Online Estimation of Dynamic Capacity of VSC-HVdc Systems –Power System Use Cases

The dynamic capacity describes the capability of high voltage direct current (HVdc) systems to operate temporarily beyond their guaranteed active and reactive power (P/Q) limitations under specific conditions. In this work, the dynamic capacity is intended to be applied in various power system use cases to ensure a more efficient and secure grid operation. In contrast to previous works, the dynamic capacity is considered with a holistic view on the HVdc system’s components. Moreover, to overcome existing limitations considering only the HVdc system design, it is introduced to estimate the dynamic capacity based on real-time operational data. In principle, dynamic capacity could help for any power system use case where temporarily additional capacity is required. The article details five use cases, including congestion management, voltage support, frequency response, offshore wind overplanting and grid planning to be of high interest for such a feature. The main HVdc applications, embedded systems, interconnectors and offshore grid connection, and anticipated time frames for dynamic capacity are highlighted from power system perspective. Also, the time-criticality of the remedial actions is outlined

Integrating short-term demand response into long-term investment planning

Planning models have been used for many years to optimize generation investments in electric power systems. More recently, these models have been extended in order to treat demand-side management on an equal footing. This paper stresses the importance of integrating short-term demand response to time-varying prices into those investment models. Three different methodologies are suggested to integrate short-term responsiveness into a long-term model assuming that consumer response can be modelled using price-elastic demand and that generators behave competitively. First, numerical results show that considering operational constraints in an investment model results in less inflexible base load capacity and more mid-range capacity that has higher ramp rates. Then, own-price and cross-price elasticities are included in order to incorporate consumers’ willingness to adjust the demand profile in response to price changes. Whereas own-price elasticities account for immediate response to price signals, cross-price elasticities account for shifting loads to other periods. As energy efficiency programs sponsored by governments or utilities also influence the load profile, the interaction of energy efficiency expenditures and demand response is also modelled. In particular, reduced responsiveness to prices can be a side effect when consumers have become more energy efficient. Comparison of model results for a single year optimization with and without demand response shows the peak reduction and valley filling effects of response to real-time prices for an illustrative example with a large amount of wind power injections. Additionally, increasing demand elasticity increases the optimal amount of installed wind power capacity. This suggests that demand-side management can result in environmental benefits not only through reducing energy use, but also by facilitating integration of renewable energy

Performance Analysis of One Model of Communication and Information System in Military Operation

This paper presents a model of communication and information system in military operations. Here OPNET MODELER simulation package is applied because it is suitable for network modelling, topology and capacity planning. Simulation of different types of IP traffic and monitor their performance to optimise the functionality of network elements, management performance network applications, and as well as in research and development of new network technologies. Application of the method of mass service are determined by the capacity needed for voice transmission on the links in the model and using the OPNET MODELER simulation program are analysed performance modeled communication information system in data transmission. The results of the simulation are presented through target the service settings: workload links communication and information system, e-mail download response time, http page download response time and packet loss in data transfer. The aim of the research has shown that modeled communication information system with defined elements (nodes), the capacity of links (according to the specification of telecommunication devices) and defined traffic can respond to the requirements of command forces in the military operation in terms of telecommunication service. The results of the analysed service target parameters show that modeled communication and information system provides an efficient flow of information and the tra nsfer of voice and IP data for the needs of command and control in military operations

Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping

Mapping floods using satellite data is crucial for managing and mitigating flood risks. Satellite imagery enables rapid and accurate analysis of large areas, providing critical information for emergency response and disaster management. Historical flood data derived from satellite imagery can inform long-term planning, risk management strategies, and insurance-related decisions. The Sentinel-1 satellite is effective for flood detection, but for longer time series, other satellites such as MODIS can be used in combination with deep learning models to accurately identify and map past flood events. We here develop a combined CNN--LSTM deep learning framework to fuse Sentinel-1 derived fractional flooded area with MODIS data in order to infer historical floods over Bangladesh. The results show how our framework outperforms a CNN-only approach and takes advantage of not only space, but also time in order to predict the fractional inundated area. The model is applied to historical MODIS data to infer the past 20 years of inundation extents over Bangladesh and compared to a thresholding algorithm and a physical model. Our fusion model outperforms both models in consistency and capacity to predict peak inundation extents.Comment: CVPR 2023: Earthvision Worksho

