In the UN Agenda 2030, tourism acquires a salient position as a critical sector, directly or indirectly influencing a number of Sustainable Development Goals (SDGs). The pursuit of Sustainable Tourism (ST) is founded on the respectful exploitation of the sector’s core ‘raw material’, i.e., the precious and vulnerable nexus of natural and cultural heritage, and a cooperative multi-actor endeavor of all those having a stake in this shared good. Strategic tourism policy decisions, formulated at the state level, frame actors’ actions, favoring a balance among economic, societal and environmental goals; and a transparent, concrete and supportive investment landscape, allowing the tourism sector to blossom. But how successful are these policy decisions in promoting a sustainable, resilient and durable tourism model by instigating the entrepreneurial community to invest in the vibrant culture–tourism complex? An effort to respond to this concern is made in this work, grounded in the ‘Culture–Tourism–Policy’ triptych and their interaction, the ‘policy cycle’ as a means of assessing policy performance towards establishing a sustainable/resilient ‘marriage’ of ‘Culture–Tourism’, and GIS-enabled spatial data management for an evidence-based assessment of policy outcomes. These three factors are closely intertwined in the assessment of strategic tourism policy decisions’ performance in a culturally vibrant and highly reputed destination, Greece.

Smart charging has a strong potential to mitigate the challenges in security of supply caused by the increasing reliance on renewable energy sources (RESs) and electric vehicles (EVs). This paper describes the performances of an autonomous distributed control for coordinating the charge of four parking lots as part of a virtual power plant. The virtual power plant consists of a wind farm and four parking lots located in different areas of the grid and connected to two different feeders. The control architecture is applied to a 24-hour simulation with input data from a wind park, the loading data of two feeders, and user behavior from 68 EVs. The objectives of the architecture are: maximization of the wind power usage to charge the EVs; minimization of feeders overloading; minimization of energy imported from the grid; assurance of sufficient charging fulfillment; wind power variability mitigation. Under simulated conditions, the control architecture keeps the feeder loading below 80% by reducing the power allowance to the parking lot during peak demand. Nonetheless the four parking lots guarantee an energy charged of 10.7 kWh for all EVs starting the charging session with less than 60% state of charge (SOC). The total energy produced by the wind power plant is 4.36 MWh, of which 1.34 MWh is used to charge EVs. The remaining 3.07 MWh is exported to the grid, and only 92 kWh is imported from the grid for charging. Further investigation is needed regarding the wind power variability mitigation, as its reduction is only marginal under simulated conditions.

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