Loss Of Power Generation Research Articles

Prospective climate change impacts on China's fossil and renewable power-generation infrastructure: Regional and plant-level analyses

The energy infrastructure has emitted massive GHGs and will suffer greatly from climate risks. Given China's largest installed capacity globally, assessing climate impacts on diverse power infrastructures will yield critical risk information and support climate-resilient policymaking. However, lacking detailed plant-level data and ignoring the integrated infrastructural management of adaptation and mitigation priorly impede an in-depth evaluation. By employing high-coverage and -resolution plant-level data and the outputs of six CMIP6 models, we evaluate the pending climate impacts on five power-production sources at the interprovincial and plant levels in China. We find a pervasive negative impact on China's power sector, and the adverse effects expand over time (short-term: 214–342 TWh, long-term: 268–397 TWh, 25–75% quantile). For different future scenarios, the greater the radiative forcing, the greater the loss of power generation. Fossil-related production loss will completely offset the gain in most provinces from renewable power, with an overall negative impact in these regions. Besides, two evident phenomena occur: First, spatial heterogeneity appears across diverse provinces; second, a critical minority of China's plants with a low capacity share contribute to the main body of climate impacts. We consider the need for a targeted coal decommissioning strategy for climate adaptation.

Optimal PV system capacity ratio and power limit value selection based on a novel fast calculation method of IGBT junction temperature and IGBT annual damage analysis

In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the XGBoost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The XGBoost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value.

A MATLAB GUI program for reservoir management to simultaneously optimise sediment release and power generation

Sedimentation affects the normal function of reservoirs and is a decisive factor in the reservoir's service life. Flushing sediment during reservoir operation is an effective non-engineering measure to alleviate reservoir sedimentation; however, lowering water level to discharge more flow conflicts with hydropower generation. In this study, reservoir management software is developed to simultaneously optimise sediment discharge and hydropower generation with the reservoir discharge as the decision variables. The sediment transport rate is calculated by an integral of the vertical distribution of suspended load concentration and flow velocity instead of empirical formulas. The model is solved by the most widely used multi-objective optimisation algorithm NSGA-II, resulting in the optimal schedule corresponding to the maximal sediment discharge and hydropower generation, which can be displayed graphically in the software. The software was developed in MATLAB with a Graphical User Interface (GUI) and applied to a large reservoir and can be generalised to other reservoirs. The results show that within the recommended discharge variation of 5%, the sediment release can be increased by 2.07 × 106 t as a reduction of per 1010 kW h in annual power generation. Compared with the original scheme, sediment release can be increased most by 3.31% at the cost of 0.03% loss of power generation. Moreover, the dual objective in the flood season was optimised by 7.30% and 3.92%, respectively.

Load forecasting and risk assessment for energy market with renewable based distributed generation

This paper projects the risk associated with social benefit gained by Independent System Operator (ISO) due to unpredictability of load demand and intermittent nature of Renewable Energy Sources (RES). The hybrid method i.e. Wavelet Transform–Auto Regression Integrated Moving Average (WT-ARIMA) is used for forecasting the load in day-ahead market. The load demand for 24 hours is forecasted using the historical hourly data of 2 months. The mean absolute percentage error (MAPE) is calculated for both classical ARIMA and WT-ARIMA model. When compared to the ARIMA model, the WT-ARIMA model has a better fit to the time series data. This paper relates the load forecasting with energy market and shows the impact of forecasting error on market parameters. The system parameters like generation cost, social benefit and power loss are analyzed in day-ahead and real-time market. This work also elaborates the effect of intermittent nature of RES on the system parameters. The optimal location of RES based distributed generation is identified by higher Locational Marginal Price (LMP) based ranking method. The optimal energy management problem is formulated with the aim to minimize the total generation cost taking into account the variability in wind power and solar power. The uncertainty modeling of wind and solar is projected by the Rayleigh and Beta distribution respectively. The risk associated with social benefit of ISO due to uncertainty of wind and solar is calculated using indices, Value at Risk and Conditional Value at Risk. The proposed approach is implemented on IEEE 30 bus system.

Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network

Blade icing is one of the main problems of wind turbines installed in cold climate regions, resulting in increasing power generation loss and maintenance costs. Traditional blade icing detection methods greatly rely on dedicated sensors, such as vibration and acoustic emission sensors, which require additional installation costs and even reduce reliability due to the degradation and failures of these sensors. To deal with this challenge, this paper aims to develop a cost-effective detection system based on the existing operation data collected from the supervisory control and data acquisition (SCADA) systems which are already equipped in large-scale wind turbines. Considering that SCADA data is essentially a multivariate time series with inherent non-stationary and multiscale temporal characteristics, a new wavelet-based multiscale long short-term memory network (WaveletLSTM) approach is proposed for wind turbine blade icing detection. The proposed method incorporates wavelet-based multiscale learning into the traditional LSTM architecture and can simultaneously learn global and local temporal features of multivariate SCADA signals, which improves fault detection ability. A real case study has shown that our proposed WaveletLSTM method achieved better detection performance than the existing methods.

Smart hybrid microgrid for effective distributed renewable energy sharing of PV prosumers

In this research article, a Distributed Energy Sharing Program (DESP) is proposed to share energy among PV prosumers in a smart hybrid microgrid (SHM), making the PV prosumer utilize the energy efficiently. The difficulties faced by the PV prosumers include the intermittent nature of Renewable Energy Sources (RESs), and PV prosumers who consume their own PV power face a power fluctuation problem. PV prosumer have one-time slot with excess PV power generation (power loss) and another slot with insufficient PV power generation (shortage). Moreover, sometimes, a total absence of PV power (affecting the prosumer day to day life). PV prosumers directly connect to the utility grid, resulting in moderate PV power selling prices, but PV prosumers purchase power from the utility grid at high prices at a time slot t. PV prosumers only earn the smallest profit under this plan. As a consequence, the DESP is proposed. SHM needs a DESP to manage the Battery Storage System (BSS) and minimize the PV prosumer electricity tariff using an internal pricing scheme to utilize RES efficiently. The Internet of Things (IoT) communicates SHM to PV prosumers or vice versa, enabling efficient and cost-effective PV energy exchange. This approach enhances PV utilization and maximizes PV prosumer’s profit through DESP.

Wind farm incorporated optimal power flow solutions through multi-objective horse herd optimization with a novel constraint handling technique

Optimal power flow plays an important role in integrating wind power into electric power networks. Because of its complexities, standard formulae are insufficient for the present scenario. Therefore, the multi-objective optimal power flow problems for wind farm incorporated power systems have been explored in this paper. The objectives are to minimize the generation costs, pollutant emissions, power losses, and voltage deviations. This paper proposes the development of a multi-objective meta-heuristic horse herd optimization for solving multi-objective optimal power flow problems. For this, a decomposition concept is introduced to the proposed algorithm leading to decomposition based multi-objective horse herd optimization algorithm. The constraint handling techniques in the previous papers have been found to be inefficient for optimal power flow problems. Therefore, a novel constraint handling technique is proposed in this paper to effectively control the variables out of bounds. In order to validate the performance and suitability of the proposed algorithm, seven case studies are examined. The algorithm is tested on wind farm incorporated IEEE-30, IEEE-57, and IEEE-118 bus test systems to demonstrate the efficiency in solving multi-objective optimal power flow problem for various problem sizes. The experimental results are demonstrating the efficiency of the proposed algorithm in solving complex optimization problems of various scales with multiple objectives.

Reliability Analysis of Wind Energy Generation System Using Stochastic Method

Operators of renewable energy systems (RESs) must always manage uncertainty to some extent to ensure the reliability and the security of the electric power supply source. The guiding principle in this regard is to ensure service reliability and quality by balancing load variations with the variable renewable energy (VRE) sources. If the power generated by these VRE sources is not properly managed in conjunction with the varying load, the power grid may fail to achieve the required balance. To ensure its reliable operation, reliability analysis is vital for wind energy generation system (WEGS). This paper evaluated and assessed the reliability of WEGS and a proposed varying load by first using a stochastic approach to model the WEGS and the proposed varying load after which power generation indices were used to evaluate and assess the performance of the model. The WEGS and the varying load were modelled separately after which the two were combined into one model. Full availability, partial availability, the expected energy not supplied (EENS) or loss of energy expectation (LOEE), the mean or average instantaneous electric power generation and mean instantaneous generation deficiency were the indices used for the evaluation of the WEGS. The results indicated that the electric power generation will meet the power demand during most of the transition states of the WEGS with the expectation that the variation in the load will not be at fast pace and in large quantum.

