Kern et al. (2022). eFlame was mainly developed to optimize the operation of bidirectional charging use-cases. ...
... eFlame was mainly developed to optimize the operation of bidirectional charging use-cases. Examples can be found in Kern et al. (2020Kern et al. ( , 2022, where arbitrage-trading and self-consumption optimization use-cases are analyzed. A peak-shaving use-case is published in Kern and Bukari (2021). ...
Bidirectional charging allows energy from the electric vehicles (EV) to be fed back into the grid, offering the possibility of price-optimized charging. However, such strategies cause higher charging cycles, which affect the cyclic aging of the battery and reduce its service life, resulting in additional costs for the user. Various approaches are used to account for battery degradation in optimizations models of bidirectional charging use-cases. In this paper, a systematic literature review is carried out to identify existing battery degradation models and to determine the most suitable one. In the models under review, degradation is integrated into the optimization’s objective function. The review shows that there are mainly two strategies suitable for vehicle-to-grid (V2G) optimization problems: A weighted Ah-throughput model (wAh-model) with a constant degradation cost factor and a performance based model (pb-model) linking the degradation to measurable parameters such as capacity loss. Both models were implemented and analyzed. The results show that the wAh-model is the better optimization option, as in the pb-model the current state of health of the battery has an excessively large impact on the calculated degradation cost. It leads to excess costs due to a higher aging rate at the beginning of life which proves to be not ideal in the optimization. The sensitivity analysis reveals that altering the initial State of Health (SoH) from 95 % in the base scenario to 100 % leads to an increase in average degradation costs by factor 9.71 in the pb-model. From the evaluated base scenario the average degradation costs for the pb-model are 0.45 cent/kWh and for the wAh-model 0.23 cent/kWh.
... Thereby, mainly increased revenue opportunities by combining use cases compared to the revenues of one individual use case. (Kern et al. 2022) for example show, that the combination of the electric vehicle smart charging use cases 'PV self-consumption optimization' and 'arbitrage trading' could yield substantially higher revenues, than the use case 'PV self-consumption optimization' individually, due to different profitability of the use cases in different seasons. (Chukwu et al. 2019) examine the revenues of combining up to four different use cases, obtaining an increase in revenues by combining multiple use cases. ...
... For example, it may be applied to the field of multi-metering to gain more advanced insights on potential synergies among different energy sectors as proposed by Cotti et al. (2013). Nevertheless, other than the assessment of benefits from multi-use by increased revenues as conducted by Kern et al. (2022); Chukwu et al. 2019) for smart charging of electric vehicles and by Englberger et al. (2020) for stationary battery storage, the methodology determines a comparative evaluation of synergies among different use case combination by a qualitative effort estimation. Therefore, a comparison of the results among different application fields cannot be performed that easily. ...
The digitalization of the energy sector enables a broad range of new digital use cases and business models. For instance, blockchain-technology can be used for the verification of tamper-resistant storage of asset data (asset logging) or manipulation-resistant guarantees of origin for electricity (labeling). Yet, it is associated with high implementation and operating effort. But many of these use cases require similar players, interfaces, data sets and data processing, so that synergies can result from a joint implementation. We thus evaluate these synergies in implementation and operating effort for use cases in the field of asset logging and labeling using a bottom-up evaluation of the components based on a methodology of Dossow (Energies 16:2424, 2022). Additionally, we extend this methodology to analyze the scalability of the use cases by assessing the relative effort reduction for an increasing number of players involved. The analysis already shows substantial synergies for combinations of two use cases. Yet, especially for combinations of three or more use cases a high effort reduction potential is derived. The highest synergies are obtained among the asset logging use cases, while a combination of asset logging and labeling use cases shows lower synergies in comparison. The analysis of the scaling of the use cases demonstrates that for labeling use cases the main effort driver is the number of consumers, while for asset logging use cases the number of asset operators shows to be more relevant. Thus, scaling effects outweigh the effort reduction potential of use case combinations especially for combinations of asset logging and labeling cases.
... While charging, it transforms AC power from the grid into DC to replenish the EV's battery. During the discharging process, the system converts the direct current (DC) power from the battery back to alternating current (AC) to ensure compatibility with the grid as per Kern et al. [146]. Bidirectional charging systems are commonly connected to the grid to manage load demands effectively. ...
Electric mobility is attracting significant attention in the current era due to its environmental benefits, sustainable transportation options, and the absence of carbon emissions. However, challenges such as the high price of batteries, inefficient charging techniques, and compatibility linking the charging station with electric vehicles (EVs) must be addressed. This article reviews advancements and identifies challenges in charging infrastructure for electric mobility. This study incorporates and analyzes an integrated review of approximately 223 research articles. Current research trends and states of charging infrastructure are prepared as per the Web of Science (WoS) database from 2013 to 2023. In light of recent extensions in wireless power transfer technology, including capacitive, inductive, and magnetic gear topology, are presented to advance the charging infrastructure. Different charging tactics based on power source, such as level-1 AC, level-2 AC, level-3 DC fast, and level-3 DC ultra-rapid charging, related to charging infrastructure are addressed. The vehicle-to-grid (V2G) integration methodology is addressed to construct a smart city by presenting the transfer of power and related data through linkage and moving systems. The exploration of artificial intelligence, global connectivity of electric vehicles (EVs), sun-to-vehicle (S2V), and vehicle-to-everything (V2X) techniques with EVs is conducted to enhance and progress the charging infrastructure. Key barriers associated with charging infrastructure are identified.
... EVs batteries act as mobile energy storage systems, capable of providing grid support and other ancillary services for instance in peak shaving, load balancing/levelling, and frequency regulation during adverse grid conditions [209]. This is possible due to the bidirectional charging technologies embedded in EVs such as Vehicle-to-Grid (V2G), Grid-to-Vehicle (G2V), and Vehicle-to-Home (V2H), where EVs will not only consume energy, but also feed power back (act as a distributed energy resources) into the power system or home to aid grid stability and fault-ride through capability [210,211]. The interaction of charging scheduling of EVs incorporating G2V and V2G technology taking into account smart grid technologies is proposed in [212]. ...
