Limited Transmission Capacity Research Articles

Long-Term Ampacity Prediction Method for Cable Intermediate Joints Based on the Prophet Model

The development of power grids is hindered by the limited transmission capacity of cable equipment, necessitating the accurate prediction of dynamic ampacity for cable expansion. This study focuses on the 110 kV cable intermediate joint, employing radial and axial inversion techniques for real-time conductor temperature inversion. Utilizing the Prophet time series model, we predict environmental changes and propose a dynamic ampacity evaluation method for cable intermediate joints. Experimental validation confirms the model’s accuracy, with prediction errors under 10 K, demonstrating its potential for enhancing cable system reliability and power grid development.

A risk-aware and second-order cone programming model for wind expansion planning considering correlated uncertainties using the copula method

Integration of large-scale renewable energy sources (RESs) into existing power grids is challenging for power system operators and planners due to the intermittent and uncertain characteristics of RESs and the limitation of transmission capacity. This paper presents a new risk-based framework to obtain the optimal placement and sizing of wind farms in the bulk power system considering the impact of mutual correlation between wind farms’ power productions in different candidate buses. In this regard, a second-order cone programming (SOCP) optimization problem is developed to minimize the total expected social cost including the operation cost of conventional generators, wind curtailment cost, and the investment cost of newly installed wind farms. In the proposed model, the correlated uncertainties of wind power production are incorporated using the copula method. Also, the conditional value-at-risk (CVaR) measure is utilized to manage the risk of the model associated with uncertainties. The proposed planning model is then implemented into the Garver 6-bus and IEEE 118-bus test systems. Given the results, employing SOCP-based AC OPF in the presented planning model results in a precise solution, demonstrating an 87% reduction in running time. Moreover, the numerical results illustrate that by considering the correlation between wind farms’ power production and reducing the risk parameter, there is a significant increase in the installed capacity of wind farms.

Low-loss weak-coupling 6-mode hollow-core negative curvature fiber based on symmetric double-ring nested tube

Few-mode optical fibers have played an increasingly important role in breaking through the transmission capacity limitations of single-mode optical fiber and alleviating the bandwidth crisis in optic fiber communication systems in recent years. Nevertheless, traditional solid core few-mode optical fibers usually suffer optical fiber nonlinearity and mode coupling, leading to mode crosstalk between channels. Hollow core negative curvature fibers (HC-NCF) have attracted widespread attention due to their advantages, such as low latency, low nonlinearity, low dispersion, low transmission loss, and large operating bandwidth. In this work, a novel low-loss few-mode HC-NCF with symmetrically double ring nested tube structure is designed, which supports six core modes including LP<sub>01</sub>, LP<sub>11</sub>, LP<sub>21</sub>, LP<sub>02</sub>, LP<sub>31<i>a</i></sub>, and LP<sub>31<i>b</i></sub>. The designed optical fiber is based on silica dioxide substrate and adopts a unique symmetrical double ring nested cladding structure, which can effectively suppress the coupling between the core mode and the cladding mode. The finite element method (FDE) is used to numerically analyze the properties of the proposed few-mode HC-NCF and optimize the structural parameters of the few-mode HC-NCF. Moreover, the confinement loss and bending loss of all core modes are investigated. The simulation results show that the proposed few-mode HC-NCF can support the independent transmission of six weakly coupled core modes (with the effective refractive index difference greater than 1×10<sup>–4</sup> between the adjacent core modes, which greatly avoids the coupling between the adjacent modes in the fiber core). In the 400 nm bandwidth (1.23–1.63 μm, covering the O, E, S, C, and L bands), all six modes in the fiber core maintain low loss transmission. Moreover, in the range of 1.3–1.63 μm, the confinement loss (CL) of LP<sub>01</sub>, LP<sub>11</sub> and LP<sub>21</sub> mode are all less than 1×10<sup>–3</sup> dB/m, and the CL of LP<sub>02</sub> and LP<sub>31<i>b</i></sub> mode are both less than 3×10<sup>–3</sup> dB/m. The CL of each mode reaches the lowest value at 1.4 μm, and the LP<sub>01</sub> mode has the lowest CL of 4.3×10<sup>–7</sup> dB/m. In addition, for a bending radius of 7 cm, each mode maintains the low bending loss characteristic in a certain operating wavelength range. In the range of 1.23–1.61 μm, the BL of LP<sub>01</sub> is less than 4.5×10<sup>–4</sup> dB/m, and the BL of LP<sub>11</sub> is less than 1.3×10<sup>–3</sup> dB/m. The tolerance analysis shows that even with the deviation of structural parameters of ±1%, the few-mode HC-NCF can still maintain the characteristic of low-loss and weak coupling. The designed few-mode HC-NCF has ultra-low CL and bending-insensitive characteristics while supporting independent transmission of six modes, which will find huge potential applications in future high performance mode division multiplexing systems.

