Scenario-based assessment of energy storage technologies for wind power generation using Bayesian causal maps

Wind power shares the major drawbacks of most renewable energy generation alternatives: higher costs and inconsistency of power generation. Power balancing requirements resulting from the intermittency of wind power suggest using energy storage assistance to improve overall generation and load characteristics. The problems in generation imbalance for wind power require multi-criteria analysis for the decision makers. In addition to the required multi-criteria analysis, there is also a problem of uncertainty inherent in future changes as a result of interdependence among these criteria. To counter this two problems, this paper describes a systematic approach of Bayesian causal maps and systematic probability generation method. Bayesian causal maps, which is built from causal maps, is used to develop a proposed framework on scenario-based assessment of energy storage technologies for wind power generation. Causal maps provides a rich representation of ideas, through the modeling of complex structures, representing the chain of arguments, as networks. However, causal maps is not easy to define and the magnitude of the effect is difficult to express in numbers. Hence, Bayesian causal maps can be used to make inferences in causal maps. A systematic probability generation method is used to estimate the conditional probability tables, which can reduce biases and inconsistencies.