A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network

Offshore wind farms are increasingly becoming the focus of clean sources market because of the huge energy potential and fast-maturing technology. The existing researches normally optimize the wind turbine layout and two-dimensional cable routing independently. This work focuses on the synchronization optimization of site selection of the offshore wind farm, three-dimensional wind turbine layout and three-dimensional cable network routing based on meta-heuristic algorithms and geographic information systems. Several practical issues, i.e., restricted areas, power generation, cable network and energy loss, are taken into consideration. A two-layer model is proposed. The outer layer model is for the site selection and the wind turbine layout optimization. The inner layer model is for the obstacle-avoiding cable routing optimization. In this stage, the seabed terrain is considered for the first time. The proposed integrated model is complex and non-convex. Thus, a hybrid method including an improved ant colony optimization combined with genetic algorithm, dual-simplex method and Kruskal algorithm is proposed to search the solution more efficiently. The initialization stage of the hybrid method is improved from random assignment to directional assignment. The directional solution is obtained by the widely used genetic algorithm. A case study based on a real offshore wind farm is established to prove the effectiveness of the proposed methodology. The results show an over one million dollars increase in annual benefit compared with conventional methods.

An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

Facilitating Large‐Scale Snow Shedding from In‐Field Solar Arrays using Icephobic Surfaces with Low‐Interfacial Toughness

AbstractLarge‐scale accrual of snow and ice on solar arrays in northern latitudes can cause significant power generation losses during winter. Depending on environmental conditions, snow can encompass a wide range in physical characteristics from dry snow (modulus ≈100 kPa and density ≈0.1 g cm−3) to bulk ice (modulus ≈8 GPa and density ≈0.9 g cm−3). This variation in snow morphology has made the development of a passive, broad‐spectrum, snow and ice‐shedding surface challenging. Here, the authors develop one of the first surfaces that simultaneously possesses both low‐interfacial strength (τ˄ice < 50 kPa) and toughness (Γice < 0.5 J m−2) with ice. These surfaces, fabricated via the addition of mobile polymer chains/oils to a thin polymeric coating, require extremely low detachment forces for ice, enabling its passive shedding at virtually any accretion length scale. Preliminary evidence that the new surfaces can shed different forms of snow and ice from field‐deployed solar arrays, over a range of subzero temperatures for several weeks, leading to significant increases in power generation is provided. The optically transparent surfaces are easily scalable and can be widely deployed by the solar industry in areas that see persistent snow. Other applications include automotive windshields, LIDAR covers for autonomous vehicles, and cold climate optical sensors.

Distribution Network Reliability and Efficiency Enhancement Using Distributed Generation

The Nigerian power sector is faced with many challenges such as: generation deficit, inefficiency and power loss over lengthy transmission and distribution lines, contribution to greenhouse gas emission, weak and dilapidated transmission and distribution infrastructure, dependence on fossil fuels, insufficient power. Efforts should be put in place by relevant authorities to improve the power sector. With the distribution network being the closest to the final consumer, efforts should be made to make it more efficient. This study therefore aims at improving the performance of poor distribution network using Distributed Generation (DG), optimally placed and sized in the network. The Asaba, 2 X 15MVA, 33/11kV injection substation in Asaba, Delta state of Nigeria consisting of Anwai road feeder and SPC feeder radiating outwardly from this injection substation was the focus of this study. Relevant data collected from Benin Electricity Distribution Company (BEDC) was used to carry out load flow study. The simulation and analysis of the result and injection of photovoltaic (PV) DG of Asaba injection substation distribution network using Newton-Raphson iteration technique in ETAP 12.6environment to ascertain the overall performance of the network under base loading condition was modelled from a drawn detailed single line diagram of the network. DGs were optimally placed in specific buses in the network using loss sensitivity analysis. The result revealed that prior to DG placement in the network, only 10.4% of the buses were within statutory voltage limit (394.25V – 435.75V or 0.95p.u – 1.05p.u) and 89.6% of the load buses in the network violated the statutory voltage limit and high losses (active and reactive) of 1329.08kW and 2031kVar. After the optimal placement of DG, the active and reactive power losses on the network reduced by 57.5% and 70.7%. While the voltage profile improved by 94.8%, thereby increasing the capacity, reliability and efficiency of distribution network.

