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In this paper five members of the "Community Psychoanalytic Track and Consortium" (CPT&C) in San Francisco, California, each holding different positionalities and functioning in different roles, come together in dialog with the shared aim to bring themselves and their readers inside the CPT&C. This writing project recapitulates principles of the CPT&C's vision itself: to form polyvocal groups with the shared task of supporting each other in our various roles, and members of community mental health organizations in their work and lives. From that effort new forms of psychoanalytic learning and work are generated; in this instance the process translates into a new writing form. [ABSTRACT FROM AUTHOR]

Accurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs' temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners' trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA. [ABSTRACT FROM AUTHOR]