Uncertainty In Weather Conditions Research Articles

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

The uncertainty of weather conditions has always been a major challenge limiting the performance of photovoltaic (PV) power prediction. Enhancing the accuracy and stability of PV power prediction is crucial for optimizing grid operation. In response to this challenge, this study introduces a hybrid model that integrates an attention mechanism with the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). This model aims to mitigate the adverse impact of weather variability on the accuracy of PV power prediction by effectively extracting key features from multidimensional time series data. Additionally, through ablation experiments, this research further assesses the contribution of each model component to performance. Validated with actual data collected from a 1 MW PV power station in China, our proposed model demonstrates significant performance advantages compared to eight advanced prediction models. Ablation study results reveal that removing the CNN component led to a 58.2% increase in MAE and a 53.9% increase in RMSE, while the removal of the attention mechanism resulted in an 83.8% increase in maximum error. These findings underscore the substantial enhancement in prediction accuracy achieved through the integration of CNN and BiLSTM, and the introduction of the attention mechanism significantly boosts the model's prediction stability.

Hybrid KNN-SVM machine learning approach for solar power forecasting

Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy.

Optimal specialty crop planning policies with yield learning and forward contract

Hops are the flowers of a specialty crop that provide unique flavors to craft beer. We study a multi‐year crop planning problem for a farmer who seeks to add hops production to the current production of a conventional crop. The farmer must dynamically determine the number of acres to allocate to each crop and design the terms of the forward contract under which brewers purchase the hops, considering the uncertainty of weather conditions, hops yield, hops spot market price, and conventional crop price. We formulate a multi‐period stochastic dynamic programming framework that incorporates statistical learning methods that depend on exogenous factors. We also develop an easy‐to‐implement learning‐based marginal total profit heuristic which can potentially be used as a decision support tool. Our numerical analyses suggest that yield learning is particularly important for a farmer who is considering investing in a high margin, but potentially risky, new crop such as hops. We also characterize the conditions under which yield learning is most beneficial for farmers. This paper contributes to the literature on specialty crop planning by introducing a multi‐year planning framework that incorporates unique characteristics of specialty crop production. We fill a gap in the crop planning literature by considering how farmers can learn about crop yields based on realized yields and exogenous factors such as weather conditions. Our paper is also of practical importance for farmers who seek to diversify their crop portfolio to hedge against risks associated with trade tensions and potential price drops for conventional crops.

Optimal utilization of integrated photovoltaic battery systems: An application in the residential sector

PhotoVoltaic (PV) panels have been increasingly favored by residential users in recent years, due to noticeable reductions in their costs. The PV systems become more effective when combined with battery packages, which store the energy produced by the PV systems for later use. This way, the PV systems are able to provide flexible and reliable services even when the peak demand for electricity misalign with the window of most efficient PV power generation. In this study, we develop an integrated charge/discharge scheme for lithium-ion batteries to maximize their total expected benefit. Specifically, we develop a Markov Decision Process (MDP) model to maximize the battery utilization, subject to uncertainty in weather conditions and electricity demands, while accounting for battery degradation due to calendar aging and charging/discharging cycles. Due to the extremely slow rate of degradation in batteries, the state space of the MDP is excessively large. To solve the problem efficiently, we establish structural properties of the MDP and exploit them to solve the problem. We improve the backward induction algorithm with established structural properties. We further improve the Deep Q-network (DQN) algorithm by proposing two novel algorithms, the augmented DQN (ADQN) algorithm and the stochastic augmented DQN (SADQN) algorithm. Computational results indicate that ADQN and SADQN solve the problem much faster than DQN, with better solution qualities. The ADQN and SADQN algorithms provide flexibility for practitioners in real-world implementations.

STOCHOS: Stochastic opportunistic maintenance scheduling for offshore wind farms

Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high Operations and Maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, transportation requirements, and production losses, significantly inflate the O&M costs relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator’s ability to identify the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler—a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the opportunities that arise due to favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed-integer linear program, which we solve using a scenario-based rolling horizon algorithm that aligns with the industrial practice. Tested on real-world data from the U.S. North Atlantic where several offshore wind farms are in-development, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across various O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions.

