Constrained Transmission Research Articles

Boundary-constrained robust regularization for single image dehazing

Generally, for single image dehazing, regularization-based schemes improve the initial transmission map iteratively by using a guidance map as a structural prior. We conducted experiments on a large number of hazy images and observed that a constrained transmission map affects the quality of the recovered image. However, regularization-based methods do not constrain the transmission map to its physically valid range during the iterative process. It degrades its robustness to outliers, and consequently, deteriorates the quality of the recovered image. In addition, conventional methods fuse the structural information of the guidance and initial transmission map without considering any structural differences between them. To address these issues, in this paper, we present a robust regularization scheme that constraints the transmission map during its enhancement. In the proposed scheme, a nonconvex energy function is constructed that leverages the mutual structural information of the guidance and transmission map. The nonconvex problem is solved by a majorize-minimization algorithm, and the intermediate transmission maps are constrained through the appropriate lower and upper bounds. The retrieved transmission map has better edge-preserving properties, and ultimately, results in a high-quality haze-free image that has faithful colors and fine details. The proposed scheme is tested on benchmark datasets and results are evaluated through quantitative metrics. The comparative analysis has revealed the effectiveness of the proposed scheme.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Downlink NOMA Based Transmission Protocol for Performance Improvement in Time-Varying Wireless Network

In this research work, the authors propose an error constrained data transmission approach using non-orthogonal multiple access (NOMA) protocol with hybrid automatic repeat request (HARQ) and different packet combining schemes. In this proposed work, the transmitter simultaneously transmits two data packets to the users (UEs) by considering the superposition coding technique in the power domain. The decoding scheme of the receiver changes according to the message decoding status in the previous rounds. This combined protocol has the capability of realizing ultra-reliability, error correction power in high error prone wireless links. The receiver adopts successive interference cancelation (SIC) to decode its own packet. Retransmission process is performed when the data is erroneously received by the destination and the receiver corrects the error by applying HARQ with Packet Combining (HARQ-PC) and HARQ with Aggressive Packet Combining (HARQ-APC) schemes. This paper analyzes the data rates, outage probabilities and bit error rates of UEs for downlink NOMA systems with respect to the transmit power using some basic parameters. The proposed schemes can greatly outperform the conventional NOMA with HARQ (NOMA-HARQ) in terms of data rates, outage probability and bit error rate in dynamic wireless channels.

Capacity Expansion Planning with Stochastic Rolling Horizon Dispatch

Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and balancing electricity markets within a capacity expansion model. We use Benders cuts and a stochastic rolling horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.

A Stochastic Rolling Horizon-Based Approach for Power Generation Expansion Planning

Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and real-time electricity markets within a capacity expansion model. We use Benders’ cuts and a stochastic rolling-horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage, and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.

A review of power system protection and asset management with machine learning techniques

Power system protection and asset management have drawn the attention of researchers for several decades; but they still suffer from unresolved and challenging technical issues. The situation has been recently exacerbated in the wake of the ever-changing landscape of power systems driven by the growing uncertainty and volatility subsequent to the vast renewable energy integration, more frequent natural extreme events due to climate changes, increasing malicious cyberattacks, and more constrained transmission systems as the result of load growth and limited investments. On the opposite side, the proliferation of advanced measuring devices such as phasor measurement units, emerging electric and non-electric sensors, and Internet of Thing (IoT)-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper aims to provide an overview on applications of ML techniques in power system protection and asset management. This paper elaborates on issues pertaining to (1) synchronous generators, (2) power transformers, (3) transmission lines, and (4) special and system-integrity protection schemes. In addition to the opportunities offered by the ML techniques, this paper discourses on the barriers and challenges to the wide-spread application of ML techniques in real-world practices.

A Network Selection Strategy Based on Joint Optimization of User Satisfaction and Transmission Efficiency in Internet of Vehicle

Network selection in the Internet of Vehicles has become a popular topic of research. Unlike existing algorithms for heterogeneous network environments that rarely consider user satisfaction, in this paper, we propose a network selection strategy that takes into account both user satisfaction and transmission efficiency. We employ the effective capacity concept, which describes the maximum throughput a system can achieve under a specific statistical Quality-of-Service (QoS) delay violation probability constraint. This strategy first analyzes the influence of different utility function weight coefficients, transmission power, and time delay on each network utility satisfaction function. It is evident that the weight coefficient is proportional to the value of the utility function. Within a constrained transmission power range, the rate of increase of the function gradually slows down until it approaches a fixed value. When the delay factor value is larger, the function value is smaller, which indicates that the pursuit of lower delay will sacrifice other network performance aspects. In order to determine the maximum value of each network utility satisfaction function, a convex optimization theory is introduced for the joint optimization of user satisfaction and transmission efficiency. Finally, simulation experiments carried out under three representative network environments show that the proposed strategy is efficient and reliable.