Modern power system is witnessing a paradigm shift due to the combination of various renewable energy sources (RESs) and the emerging innovative technologies that are integrated into the existing grid in order to improve the reliability and stability. The grids have expanded across larger geographical areas and transcended the boundaries of conventional power generation with integration of cutting-edge innovations. Maintaining frequency stability and instantaneous power balance between generation and demand in this system have become a difficult task as a result of the stochastic and uncertainties characteristics of the modern grid. The review investigates the role of load frequency control (LFC) in modern power systems, focusing on its implementation in both single-area and multi-area power system (MAPS). It explores the development of LFC from centralized to decentralized control in MAPS, emphasizing the growing significance of decentralized strategies in modern grids. LFC scheme ensures a real-time power balance, well-coordinated utilization of resources, and prompt response to load variation and other external disturbances across the diverse regions. The interoperability of LFC along with other ancillary services, such as demand-side management (DSM), smart grid, microgrid, energy storage system, electric vehicles (EVs), high-voltage direct current (HVDC) systems, and cybersecurity technologies are analysed for their potential capability in reshaping the resilience, reliability, and adaptability of the power system operations. Implementation of these robust strategies is faced with technical and economic challenges, including high cost of infrastructure, complexities coordination, cyber threats, and integration of intermittent renewable sources. Furthermore, future trends such as the growing role of artificial intelligence, machine learning, and real-time communication technologies in enhancing LFC performances are highlighted. Finally, the paper introduces cutting-edge approaches for LFC in modern power systems, while also identifying future research prospects for improving current techniques and developing novel ones.
... The model can optimize the components' operation for several use cases, such as arbitrage trading, PV self-consumption optimization, FCR (Frequency Containment Reserve) trading, and peak shaving. [12], [13], [14] This case study considers a household with an EVSE and an EV with variable electricity prices based on the Intraday Auction 2023. The parameterization is based on the unIT-e² project ( [15] and [16]), as in [13] but updated for 2023; see Table 2. ...
This paper investigates the impact of the 2023 framework of rules to § 14a of the Energiewirtschaftsgesetz-German Energy Industry Act, EnWG-(§ 14a framework) on electric vehicle (EV) users. The § 14a framework requires distribution grid operators (DSOs) to connect new heat pumps (HPs) and EV charging infrastructure (EVSE) while allowing them to reduce power drawn from HPs and EVSEs in case of grid overload. The DSOs are mandated to provide remuneration for these components. The study develops remuneration mechanisms in the form of variable grid fee tariffs together with DSOs, which are then implemented into an optimization model for the flexible marketing of EVs to analyse their effect on the EV charging behaviour and the total costs. The analysis reveals that the § 14a framework gives high flexibility in designing variable grid fee tariffs to meet the needs of the different distribution grids. These different tariffs enable flexible EV users to achieve substantial cost savings, thereby providing a strong incentive for the load shifting required by DSOs. This research fills a gap in understanding how the incentives outlined in the § 14a framework affect EV users.
... Compared to large-scale wind resources, local solar energy is connected closer to consumers (easier to obtain and store), which explains why the benefits of V2G were discussed more about solar energy in [144]. Optimized charging time with the complement of self-consumption from PV energy entails a reduced electricity bill, although the revenue is highly dependent on photovoltaic systems' dimensions and household size [145]. Charging the vehicle during off-peak time and using the available and more affordable electricity stored in the vehicle when it is necessary can also save the household's electricity cost. ...
In light of governmental policies phasing out petrol/diesel car sales, the vehicle retail sector is transforming to focus solely on electric vehicles (EVs). Given their available physical space and access to a high volume of EVs, future vehicle retailers are ideally positioned to operate as bidirectional charging hubs. This paper explores the challenges and opportunities this presents for EV retailers. Current EV battery technology is examined, including degradation mechanisms associated with grid-to-vehicle and vehicle-to-everything applications. Next, bidirectional chargers and relevant industry protocols are analyzed in detail. The UK energy market's ancillary services are also investigated, with a focus on the specific performance requirements of different market types. Leveraging publicly available datasets from six mainstream EV models, the suitability of various EV fleets for each market is assessed. Finally, recent V2G projects are analyzed, and the broader societal implications of bidirectional charging hubs are discussed.
... In addition, the remuneration fee decreases every year, and the costs of energy from the grid increase. As a result, self-consumption becomes more economically attractive [21]. As for the use case emergency power supply, the electricity supply in Germany is reliable, and few to no very rural areas exist, such that the use case is rated as not relevant. ...
The increasing utilisation of the distribution grid caused by the ramp-up of electromobility and additional electrification can be eased with flexibility through smart and bidirectional charging use cases. Implementing market-oriented, grid-, and system-serving use cases must be tailored to the different national framework conditions, both in technical and regulatory terms. This paper sets out an evaluation methodology for assessing the implementation of smart and bidirectional charging use cases in different countries. Nine use cases are considered, and influencing factors are identified. The evaluation methodology and detailed analysis are applied to Austria, the Czech Republic, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, and Sweden. In every country, the implementation of vehicle-to-home use cases is possible. Realising market-oriented use cases is feasible in countries with a completed smart meter rollout and availability of tariffs with real-time pricing. Grid-serving and ancillary service use cases depend most on country-specific regulation, which is why no clear trend can be identified. Use cases that require direct remote controllability are the most distant from implementation. The overarching analysis provides orientation for the design of transnational products and research and can serve as a basis for a harmonisation process in regulation.
... Through the SG's framework, the initiatives regarding RERs, non-renewable distributed generation units, and EVs would be more achievable. The bidirectional flow of power and information in SGs facilitates the operation of uncertain RERs and EVs [5,6]. Much attention has been paid to the SGs' reliability, which is one of the essential issues in new modernized grids. ...
