Due to lack of operating experience in the field of offshore wind energy and large costs associated with maintaining offshore wind farms, there is a need to develop accurate operation and maintenance models for strategic planning purposes. This paper provides an approach for verifying such simulation models and demonstrates it by describing the verification process for four models. A reference offshore wind farm is defined and simulated using these models to provide test cases and benchmark results for verification for wind farm availability and O&M costs. This paper also identifies key modelling assumptions that impact the results. The calculated availabilities for the four models show good agreement apart from cases where maintenance resources are heavily constrained. There are also larger discrepancies between the cost results. All the differences in the results can be explained by different modelling assumptions. Therefore, the models can be regarded as verified based on the presented approach.

Flexible distributed energy resources, such as energy storage systems (ESSs), are increasingly considered as means for mitigating challenges introduced by the integration of stochastic, variable distributed generation (DG). The optimal operation of a distribution system with ESS can be formulated as a multi-period optimal power flow (MPOPF) problem which involves scheduling of the charging/discharging of the ESS over an extended planning horizon, e.g., for day-ahead operational planning. Although such problems have been the subject of many works in recent years, these works very rarely consider uncertainties in DG, and almost never explicitly consider uncertainties beyond the current operational planning horizon. This article presents a framework of methods and models for accounting for uncertainties due to distributed wind and solar photovoltaic power generation beyond the planning horizon in an AC MPOPF model for distribution systems with ESS. The expected future value of energy stored at the end of the planning horizon is determined as a function of the stochastic DG resource variables and is explicitly included in the objective function. Results for a case study based on a real distribution system in Norway demonstrate the effectiveness of an operational strategy for ESS scheduling accounting for DG uncertainties. The case study compares the application of the framework to wind and solar power generation. Thus, this work also gives insight into how different approaches are appropriate for modeling DG uncertainty for these two forms of variable DG, due to their inherent differences in terms of variability and stochasticity.

It is essential to implement cost-effective operation and maintenance concepts and strategies to achieve a significant reduction of cost of energy from offshore wind farms. A decision support tool (Norwegian offshore wind cost and benefit model – NOWIcob) was developed that simulates the operational phase of an offshore wind farm with all maintenance activities and costs. This tool can be used to understand sensitivities of the operation and maintenance costs due to changes in the maintenance and logistic strategy, and helps to choose an optimized strategy for a specific wind farm.The model is based on a time-sequential event-based Monte Carlo technique and takes into consideration weather uncertainty and other relevant aspects for the operational phase of an offshore wind farm. Different vessel concepts for accessing the wind farm and time-based, condition-based and corrective maintenance tasks can be simulated. The model delivers results in form of availability, life cycle profit, operation and maintenance cost, produced electricity and other performance criteria.The main characteristics and the underlying scientific models will be described. In addition, application of the NOWIcob model will be demonstrated for a theoretical case of a far-offshore wind farm using a mothership/daughter ship vessel concept, and benefits of using such a tool will be highlighted