A Cyber Attack Mitigation Scheme for Series Compensated DFIG-Based Wind Parks

Subsynchronous Interaction (SSI) phenomenon is known to be one of the most frequent and severe stability issues of a Wind Park (WP), and can potentially lead to a significant loss of power generation. The broad impacts of this phenomenon on a power grid have made WPs interesting targets for cyber attacks. To initiate the SSI, an adversary can target either the power grid (external attacks) or the cyber system of WPs (internal attacks). This paper proposes a mitigation scheme for attacks that initiate the SSI phenomenon in series compensated doubly-fed induction generator (DFIG)-based WPs. External attacks are addressed by employing a robust static-output-feedback Subsynchronous Damping Controller (SSDC), which is designed based on the insensitive strip region and Linear Matrix Inequality (LMI) techniques. Internal attacks, however, are detected by comparing the estimated and measured converters’ currents. Once the compromised measurements are detected, the designed SSDC is restructured to mitigate the attacks. The effectiveness of the proposed method is demonstrated using detailed Electromagnetic Transient (EMT) simulations for both internal and external cyber attacks. Additionally, the performance of the proposed method is corroborated using a real-time co-simulation framework.

Equivalent circuit modelling of large hydropower plants with complex tailrace system for ultra-low frequency oscillation analysis

Focused on the hydraulic factor-induced ultra-low frequency oscillation phenomenon occurring in a large hydropower plant, this paper investigates the precise modelling of hydraulic transient characteristics in its complex tailrace system and reveals the feasible oscillation suppression schemes from the source side. Combining the telegraphist's equations with the Saint-Venant equations, the mathematical expression of equivalent circuit model (ECM) for open channels is deduced, thus to expand the utilization of ECM to hydraulic transients description in pressurized pipe and open channel combination systems and to establish the high-order equivalent circuit topology of the overall tailrace system in a real hydropower plant. Based on the mathematical model, the feasibility and effectiveness of two different oscillation suppression approaches involving increasing the hydraulic losses on the branch tailrace channels or the main tailrace channel, respectively, are discussed in detail via numerical simulation. Furthermore, the superiority of the oscillation suppression scheme involving increasing hydraulic losses in branch channels in reducing the extra power generation loss is demonstrated. The application of equivalent circuit theory in complex hydraulic systems can contribute to the mechanism-related research on ultra-low frequency oscillations in hydro-dominant power systems.

Optimal Placement of Wind Power Plants in Transmission Power Networks by Applying an Effectively Proposed Metaheuristic Algorithm

In this paper, a modified equilibrium algorithm (MEA) is proposed for optimally determining the position and capacity of wind power plants added in a transmission power network with 30 nodes and effectively selecting operation parameters for other electric components of the network. Two single objectives are separately optimized, including generation cost and active power loss for the case of placing one wind power plant (WPP) and two wind power plants (WPPs) at predetermined nodes and unknown nodes. In addition to the proposed MEA, the conventional equilibrium algorithm (CEA), heap-based optimizer (HBO), forensic-based investigation (FBI), and modified social group optimization (MSGO) are also implemented for the cases. Result comparisons indicate that the generation cost and power loss can be reduced effectively, thanks to the suitable location selection and appropriate power determination for WPPs. In addition, the generation cost and loss of the proposed MEA are also less than those from other compared methods. Thus, it is recommended that WPPs should be placed in power systems to reduce cost and loss, and MEA is a powerful method for the placement of wind power plants in power systems.