The reliability‐oriented optimized sizing and placement of electric vehicle (EV) charging stations (EVCSs) has received less attention. In addition, the literature review shows that a research gap exists regarding a clustering‐based method to optimize the allocation of DGs and EVCSs, considering the system uncertainties. This article tries to fill such a knowledge gap by proposing a new clustering‐based method to optimize the allocation of DGs and EVs simultaneously, considering the uncertainties of EV behaviours and stochastic behaviours of renewable DGs. Developing a new stochastic model for EVs using the clustering algorithm is one of the essential contributions. The uncertain parameters, e.g. EV charging loads based on EV owners’ behaviours (arrival time, departure time, and driving distance) and renewable DGs, would be clustered. The proposed method could solve the execution time challenges of Monte Carlo simulation‐based approaches to concern the stochastic behaviours of smart grids. The simultaneously reliability‐oriented optimal allocation of EVCSs, DGs, and protection equipment, using the proposed clustering‐based algorithm is another main contribution. The IEEE 33‐bus test system is studied to examine the introduced method. Simulation results imply that a 1.45% accuracy improvement could be obtained compared to available analytical ones, while its execution time is appropriate.
... The fourth level represents a variable charging process with guaranteed SoC at a desired time, where a safety buffer of 25% SoC is charged immediately. c) Saving potential for the consumers relative to their previous charging tariff (four levels): In line with estimates from recent publications on potential V2G revenues in Germany (FfE, 2023;Kern et al., 2022), we chose 30% as maximum savings potential for our study and set the remaining three levels to 10% less each, i.e., 20%, 10% or no savings. d) Intervention as possibility to manually intervene with the charging mode (two levels: yes/no): In line with the literature (also framed as opt-out or overruling option), this attribute was specified as a binary choice. ...
Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.
... An essential component of this urban ecosystem is the home, which is currently a sophisticated structure with a number of interrelated systems and frameworks, including utilities, security, and lighting [156]. Building complexity increases with building growth, and buildings are susceptible to disturbing disturbances that could jeopardize resources and life safety. ...
The idea of the IOT started way back in 1982 when a bending machine is connected to the internet, then to the concept of Mark Weiser in 1992, then RFID, and so on. A detailed evolution of the IOT and how it was transforming is given. Due to the wide embracement of IOT by industries and home users, it has become the cornerstone of the Information and Communication Technology (ICT) market. The market value of the IOT was worth 25 billion in 2020; 6 trillion in 2025 at 15.12% growing rate [1]. This work presented some major technologies integrated with the IOT to achieve a certain goal and improve an existing system, the capability of IOT to create smart applications is demonstrated and some major distinctions between IOT & the Internet were highlighted.
... V2G services offer EV owners the opportunity to generate revenue by purchasing electricity at lower prices during offpeak periods and selling excess energy at higher prices during on-peak periods [24], [25]. Existing power generation units also benefit in that V2G reduces the need for expansion or new power generation plants, thereby reducing investment costs [26]. ...
The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) has significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems as a global shift towards sustainable energy solutions. Therefore, this survey paper provides a comprehensive discussion on improving MG operation through EV integration. This study evaluates the status of EV integration into MGs, focusing on technological advancements, and emerging trends, while pinpointing key technical challenges and opportunities. Furthermore, this paper examines the pivotal role of EVs in participating in vehicle-to-grid (V2G) services, providing ancillary support to improve MG performance. The importance of a reliable communication infrastructure for information exchange between EV, EV charging stations (EVCSs), and MG has been emphasized for the effective implementation of V2G services. This discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into AC and DC MGs and further proposes configurations for both MG cases. Finally, the paper concludes by providing recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby contributing to the ongoing advancement of sustainable and resilient energy systems. The key findings of this work include solutions for MG voltage and frequency regulation implemented through EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC, EV contributions in improving the MG’s operational resilience and adaptability, and the noteworthy challenges arising from V2G implementation in such systems.
... The optimization model eFlame was primarily developed to optimize several use cases for bidirectional charging separately. In [14,26], the use cases arbitrage trading and self-consumption optimization are elaborated upon. The use case peak shaving is dealt with in [7]. ...
Battery-electric trucks offer a high battery capacity and good predictability, making them attractive for the implementation of bidirectional charging strategies. Nevertheless, most of the previous charging strategy studies focus on electric passenger cars. These charging strategies are usually formulated as separate use cases like tariff-optimized charging, arbitrage trading, peak shaving, and self-consumption optimization. By combining different use cases, their economic potential can be increased. In this paper, we introduce a model to optimize charging processes in depots for electric vehicles considering the combination of different use cases. This model is applied to a depot for battery-electric trucks. The savings obtained through optimized bidirectional charging highlight the enormous potential of this technology for the future, especially in the heavy-duty sector.
... The available capacity of V2G technology is constrained by the anticipated departure time and target SOC, while the online duration is limited by idle period. A large amount of research indicates that V2G technology mainly participates in the electricity market services including peak-to-valley arbitrage [32], the spot market [33], DR [34], and ancillary services [22,29,[35][36][37]. The ancillary services mainly include four aspects: frequency regulation, voltage regulation, spinning reserve, and black start [17,38]. ...
Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology’s business schemes are investigated. Furthermore, the challenges faced by the V2G technology’s business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology’s research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector.
... The utilization of EVs not only provides system flexibility but also offers various revenue-generating opportunities for users across multiple scenarios. In a recent study, researchers employed linear and mixed-integer linear programming algorithms to model the optimization of PV systems and the integration of PV with trading, specifically focusing on households equipped with bidirectional charging EVs [37]. The study focused on charging and discharging efficiencies of bidirectional EVs using linear the mixed-integer linear programming MILP. ...
