Hongye Guo

Addressing uncertainty is essential in power systems with high levels of renewable energy penetration. Distributed energy resources (DERs), due to their partially controllable nature, are a major source of uncertainty. However, due to their large numbers and complex correlations, their aggregated uncertainty is highly complex. This paper aims to track how the uncertainty is modeled from individual DER prediction errors to the aggregated-level. By enforcing a specified confidence level, the aggregated-level probabilistic flexibility boundary is formulated as a Minkowski sum under joint chance constraints (JCCs). Despite the inherent intractability of this problem, we establish an equivalent representation that allows for an effective approximation using the proposed quantile cube approximation method. An iterative algorithm is also developed to enhance computational efficiency in implementing the method. Numerical tests demonstrate that the proposed method effectively reduces the conservativeness of the aggregated confidence boundary and the computation time at the same time.

The power consumption flexibility of distributed energy resources (DERs) must be aggregated to enable effective interaction with power systems. However, model heterogeneity, geographical dispersion, and the large number pose significant challenges to aggregation. This paper first models DER flexibility by explicitly incorporating heterogeneity in both state variables and available time periods, represented through polytopes of heterogeneous dimensions. The aggregation of DERs is then formulated as a standard projection maximal inner approximation (MIA) problem. To efficiently and accurately solve this problem, a novel linear programming (LP)-based algorithm is developed. Furthermore, a hierarchical framework is introduced to enable large-scale aggregation, within which a Minkowski-closed family is proven, allowing accurate and efficient secondary aggregation through vector addition. In addition, generalized operating envelopes (OEs) are proposed for distribution system operators (DSOs) to establish and communicate network constraints, enabling integration into the aggregation process without disclosing sensitive network information. Numerical experiments validate the proposed formulations and demonstrate superior accuracy and scalability of the proposed method while maintaining high computational efficiency.

Achieving energy sustainability requires a transition from fossil fuel-based to renewable-dominant power systems (RDPSs). However, this shift introduces significant grid stability challenges, primarily due to the reduced synchronous dynamics traditionally provided by conventional thermal power plants. To enhance RDPS resilience, grid-forming battery energy storage systems (GFM-BESSs) have emerged as a pivotal solution, emulating the dynamic behaviors of synchronous generators (SGs). With efficient bidirectional power interaction and fast response, GFM-BESSs actively enhance grid stability through virtual inertia, voltage support, enhanced synchronization, and restoration capability. In this review, we establish the GFM-BESS as a standalone and integral grid infrastructure in RDPSs. We first identify the stability risks in RDPSs and systematically examine the role of the GFM-BESS in supporting grid stability. We then investigate the coordination between multiple hierarchical control layers of the GFM-BESS, including the battery management system (BMS), power converter system (PCS), energy management system (EMS), and grid EMS (GEMS), which collectively address its operational complexities across different timescales. Based on the grid stability requirements, we evaluate the technical benefits of GFM-BESS deployment and discuss potential monetization approaches for the economic value of GFM-BESSs in RDPSs. Finally, highlighting the potential of GFM-BESSs in driving the sustainable energy transition, we conclude with a strategic road map that identifies the collaborative synergies of multiple stakeholders, from hardware manufacturers to policymakers, outlining key opportunities for future research to advance GFM-BESSs as a cornerstone of the 100% renewable energy goal.

The resource-task network (RTN) model has been widely applied to represent the technical constraints of complex industrial processes (IPs) such as steel-making, providing the basis for industrial demand response. However, the legacy RTN model contains numerous binary variables and applies different formulations for non-flexible and flexible processes, restricting its computational efficiency and applicability. To systematically improve the computational performance of IP models, we propose continuous RTN model (cRTN), a novel modeling approach that uses continuous variables to represent production tasks and progresses, which are then integrated into unified as well as computationally favorable formulations for the technical constraints in discrete IPs, including resource balance, task execution, waiting time limits, and production targets. Compared to the legacy models, cRTN features fewer binary variables, shorter solving time, and better scalability while maintaining the same accuracy. Numerical tests based on a steel plant demonstrate that cRTN is in typical cases 10 times faster than legacy models and remains tractable with increasing batch sizes, which in legacy models leads to larger problem scales and infeasible solving time. cRTN also achieves a reduction in energy costs by resolving the issue of rounding errors reported in legacy models.

The intricate mixed-integer constraints in industrial load models not only pose challenges for their direct integration into economic dispatch or market clearing processes but also render current analytical dimension-reduction methods ineffective. We propose a novel data-driven dimension-reduction approach for industrial load modeling, which uses the optimal energy usage data from industrial loads to train a dimension-reduced model that best fits the original constraints. Our approach, implemented by the adjustable load fleet model, outperformed analytical methods across three industrial load datasets.