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In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.

This paper proposes a day-ahead and intra-day co-optimized resilience enhancement strategy for distribution systems with outages under extreme events, namely, dynamic programming formulated multi-stage hybrid stochastic-robust optimization (DSRO). The DSRO model is a pioneering effort to simultaneously determine the day-ahead decision and the corresponding intra-day policies for microgrids formation, via coupling the two problem with value functions. It establishes a multi-stage framework to address the anticipative challenges inherent in the widely-used two-stage model. For coping with uncertainties, discrete Markov chains are incorporated to model subsequent contingencies, and an uncertainty set for wind generation is constructed to improve computational efficiency. To efficiently solve the DSRO model, a robust dual dynamic programming (RDDP) based solution method is presented, where the relaxed inner approximation method and Lagrangian hyperplanes are employed to handle the nonconvex challenges arising from discrete recourse variables. Case studies on the modified IEEE 14-Bus and 123-Bus distribution systems demonstrate the effectiveness of the proposed model and the solution methodology.

The scale-up of AI models for analyzing large-scale power systems necessitates a thorough understanding of their scaling properties. Existing studies on these properties provide only partial insights, showing predictable decreases in loss function with increased model scales; yet no scaling law for power system AI models has been established, resulting in unpredictable performance. This letter introduces and explores the concept of “emergent abilities” in graph neural networks (GNN) used for analyzing large-scale power systems–a phenomenon where model performance improves dramatically once its scale exceeds a threshold. We further introduce an empirical power-law formula to quantify the relationship between this threshold and the power system size. Our theory precisely predicts the threshold for the emergence of these abilities in large-scale power systems, including both a synthetic 10,000-bus and a real-world 19,402-bus system.

In pursuit of achieving carbon neutrality goals, modern power systems are increasingly characterized by low-carbon and low-inertia properties, leading to significant concerns regarding the security of system frequency. These ancillary services for providing frequency regulation (FR) can contribute to the system inertia, FR reserve capacity, and the response rate of FR reserves. However, it could be challenging to motivate low-carbon resources, like carbon-capture-utilization-and-storage (CCUS) systems and grid-forming inverter-based renewable energy systems (RESs), to participate in FR ancillary service markets. A critical focus of this paper lies in assessing the marginal value of diverse FR ancillary services for improving the performance of frequency-secured systems in under-frequency and over-frequency cases. Given the tight relations among energy and FR ancillary services, the frequency-secured performance criteria are introduced, including maximum rate of change of frequency (RoCoF), maximum frequency deviation, and quasi-steady-state (Q-S-S) frequency which are devised in a joint energy, carbon, and FR ancillary service market. To solve this nonconvex and nonlinear market problem, while minimizing the uplift payment, a tractable shrunken convex hull pricing method is presented. Multiple case studies confirm the proposed method's effectiveness in enhancing the system frequency stability, reducing total costs, and curtailing carbon emissions.

The power industry's carbon emissions stand out as a primary contributor to the overall carbon dioxide emissions within the energy system under the context of energy Internet. Thus, reducing emissions in the power sector has become crucial for achieving carbon neutrality. However, challenges from the electricity–carbon nexus have surfaced in effectively coordinating and integrating the carbon market with the electricity market. This paper initiates the exploration of such nexus by analysing the current status of major carbon emission trading markets on a global scale. Subsequently, it delves into a comprehensive examination of the coupling between the electricity and carbon markets across three levels: market participants, operational models and market mechanisms. Four key issues are then identified in the electricity–carbon nexus: challenges in decision-making for market participants, discrepancies in operational timelines, the intricate design of market coupling mechanisms and the spillover effects of market risks. To tackle the above challenges in the electricity–carbon nexus, this paper takes a deep dive into two different models in understanding the nexus including econometric/statistical models and optimisation models, serving as the foundation for understanding the intricacies of electricity–carbon market coupling. This paper concludes with a detailed exploration for future roadmap and research prospects in the electricity–carbon market nexus.

This paper presents a privacy-preserving operational decision-making approach for autonomous microgrids networked via a power distribution system, where the distribution system operator (DSO) and microgrid master controllers (MMCs) make independent decisions as different stakeholders. First, the scheme of bilevel optimization (i.e., the Stackelberg leader-followers game) is applied to investigate the sequential interactions between DSO and MMCs. Then, given the coexistence of continuous and binary variables in the lower-level problems, an exact strong-duality-based reformulation and decomposition framework is customized to cope with the nonconvex nature of the bilevel mixed-integer optimization. Meanwhile, a fast-enhanced Benders decomposition algorithm is proposed to realize local privacy-preserving decision-making, where multiple unified Benders cuts are generated once and the strongest one is selected back to Benders mater problem. Mathematically, the proposed algorithm will not change the equilibrium point and corresponding optimal solution after finite iterations. Finally, through numerical experiments on a simplified two-bus test system and a modified IEEE 123-bus system, we demonstrate the effectiveness of privacy preservation, as well as the robustness and scalability of the proposed approach.

The variability caused by the proliferation of distributed energy resources (DERs) and the significant growth in unbalanced three-phase loads pose unprecedented challenges to distribution network operations. This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network. First, a distributionally robust chance constraint (DRCC) method is devised to solve the unbalanced three-phase optimal power flow using a semidefinite programming (SDP) model. The DSO can apply the proposed solution to jointly clear energy and flexibility markets. Then, the DRCC model accuracy is improved by an information-sharing mechanism characterized by spatially-correlated uncertainties in the distribution grid. Further, a novel system-wide response function is derived to make the DRCC model tractable. Using the duality theory, the paper further investigates the physical composition of the DSO’s cleared flexibility prices to guide the unbalanced distribution network operation. Finally, the effectiveness of the risk-aware flexibility market is verified in a modified three-phase IEEE 34-node system. Results demonstrate that the flexibility market can quantify the impact of spatially correlated uncertainties and facilitate the utilization of flexible resources to mitigate uncertainties across the network.