Output Volatility Research Articles

A Comparative Analysis of the Effects of Price Instability and Output Volatility on Economic Growth

Historical data indicate that the relationship between inflation and output does not align closely with the Philips curve's implications. Since the late 1980s, monetary policy has prioritized price stability as a key component of sustainable long-term economic growth. This emphasis reflects policymakers' assumption that output volatility poses less risk to long-term growth than inflation volatility, though this assumption lacks empirical support in the literature. To address this gap, this study investigates the impact of inflation and output volatility on economic growth by analyzing panel data from 1990 to 2020 across 68 countries. Fixed Effect Models are employed to account for country-specific, time-invariant factors that may influence the relationship between volatilities and growth. The findings reveal a significant negative impact of output volatility on economic growth, even when inflation volatility is accounted for, suggesting that policymakers should consider output volatility alongside price stability to support sustained economic growth.

Optimal strategies for contract farming supply chain with government subsidies under e-commerce assistance to farmers.

The agricultural issue is a focal point of concern for each country, and e-commerce assistance to farmers, as an emerging model, is gaining increasing attention. Considering this trend, this paper bases on e-commerce assistance to farmers and considers four types of government subsidies: no subsidy, price subsidy model with the farmer as the subsidy target, price subsidy model with the e-commerce platform as the subsidy target, and area subsidy model. Based on this, a game model of the contract farming supply chain involving a farmer and an e-commerce platform was established to explore the optimal decision-making in the contract farming supply chain considering various government subsidies under the background of e-commerce assistance to farmers. The results indicate that: (1) Consumer preference for supporting farmers and consumer premiums can stimulate the farmer to expand farm size, increase agricultural production, increase the purchase price of agricultural products and promote the growth of profits for the farmer and the e-commerce platform, contributing to the growth of social welfare. Output volatility can lead to a decrease in farm size and a reduction in the purchase price of agricultural products. (2) Government subsidies can support the farmer in expanding farm size and contribute to increased profits for the farmer and the e-commerce platform. However, government subsidies do not universally have a positive impact on every variable. The influence of government subsidies on the purchase price of agricultural products is contingent upon the method of subsidy distribution. Similarly, the effects of government subsidies on social welfare are influenced by factors such as price elasticity and agricultural productions per unit area. (3) From the perspectives of farm size, the purchase price of agricultural products, and profits of the farmer and the e-commerce platform, the government will have different and more targeted subsidy models, and the corresponding subsidy models are influenced by the agricultural productions per unit area. The research conclusion can provide references for optimal decision-making in contract farming under the model of e-commerce assistance to farmers.

Heterogeneous biased expectations of young and old individuals: macroeconomic effects and policy implications

Abstract This paper examines the effects of heterogeneous biased expectations between the young and old on business cycles and explores its policy implications. Empirical findings reveal that individuals, particularly the young, can have more optimistic or pessimistic views about the future state of the economy compared to the data-generating measure. This study relates these results to the learning-from-experience literature, which suggests that individuals, particularly the young, place greater weight on recent observations when forming their expectations. Incorporating household weighting schemes into a life-cycle learning model, I show that household sensitivity to recent observations amplifies the effects of economic shocks. However, the amplification effects become less extensive as the population ages due to the lower sensitivity of the old. My simulation results indicate that a 10 percentage point increase in the old population ratio leads to a 16 percent decrease in output volatility. Regarding policy implications, this paper suggests that the government spending multiplier declines by approximately 10 percent when the old population ratio rises by 10 percentage points due to weak amplification effects. Moreover, the weakened output effects deteriorate the welfare of the population, particularly that of the young.

Robust Load Frequency Control of Interconnected Power Systems with Back Propagation Neural Network-Proportional-Integral-Derivative-Controlled Wind Power Integration

As the global demand for energy sustainability increases, the scale of wind power integration steadily increases, so the system frequency suffers significant challenges due to the huge fluctuations of the wind power output. To address this issue, this paper proposes a Back Propagation Neural Network-Proportional-Integral-Derivative (BPNN-PID) controller to track the output power of the wind power generation system, which can well alleviate the volatility of the wind power output, resulting in the slighter imbalance with the rated wind power output. Furthermore, at the multi-area power system level, to mitigate the impact of the imbalanced wind power injected into the main grid, the H∞ robust controller was designed to ensure the frequency deviation within the admissible range. Finally, a four-area interconnected power system was employed as the test system, and the results validated the feasibility and effectiveness of both the proposed BPNN-PID controller and the robust controller.

