In recent years, it has become crucial to improve the resilience of electricity distribution networks (DNs) against storm-induced failures. Microgrids enabled by Distributed Energy Resources (DERs) can significantly help speed up re-energization of loads, particularly in the complete absence of bulk power supply. We describe an integrated approach which considers a pre-storm DER allocation problem under the uncertainty of failure scenarios as well as a post-storm dispatch problem in microgrids during the multi-period repair of the failed components. This problem is computationally challenging because the number of scenarios (resp. binary variables) increases exponentially (resp. quadratically) in the network size. Our overall solution approach for solving the resulting two-stage mixed-integer linear program (MILP) involves implementing the sample average approximation (SAA) method and Benders Decomposition. Additionally, we implement a greedy approach to reduce the computational time requirements of the post-storm repair scheduling and dispatch problem. The optimality of the resulting solution is evaluated on a modified IEEE 36-node network.
A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is to provide electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event. The opera…
The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, o…
Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs will wirelessly collect information from hardware installed in the power system and in homes with the objective to intelligently and efficiently optimize electricity usage and minimize costs. However, HEMSs can be vulnerable to cyberattacks that target the electricity pricin…
Local electricity markets represent a way of supplementing traditional retailing contracts for end consumers -- among these markets, the renewable energy community has gained momentum over the last few years. This paper proposes a practical and readily to be adopted modelling solution for these communities, one that allows their members to share the economic benefits derived from them. The proposed solution relies on an \emph{ex-post} allocation of the electricity that is generated within energy communities (i.e., local electricity) based on the optimisation of \emph{repartition keys}. Reparti…
This paper presents an original collaborative framework for power exchanges inside a low voltage community. The community seeks to minimize its total costs by scheduling on a daily basis the resources of its members. In this respect, their flexibility such as excess storage capacity, unused local generation or shiftable load are exploited. Total costs include not only the energy commodity, but also grid fees associated to the community operation, through the integration of power flow constraints. In order to share the community costs in a fair manner, two different cost distributions are propo…
A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation
Univ. Grenoble Alpes, CNRS, Grenoble INP（格勒诺布尔阿尔卑斯大学、法国国家科学研究中心、格勒诺布尔理工学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Offline-Tuned Service-Aware TN-NTN Feeder Allocation for 6G Post-Disaster Service Resilience
Rhineland-Palatinate Technical University of Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）；German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心）
The implementation of electricity markets based on locational marginal pricing in a multi-settlement process has allowed wholesale competition, with pricing mechanisms that incentivize the optimal allocation of generation, transmission, and demand-response resources. While efficiency and reliability gains have been achieved in the US at a regional level, the lack of adequate inter-regional coordination mechanisms has limited broader gains. The shortcomings of cross-border coordination of electricity markets become more apparent as the industry transitions towards higher penetration of renewabl…
As the society becomes more dependent on the presence of electricity, the resilience of the power systems gains more importance. This paper develops a decision support method for distribution system operators to restore electricity after an earthquake to the maximum number of customers in the minimum expected duration. The proposed method employs Markov Decision Process (MDP) to determine the optimal restoration scheme. In order to determine the probability of the field component damage due to the earthquake, the Probability of Failure ($P_f$) of structures are calculated using the Peak Ground…
This paper presents sensitivity analyses of resilience-based active distribution system planning solutions with respect to different parameters. The distribution system planning problem is formulated as a two-stage risk-averse stochastic optimization model with conditional value-at-risk (CVaR) as the risk measure. The probabilistic scenarios are obtained using regional wind profiles, and Monte Carlo simulations are conducted to obtain failure scenarios based on component fragility models. The planning measure includes advanced distribution grid operations with intentional islanding measures. T…
This paper proposes a novel cost-reflective and computationally efficient method for allocating distribution network costs to residential customers. First, the method estimates the growth in peak demand with a 50% probability of exceedance (50POE) and the associated network augmentation costs using a probabilistic long-run marginal cost computation based on the Turvey perturbation method. Second, it allocates these costs to customers on a cost-causal basis using the Shapley value solution concept. To overcome the intractability of the exact Shapley value computation for real-world applications…
This paper proposes a stochastic optimal preparation and resource allocation method for upcoming extreme weather events in distribution systems, which can assist utilities to achieve faster and more efficient post-event restoration. With the objective of maximizing served load and minimizing operation cost, this paper develops a two-stage stochastic mixed-integer linear programming (SMILP) model. The first-stage determines the optimal positions and numbers of mobile resources, fuel resources, and labor resources. The second-stage considers network operational constraints and repair crew schedu…
Technical Report on Resilient and Secure Large-Scale Energy Internet Systems
University of Massachusetts Boston（马萨诸塞大学波士顿分校）；The University of Tulsa（塔尔萨大学）；University of Warwick（华威大学）；Sandia National Laboratories（桑迪亚国家实验室）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Orennia（奥伦尼亚）；University of Central Florida（中佛罗里达大学）；National Laboratory of the Rockies（落基山国家实验室）；Idaho National Laboratory（爱达荷国家实验室）
In heterogeneous networks (HetNets), how to improve spectrum efficiency is a crucial issue. Meanwhile increased energy consumption inspires network operators to deploy renewable energy sources as assistance to traditional electricity. Based on above aspects, we allow base stations (BSs) to share their licensed spectrum resource with each other and adjust transmission power to adapt to the renewable energy level. Considering the sharing fairness among BSs, we formulate a multi-person bargaining problem as a stochastic optimization problem. We divide the optimization problem into three parts: da…
With the commitment to climate, globally many countries started reducing brownfield energy production and strongly opting towards green energy resources. However, the optimal allocation of distributed energy resources (DERs) in electrical distribution systems still pertains as a challenging issue to attain the maximum benefits. It happens due to the systems complex behaviour and inappropriate integration of DERs that adversely affects the distribution grid. In this work, we propose a methodology for the optimal allocation of DERs with vulnerable node identification in active electrical distrib…
Phasor measurement units (PMUs) enable better system monitoring and security enhancement in smart grids. In order to enhance power system resilience against outages and blackouts caused by extreme weather events or man-made attacks, it remains a major challenge to determine the optimal number and location of PMUs. In this paper, a multi-objective resilient PMU placement (MORPP) problem is formulated, and solved by a modified Teaching-Learning-Based Optimization (MO-TLBO) algorithm. Three objectives are considered in the MORPP problem, minimizing the number of PMUs, maximizing the system observ…
Community-Centered Resilience Enhancement of Urban Power and Gas Networks via Microgrid Partitioning, Mobile Energy Storage, and Data-Driven Risk Assessment
The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility o…
Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a "hotline" communication network. According to this protocol, agents act on local readings unless their contro…
In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize the damaged assets so that the lost loads, especially critical and important loads, can be restored faster. In addition, correctly predicting the number of repair teams during critical times contributes to restoring the network to the initial resilience level. For this reason, this paper discusses the prioritization of electricity supply lines for evaluating the number of required repair teams. To this end, the economic value of distribution system lines …
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）
Smart grid (SG) systems enhance grid resilience and efficient operation, leveraging the bidirectional flow of energy and information between generation facilities and prosumers. For energy demand management (EDM), the SG network requires computing a large amount of data generated by massive Internet-of-things sensors and advanced metering infrastructure (AMI) with minimal latency. This paper proposes a deep reinforcement learning (DRL)-based resource allocation scheme in a 6G-enabled SG edge network to offload resource-consuming EDM computation to edge servers. Automatic resource provisioning …