Application of IRT Models to Selection of Bidding Paths in Financial Transmission Rights Auction: U.S. New England

This paper explores a way to apply Item Response Theory (IRT), one of the popular statistical methodologies in measurement and psychometrics, to evaluate Financial Transmission Rights (FTR) paths in the U.S. electricity market. FTR is an energy derivative product to hedge congestion cost risks inherent in constrained transmission lines. In New England, with about 1200 pricing locations, the theoretical combinations of FTR paths amount to 1.4 million in prevailing flows alone. With capital constraints, it is imperative that FTR market participants build the capability to evaluate FTR paths to bid on. IRT provides a framework of how well tests work, and how individual items work on tests, estimating respondents’ latent abilities, and individual item parameters. IRT is utilized to analyze historical electricity data of 2019 for a daily congestion cost of eight customer load zones and one hub in the U.S., New England, for the evaluation of FTR paths. In the analysis, an item represents an FTR path, while item difficulty, item discrimination, and a latent trait variable for the path correspond to the path profitability, risk level, and daily congestion ability, respectively. This paper explores the experimental procedures by which IRT, a psychometric tool, may also be applicable in complex energy markets, providing a consistent and standardized analytical framework to address the issues of selection and prioritization among multiple opportunities. FTR path evaluation is conducted in three steps to determine bid priority paths in FTR auctions: parameter significance tests, ranking on path profitability and risk level, and weighting scores of individual rankings on the two criteria.

Value of hydro power flexibility for hydrogen production in constrained transmission grids

The cost of large scale hydrogen production from electrolysis is dominated by the cost of electricity, representing 77–89% of the total costs. The integration of low-cost renewable energy is thus essential to affordable and clean hydrogen production from electrolysis. Flexible operation of electrolysis and hydro power can facilitate integration of remote energy resources by providing the flexibility that is needed in systems with large amounts of variable renewable energy. The flexibility from hydro power is limited by the physical complexities of the river systems and ecological concerns which makes the flexibility not easily quantifiable. In this work we investigate how different levels of flexibility from hydro power affects the cost of hydrogen production.We develop a two-stage stochastic model in a rolling horizon framework that enables us to consider the uncertainty in wind power production, energy storage and the structure of the energy market when simulating power system operation. This model is used for studying hydrogen production from electrolysis in a future scenario of a remote region in Norway with large wind power potential. A constant demand of hydrogen is assumed and flexibility in the electrolysis operation is enabled by hydrogen storage. Different levels of hydro power flexibility are considered by following a reservoir guiding curve every hour, 6 h or 24 h.Results from the case study show that hydrogen can be produced at a cost of 1.89 €/kg in the future if hydro power production is flexible within a period of 24 h, fulfilling industry targets. Flexible hydrogen production also contributes to significantly reducing wasted energy from spillage from reservoirs or wind power curtailment by up to 56% for 24 h of flexibility. The results also show that less hydro power flexibility results in increased flexible operation of the electrolysis plant where it delivers 39–46% more regulating power, operates more on higher power levels and stores more hydrogen.

Production of Hydrogen from Wind and Hydro Power in Constrained Transmission grids, Considering the Stochasticity of Wind Power

Producing hydrogen from renewable energy sources can be used as a way of extracting large quantities of energy from remote regions far from load centers. These regions have weak transmission grids and building new transmission lines are expensive due to large distances. The tight restrictions on the power systems in these regions makes daily operation difficult and unexpected variations in wind power production can have significant negative impacts, such as rationing of power.A stochastic rolling horizon model is formulated and implemented to consider the importance of including wind power stochasticity when operating flexible hydrogen loads in a congested power system. Wind power scenarios are created using realized wind power production and meteorological weather forecasts. The resulting operation plans of hydrogen storage and hydro power plants, using expected values or wind power scenarios, are tested and compared in a simulator with the realized wind power production.Results from the case study show that the stochastic model gives a better strategy than the deterministic model which use the expected value of wind production by about 5.6% and there is potential for further cost reductions by improving the forecasting. When including more than 27 wind power scenarios the changes in results are small. The case study also shows that hydrogen storage is important to avoid rationing in certain situations and increase power flow.

Blackout Mitigation of Voltage Stability Constrained Transmission Corridors through Controlled Series Resistors

Blackout Mitigation of Voltage Stability Constrained Transmission Corridors through Controlled Series Resistors

Supply Function Equilibrium Over a Constrained Transmission Line I: Calculating Equilibria

Competition between oligopolist electricity generators is inhibited by transmission constraints. I present a supply function equilibrium (SFE) model of an electricity market with a single lossless, but constrained, transmission line. The market admits equilibria in which generator withhold energy in order to induce congestion, which further increases their local market power. Under appropriate assumptions on cost and demand functions, I obtain a planar autonomous system of ordinary differential equations for the SFE. Computational methods are developed to solve the system while respecting monotonicity constraints on the supply functions. Using these methods I can calculate SFE in network markets that range from fully isolated to fully integrated. I also find network markets for which the SFE is not unique.

