Constrained power regulation for nonlinear affine wind turbines remains a challenging problem due to strong nonlinearities, multiple operating regions, and strict actuator and state constraints. This study presents a linear programming (LP)-based control strategy that directly uses the nonlinear state-space model without requiring model linearization or gain scheduling. Auxiliary variables are introduced to capture the nonlinear aerodynamic power and thrust coefficients, preserving the affine structure necessary for LP optimization. The controller simultaneously regulates generator power, rotor speed, and aerodynamic performance while explicitly enforcing state and actuator constraints. The proposed strategy is evaluated using the NREL 5 MW reference wind turbine under three wind speed profiles. Simulation results demonstrate accurate maximum power point tracking in the partial-load region, effective rated power regulation in the full-load region, and smooth transitions between operating regions, all while satisfying the imposed constraints. Furthermore, the LP formulation achieves these objectives with low computational complexity, making it well-suited for real-time implementation in nonlinear wind turbine control.
Independent energy storage stations (IESS) play a crucial role in ensuring the stable operation of new-type power systems, while their economic viability across multiple electricity markets remains a central challenge for rational investment. Studying the synergistic value of IESS in energy, frequency regulation, and capacity markets is therefore of significant importance. This study constructs a three-layer distributionally robust optimization (DRO) model encompassing investment, allocation, and clearing to capture the coordinated operation mechanism of IESS, and, building on the existing theoretical foundation of Sinkhorn DRO, employs the Sinkhorn distance to construct an ambiguity set for market price distributions to characterize uncertainties in energy and frequency regulation market prices, while developing an iterative solution framework integrating a genetic algorithm and Gurobi. Case studies with five comparative schemes show that compared with classical robust optimization, the proposed method reduces the configured capacity by 88.64 MWh, corresponding to a 32.0% decrease, while sustaining an annual net profit of $1.76 million; compared with the two-market participation schemes, the annual net profit exceeds those schemes by $0.22 million and $0.52 million, respectively. The research verifies that the proposed model and method effectively mitigate overinvestment and enhance economic viability, providing a decision-making reference for promoting large-scale IESS participation in electricity markets.
This paper proposes the stochastic cubature rule (SCR)-based uncertainty quantification method for probabilistic power flow (PPF) analysis under high-dimensional uncertainties. By applying the Haar-distributed orthogonal transformation to the conventional cubature rule, the coordinate components of the cubature points are redistributed across dimensions. SCR requires only a small number of sample points with equal positive weights. Analytical results indicate that SCR can improve higher-order moment approximation and help mitigate the “non-local sampling effect”. Combined with the copula transformation, the proposed method further accommodates complex distributions and correlations. Tests on modified IEEE 300- and 3120-bus systems evaluate the proposed method against several representative methods in terms of statistical moment calculation, probability density function (PDF) fitting, and confidence interval (CI) estimation. The results demonstrate that SCR achieves satisfactory accuracy, computational efficiency, and robustness under diverse uncertainty conditions.
Although existing grid-forming (GFM) control strategies have made substantial progress in device-level functionality, how GFM equipment should support critical-bus voltages throughout complex grid disturbance processes has not been fully investigated. This paper proposes a staged multi-objective GFM control strategy based on internal electromotive force (EMF) vector reshaping. First, stage-specific support requirements throughout grid disturbance sequences are analyzed, and a hierarchical control architecture employing internal EMF vector reshaping is developed. Second, the specific implementation is presented, including offline presetting of disturbance scenarios and corresponding support requirements, rule-based staged support-command generation and switching, and converter-level implementation. Furthermore, key control parameters for both staged support command generation and converter-level control are designed, and the large-disturbance stability characteristics of the GFM converter under the proposed strategy are investigated through phase plane analysis. Finally, hardware-in-the-loop experiments based on the Chinese Society of Electrical Engineering Voltage Stability (CSEE-VS) benchmark system validate multiple grid voltage support functions of the GFM-VSC under the proposed strategy.
Battery energy storage and demand response are critical technologies to provide flexibility in modern decarbonized electric power systems. This study presents a comprehensive techno-economic assessment of their combined value for the Spanish bulk power system and electricity distribution networks by 2030. The study integrates generation expansion planning, detailed hourly operational system dispatch, and distribution network planning modeling. The case study analyzes multiple deployment scenarios of batteries and demand response to quantify their benefits. For instance, the central scenario assumes 20 % residential and commercial participation in demand response and the installation of 6.4 GW of batteries. This deployment scenario of demand response and batteries results in bulk power system cost savings of approximately 1,447 M€/yr. In relative terms, this results in a reduction of 69 % in investments in additional peak conventional generation (i.e., open-cycle gas turbines), 28 % in variable generation costs, 26 % in emission costs, 55 % in average electricity market prices, and 7 % in renewable energy curtailment. Furthermore, of the total battery capacity installed in the central scenario, 1.4 GW is deployed as distributed batteries, which, together with demand response, defer 77 % of the grid reinforcements required to accommodate new demand and generation in medium and low voltage distribution networks, resulting in additional savings of 375 M€/yr. These results could be used to design policies to accelerate the deployment of these technologies while ensuring system reliability and economic efficiency.