The future development of the energy sector is influenced by Renewable Energy Sources (RES) and their integration. The main hindrance with RES is that their output is highly volatile and less predictable. However, the utility of the RES can be further enhanced by prediction, optimization , and control algorithms. The scope of this paper is to disseminate a smart Adaptive Optimization and Control (AOC) software for prosumers, namely PV-OPTIM, that is developed to maximize the consumption from local Photovoltaic (PV) systems and, if the solar energy is not available, to minimize the cost by finding the best operational time slots. Furthermore, PV-OPTIM aims to increase the Self-Sustainable Ratio (SSR). If storage is available, PV-OPTIM is designed to protect the battery lifetime. AOC software consists of three algorithms: (i) PV Forecast algorithm (PVFA), (ii) Day Ahead Optimization Algorithm (DAOA), and (iii) Real Time Control Algorithm (RTCA). Both software architecture and functionalities, including interactions, are depicted to promote and repli-cate its usage. The economic impact is related to cost reduction and energy independence reflected by the SSR. The electricity costs are reduced after optimization and further significantly decrease in case of real-time control, the percentage depending on the flexibility of the appliances and the configuration parameters of the RTCA. By optimizing and controlling the load, prosumers increase their SSR to at least 70% in the case of small PV systems with less than 4 kW and to more than 85% in the case of PV systems over 5 kW. By promoting free software applications to enhance RES integration, we estimate that pro-environmental attitude will increase. Moreover, the PV-OPTIM provides support for trading activities on the Local Electricity Markets (LEM) by providing the deficit and surplus quantities for the next day, allowing prosumers to setup their bids.
... This means that whenever a charging station is available, we assume that the EV is directly connected to it. In reality, people are currently less likely to plug in their EVs, especially at public charging stations -unless they must because of a low battery level (Kern et al., 2022;Muratori, 2018;San Román et al., 2011). ...
The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs’ system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs’ system-level impact and that a naïve aggregation of individual profiles leads to an overestimation of the fleet’s flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2 kWh/EV, only 10% of the result of a naïve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.
... A review of the unidirectional and bidirectional charging algorithms that leverage V2G and V2H concepts is presented in [15]. In [16], taking into account the varying charging and discharging efficiencies in the use of V2H, a model has been developed that optimizes the flow of electricity between the home and the vehicle and is adapted for the average German household. In [17], the V2H concept with 10 battery EVs and 5 fuel-cell EVs is conducted and reported that the imported electrical energy from the grid could be reduced by 71%. ...
This paper introduces the design and analysis of an isolated bidirectional five-level (5L) neutral point clamped (NPC) dual active bridge dc-dc converter with an improved anti-windup strategy using proportional-integral (PI) controller for electric vehicle-to-home applications. The proposed converter is more advantageous in that it provides a 5L output voltage at the high-frequency isolation transformer sides thanks to its NPC structure compared to the conventional full-bridge circuit. Reducing switching stresses, providing galvanic isolation, ensuring bidirectional power flow and simple control strategy are the highlights of the proposed structure. A single phase shift (SPS) modulation strategy has been employed to regulate the output voltage and to generate switching signals of the proposed dc-dc converters' switches. To improve the dc-link voltage and to reduce the output voltage tracking errors, the anti-windup PI (PIAW) controller has been utilized instead of the conventional PI controller. To demonstrate the verification of the proposed converter and its controller, a simulation model has been developed in MATLAB/Simulink software. The performance and effectiveness of the proposed converter have been evaluated under steady-state and dynamic variations by the simulation results. Keywords-anti-windup PI controller, five-level neutral point clamped, dc-dc converter, dual active bridge
... Cheng et al. [45] proposed a centralized charging algorithm for fast charging stations with PV, aiming at taking the role of energy storage to maximize the PV output and utilization rate of gridconnected interlinking converters. Kern et al. [46] applied linear and mixed-integer linear programming to optimize V2H and V2G in smart homes for PV self-consumption. Welzel et al. [47] developed a nonlinear optimization model to real-time coordinate various EVs' charging demands, which can save up to 40% of electricity costs. ...
Charging electric vehicles (EV) by photovoltaics (PV) contributes to achieving carbon neutrality, but puts pressure on urban renewal, e.g., large investments in distribution grid upgrade and energy storage (ES). To solve this problem, we proposed a charging system aiming at providing intermittent but free solar charging service for private EV drivers to cover their daily intra-urban transportation demand. It is a battery-free direct-current (DC) microgrid with a distributed charging strategy, taking variable DC bus voltage as a control signal. The system was optimized and tested by simulation on an office building with 80 private EVs. We performed a system performance comparison, when achieving the same guarantee level of EV use with the parking shed fully cover by PV. A 10-kW charger (DC Level 1) with the strategy can take the role of a 3.5-kW charger (AC Level 2) with ES (26.0 kWh/parking space) to coordinate PV generation and charging load. Furthermore, connecting the system with the adjacent building can increase the annual solar self-consumption rate from 33.3% to 67.9%, and avoid the distribution grid upgrade (average 5.6 kVA/charger). The discounted payback time of this building-connected system decreases from 9.4 to 4.5 years, compared with a conventional solar charging station.
... It was shown that bilateral contracts for prosumers and consumers can lead to benefits for both. Kern et al. [27] analyzed the potential of vehicle-to-home and vehicle-to-grid arbitrage trading for a single-family household and a single EV complemented by a PV system, stationary battery storage and a heat pump. They concluded that V2H is mostly conducted during summer months (over 90%) and that V2G arbitrage trading is more profitable during winter months. ...
To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place. In the long term, it can be assumed that a large proportion of passenger cars will be battery–electric. On the one hand, this conversion will result in higher energy and power requirements for the electricity network; on the other hand, it also offers the potential for vehicles to provide energy for various systems in the future. Battery–electric vehicles can be used to shift grid purchases, optimize the operation of other components and increase the self-consumption rate of photovoltaic systems. An LP model for the optimal energy management of the neighborhood consisting of buildings with electricity and heat demand, a PV system, a BEV fleet, a heat pump and thermal storage was formulated. The potential of the BEV fleet to provide energy via V2B in the neighborhood was investigated, considering electricity tariff models and individual charging/discharging efficiencies of vehicles and stochastic mobility profiles. The vehicle fleet provides between 4.8kWh−1sqm−1a (flat-fee) and 25.3kWh−1sqm−1a (dynamic tariff) per year, corresponding to 6.7, 9.5% and 35.7% of the annual energy demand of the neighborhood. All tariff models lead to optimization of self-consumption in summer. Dynamic pricing also leads to arbitrage during winter, and a power price tariff avoids peaks in grid draw. Due to individual charging efficiencies, the power supplied by the fleet is distributed unevenly among the vehicles, and setting limits for additional equivalent full cycles distributes the energy more evenly across the fleet. The limits affect the V2B potential, especially below the limits of 20 yearly cycles for flat and power tariffs and below 80 cycles for a dynamic tariff.