Supply Function Equilibrium Over a Constrained Transmission Line II: Multiple Plants and Nodal Price Derivatives

Market power in electricity wholesale markets arises when generators have incentives to mark up their offers above the cost of production. I model a transmission network with a single line. I derive optimality conditions for supply functions for generators who supply energy at both ends of the line, and also for generators who hold financial derivatives on the locational prices. These financial derivatives include contracts for differences as well as financial transmission rights. One way that generators can manipulate prices in their favor is by inducing congestion in the network. I find that dispersed ownership and financial transmission rights are both effective ways to reduce strategic congestion of the line. I also find that certain portfolios of contracts for differences can lead to multiple supply function equilibria.

Network Expansion to Mitigate Market Power

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.

Transmission needs across a fully renewable European power system

The residual load and excess power generation of 30 European countries with a 100% penetration of variable renewable energy sources are explored in order to quantify the benefit of power transmission between countries. Estimates are based on extensive weather data, which allows for modelling of hourly mismatches between the demand and renewable generation from wind and solar photovoltaics. For separated countries, balancing is required to cover around 24% of the total annual electricity consumption. This number can be reduced down to 15% once all countries are networked together with unconstrained interconnectors. The reduction represents the maximum possible benefit of transmission for the countries. The total Net Transfer Capacity of the unconstrained interconnectors is roughly 11.5 times larger than current values. However, constrained interconnector capacities 5.7 times larger than the current values are found to provide 98% of the maximum possible benefit of transmission. This motivates a detailed investigation of several constrained transmission capacity layouts to determine the export and import capabilities of countries participating in a fully renewable European electricity system.

Trajectory Compensation of Chute Constrained Chain Drive

A method for path compensation of constrained chain transmission with heavy loads is presented in the paper. Polygon effects generated in chain transmission has been analyzed in the paper. Relevant position compensation programs and compensation trajectory have been given. As a result, polygon effects have been greatly reduced. This approach also provides a reference for other chain drive with constrained path.

Imperfect CSI Based Joint Bit Loading and Power Allocation for Deadline Constrained Transmission

This paper studies the imperfect channel state information (CSI) based joint resource scheduling problem for deadline constrained transmission. We present a holistic system model in terms of performance constraints of imperfect CSI, scheduling deadline and bit loading. By tackling channel estimation error, robust scheduling problems are formulated with the ellipsoidal approximation and transmission reliability constraints respectively. Dynamic programming (DP) based robust scheduling schemes are derived. The numerical results show that the proposed robust scheduling schemes are efficient in solving the jointly scheduling problem. It is also shown that the impact of imperfect CSI is non-negligible, thus highlighting the importance of robust design in time-sensitive communications.

Against All Odds

Terrorist attacks on Electricity infrastructure, a special kind of disaster, are capable of causing large economic losses and disruptions of service. Demand reduction, higher operating costs for the electricity sector, the costs of repair or replacement of towers, and the impact on users are some of the most important effects of such attacks. This article summarizes the impact of terrorism on transmission lines and security of supply in Colombia's power system during the last 11 years. Attacks on the electrical infrastructure, power failures, restoration procedures after a blackout, the development of constrained transmission, the impact of the related costs on business profitability, recovery costs, and the lessons learned from emergency situations are the principal topics discussed.

Maximizing Firm Wind Connection to Security Constrained Transmission Networks

Prudent use of existing transmission capacity could be achieved by an optimal allocation of wind capacity to distinct transmission nodes. The statistical interdependency of geographically separate wind sites and the partially-dispatchable nature of wind power require a collective analysis of all potential wind farms over an extended time-frame in any optimized transmission planning study. The methodology presented in this paper separates this large optimization problem into smaller subtasks, including a year-long sequential time series hourly integer unit commitment, a linear dc load-flow network model with hourly security constraints, and a linear programming optimization model to estimate the maximum firm wind energy penetration for a given network. A novel maximal- vector based constraint redundancy analysis is employed to significantly reduce the linear programming optimization dimensionality. Firm wind capacity connections are facilitated in this paper - i.e., those to which wind curtailment to manage congestion is not applicable within a typical system ?planning? timeframe analysis. Each bus is allocated firm capacity on the basis of maximizing the possible firm wind energy penetration in the transmission system as a whole, while preserving traditional network security standards.

A Chance Constrained Transmission Network Expansion Planning Method With Consideration of Load and Wind Farm Uncertainties

This paper proposes a chance constrained formulation to tackle the uncertainties of load and wind turbine generator in transmission network expansion planning. A combined Monte Carlo simulation/analytical probabilistic power flow analysis method is first presented to obtain the probability density function of wind turbine generator output. The paper then shows the development of the chance constrained formulation with the inclusion of the wind turbine generator probability density function and probabilistic power flow in the formulation. The proposed formulation is more computationally efficient and can deal with uncertainties in transmission network expansion planning. The power of the new method is shown through the application of the formulation to two test systems.