Booking in the Rain. Testing the Impact of Public Information on Prices

Weather forecasts are a rare example of public information which is, at the same time, relevant for agents' decisions and entirely exogenous for both sides of the (tourism) market. We develop a model where signals of good weather have a positive impact on accommodation prices, the effect being stronger the higher the accuracy of the forecast and the ex-ante uncertainty in weather conditions. Using data from a sea and sun destination, we estimate an augmented hedonic price model and find that results robustly support the theory. We also find that the response of prices to weather forecasts is larger for upper-scale hotels than for low- and mid-scale hotels, a result we link to the superior pricing capability of the former.

Co-inventions, uncertainties and global food security

This paper examines the effects of international collaborative efforts on climate-friendly agricultural technologies on global food security. In particular, we use patent data on environmental technological innovations for OECD countries and global food prices from the period 1990 to 2016. Also, we investigate the impact of uncertainties in weather conditions in terms of rising global temperature created by climate change using data on global surface temperature from the Energy Information Administration and the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis of the National Aeronautics and Space Administration (NASA). We used both impulse response functions and variance error decomposition from a panel Vector Auto-Regressive (VAR) model to examine both the response of global food prices to shocks on the concerned variables and the decomposition of error variance in global food prices. First, our results show that international collaborative efforts on climate-friendly agricultural technologies reduce global food prices while increasing global surface temperature increases food prices. Regarding the variance decomposition of global food prices, results show that surface temperature followed by international collaborations in climate-friendly innovations and other environment-related technologies are the main drivers of forecast error variance in global food prices. The food price variance share associated with greenhouse gas emissions is less when compared to that of technological innovations.

Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation

AbstractOperations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi‐stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi‐stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.

Social Welfare Maximization in Smart Grid: Review

The modernized power grid system intelligently integrates various advanced Information and Communication Technologies (ICT’s) with various renewable energy sources. Moreover, recently huge developments are happening in renewable sources, with enhancement in technologies eras, the role of consumers is shifting towards producers so-called “prosumers”. Furthermore, main challenges are enlarged energy demands, uncertainty in weather conditions and varied occupant’s behavioural interventions in energy consumption. So, residential user-end is having potential for energy optimization by energy efficiency, conservation, and active participation. Besides, the dynamic fair energy pricing with respect to generation cost and load demands is motivational parameters to users. On the same, this paper focuses on analysing a novel, attractive business model that can actively participate prosumers through dynamic pricing and incentive based demand response programs for the maximization of social welfare. Further this review paper focusses on various optimization models to achieve social welfare maximization. And also bibliometric analysis has been done through the Scopus database.

A multi-objective optimization method for industrial park layout design: The trade-off between economy and safety

The general layout design significantly impacts the economy and safety performance of an industrial park. In most of the previous works, safety issues are converted to economic numbers in objective functions. However, this conversion is not appropriate. In this work, a multi-objective optimization method is proposed to obtain a set of solutions that achieves different trade-offs between economy and safety. An improved FLUTE algorithm is employed to obtain the most economical pipe networks. An extended risk map method is proposed to describe the distribution of risk considering the uncertainty of weather conditions. The proposed model is compared with a single-objective method in one case study to show the superiority of the proposed method. In addition, three different evolutionary algorithms are employed to solve a second case for comparison. The results show that the non-dominated sorting genetic algorithm II (NSGA-II) is more effective for industrial facility layout problems with multi-objective.

Optimal design and cost of ship-based CO2 transport under uncertainties and fluctuations

This study investigates the impact of operational fluctuations and uncertainties on the design and expected cost of ship-based CO2 transport. The model analysis is based on a two-stage stochastic investment model for a single-source single-sink CCS value chain with a ship-based transport system. The sailing time of the ship is uncertain due to changing weather conditions. The optimal investment decisions are driven by the expected cost of operating the value chain in the stochastic operational scenarios. This approach is demonstrated on a case study in which 0.4 MtCO2/y is transported over 715 km, from a cement plant located in Brevik to a harbor in Kollsnes in Norway.The results show that a transport rate of 99 % of the available CO2 leads to the lowest average cost of transport at 33.8 €/ton. Once the delays caused by the weather are considered, the buffer storage capacity that is 18 % above the ships transport capacity, seems to be the most efficient solution for recovering normal operation after weather delays. The expected transport cost increases with 1.9 €/ton (i.e. 5%) when the uncertainty in weather conditions is neglected in the value chain design decisions. Furthermore, seasonal variations in emissions lead to the need of a larger ship rather than maintaining the same ship size and increasing the power when required. The seasonal storage of CO2 never appears to be a cost-efficient strategy, compared to increasing ship capacity. Finally, the risks of higher future fuel prices and ship breakdowns will cause the value of buffer storage capacity to increase, and thus resulting to select a buffer capacity up to 73 % larger than the ship size.