Age of Information Cost Minimization with No Buffers, Random Arrivals and Unreliable Channels: A PCL-Indexability Analysis

Over the last decade, the Age of Information has emerged as a key concept and metric for applications where the freshness of sensor-provided data is critical. Limited transmission capacity has motivated research on the design of tractable policies for scheduling information updates to minimize Age of Information cost based on Markov decision models, in particular on the restless multi-armed bandit problem (RMABP). This allows the use of Whittle’s popular index policy, which is often nearly optimal, provided indexability (index existence) is proven, which has been recently accomplished in some models. We aim to extend the application scope of Whittle’s index policy in a broader AoI scheduling model. We address a model with no buffers incorporating random packet arrivals, unreliable channels, and nondecreasing AoI costs. We use sufficient indexability conditions based on partial conservation laws previously introduced by the author to establish the model’s indexability and evaluate its Whittle index in closed form under discounted and average cost criteria. We further use the index formulae to draw insights on how scheduling priority depends on model parameters.

Simulation and calculation of maximum transmission power for offshore wind plants accounting for the Electro-Magnetic transient process

The offshore wind power industry is witnessing a shift towards larger scale and longer offshore distances, pushing the transmission capacity and transmission distance of submarine cables to their maximum limits. Consequently, it is essential to evaluate the maximum transmission limit of offshore wind power. At the same time, the larger scale of the wind farm will make the Electro-Magnetic transient (EMT) processes have a greater impact on the system stability. To address these issues, this paper firstly established a wind generator (WG) model through a permanent magnet synchronous generator (PMSG) model with averaging parameters, as well as a cross-linked polyethylene (XLPE) submarine cable model to construct an offshore wind power system via AC transmission. Subsequently, a continuous EMT simulation method is proposed to calculate the maximum transmission capacity and transmission distance limit. Real-time transient simulations were conducted to check the transient stability constraints during the calculation process. Finally, through several case studies on different types of cables, the simulation and calculation results are presented to provide useful references for engineering applications.

Dynamic output-feedback model predictive control for polytopic uncertain systems with amplify-and-forward relays

In this paper, the dynamic output-feedback model predictive control (MPC) problem is investigated for systems with polytopic uncertainties and amplify-and-forward (AF) relays, where the signal transmission with random power is adopted to facilitate the remote transmission with the limited transmission capacity of sensors. With respect to the parameter uncertainties of the system and the randomness of AF relays, a new objective function is constructed, which is defined by the mathematical expectation of the conventional quadratic function over the infinite time horizon. Then, the singular value decomposition technique is applied to handle the inherent couplings of variables to formulate a solvable optimization problem. Furthermore, sufficient conditions are provided to guarantee the recursive feasibility of the proposed MPC algorithm, meanwhile the underlying system can be stabilized in the mean-square sense by the designed controllers. Finally, two simulation examples are used to show the effectiveness of the proposed MPC strategy.

