Estimating the Benefits of Electric Vehicle Smart Charging at Non-Residential Locations: A Data-Driven Approach

In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 40% when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25kWh (~2.8%) of energy per non-residential EV charging session from peak periods (12PM-6PM) to off-peak periods (after 6PM) in Northern California for the year of 2013.Comment: Pre-print, under review at Applied Energ

Co-optimisation of Planning and Operation forActive Distribution Grids

Given the increased penetration of smart grid technologies, distribution system operators are obliged to consider in their planning stage both the increased uncertainty introduced by non-dispatchable distributed energy resources, as well as the operational flexibility provided by new real-time control schemes. First, in this paper, a planning procedure is proposed which considers both traditional expansion measures, e.g. upgrade of transformers, cables, etc., as well as real-time schemes, such as active and reactive power control of distributed generators, use of battery energy storage systems and flexible loads. At the core of the proposed decision making process lies a tractable iterative AC optimal power flow method. Second, to avoid the need for a real-time centralised coordination scheme (and the associated communication requirements), a local control scheme for the operation of individual distributed energy resources and flexible loads is extracted from offline optimal power flow computations. The performance of the two methods is demonstrated on a radial, low-voltage grid, and compared to a standard local control scheme

A Centralised Control Method for Tackling Unbalances in Active Distribution Grids

Traditional distribution network operators are gradually being transformed to system operators, using modern technologies to ensure a secure and efficient operation in a rapidly changing and uncertain environment. One of their most challenging tasks is to tackle the unbalanced operation of low-voltage networks, traditionally caused by unequal loading and structural asymmetries, and exacerbated by the increased penetration of single-phase distributed energy resources. This paper proposes a centralized operation scheme based on a multi-period optimal power flow algorithm used to compute optimal set-points of the controllable distributed energy resources located in the system. The algorithm reduces the operational cost while satisfying the appropriate security and power quality constraints. Furthermore, the computational tractability of the algorithm and the incremental cost of tackling imbalances in the network are addressed. Finally, the performance of the proposed method is tested on an unbalanced low-voltage distribution network

Optimized Local Control for Active Distribution Grids using Machine Learning Techniques

Modern distribution system operators are facing a changing scenery due to the increasing penetration of distributed energy resources, introducing new challenges to system operation. In order to ensure secure system operation at a low cost, centralized and decentralized operational schemes are used to optimally dispatch these units. This paper proposes a decentralized, real-time, operation scheme for the optimal dispatch of distributed energy resources in the absence of extensive monitoring and communication infrastructure. This scheme uses an offline, centralized, optimal operation algorithm, with historical information, to generate a training dataset consisting of various operating conditions and corresponding distributed energy resources optimal decisions. Then, this dataset is used to design the individual local controllers for each unit with the use of machine learning techniques. The performance of the proposed method is tested on a low-voltage distribution network and is compared against centralized and existing decentralized methods

Operational Planning of Active Distribution Grids under Uncertainty

Modern distribution system operators are facing constantly changing operating conditions caused by the increased penetration of intermittent renewable generators and other distributed energy resources. Under these conditions, the distribution system operators are required to operate their networks with increased uncertainty, while ensuring optimal, cost-effective, and secure operation. This paper proposes a centralized scheme for the operational planning of active distribution networks under uncertainty. A multi-period optimal power flow algorithm is used to compute optimal set-points of the controllable distributed energy resources located in the system and ensure its security. Computational tractability of the algorithm and feasibility of the resulting flows are ensured with the use of an iterative power flow method. The system uncertainty, caused by forecasting errors of renewables, is handled through the incorporation of chance constraints, which limit the probability of insecure operation. The resulting operational planning scheme is tested on a low-voltage distribution network model using real forecasting data for the renewable energy sources. We observe that the proposed method prevents insecure operation through efficient use of system controls

Optimal planning of distribution grids considering active power curtailment and reactive power control

In this paper, a new planning methodology is proposed for existing distribution grids, considering both passive and active network measures. The method is designed to be tractable for large grids of any type, e.g., meshed or radial. It can be used as a decision-making tool by distribution system operators which need to decide whether to invest in new hardware, such as new lines and transformers, or to initiate control measures influencing the operational costs. In this paper, active power curtailment and reactive power control are taken into account as measures to prevent unacceptable voltage rises as well as element overloads, as these allow postponing network investments. A low-voltage, meshed grid with 27 nodes is used to demonstrate the proposed scheme. In this particular case, the results show that by using control measures, an active distribution system operator can defer investments and operate the existing infrastructure more efficiently. The methodology is able to account for variations in operational and investment costs coming from regulatory influences to provide an insight to the most cost-efficient decision

Wear and Electrical Resistance on Diesel Lubricated Surfaces Undergoing Reciprocating Sliding

Tribological tests, using the high-frequency reciprocating rig (HFRR), were carried out on commercial diesel fuels with a range of lubricities as well as on solutions of methyl stearate and stearic acid in ultra-low-sulphur, additive-free diesel. The surfaces of discs used in the HFRR tests were measured with micro-Raman and time-of-flight secondary ion mass spectrometry (ToF-SIMS). These measurements showed that amorphous carbon was formed during reciprocating sliding, the structure of which was related to the lubricity as measured by the average wear scar on the ball. Magnetite (Fe3O4) and hematite (α-Fe2O3) were also detected on the surfaces. In most cases, the detection of hematite in the wear track was also associated with low wear. However, hematite was found in the wear particles of all samples. The build up of a film, measured by electrical resistance, was attributed to the presence of hematite, which has a much lower electrical conductivity than magnetit

Ab initio simulations of peptide-mineral interactions

We performed Car-Parrinello Molecular Dynamics (CPMD) simulations of two amino acids, aspartic acid (Asp) and phophoserine (pSer), on a calcium oxalate monohydrate (COM) surface as a model of the interactions of phosphoproteins with biominerals. In our earlier work using in vitro experiments and classical Molecular Dynamics (MD) simulations we have demonstrated the importance of phosphorylation of serine on the interactions of osteopontin (OPN) with COM. We used configurations from our previous classical MD simulations as a starting point for the ab initio simulations. In the case of Asp we found that the a-carboxyl and amine groups form temporary close contacts with the surface. For the dipeptide Asp-pSer the carboxyl groups form permanent close contacts with the surface and the distances of its other functional groups do not vary much. We show how the interaction of carboxyl groups with COM crystal is established and confirm the importance of phosphorylation in mediating the interactions between COM surfaces and OPN. Keywords: Molecular dynamics; Ab initio; Car-Parrinello; Osteopontin; Calcium oxalate monohydrate; Aspartic acid; Phosphoserin

Local spectroscopy and atomic imaging of tunneling current, forces and dissipation on graphite

Theory predicts that the currents in scanning tunneling microscopy (STM) and the attractive forces measured in atomic force microscopy (AFM) are directly related. Atomic images obtained in an attractive AFM mode should therefore be redundant because they should be \emph{similar} to STM. Here, we show that while the distance dependence of current and force is similar for graphite, constant-height AFM- and STM images differ substantially depending on distance and bias voltage. We perform spectroscopy of the tunneling current, the frequency shift and the damping signal at high-symmetry lattice sites of the graphite (0001) surface. The dissipation signal is about twice as sensitive to distance as the frequency shift, explained by the Prandtl-Tomlinson model of atomic friction.Comment: 4 pages, 4 figures, accepted at Physical Review Letter