Solar parabolic trough thermal energy output forecasting based on K-Nearest Neighbors approach

Solar thermal output forecasting that is derived from the solar irradiance forecasting, is very much exposed to high forecasting error. This is because of the heavy dependence on the solar irradiance prediction accuracy that could be very low in certain situations owing to the high uncertainty in weather conditions. By considering this fact, this paper proposes to develop a solar thermal output forecasting model using measured solar irradiance data, instead of the predicted data. The proposed model applies K-Nearest Neighbors (K-NN) algorithm to generate 24-hour ahead forecasting data on solar thermal output from a solar parabolic trough system For the purpose of illustrating the forecasting model performance, Kuala Lumpur, Malaysia is used as a case study, with PolyTrough 1800 model is selected as the solar parabolic trough collector under investigation. Simulation has been carried out using Matlab software to verify the effectiveness of the proposed K-NN-based forecasting model. The results show that the model is able to produce acceptable results in certain conditions.

Justification of Ice Melting Power on 10 kV Overhead Power Lines

The fuzzy multicriteria model of the ice melting system on radial 10 kV overhead power lines was developed. Three criteria were taken into account: the minimum of reduced annual costs for ice melting system, the minimum of amount of electric energy not provided to consumers during melting, and the minimum of the amount of electricity consumed for the ice melting. The model also takes into account the fuzzy limitation of the permissible ice melting power associated with the uncertainty of weather conditions. Based on the model we justified the value of ice melting power, which should be provided by serial ice melting devices of industrial production, intended for use on radial 10 kV overhead power lines, taking into account the uncertainty of initial data.

Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis

This work proposes a framework for the robust design of multi-energy systems when limited information on the input data is available. The optimal design of a decentralized system involving renewable energy sources and energy storage technologies is considered by formulating a mixed integer linear program that determines the optimal selection, size, and operation of the system to provide energy to an end user, while minimizing its total annual costs and CO2 emissions. Different aspects related to the feasibility and the optimality resulting when operating the multi-energy system on input data different than those used for the design are studied. Input data include weather conditions, energy demands and energy prices.First, considering a single input scenario, we define the resolution required to describe the time profiles of the input data. To do so, we aggregate hourly-resolved yearly time-series through a different number of typical design days, and we define the minimum number of typical design days necessary to obtain feasible and optimal system designs when these are operated on the original input profiles. Next, the uncertainty of weather conditions and energy demands is analyzed. This is described through several scenarios, which are created by combining different climate models, greenhouse gas emissions forecasts and models of buildings. The system configurations obtained with one of these scenarios are evaluated by introducing performance indicators that quantify the robustness and the cost optimality attained when operating the selected design on all other possible scenarios. Furthermore, correlations between performance indicators, structure and size of the system, and relevant characteristics of the input scenarios are identified. Based on this analysis, a robust scenario is defined, which requires information on the average scenario only, and its performance is compared against those of the average and of the worst-case scenarios when used to design a multi-energy system. Several designs are investigated, focusing on the minimization of costs and CO2 emissions, showing that the proposed robust scenario allows obtaining a robust and optimal system design through a deterministic optimization problem, hence maintaining the computation complexity at a minimum. Despite the higher robustness and cost optimality, slightly higher CO2 emissions are observed than the average and the worst-case scenarios.The proposed method is illustrated by performing the optimal design of a multi-energy system providing electricity and heat to a Swiss urban neighborhood, typical of the city of Zurich.

Optimizing vessel fleet size and mix to support maintenance operations at offshore wind farms

This paper considers the problem of determining the optimal vessel fleet to support maintenance operations at an offshore wind farm. We propose a two-stage stochastic programming (SP) model of the problem where the first stage decisions are what vessels to charter. The second stage decisions are how to support maintenance tasks using the chartered vessels from the first stage, given uncertainty in weather conditions and the occurrence of failures. To solve the resulting SP model we perform an ad-hoc Dantzig–Wolfe decomposition where, unlike standard decomposition approaches for SP models, parts of the second stage problem remain in the master problem. The decomposed model is then solved as a matheuristic by apriori generating a subset of the possible extreme points from the Dantzig–Wolfe subproblems. A computational study in three parts is presented. First, we verify the underlying mathematical model by comparing results to leading work from the literature. Then, results from in-sample and out-of-sample stability tests are presented to verify that the matheuristic gives stable results. Finally, we exemplify how the model can help offshore wind farm operators and vessel developers improve their decision making processes.