Investment and Bidding Strategies for Optimal Transmission Management Dynamics: The Italian Case

This paper explores the allocation process of Financial Transmission Rights (FTRs) in the Italian electricity market. FTRs are financial instruments allowing market participants to hedge against transmission congestion, also playing a critical role in ensuring the efficient use of the transmission system. We present a linear programming (LP) model that simulates the FTRs allocation process, taking into account the transmission capacity limits of the electric network when the total revenue is considered as the utility function. Obtained results highlight that our solution allows us to develop better investment and bidding strategies for optimal transmission management dynamics. In particular, numerical simulations show good results, with an overall MAPE of approximately 7%, indicating that the model accurately predicts the allocation of transmission rights across the network. Overall, the paper provides insights into the inner workings of the FTR allocation process in Italy, also providing improved market efficiency while increasing revenue for market participants.

User Features-Aware Content Delivery in Cache-Enabled Mobile MD2D Network

Device-to-device (D2D) communication is one of the most promising technologies for relieving the pressure of demands in the 5G mobile networks. However, due to randomness of user request, limitation of storage space and transmission capacity, it is still a challenge for channel allocation to optimize the successful delivery ratio of contents. Thus, in this paper, we first consider the link selection problem for mobile users in Multi-D2D (MD2D) based content delivery networks. We establish a content delivery utility that combines physical and social aspects, taking into account energy consumption, user mobility, and trust relationships. Second, we model the content delivery link selection as the maximum weighted matching problem. For this NP-hard problem, by relaxing the integer constraints, we propose a BnB-CDLS algorithm based on the branch-and-bound method for the proposed content delivery scheme. Furthermore, we prove that the problem can be computationally reduced to a monotone submodular problem subject to matroid and knapsack constraints, which can be solved by a greedy GA-CDLS algorithm. Numerical results show that as compared with the existing schemes, the proposed scheme can significantly improve the successful delivery ratio of contents.

Price formation in short-term electricity market scheduling

Price formation is important for monitoring/development of an electricity market since the short/long-run efficiencies depend on the prices representing the economic signals. However, existing approaches cannot capture the contribution of suppliers’ bidding behavior to short-term price formation. As a result, regulators cannot evaluate these contributions. This can result in market monitoring failure/inefficient regulatory decisions that entail short-run market distortion or compromised investment respectively. This paper proposes a novel approach enabling regulators to retrace these contributions for every single non-zero shadow price formed in a dynamic security-constrained economic dispatch (SCED) problem. The decomposition of dual variables in a linear representation of SCED is adopted to evaluate the marginal contribution of the offered prices. The proposed theory yields explicit functions mapping the SCED shadow prices (i.e., locational marginal prices, and marginal values of (i) binding generation capacity limits, (ii) binding transmission capacity limits, and (iii) binding ramping limits) to the offered prices. Counterintuitively, the results show that increasing the offered prices will not necessarily leads to an increase in the shadow prices, especially the locational marginal prices. In addition, it is shown that the offered prices can pose long-run implications by contributing to the shadow prices associated with the marginal value of transmission/generation capacity.

Negative electricity prices as a signal for lacking flexibility? On the effects of demand flexibility on electricity prices

PurposeThe purpose of this paper is to examine how active consumers, i.e. consumers that can inter-temporally shift their load, can influence electricity prices. As demonstrated in this paper, inter-temporal load shifting can induce negative electricity prices, a recurring phenomenon on power exchanges.Design/methodology/approachThe paper presents a novel electricity-market model assuming a nodal-pricing, energy-only spot market with active consumers. This study formulates an economic equilibrium problem as a linear program and uses an established six-node case study to compare equilibrium prices of a model with inflexible demand to a model with flexible demand of active consumers.FindingsThis study illustrates that temporal coupling of hourly market clearing through load shifting of active consumers can cause negative electricity prices that are not observed in a model with ceteris paribus inflexible demand. In such situations, where compared to the case of inflexible demand more flexibility is available in the system, negative electricity prices signal lower total system costs. These negative prices result from the use of demand flexibility, which, however, cannot be fully exploited due to limited transmission capacities, respectively, loop-flow restrictions.Originality/valueLiterature indicates that negative electricity prices result from lacking flexibility. The results illustrate that active consumers and their additional flexibility can lead to negative electricity prices in temporally coupled markets, which in general contributes to increased system efficiency as well as increased use of renewable energy sources. These findings extend existing research in both the area of energy flexibility and causes for negative electricity prices. Therefore, policymakers should be aware of such (temporal coupling) effects and, e.g. continue to allow negative electricity prices in the future that can serve as investment signals for active consumers.