Purpose
In recent years, both homeowners and the research community have shown a growing interest in home automation devices and smart homes. About one-third of all primary energy resources are used by homes worldwide, which consume significant energy. This has raised concerns regarding energy accessibility and the quick depletion of energy sources, the growing need for building services, the improvement of comfortable lifestyles and the increased time spent at home. This study aims to offer a comprehensive and significant examination of state-of-the-art intelligent control systems used for managing energy and ensuring comfort in smart homes.
Design/methodology/approach
After conducting a comprehensive search in the Scopus database, a total of 55 articles were carefully selected. Using the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4 SLR) technique for systematic reviews, the current study synthesized prior research on energy efficiency in smart homes and conducted a detailed descriptive analysis to describe the current state of knowledge.
Findings
Future research on energy efficiency in smart homes could delve into various prospective areas that would strengthen existing knowledge and practices. Using innovative technologies in smart homes can reduce energy consumption in residential areas by offering convenience and improved features.
Originality/value
To the best of the authors’ knowledge, this is the first systematic literature review focused on intelligent control systems for energy and comfort management in smart homes, as well as residents’ interaction with indoor comfort.
In the context of developing modern smart energy systems, integrating Vehicle-to-Grid (V2G) technology within microgrids offers valuable prospects for advancing energy efficiency and sustainability. In this perspective, motivation for the present work mainly stems from the rising need for intelligent and innovative grid management strategies that are capable of balancing energy demand and supply, sustaining economic viability, securing system stability as well as accommodating the growing penetration of Electric Vehicles (EVs). However, strategically managing the dynamic interaction between EVs participating in V2G operations and the microgrid can become complex due to various shifting factors. These include different charging/discharging preferences, EVs flexibility constraints as well as stochastic V2G variables. Thereby, a robust and comprehensive framework is needed to optimize EV-microgrid interactions. In response to this challenge, this study investigates a non-cooperative game approach for decentralized optimization of V2G operations within a microgrid environment. A case study with 5 EVs then with a fleet of 100 EVs is developed and analyzed to showcase the suggested approach. Numerical results show that with 5 EVs, the contribution to the microgrid's daily energy demand is about 5.5%, which increases to 35.37% with 100 EVs. These results highlight the effectiveness of the proposed framework and provide valuable insights for enhancing microgrid sustainability and smart grid management.
Vehicle-to-grid technology enables electric vehicles to contribute their large, high-power batteries to power systems reserves. Here we report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. Our results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 s of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.
Increased adoption of the electric vehicle (EV) needs the proper charging infrastructure integrated with suitable energy management schemes. However, the available literature on this topic lacks in providing a comparative survey on different aspects of this field to properly guide the people interested in this area. To mitigate this gap, this research survey is an effort to provide know-how about the important aspects of the overall EV charging setup with 239 relevant references. It reviews the achievements of energy management systems in terms of improving fuel consumption efficiency and reducing carbon dioxide emissions in EV charging systems. State-of-the-art and most up-to-date standards of EV technology and charging infrastructure are presented. EV charging schemes based on standard grid and renewable energy resources are introduced with a brief comparison of the standard grid and photovoltaic-grid charging systems. Moreover, this article describes centralized and decentralized control configurations of EV charging. A comparative survey of different energy management algorithms is presented while highlighting the benefits and drawbacks of each algorithm. The work presented in this review will help frame future research needs. It is anticipated that the material gathered in this article will become a valuable source of information for the researchers working on this area of study.
Vehicle-to-grid technology enables electric vehicles to contribute their large batteries and high (dis)charging powers to power systems reserves. Here we report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. Our results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 seconds of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.
The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for the user is PV self-consumption optimization using smart charging EVs. Yet, more research is needed to explore the use case’s impacts on the German/European energy systems. Therefore, PV self-consumption optimization using EVs is integrated into an energy system model, assessing its impact on the energy system in 2030. For this purpose, the use case is modeled for different groups of people—personas—which are defined in a way that creates a diverse set of personas reflecting the distribution of different statistical values within Germany. The modified (dis)charging profiles are then aggregated and integrated into the energy system model. With a high implementation of PV self-consumption optimization in Germany in 2030, a positive system effect (with a system cost reduction of 53 million EUR/a) can be observed with a lower need for further storage and less curtailment of renewable energies (RES). Furthermore, the market values for RES increase by 0.7%, which fosters the integration of RES.
The vehicle-to-grid (V2G) network is an infrastructure for electric vehicles (EVs) to be charged and discharged. As EVs are increasingly connected to V2G networks, V2G networks are facing serious data security problems. Attackers may eavesdrop on, tamper with, and forge the electricity transaction data, which will seriously damage the security of the transmitted data and affect the V2G network's normal operation. To ensure secure and efficient transmission of electricity transaction data, a pairing-free certificateless agregate signcryption (PCAS) scheme is proposed in this article. The PCAS scheme is theoretically proven to provide confidentiality, unforgeability, etc. On the premise of ensuring data security, time-consuming pairing operation is avoided in the PCAS scheme, the certificateless mechanism is used to avoid certificate management and key escrow issues, the verification time of ciphertext is reduced by using aggregate signcryption, and the special random number processing method is adopted to reduce the complexity of signcryption process. The scheme is more efficient by adopting these measures. The performance analysis results demonstrate that the PCAS scheme takes less computing time than compared schemes. When massive EVs are charged and discharged through V2G networks, the data are transmitted more securely and efficiently by using the PCAS scheme.