Booking in the Rain: Testing the Impact of Public Information on Prices

The key role played by public information in market economies is paramount. Yet, investigation on how prices are affected is hindered by intricate endogeneity and parametrization issues. This paper contributes to the literature by identifying and studying a case where public information is related to weather forecasts, in a sector (tourism) where meteorological conditions exogenously impact on both suppliers’ and consumers’ decisions, especially in leisure destinations. We first develop a theoretical model where forecasts affect demand and hence equilibrium prices in accommodation: an improvement in the weather forecast has a positive impact on the price, the effect being stronger the higher the level of accuracy of the forecast and the level of ex ante uncertainty in weather conditions. We then estimate an augmented hedonic price model where dynamic characteristics (typical of advanced booking strategies) are included together with information about weather forecasts. Consistent with the theory, we find that, ceteris paribus, good weather forecasts are associated to higher prices, the impact being larger the higher their level of accuracy and the higher the ex ante level of uncertainty in weather conditions.

Dispatch for Urban Integrated Heat and Power System Considering Secondary PM2.5 Under Smart Environmental Sensing

To improve the air quality and reduce the human settlement pollution, the effects of initial air pollutant emissions from urban heat and power generation with the diffusion of pollutants were often considered in the energy system dispatch, but the effects of secondary PM2.5 hazards caused by the urban energy system were almost ignored. In the urban integrated heat and power systems (UIHPS), under the support of smart environmental sensing, the introduction of integrated demand response (IDR) between the energy production side and consumer side is an effective means of reducing primary and secondary PM2.5 hazards. As for the diffusion of primary pollutants and the generation of secondary PM2.5 are affected by the uncertainty of weather conditions, a stochastic environmental economic dispatch (SEED) model for UIHPS considering IDR is proposed in this paper. Linearized secondary PM2.5 generation and diffusion functions for UIHPS dispatch are introduced into the objective function. The price-based IDR is performed on the Stackelberg game mechanism, and the game equilibrium solution is transformed as the SEED's constraint set by using the backward induction method. Simulations show that under the partly data supply from smart environmental sensing, the SEED model considering the secondary PM2.5 and IDR can effectively alleviate the comprehensive hazard of PM2.5 under severe haze weather.

Measuring farmer’s satisfaction and brand loyalty toward Indian fertilizer brands using DEA

The present study has attempted to identify Indian farmers’ level of satisfaction and loyalty pertaining to the purchase and use of different fertilizer brands. Fertilizer brands have to operate under certain prevailing constraints, such as uncertainty in weather conditions, the nature of the product and its usages. Added to this, there has been a reduction in the subsidy offered by the government of India, resulting in the escalation of complex fertilizer prices from INR 500 per bag to more than INR 1200 per bag. In order to gain competitive advantage through larger market share and increased profitability under the aforementioned constraints, retention of existing customers and strengthening brand loyalty have become significant preferences for fertilizer-selling firms. The study has employed data envelopment analysis as a tool to measure farmers’ satisfaction and brand loyalty efficiency toward existing fertilizer brands. The European Customer Satisfaction Index model has been used as a reference for the paper. The data analysis reveals that Paradeep Phosphates Limited featured as the most preferred brand. The study has several implications. Managers can use these findings for their self-assessment of the fertilizer brands and then improve upon or manage the farmers’ satisfaction and loyalty patterns.

Double Integral Sliding Mode MPPT Control of a Photovoltaic System

This brief proposes a new maximum power point tracker (MPPT) for a stand-alone photovoltaic (PV) system using the concept of double integral sliding mode controller (DISMC). Performance of a sliding mode controller (SMC) is greatly influenced by the choice of the sliding surface. A double integral SMC uses double integral of tracking voltage error term in its sliding surface to eliminate steady-state error apart from providing robust control actions in face of system uncertainties. However, there is still difficulty of increased chattering and slow transient response in DISMC. In this brief, the objective is to exploit the merits of the DISMC for designing an MPPT for PV system whilst the disadvantages of chattering and slow transient response are alleviated by choosing a new sliding surface. The design of this new DISMC-MPPT system considers the uncertainties in weather conditions and variations in load. This MPPT has been implemented using a pulsewidth modulator controlled dc/dc converter to keep the switching frequency constant. Thus, designing the control and filter circuits becomes simpler. The effectiveness of the proposed DISMC has been validated using simulation and experimental results using a prototype PV control system setup.