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Considering the increasing amounts of renewable energy generation and energy storage required to meet ambitious environmental goals, it is clear that the next generation of power grids will be dominated by converter-connected devices. In addition, the increasing share of loads connected via power electronics and the general transition to non-synchronous grids with distributed generators make dc microgrids an attractive future alternative. However, achieving optimal utilization of distributed generators in such cases is a complex task, as the performance depends on both the grid and control design. In this paper, we consider such a case where the optimal utilization of distributed generators is achieved by optimal power sharing while taking into account the grid topology, the available generators, and the way they are controlled. For the latter, we consider a droop-based decentralized control scheme whose primary objective is to achieve voltage regulation in the allowable operating range. A novel mixed-integer optimization approach is proposed to identify the optimal converter size and location in the network so that the microgrid can operate safely and with optimal use of the available resources. Time-domain simulations are used to validate the proposed approach and demonstrate its robustness to uncertainty in generator availability.

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated. © 2023, The Author(s).

Modern isolated power grids are constantly evolving to adopt smart grid concepts that can permit higher renewable energy penetration and energy management optimization, in the view of a sustainable RES based energy production EU policy with reduced pollutant emissions. Nevertheless, many islandic power systems like the islands in Southern Europe are still depending on oil-fired diesel engines, while the renewable energy production is limited due to financial, technical and environmental reasons. In this study, the power system of a typical non-interconnected South European island consisting of diesel generators and a PV farm is modelled and simulated. Scope of this paper is to examine the ability of a Battery Energy Storage System (BESS) to achieve load peak shaving combined with maximization of the PV power penetration into the grid leading to pre-planned zero curtailment. For this purpose, a novel peak shaving algorithm is developed and implemented into an Energy Management System (EMS), for optimal scheduling of the diesel engines. Thereinafter, dynamic simulations of the island’s power system are carried out employing a predictive control strategy for different time scales, ranging from a supervisor BESS controller based on load forecasting, to a real-time battery power regulation. The predictive BESS controller is based on future consumption values forecasting, which in turn result from an Artificial Neural Network (ANN) and an optimization procedure taking into account PV power generation and a peak shaving threshold. Thus, a new diesel engine scheduling is obtained capable of replacing the maximum peak power demand with renewable power while at the same time load curve smoothening and reduced diesel generators ramps-up are achieved. The simulations are executed in APROS (Advanced Process Simulator) dynamic simulation platform, using built-in components for the BESS modelling, an external model for load forecasting and a user-developed EMS structure.

For å tilfredsstille målet om høy andel av fornybar energi på offshore olje & gass plattformer, må det utvikles nye regulerings algoritmer som takler frekvensregulering under usikkerhet. På grunn av det lave treghetsmomentet og den stokastiske energiproduksjonen, er slike isolerte strømnett mer volatile og utsatt for frekvenssvingninger enn tradisjonelle strømnett. I denne oppgaven utvikles og sammenlignes en scenario stokastisk modell-prediktiv regulator med ulike pådrags-parametriseringer og de ulike parametriseringene sammenlignes opp mot hverandre og mot en tradisjonell deterministisk modell-prediktiv regulator samt en robust «mixed synthesis» H∞ regulator. Resultatene bekrefter at scenario stokastisk modell-prediktiv regulering gir lavere sannsynlighet for å overskride begrensninger og at hvilken type pådrags-parametrisering man benytter har stor påvirkning på oppførselen til regulatoren. Resultatene viser også hvordan H∞ regulatoren ikke evner til å koordinere de ulike resursene optimalt for å bevare frekvensen. Til slutt drøfter denne oppgaven hvorvidt en scenario stokastisk modell prediktiv regulator er oppnåelig for frekvensregulering med et kort diskre tids steg eller om løsningstiden på optimaliseringsproblemet er for lang. Konklusjonen på dette spørsmålet står ubesvart også etter denne oppgaven. To accommodate the visions of high penetration renewable energy power generation at offshore oil & gas platforms, control algorithms that effectively can deal with uncertainty and manage frequency control need to be developed. Due to the low system inertia and stochastic nature of the power generation, the uncertainty and vulnerability of such offshore isolated power systems with no connection to the main grid are significantly higher than for the main grid. In this thesis, scenario stochastic model predictive controllers with different control action parametrizations, are developed and compared to each other, the traditional deterministic model predictive controller, and the robust mixed synthesis H∞ controller. The results confirm how the scenario stochastic model predictive controllers give lower constraint violation probability and how the different control parametrizations have a big influence on the behaviour. The results also highlight how the H∞ controller lacks the ability to coordinate the energy resources optimally to preserve the frequency. Lastly, a challenge with the scenario model predictive controller is presented with regards to the computational time for such short time step applications as frequency control. The question of the feasibility of scenario stochastic model predictive control for frequency control remains open.