Battery-based electric vehicles (BEVs) in the United States (U.S.) set a new sales record in 2022, driven by technology, policy, environmental, and economic objectives. However, the rapid deployment of BEVs and charging infrastructure without a careful review of their integration with the electric grid can have negative economic impacts on reliable and resilient electricity supply. Bi-directional power transfer (Bi-Di) vehicle-grid integration technologies and services such as vehicle-to-home or building (V2H/B) and vehicle-to-grid (V2G) can potentially lower local and system peak demand, improve economics for grid operators, and benefit BEV customers. Original equipment manufacturers (OEMs) in the automotive industry are exploring technologies and economics (techno-economics) for Bi-Di services. The study conducted a literature review of eleven case studies in the U.S. and Europe that featured Bi-Di demonstrations from 2005 to 2022 to highlight insights and techno-economic opportunities and challenges for OEMs. The findings should motivate the OEMs to prioritize technology innovation and business models to increase BEV sales and gain continuous revenue from Bi-Di services, which can potentially transition "car makers" to "technology solution" companies.
Vehicle-to-grid (V2G) technology, a key driver for reducing carbon emissions and promoting sustainability, promises significant economic benefits through efficient energy exchanges between electric vehicles (EVs) and power distribution grids. However, the inherent uncertainty and variability in EV charging behavior pose challenges in accurately estimating potential economic gains from coordinated smart charging, presenting difficulties for charging service providers. In response to this, our study introduces a data-driven framework for assessing the profitability of fast-charging stations based on real-world operational data. This framework integrates data analytics, mixed-integer optimization, and behavioral theory. To address the computational challenge posed by optimizing charging schedules for numerous sessions, a tailored sub-gradient method is proposed. Additionally, a logit-based choice model is incorporated to account for the participation decisions of users. The flexibility of 0.7 million charging session profiles is thoroughly analyzed under peak-shaving incentives from the grid and quantifies the resultant monetary benefits. The analysis further extended to multiple influencing factors such as climate and the Covid-19 pandemic. Results suggest that fast charging stations under a coordinated charging scheme could experience a monthly bill curtailment of 20–30% compared to uncoordinated circumstances. These findings could pave the way for more efficient and profitable V2G operations, thereby accelerating the transition toward a more sustainable transportation infrastructure.
The research project “Bidirectional Charging Management” (BCM) tests bidirectional charging applications in a comprehensive field trial to demonstrate the customer benefits and value of this technology. Various data are collected for the evaluation of the field trial and the assessment of the different use cases. This study gives an overview of first results while focusing on the private customers. The functionality of the increase of self-consumption use case is described and key values are presented. Due to the delayed start of the field trial, only the unfavorable winter months with less solar radiation could be analyzed so far. Nevertheless, the data show that customers with a large PV system, high household power consumption and connection time during the night as well as a low target State of Charge (SoC) reach the best results in this use case. Furthermore, the own consumption rate and self-sufficiency are compared to different charging methods and the efficiency of the system is evaluated.KeywordsBidirectional charging managementElectromobilityElectric vehiclesField trialPilot operationUse casesEvaluationIncrease of self-consumption
The main objectives of the GREENROAD study are
▪ to provide a national quantity structure and detailed spatial allocation for
the projected requirements of zero emission infrastructure for up to 2040,
▪ to estimate the expected infrastructure costs,
▪ to evaluate the need for public support measures and
▪ to recommend necessary complementary actions.
Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.
Zusammenfassung
Durch ungesteuertes Laden von mehreren, batteriebetriebenen Elektrofahrzeugen kann es in Verteilnetzen zu Netzengpässen oder Überlastung einzelner Netzanschlüsse kommen. Zur Verminderung dieser Netzbelastungen im Kontext gleichzeitig stattfindender Ladevorgänge von Elektrofahrzeugen, können intelligente Flotten-Lademanagementstrategien genutzt werden. Im Rahmen dieses Papers werden in Fallstudien simulativ die Auswirkungen uni- und bidirektionaler Flotten-Lademanagementstrategien auf die Verteilnetzbelastung analysiert. Die Fallstudien verdeutlichen, anhand eines exemplarischen Lastgangs eines Gewerbebetriebs, dass der Spitzenlastwert des Betriebs durch den Einsatz von Lastmanagement, trotz den Ladevorgängen einer Elektrofahrzeug-Flotte unverändert bleibt, während es zeitgleich zu annähernd keinen Einschränkungen für die Fahrzeugnutzer kommt. Des Weiteren wird verdeutlicht, dass durch den Einsatz bidirektionaler Elektrofahrzeuge die Spitze der ursprünglichen Last weiterführend um 40 % reduziert werden kann, während auch in diesem Fall 84 % der Fahrten vollelektrisch durchgeführt werden können. Zudem zeigen die Simulationen, dass durch den Einsatz von Lastmanagement die Netzbelastung in Relation zum ungesteuerten Fall vermindert wird.
The electrification of the mobility and heating sectors will significantly change the electrical behavior of households in the future. To investigate this behavior, it is important to include the heating and mobility sectors in load profile models. Existing models do not sufficiently consider these sectors. Therefore, this work aims to develop an integrated, consistent model for the electrical and thermal load of private households and their mobility behavior. The model needs to generate regionally distinct profiles depending on the building, household and resident type and should be valid for Germany. Based on a bottom-up approach, a model consisting of four components is developed. In an activity model based on a modified Markov chain process, persons are assigned to activities. The activities are then allocated to devices in the electrical and thermal models. A mobility model assigns distances to the journey activities. The results of the simulation to validate the model shows an average annual energy consumption per household of 2751 kWh and a shape of the average load profile, both in good agreement with the reference. Furthermore, the temporal distribution of the vehicles to the locations is in accordance with the reference but the annual mileage is slightly underestimated with 10,730 km.