Quantization-Based Event-Triggered Consensus of Multiagent Systems Against Aperiodic DoS Attacks

This article focuses on the secure consensus problem of linear multiagent systems (MASs) with quantized event-triggered control (ETC) against denial-of-service (DoS) attacks. DoS attacks with constrains on frequency and duration are discussed, which intend to block the communication links between agents to destroy the consensus. Due to the limited transmission capacity and communication resources, a uniform quantizer and an event-triggered mechanism (ETM) are considered to economize energy consumption. An ETC protocol based on the quantized relative state is designed to resist malicious DoS attacks. Then, sufficient conditions to guarantee the practical consensus of MASs are derived with finite data transmission rates, and the tolerance of DoS attack frequency and duration are given. The Zeno behavior does not exhibit by proving that lower positive bounds exist for all agents, indicating the feasibility of the proposed ETM. Finally, two simulation examples are given to verify the validity and superiority of the theoretical results.

Microvibration streaming measurements using dynamic compressed sensing for satellites

Long-term monitoring of satellite microvibrations generates a significant amount of data streams, placing strain on satellites with limited transmission capacity. To relieve this transmission strain, a dynamic compressed sensing (CS) framework is proposed for measuring satellite microvibrations. Microvibration streams are measured block by block and then reconstructed using a dynamic recovery algorithm. The recovery solution of one block can be used as a priori knowledge for the next block, allowing for faster updates. However, existing dynamic recovery algorithms are only applicable in the real domain and cannot be applied to microvibrations projected on a Fourier basis in the complex domain. In light of this event, the dynamic homotopy algorithm is expanded to the complex domain to deal with microvibration signals that are sparse in the Fourier basis. In comparison to conventional uniform sampling methods, the experimental results show that the dynamic CS with the expanded recovery algorithm can achieve a maximum root-mean-square acceleration (Grms) deviation of 4% in power spectrum density with one-fifth of the sampling points. Compared to recovery algorithms applicable to fixed measurements, the dynamic algorithm can achieve comparable accuracy in about one-third of the computation time. The experimental findings demonstrate the feasibility of satellite microvibrations measurements using dynamic CS.

Profitability Assessment of Windfarm Overplanting in Spain

The efforts made by European institutions to decarbonise the electricity system over the last decade have led Spain to become the fifth country in the world in terms of wind power plant capacity in 2021. This major achievement is still far from being able to contain and limit CO2 emissions. By 2030, it is expected that, together with photovoltaic energy, Spain will reach 74 % of renewable generation in its electricity system. Wind technology is currently very mature in onshore wind farms and its relatively low cost makes this technology attractive to investors. However, the best wind sites were the first places to be occupied by the first turbine generations. On the other hand, newer wind farms occupy sites with less wind resource but have more efficient turbines. To improve the profitability of the wind farm and increase its production, the feasibility of other alternatives such as overplanting or storage is beginning to be investigated. Overplanting aims to optimise the use of the transmission system by increasing wind capacity above the transmission capacity limit. In this work, we measure the profitability of an overplanting strategy by quantifying the parameters that make profitable the investment. The developed model optimised the production of energy taking into account technical and financial parameters in order to cover a wide range of situations. We analysed the case of a specific site with 25 2 MW turbines with the Python tool PyWake. The results obtained show that for each new turbine the annual energy produced (AEP) grows by around 3.87 GWh per turbine added (a cut of 1.69 GWh due to congestion of the transmission system). The study shows improvements in profitability when the electricity price exceeds 70 €/MWh.

