The inevitable transition of the power system toward a sustainable and renewable-energy centered power system is accompanied by huge versatility and significant challenges. A corresponding shift in operation strategies, embracing more intelligence and digitization, e.g., a Cyber-Physical System (CPS), is needed to achieve an optimal, reliable and secure operation across all system levels (components, units, plants, grids) and by the use of big data. Digital twins (DTs) are a promising approach to realize CPS. In this paper, their applications in power systems are reviewed comprehensively. The review reveals that there exists a gap between available DT definitions and the requirements for DTs utilized in future power systems. Therefore, by adapting the current definitions to these requirements, a generic definition of a “Digital Twin System (DTS)” is introduced which finally allows proposing a multi-level and arbitrarily extendable “System of Digital Twin Systems (SDTSs)” idea. The SDTSs can be realized with an open-source framework that serves as a central data and communication interface between different DTSs which can interact by “Reporting Modules” and are regulated by “Control Modules” (CMs). Exemplary application scenarios involving multiple system levels are discussed to illustrate the capabilities of the proposed SDTS concept.

Concepts for control of wind farms (WFs) can be clustered into two distinct concepts, namely, wind power plant control (WPPC) and wind farm flow control (WFFC). WPPC is concerned with the connection to the power system, compliance with grid codes, and provision of power system services. This comprises the traditional way of operating a WF without consideration of aerodynamic turbine interaction. However, flow phenomena like wake effects can have a large impact on the overall performance of the WF. WFFC considers such aerodynamic phenomena in the WF operation. It can be viewed as a new feature that shall be integrated with the existing control functions. The interaction of these different control concepts is discussed in this article, leading to an identification of the challenges whose solutions will contribute to a successful integration of electrical system and aerodynamic aspects of WF control.

Future inverter-based resources (IBRs) must provide grid-forming functionalities to compensate for the declining share of conventional synchronous machines (SMs) in the power generation mix. Specifically, decreasing power system inertia poses a significant challenge to grid frequency stability, as system inertia limits the rate of change of frequency (ROCOF). Conventional grid-following control decouples the physical inertia of wind turbines (WTs) from the grid frequency. Novel grid-forming control methods, such as virtual synchronous machine (VSM) control, provide (virtual) inertia to the system, e.g. by extracting kinetic energy from WTs. Since the grid-forming capability of IBRs depends on volatile operating conditions, future market designs will remunerate inertia provision based on its availability. Thus, estimating grid-forming capabilities of WTs and forecasting inertia of wind farms (WFs) are of interest for both WF and system operators. In this paper, we propose a method to forecast inertia that accounts for wake effects in a WF. The approach is based on mapping forecasted site conditions to each single WT in the WF through a wake model. The resulting inflow conditions are used to predict the WT grid-forming capabilities, taking WT control strategies and operating limits into account.

We present the economic lifetime-aware formulation of wind farm control (WFC), a novel approach that incorporates the current state of damage of turbines – either provided by a digital twin or estimated from operational data – to compute control policies that optimize the long-term life management of a wind farm. The optimization is guided by an economic value function that balances power capture (taking into account variable spot market prices) with operation and maintenance (O&M) costs, including potential revenue losses due to turbine downtime. The optimization process is subject to constraints ensuring that the target lifetime of selected turbine components is met, considering varying inflow conditions. This results in optimal control setpoints for each turbine as functions of ambient conditions. After introducing the economic lifetime-aware WFC approach, the paper analyzes two case studies. The first is a synthetic scenario with a simplified set of environmental conditions, designed to demonstrate the behavior of the new control strategy and its impact on fatigue loads. The second case features the more realistic setup of a small wind farm, with wind climate data derived from a real meteorological mast. The benefits of the proposed methodology are highlighted by comparing it to a range of reference control strategies, which either ignore component damage or address fatigue loads in alternative simplified ways. When compared to greedy and power-maximizing WFC strategies, results show that only the lifetime-aware formulation ensures the achievement of the desired lifetime targets. Additionally, it also results in the best economic performance.