The use of batteries of electric vehicles (EVs) for home electricity applications using a bidirectional charger, a process called vehicle-to-home (V2H), is attracting the attention of EV owners as a valuable additional benefit of EVs. To motivate owners to invest in V2H, a quantitative evaluation to compare the performance of EV batteries with that of residential stationary batteries (SBs) is required. In this study, we developed a multi-objective optimization method for the household of EV owners using energy costs including investment and CO2 emissions as indices and compared the performances of V2H and SB. As a case study, a typical detached house in Japan was assumed, and we evaluated the economic and environmental aspects of solar power self-consumption using V2H or SB. The results showed that non-commuting EV owners should invest in V2H if the investment cost of a bidirectional charger is one third of the current cost as compared with inexpensive SB, in 2030. In contrast, our results showed that there were no advantages for commuting EV owners. The results of this study contribute to the rational setting of investment costs to increase the use of V2H by EV owners.
The well-known Annuity Factor, restricted to constant payments only, can be generalized for time dependent payments. A Generalized Annuity Factor (GAF) broadens the application potential considerably as is shown exemplarily for the valuation of loans and pension obligations. For the first time for such linear and nonlinear payments over time, compressed closed-form formulae for important financial key numbers such as present value, duration, convexity or value at risk can be derived. Moreover, easy computation makes General Annuity Factors a useful valuation tool especially in the field of finance and accounting. As General Annuity Factors can be implemented as User Defined Functions in a spreadsheet program, calculations can also be done in smaller firms or public services. Because of its computational efficiency the new instrument is also suitable for far-sighted economical models such as Asset Liability Management models (ALM) or life-cycle valuation models concerning products or investments.
The usage of renewable energy sources (RESs) to achieve greenhouse gas (GHG) emission reduction goals requires a holistic transformation across all sectors. Due to the fluctuating nature of RESs, it is necessary to install more wind and photovoltaics (PVs) generation in terms of nominal power than would otherwise be required in order to ensure that the power demand can always be met. In a near fully RES-based energy system, there will be times when there is an inadequate conventional load to meet the overcapacity of RESs, which will lead to demand regularly being exceeded and thereby a surplus. One approach to making productive use of this surplus, which would lead to a holistic transformation of all sectors, is "sector coupling" (SC). This paper describes the general principles behind this concept and develops a working definition intended to be of utility to the international scientific community. Furthermore, a literature review provides an overview of relevant scientific papers on the topic. Due to the challenge of distinguishing between papers with or without SC, the approach adopted here takes the German context as a case study that can be applied to future reviews with an international focus. Finally, to evaluate the potential of SC, an analysis of the linking of the power and transport sectors on a worldwide, EU and German level has been conducted and is outlined here.
The European Earth observation programme Copernicus aims at providing environmental
information to support policymakers, public authorities and both public and commercial users.
A systematic monitoring and forecasting of the state of the Earth's subsystems is provided within six
thematic areas: marine, land, atmosphere, emergency, security, and climate change.
The pre‐operational atmosphere service of Copernicus was provided through the FP7 projects
MACC and MACC‐II/‐III (Monitoring Atmospheric Composition and Climate). This is now continued
in the operational Copernicus Atmosphere Monitoring Service (CAMS). CAMS combines state‐ofthe‐
art atmospheric modelling with Earth observation data to provide information services covering
European Air Quality, Global Atmospheric Composition, Climate, and UV and Solar Energy. Within
the CAMS Radiation Service (CAMS‐RAD) existing historical and daily updated databases for
monitoring incoming surface solar irradiance are made available. The CAMS Radiation Service is
subject to a continuous validation and development. The service meets the needs of European and
national policy development and the requirements of (commercial) downstream services (e.g.
planning, monitoring, efficiency improvements, integration into energy supply grids).
The Users’ Guide describes the data, methods and operations used to deliver time‐series of solar
radiation available at ground surface.
Section 2 includes a short description of the CAMS Radiation Service ‘in a nutshell’. It is meant for
the ‘fast‐track readers’ as a first orientation about the available products.
Section 3 describes shortly the previously provided databases HelioClim‐3 and SOLEMI. These
databases were operated by DLR for SOLEMI and ARMINES and its subsidiary Transvalor for
HelioClim‐3. The historical evolution of methods used to convert satellite images into solar surface
irradiance is shortly presented.
Section 4 describes the new Heliosat‐4 method, including the McClear model describing the
irradiance under clear‐sky.
Section 5 intends to summarize existing knowledge and lessons learnt on satellite‐based irradiances,
which has been published previously only in a scattered manner and is hardly available to users.
Section 6 provides an overview of the operations and the workflow. The products are defined. It
also discusses the means to control the quality of the products and the processing chain.
Section 7 defines the method applied in the quarterly validation reports. It discusses the way
validation results are presented and how they may be interpreted.
It is intended to update this User’s Guide regularly with in the CAMS Radiation Service line.
An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naïve forecast for all variables of the problem.
Legal commitments to reduce CO2 emissions require policy makers to find cost-efficient means to meet these obligations. Marginal abatement cost (MAC) curves, which illustrate the economics associated with climate change mitigation, have recently attracted a great amount of attention. A number of limitations with MAC curves are explained by the implication they should be just one tool in a broader set of decision-making aids used in assessing climate policy. MAC curves, for example, omit ancillary benefits of greenhouse gas emission abatement, treat uncertainty in a limited manner, exclude intertemporal dynamics and lack the necessary transparency concerning their assumptions. MAC curves based on the individual assessment of abatement measures suffer from additional shortcomings such as the non-consideration of interactions and non-financial costs, a possibly inconsistent baseline, double counting and limited treatment of behavioural aspects. Reducing emissions from deforestation and forest degradation exhibit many of the above-mentioned problems, making it particularly difficult to capture in a cost curve. Policy makers should therefore be cautious when interpreting MAC curves, pay attention to the underlying assumptions, consider non-financial costs and be aware of the important uncertainties and underlying path dependencies.
