After the Energy Bill was announced in 2005, a transition of national energy system from hydrocarbon to hydrogen had been envisioned. However, safe and convenient refueling and widespread availability of hydrogen from various energy sources are the prerequisite for the development of hydrogen economy. This paper focuses on the design of a hydrogen filling station with on-site hydrogen production by electrolysis of water. The proposed virtual wind farm and on-site installed photovoltaic (PV) supply the main and renewable energy for electrolyzers in the station. The utility electricity is served as an auxiliary energy resource to compensate the intermittence of wind and solar. Since the market clearing price (MCP) can change dramatically in the deregulated power market, the station can participate in the power market with a hydrogen storage tank. Based on the forecasting of wind power with artificial neural network (ANN) model and MCP with the statistical model, this paper proposes an optimization algorithm for the hydrogen production schedule and the strategy of power trading to optimize the production costs of the station. Index Terms-Hydrogen economy, hydrogen filling station, renewable energy, power trading. Perfect forecasting 20% Uncertainty 30% Uncertainty 40% Uncertainty 50% Uncertainty No OPT 978-1-4244-5602-4/09/$25.00 © IEEE 2010

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A scalar equilibrium (SE) is defined for n-person prescriptive games in normal form. When a decision criterion (notion of rationality) is either agreed upon by the players or prescribed by an external arbiter, the resulting decision process is modeled by a suitable scalar transformation (utility function). Each n-tuple of von Neumann-Morgenstern utilities is transformed into a nonnegative scalar value between 0 and 1. Any n-tuple yielding a largest scalar value determines an SE, which is always a pure strategy profile. SEs can be computed much faster than Nash equilibria, for example; and the decision criterion need not be based on the players' selfishness. To illustrate the SE, we define a compromise equilibrium, establish its Pareto optimality, and present examples comparing it to other solution concepts.