Citation: GOUGH, R. ...et al., 2017. Vehicle-to-grid feasibility: A technoeconomic analysis of EV-based energy storage. Applied Energy, 192, Additional Information:• This paper was accepted for publication in the journal Ap- 1 Vehicle-to-Grid Feasibility: A Techno-economic Analysis of EV-based Energy StorageRebecca Gough*⁺ Abstract:The potential for electric vehicles to obtain income from energy supplied to a commercial building together with revenue accruing from specific ancillary service markets in the UK is evaluated in this work. A hybrid time-series/probabilistic simulation environment using real-world data is described, which is applied in the analysis of electricity trading with vehicle-to-grid to vehicles, buildings and markets. Key parameters are found to be the electric vehicle electricity sale price, battery degradation cost and infrastructure costs. Three vehicle-to-grid scenarios are evaluated using pool vehicle trip data, market pricing index data and half-hourly electricity demand for a commercial building. Results show that provision of energy to the wholesale electricity market with additional income from the capacity market results in the greatest projected return on investment, producing an individual vehicle net present value of ~£8,400. This is over 10 years for a vehicle supplying energy three times per week to the half-hour day-ahead market and includes the cost of installing the vehicle-to-grid infrastructure. The analysis also shows that net income generation is strongly dependent upon battery degradation costs associated with vehicle-to-grid cycling.

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Sound recordings were made of two dredging operations at hydrophone depths of 3 and 9.1 m at distances up to 1.2 km from the source in shallow waters (<15 m) of New York Harbor. Sound sources included rock fracturing by a hydraulic cutterhead dredge and six distinct sources associated with a mechanical backhoe dredging operation during rock excavation. To place sound emitted from these dredges in perspective with other anthropogenic sounds, recordings were also made of several deep-draft commercial vessels. Results are presented as sound pressure levels (SPLs) in one-third octave versus range across the 20 Hz to 20 kHz frequency band. To address concerns for protection of fishery resource occupying the harbor, SPL were examined at frequency bands of 50-1000 Hz and 100-400 Hz, the ranges where the majority of fishes without hearing specializations detect sound and the range of greatest sensitivity, respectively. Source levels (dB re 1 μPa-1 m rms) were back calculated using fitted regression (15LogR). The strongest sound sources (180-188.9 dB) were emitted by commercial shipping. Rock fracturing produced a source level of 175 dB, whereas six distinct sources associated with rock excavation had source levels ranging from 164.2 to 179.4 dB re 1 μPa-1 m (rms).

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This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system’s operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.

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