Assessment of novel distributed control techniques to address network constraints with demand side management

The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid.The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

The National Aeronautics and Space Administration's (NASA) Airspace Systems Program is contributing air traffic management research in support of the 2025 Next Generation Air Transportation System (NextGen). Contributions support research and development needs provided by the interagency Joint Planning and Development Office (JPDO). These needs generally call for integrated technical solutions that improve system-level performance and work effectively across multiple domains and planning time horizons. In response, the Airspace Systems Program is pursuing an integrated research approach and has adapted systems engineering best practices for application in a research environment. Systems engineering methods aim to enable researchers to methodically compare different technical approaches, consider system-level performance, and develop compatible solutions. Systems engineering activities are performed iteratively as the research matures. Products of this approach include a demand and needs analysis, system-level descriptions focusing on NASA research contributions, system assessment and design studies, and common systemlevel metrics, scenarios, and assumptions. Results from the first systems engineering iteration include a preliminary demand and needs analysis; a functional modeling tool; and initial system-level metrics, scenario characteristics, and assumptions. Demand and needs analysis results suggest that several advanced concepts can mitigate demand/capacity imbalances for NextGen, but fall short of enabling three-times current-day capacity at the nation s busiest airports and airspace. Current activities are focusing on standardizing metrics, scenarios, and assumptions, conducting system-level performance assessments of integrated research solutions, and exploring key system design interfaces

Making space for proactive adaptation of rapidly changing coasts: a windows of opportunity approach

Coastlines are very often places where the impacts of global change are felt most keenly, and they are also often sites of high values and intense use for industry, human habitation, nature conservation and recreation. In many countries, coastlines are a key contested territory for planning for climate change, and also locations where development and conservation conflicts play out. As a “test bed” for climate change adaptation, coastal regions provide valuable, but highly diverse experiences and lessons. This paper sets out to explore the lessons of coastal planning and development for the implementation of proactive adaptation, and the possibility to move from adaptation visions to actual adaptation governance and planning. Using qualitative analysis of interviews and workshops, we first examine what the barriers are to proactive adaptation at the coast, and how current policy and practice frames are leading to avoidable lock-ins and other maladaptive decisions that are narrowing our adaptation options. Using examples from UK, we then identify adaptation windows that can be opened, reframed or transformed to set the course for proactive adaptation which links high level top-down legislative requirements with local bottom-up actions. We explore how these windows can be harnessed so that space for proactive adaptation increases and maladaptive decisions are reduced

Policy into practice: Adoption of hazard mitigation measures by local government in Queensland:A collaborative research project between Queensland University of Technology and Emergency Management Queensland in association with Local Government of Queensland Disaster Management Alliance

The focus of the present research was to investigate how Local Governments in Queensland were progressing with the adoption of delineated DM policies and supporting guidelines. The study consulted Local Government representatives and hence, the results reflect their views on these issues. Is adoption occurring? To what degree? Are policies and guidelines being effectively implemented so that the objective of a safer, more resilient community is being achieved? If not, what are the current barriers to achieving this, and can recommendations be made to overcome these barriers? These questions defined the basis on which the present study was designed and the survey tools developed.\ud \ud While it was recognised that LGAQ and Emergency Management Queensland (EMQ) may have differing views on some reported issues, it was beyond the scope of the present study to canvass those views.\ud \ud The study resolved to document and analyse these questions under the broad themes of: \ud \ud • Building community capacity (notably via community awareness).\ud • Council operationalisation of DM. \ud • Regional partnerships (in mitigation/adaptation).\ud \ud Data was collected via a survey tool comprising two components: \ud \ud • An online questionnaire survey distributed via the LGAQ Disaster Management Alliance (hereafter referred to as the “Alliance”) to DM sections of all Queensland Local Government Councils; and\ud • a series of focus groups with selected Queensland Councils\u