• Energy Research

We present a new privacy-preserving probabilistic forecasting of nodal voltage levels in renewable energy communities.

This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation.

Abstract Full converter-based wind power generation (FCWG, e.g. permanent magnet synchronous generator (PMSG)) becomes prevalent in power electronics dominated multi-machine power system (MMPS). With flexibly modified FCWG oscillation modes (FOMs), FCWG has the potential to actuate conducive dynamic interactions with electromechanical oscillation modes (EOMs) of MMPS. In this paper, a mathematical model of FCWG and MMPS is firstly derived to examine the dynamic interactions. Then a novel modal superposition theory is proposed to classify the modal interactions between FOMs and EOMs in the complex plane for the first time. The modal coupling mechanism is graphically visualized to investigate the dynamic interactions, and the eigenvalue shift index is proposed to quantify the dynamic interaction impact on critical EOM. Based on different manifestos in modal coupling mechanism and eigenvalue shift index, a novel methodology to optimize the dynamic interactions between the FCWG and MMPS is designed within the existing control frame. The optimized dynamic interactions (i.e. modal counteraction) can significantly enhance the LFO stability of MMPS, effectiveness of which is verified by both modal analysis and time domain simulations.

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines.

The thermal limit of overhead lines is one of the key constraints in system scheduling problem to ensure the system security and efficiency. In order to achieve better utilization of overhead lines, especially in a distribution system with high renewable energy penetration, this paper proposes a stochastic system scheduling model with Dynamic Line Rating (DLR) constraints. The nonlinear nonconvex DLR constraints derived from the Heat Balance Equation (HBE) are relaxed to Second-Order Cone (SOC) formulation. Therefore, mixed-integer SOC Programming (SOCP) is developed, which is more accurate than the traditional linear programming and can be solved efficiently. A modified IEEE 14-bus system is applied to investigate the effectiveness of relaxation and demonstrate the economic benefit.

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variability introduced by renewable sources start to drive the system stability consideration from deterministic to probabilistic, which further exaggerates the complexity. In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem. First, a graph neural network-based transient simulator is trained by supervised learning to efficiently generate post-contingency rotor angle curves with the steady-state and contingency as inputs, which serves as a feature extractor for operating states and a surrogate time-domain simulator during the environment interaction for reinforcement learning. Distributional deep reinforcement learning with explicit uncertainty distribution of system operational conditions is then applied to generate the redispatch strategy to balance the user-specified probabilistic stability performance and economy preferences. The full distribution of the post-redispatch transient stability index is directly provided as the output. Case studies on the modified New England 39-bus system validate the proposed method. 13 pages,11 figures,accepted by IEEE Transactions on Power Systems on 24-Jun-2024