It has been noted that the models typically used to represent inverters in simulation and design tools at the present are inadequate because they do not capture the variations in electrical efficiency over the full range of operating conditions. Data to develop more detailed models have been scarce in the past, but are now increasingly available from multiple sources, therefore it is time to rectify the situation. This paper examines efficiency measurements for a wide range of different inverter products at multiple power levels and input voltages. A model is developed that expresses efficiency as a function of both power and voltage, and it is demonstrated that this model can approximate the efficiency with an appropriate level of accuracy using a small number of parameters. This combination of accuracy and simplicity should facilitate implementation in software and dissemination of model parameters.
The cost to charge an electric vehicle (EV) varies depending on the price of electricity at different charging sites (home, workplace, public), vehicle use, region, and time of day, and for different charging power levels and equipment and installation costs. This paper provides a detailed assessment of the current (2019) levelized cost of light-duty EV charging in the United States, considering the purchase and installation costs of charging equipment and electricity prices from real-world utility tariffs. We find national averages of 0.14/kWh for plug-in hybrid EVs in the United States. Costs, however, vary considerably (e.g., 0.27/kWh for battery EVs) for different charging behaviors and equipment costs, corresponding to a total projected fuel cost savings between 10,500 compared with gasoline vehicles (over a 15-year time horizon). Regional heterogeneities and uncertainty on lifetime vehicle use and future fuel prices produce even greater variations.
The energy utilization optimization strategies in a smart house without and with vehicle to home (V2H) and/or home distributed photovoltaic (HDPV) in Shanghai are investigated in detail for the efficient household energy utilization and the reduction of net electricity expenditure. Such influences as EV travel distances, weather conditions and different PV subsidies are also taken into account. The results show that transferring valley electricity and PV by V2H can not only improve the utilization rate of valley electricity and PV, but also obtain considerable economic benefits. Transferring PV by V2H can get more revenues than transferring valley electricity by V2H. The energy arbitrage of V2H decreases with the increase of the EV travel distance. The HDPV-V2H mode in the case studied can completely cover the electricity demand of the household load in sunny and cloudy days without additional grid electricity while the combination of PV with transferred valley electricity by V2H is enough to support the household load demand in rainy days. The positive return of HDPV still can’t do without the support from government’s subsidy in Shanghai in the coming time. However, the HDPV-V2H mode can improve the benefit of HDPV. Meanwhile, there are a lot of EVs in Shanghai, charging with green power in priority. The HDPV-V2H mode can promote the synergetic development of HDPV and EVs in Shanghai.
With the increasing penetration of distributed renewable energy generation and dynamic electricity pricing schemes, applications for residential demand side management are becoming more appealing. In this work, we present an optimal control model for studying the economic and grid interaction benefits of smart charging of electric vehicles (EV), vehicle-to-grid, and space heating load control for residential houses with on-site photovoltaics (PV). A case study is conducted on 1–10 net zero energy houses with detailed empirical data, resulting in 8–33% yearly electricity cost savings per household with various electric vehicle and space heating system combinations. The self-consumption of PV is also significantly increased.
This paper proposes optimal scheduling of imported power of a load aggregator (LA) with the utilization of electric vehicles (EVs) to maximize its profit. As with the increase of renewable energy resources, electricity price in competitive market becomes more uncertain and on the other hand with the penetration of renewable distributed generators in the distribution network the predicted load of an LA also becomes uncertain in real time. Though there is uncertainties in both load and price but the use of EVs storage capacity can make the operation of load aggregator flexible. LA submits its offer to day-ahead market based on predicted loads and optimized use of its EVs to maximize its profit as well as in real time operation it uses its energy storage capacity such as way that it can maximize its profit. In this project load aggregators profit maximization algorithm is formulated and the optimization problem is solved with the help of a MATLA based optimization tool CVX (convex). As in real time operation the forecasted loads differ from actual load, the mismatches are settled in real time balancing market. Simulation results compare the profit of an LA with a hypothetical group of 1000 EVs and without EVs.
The worldwide launch of electric vehicles (EV) requires new approaches of market integration. At present several projects are performed investigating the vehicle-to-grid concept in terms of technical and economic feasibility. Numerous papers in the field of electric vehicle integration exist in national and international research community. It is difficult to compare the contents of these papers because of the different energy supply system and energy markets. Therefore this paper analyses and compares various approaches to integrate EVs into the energy market. The analysis includes worldwide scientific results. This examination results in a qualitative as well as a quantitative evaluation of vehicle-to-grid concepts. The results support two targets. First, the evaluation is used to identify the most feasible business case in terms of technical and economic market integration of EVs. And second, the business case is adapted to the German energy supply system and energy market. A detailed description of the German business to integrate EVs is given. Thereby this paper lists and explains the requirements to launch EVs successfully.
Bidirectional Charging Management - Field Trial and Measurement Concept for Assessment of Novel Charging Strategies
Hinterstocker Michael
Müller Mathias
Kern Timo
Ostermann Adrian
Dossow Patrick
Pellinger Christoph
dena-Leitstudie Integrierte Energiewende - Impulse für die Gestaltung des Energiesystems bis 2050 - Impulse für die Gestaltung des Energiesystems bis 2050
Bründlinger Thomas
König Julian Elizalde
Frank Oliver
Gründig Dietmar
Jugel Christoph
Kraft Patrizia
Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR
Erdinc Ozan
Paterakis Nikolaos
Mendes Tiago
Bakirtzis Anastasios
Catalão João
Wissenschaftliche Unterstützung bei der Erstellung von fahrzeugbezogenen Analysen zur Netzintegration von Elektrofahrzeugen unter Nutzung erneuerbarer Energien - Endbericht zum Vorhaben FKZ UM 11 96 107
Wickert Manuel
Gerhard Norman
Trost Tobias
Prior Johannes
Cacilo Andrej
Hartwig Matthias
Angewandte Simulation und Optimierung in der Energiewirtschaft Operations Research - Detailliertere Modellierung des Kraftwerkbetriebs: Umsetzung nichtlinearer und ganzzahliger Probleme in der linearen Programmierung
Roth Hans
Munich: Verband der Bayerischen Energie- und Wasserwirtschaft e