Model predictive control (MPC) for wind turbines offers several interesting advantages over simpler techniques, as for example the direct optimization of a goal function, the inclusion of constraints, non-linear coupled dynamics, and wind preview (when available). To enable real-time execution, MPC uses a reduced order model (ROM) that approximates the dynamics of the controlled system using only a limited number of degrees of freedom. As a result, the accuracy of the ROM is often the main limit to the performance of MPC. To address this problem, an adaptive controller-internal model can reduce plant-model mismatches, potentially leading to improved performance. This work proposes an adaptive economic nonlinear MPC (ENMPC) for wind turbines. The controller maximizes profit by optimally balancing fatigue damage cost with revenue due to power generation. The cyclic fatigue cost is formulated directly within the controller using the novel parametric online rainflow counting (PORFC) approach. PORFC provides a rigorous continuous expression of the discontinuous cyclic fatigue cost using time-varying parameters. Adaptivity is obtained by a controller-internal grey-box model that combines reduced order physical dynamics with data-driven correction terms. These are implemented via a neural network that is trained offline. Additionally, system state and disturbance estimators are included in the closed-loop controller. The improvement in state predictions due to model adaptation is first assessed and compared with respect to the non-adapted baseline ROM in open loop. The performance of the adaptive ENMPC and the impact of a reduced plant-model mismatch is then assessed in closed loop for a reference multi-MW onshore wind turbine in a realistic simulation environment. Results show that the adaptive ENMPC yields higher economic profits at significantly lower pitch and torque travels, compared to the baseline non-adaptive ENMPC. While the enhanced closed-loop performance and economic gains of the proposed model adaptation are significant, they come at the cost of a slight increase in the computational burden of the controller.

Accurate and computationally efficient modeling of wind farm response is essential to support a wide range of stakeholders, including research institutes, wind turbine and wind farm designers, operators, and control algorithm developers. This paper presents a modular and integrated framework for modeling wind farm response, enabling consistent and multi-purpose predictions across turbine- and farm-level applications. The proposed approach combines computationally efficient and site-specific wind farm flow modeling, high-fidelity aero-servo-elastic simulations, wake-resolved inflow characterization, and data-driven response surrogates within a flexible architecture that allows individual components to be independently developed, validated, and exchanged. Within this framework, key novelties are introduced such as a modular and holistic wind farm model, as well as a wake-slice methodology to represent local waked inflow conditions in a compact and physically meaningful form, enabling efficient training of response surrogate models using single-turbine simulations. Artificial neural network surrogates are developed to predict individual turbine responses based on a reduced set of local inflow and control descriptors, allowing the effects of wakes, turbulence, and operational strategies to be captured without resorting to full farm-level aeroelastic simulations. Another key feature of the proposed framework is its ability to consistently model multiple turbine types as well as a wide range of operational modes (power production, start-up, shut-down and parking) combined with several control modes (normal operation, yaw-steering, derating, down-regulation and noise-curtailment) within a single formulation. To this end, the methodology employs location-agnostic load surrogates, applicable to a given turbine type irrespective of its position within a farm and at any site. The overall framework is wind farm agnostic, with a modular structure that enables application to arbitrary farm layouts, environmental conditions, and operating modes without structural modification. The framework is tested using one open-source reference turbine and two anonymized commercial turbines. For each turbine type, surrogates were developed using a single holistic library of inflow profiles representing clean and waked conditions. The performances are evaluated through an exemplary wind farm configuration composed of six turbines, demonstrating the location agnosticism of the proposed approach. Furthermore, the framework is systematically evaluated through surrogate validation and analysis across different turbine types, environmental conditions, and operational and control modes. The results demonstrate that the proposed toolchain accurately reproduces the load variations induced by wake interactions, operational modes and control modes, while maintaining a low computational cost. By combining modular physics-based modeling with scalable data-driven surrogates, the framework provides a multi-stakeholder solution for wind farm response modeling, supporting applications ranging from design analysis to operational assessment and wind farm control studies.