Reliability assessment of performance-based balanced systems with rebalancing mechanisms

Performance-based balanced systems (PBSs) are widely used as critical equipment in many fields, so their reliability assessment has become a hot topic. Taking the battery pack PBS as an example, the balance means the performance differences between components are within an acceptable range, where the common bus is used to redistribute the performance for rebalancing, and the redistribution is affected by the transmission loss and transmission capacity limit. To reflect the above rebalancing mechanisms, a reliability evaluation method is proposed for PBSs with common bus performance sharing (PBSs-CBPS) considering balance degree threshold, transmission loss, and transmission capacity limit. Firstly, a continuous-time discrete-state Markov model is built to address the transition behaviors of components. Next, considering the effects of balance degree threshold and transmission loss, the rebalancing process is formulated as a nonlinear programming problem to obtain the performance of components after rebalancing, and the system reliability model is established. Then, the universal generation function (UGF) method combined with nonlinear programming is proposed to calculate system reliability, and Paper-Cut UGF Algorithm (PCUGF) is proposed to improve computational efficiency. Finally, an analytical example and a numerical example of 6S1P lithium-ion battery packs are given to show the effectiveness of the proposed method.

Hardware-in-the-loop testing of a battery energy storage controller for harbour area smart grid: A case study for Vaasa harbour grid

A battery energy storage controller (BESC) can balance the mismatch of power demand and supply and improve flexibility and resiliency of seaport microgrids. However, it is required to test functionality of the BESC, and validate that it can balance the power supply–demand imbalance by charging and discharging the battery. The main objective of this study is to implement hardware-in-loop (HIL) tests for validating the controller’s functionality. This article investigates the testing performance of the BESC that will be used in harbour grids to adjust for the mismatch of power supply and load demand by appropriately charging and discharging the battery energy storage system. The proposed BESC can effectively save energy and reduce peak load demand in harbour grids with limited transmission and distribution network power capacities. The BESC is initially developed offline in MATLAB/Simulink and then implemented in a FPGA based external controller interfaced with the OPAL-RT real-time simulator by using the IEC61850 communication protocol and GOOSE messages. The BESC is configured and implemented on the external FPGA board. In addition, real data from the local distribution system operator Vaasan Sahkoverkko and the harbour operator Kvarken port of Vaasa have been utilized to evaluate the efficacy of the suggested control algorithm for the battery energy storage system with a realistic scenario. The simulation findings indicate that the BESC can balance electricity demand within the microgrid by charging and discharging batteries.

The Role of Optimal Transmission Switching in Enhancing Grid Flexibility: A Review

The integration of variable renewable energy sources (RES) into power grids has resulted in a more complex operating environment for power system operators (SO), necessitating the need for increased grid flexibility. This is crucial for utilities, as the cost-effective utilization of existing infrastructure is necessary, given the prolonged construction period and high investment costs associated with building new power delivery facilities. Under the umbrella of smart grid, optimal transmission switching (OTS) is a cost-effective transmission technology that can alleviate concerns related to network congestion, limited transmission capacity, and high penetration of renewables. OTS is a concept that is integrated into the optimal power flow (OPF) problem such that it provides SO the choice to temporarily switch one or more lines out of service from the network. OTS is shown in the literature to have economic benefits, reduce operational cost, relieve network congestion, serve as a corrective mechanism for reducing voltage violations, improve system reliability, and minimize system loss. Despite the significant attention given to OTS by researchers over the past decade, no extensive review paper exists on the topic in the literature. Therefore, this paper provides a state-of-the-art overview of the OTS problem. The concept of OTS is explained by approximating the alternating current OPF problem into a linear direct current OPF problem. Various alternative models of the OTS problem proposed in the literature are discussed. The paper also analyzes the interaction between OTS and other flexibility options such as Dynamic thermal rating (DTR), Energy storage systems (ESS), and RES. Furthermore, the paper presents the general framework and impact of the short-term OTS problem on long-term expansion planning problems. Finally, an extensive literature review on the impact of OTS on system reliability is provided.