Upper Limit and Power Generation Loss of Water Supplement from Cascade Hydropower Stations to Downstream under Lancang-Mekong Cooperation

In cross-border water supplement cooperation, the supplement water discharged from upstream hydropower stations is the key to improving downstream benefits, but will lead to upstream power generation loss, so the upstream hydropower stations have to be aware of how much water they can offer and how much power they will lose to make the water supplement cooperation more reasonable. Therefore, this study puts forward a model to calculate the upper limit flow of water supplement of cascade hydropower stations under firm power constraints and water level constraints and proposes a new optimization method called the “collaborative-independent” joint optimization method to calculate the power generation loss under water supplement constraints. The results show that the upper limit flow will increase with the increase of annual inflow, and the uncertainty of the distribution of inflow in the year will also affect the upper limit flow: the larger the proportion of non-flood season inflow, the higher the upper limit flow. In normal and wet years, delaying water supplement time can significantly increase the upper limit flow by about 5% per month. Additionally, the “collaborative-independent” joint optimization method newly proposed in this paper can significantly improve the local optimization problem compared to the traditional optimization method. The power generation loss increases with the increase of water supplement flow, and delaying water supplement time can significantly reduce the power generation loss. The results of this paper can provide essential data support for future water resources cooperation negotiations in the Lancang-Mekong river basin to promote efficient and orderly water resources cooperation in the basin.

Analysis of the impact of power loss due to snail trails in a 95-kWp photovoltaic power system

Snail trails are a prevalent degradation phenomenon observed in solar power plants. In a previous study, it was determined that snail trails affected only the appearance of solar cells, without significantly contributing to actual power loss. However, when snail trails are accompanied by solar cell cracks, power loss occurs. We analyzed the snail trails in a specific module in a 95 kW power plant that has been operational since 2015. Moreover, we evaluated the power generation loss arising from snail trails using current–voltage measurements and electroluminescence analysis.

Optimal Maintenance Policy for Offshore Wind Systems

Employing maintenance threshold plays a critical step in determining an optimal maintenance policy for an offshore wind system to reduce maintenance costs while increasing system reliability. Considering the limited works on this topic, we propose a two-stage procedure to determine the optimal maintenance thresholds for multiple components of an offshore wind power system in order to minimize maintenance costs while achieving the highest possible system reliability. First, using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds of individual components where the cost of maintenance and the rate of failure are critical. Then, fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold considering all components. A variety of factors including weather conditions, system reliability, power generation losses, and electricity market price are carefully considered to enhance the system’s reliability and reduce the costs of maintenance. When maintenance threshold results are compared, component-wise versus system-wise, an average system savings of 1.19% for maintenance cost is obtained while the system reliability is increased by 1.62% on average.

Optimal power flow solution of an integrated power system using elephant herd optimization algorithm incorporating stochastic wind and solar power

ABSTRACT In this paper an improved optimization strategy is proposed to address issues related to uncertainty of the optimum power flow (OPF) considering the cost analysis. Elephant Herd Optimization (EHO) method roused by the crowding conduct of elephant gathering has been modified to achieve the minimization of the objective functions. Initially, the OPF is analyzed with the Newton-Raphson (N-R) approach by considering only conventional resources, later on the wind and PV-based power scheduling is performed. The output power of the wind and PV systems is computed from the Weibull probability distribution function (PDF) and Lognormal PDF. Initially, the objective function is defined to analyze the power loss, voltage deviation, carbon emissions and generation cost. The defined multi-objective function is solved for PV and Wind power generation costs, emission, voltage stability, and losses. The considered constraints are the cost of generation and the risks associated with the renewable energy sources apart from voltage and reactive power limits. Moreover, the penalty deviation charges have also been considered during extreme conditions for sustainable power sources. The proposed approach has been applied to IEEE 57 bus system and the resulting emissions, generation cost, losses and voltage deviation are evaluated and the direct, reserve, and penalty costs of wind and PV are analyzed It is contrasted to standard approaches such as Differential Evolutionary (DE) and Firefly Algorithm (FA) to substantiate the efficiency of the EHO method. The proposed approach is deployed in the MATLAB for various cases.

Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials

The European Union has set ambitious CO2 reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms−1 depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.