Spectrum Reconstruction via Deep Convolutional Neural Networks for Satellite Communication Systems

Satellite based spectrum sensing is studied for a system consisting of multiple satellites and a gateway (GW), where these satellites perform spectrum sensing and send mass spectrum-sensing data to the GW. To address the challenges of mass spectrum-sensing data and limited transmission capacity of the links from spectrum-sensing satellites to the GW, we propose a method called joint anomalous data repairing and deep convolutional neural network based spectrum reconstruction (ADRD-SR), which can reconstruct the original spectrum-sensing data from the incomplete data. Specifically, the GW preprocesses the incomplete data using the anomalous data repairing algorithm. A deep convolutional neural network is constructed and well trained, then it is activated to reconstruct the preprocessed spectrum data. Additionally, to sustain good reconstruction performance by tracing the dynamical spectrum-sensing data, we design a real-time evaluation oriented spectrum reconstruction framework, through seeking the events when the mean absolute error (MAE) becomes larger than a predefined threshold. Furthermore, the ADRD-SR method can reduce the MAE by more than 68% over the conventional reconstruction methods. Moreover, the reconstructed spectrum data can be used to assist spectrum sensing, and the corresponding probability of correct detection is only degraded by 5% even when 75% of the data is discarded.

Consensus in networks of dynamical systems with limited communication capacity

This paper provides a solution for the preservation of consensus in a network of several agents, described by discrete-time nonlinear dynamical systems. Consensus preservation is the problem of maintaining a certain distance between the states of several systems, given that the systems’ initial states are close to each other. The agents are equipped with both a smart sensor, capable of measuring the state of the system and performing some computations, and a controller. The sensors and controllers are placed at locations that are remote from one another. A network of communication channels with limited transmission capacity connects the agents by allowing the sensors to send messages to the controllers of their system as well as to the controllers of other systems. The controllers use the messages that they receive to steer the agents such that the consensus is preserved. Sensors and controllers that achieve this feature are called consensus-preserving protocols. In this paper, three distinct consensus-preserving protocols are presented, each with an increasing degree of interaction between the systems. For each of these protocols, a theorem providing conditions on the minimum communication capacity sufficient to implement them is presented. The protocols are tested by simulations for a network of logistic maps and a network of Hénon maps. For both of these networks, analytical bounds on the sufficient transmission capacities in the communication channels to implement the protocols are provided. These bounds are compared to rates observed in simulations of the consensus-preserving protocols.

Video encryption/compression using compressive coded rotating mirror camera

Compressive coded rotating mirror (CCRM) camera is a novel high-speed imaging system that operates under amplitude optical encoding and frame sweeping modalities in a passive imaging mode that is capable of reconstructing 1400 frames from a single shot image acquisition and achieves the highest compression ratio of 368 compared to the other compressive sensing (CS) based single-shot imaging modalities. The integrated optical encoding and compression adds a strong layer of encryption on the observed data and facilitates the integration of the CCRM camera with the imaging applications that require highly efficient data encryption and compression due to capturing highly sensitive data or limited transmission and storage capacities. CCRM uses amplitude encoding that significantly extends the key space where the probability of having the exact encoder pattern is estimated as Pleft( Aright) = 1/{10}^{122,500}, hence drastically reducing the possibility of data recovery in a brute force manner. Data reconstruction is achieved under CS based algorithms where the obtained amplitude-based pattern from optical encoder operates as the key in the recovery process. Reconstruction on the experimental as well as the synthetic data at various compression ratios demonstrate that the estimated key with less than 95% matching elements were unable to recover the data where the achieved averaged structural similarity (SSIM) of 0.25 before 95% encoder similarity and 0.85 SSIM at 100% encoder similarity demonstrates the high-sensitivity of the proposed optical encryption technique.

Evolution of Fundamental Price Determination within Electricity Market Simulations

Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of market coupling and price determination. In this paper, we analyze a new single-stage approach that combines both steps and theoretically and empirically demonstrate its precision by a backtest. For this purpose, we compare a simulated versus a historical electricity price distribution. Moreover, we explain the necessary adjustments for future regulatory developments of the European electricity market regarding flow-based market coupling and propose a concept for the application of future regulatory developments. We demonstrate further developments using a future scenario.