Determinants of the choice of exchange rate regime in oil-exporting countries

This study examines the factors that determine exchange rate regime choice in oil-exporting countries. We run ordered logit regressions for an unbalanced panel dataset of 138 countries covering 1974–2021 and confirm that oil-exporting countries are more likely to adopt a fixed exchange rate regime than other countries. The main reason for this is that, given the volatility and uncertainty of oil revenues, traditional monetary policy tools are ineffective, resulting in the exchange rate serving as a nominal anchor. This is supported by our findings on the distinct roles of output volatility, government spending, fiscal cyclicality, and central bank independence in determining exchange rate regimes in oil-exporting countries.

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting

Ultra-short-term photovoltaic (PV) power forecasting is crucial in the scheduling and functioning of contemporary electrical systems, playing a key role in promoting renewable energy integration and sustainability. In this paper, a novel hybrid model, termed AI_VMD-HS_CNN-BiLSTM-A, is introduced to tackle the challenges associated with the volatility and unpredictability inherent in PV power output. Firstly, Akaike information criterion variational mode decomposition (AI_VMD) integrates the Akaike information criterion with variational mode decomposition (VMD) and reduces data complexity, enhancing grid optimization and energy efficiency. The adaptive selection of optimal parameters enhances VMD decomposition performance, supporting sustainable energy management. Secondly, the hierarchical scale-transform convolutional architecture (HS_CNN) supplements the traditional convolutional neural network (CNN) with two channels featuring distinct dilation rates, thereby extracting dual levels of time-scale information for a more comprehensive data representation. Finally, a bidirectional long short-term memory neural network (BiLSTM) with an attentional mechanism combines past and future data to enable more accurate forecasts, aiding in carbon reduction and smart grid advancements. Experimentation with data from the Alice Springs PV plant in Australia demonstrates that the proposed AI_VMD-HS_CNN-BiLSTM-A model exhibits superior adaptability and accuracy in multiple time-scale forecasting compared to the baseline models. This approach is important for decision-making and scheduling in grid-connected photovoltaic systems, enhancing energy resilience and promoting the sustainable development of renewable energy.

Prediction method for power fluctuations in cross regional consumption and transportation under the integration of new energy

Against the backdrop of the increasing development of the new energy industry, the volatility of new energy output poses significant challenges to regional power grid balance and energy absorption. Therefore, this article proposes a prediction method for cross regional transmission power fluctuations under new energy integration conditions. A comprehensive and representative sample dataset was constructed by comprehensively considering factors such as fluctuations in new energy output, capacity confidence, and peak shaving characteristic parameters, combined with numerical weather forecast data. Normalize the sample data to eliminate dimensional differences between parameters. The sparrow search algorithm is used to optimize the weights and thresholds of the double hidden layer BP neural network, effectively avoiding local optimization problems caused by over training. The experimental results show that this method has significant advantages in predicting power fluctuations in cross regional absorption and transportation of new energy, with a predicted power to power ratio of over 0.85.

Finance and Output Volatity During The Global Financial Crisis

This paper assesses the effectiveness of financial depth and financial inclusion in the mitigation of output volatility during the 2008-2010 Global Financial Crisis. The paper also evaluates whether finance is more effective in countries with more developed financial systems, higher levels of economic developments, sounder banking systems, and better political stability. Employing a cross-sectional dataset covering more than 100 countries, our results indicate the ability of finance in subduing output volatility during the crisis. Our evidence is also concrete in suggesting the success of finance in reducing output volatility in more financially developed, advanced, and politically stable countries. However, the evidence is unclear on whether finance is beneficial for countries characterized by low, intermediate, or high banking stability. In addition to these findings, the preponderance of evidence tends to suggest the better ability of financial usage as compared to financial access in mitigating aggregate fluctuations during the crisis. Finally, our additional analysis reveals that finance functions well in stabilizing output when output volatility is high.

Analyzing the Impact of Monetory Policy Shocks on Output, Inflation and Exchange Rate Dynamics of Pakistan

The major objective of this study is to analyse that how monetary policy of Pakistan had impact on output, inflation and exchange rate fluctuations during Global Financial Crisis 2008-2009 in Pakistan. Quarterly data was used for this purpose. Data spans from 1990Q1 to 2022Q4. Time varying parameter – vector auto regression model (TVP-VAR) of Jouchi Nakajima (2011) has been used for analysis. Through this model we selected the time horizons such as 2008, 2009 and 2015 mainly to study the impact of monetary policy on different selected variables. Our empirical finding indicates that monetary policy shock plays an important role in the economic dynamics of Pakistan. The impulse response of inflation is negative to tight monetary policy shock (εi↑→ p), this is in accordance with the standard economic theory. In case of Pakistan, after 1990 there is declining trend in volatility of output and a considerable reduction in business cycle fluctuations. Policy rate is more volatile during 1998. After 2000 there is decline in volatility of policy rate but in 2008-09 there is rise in interest rate because of Global Financial Crises in 2007 and 2008. The response of policy rate to inflation shock is in rising trend in 2007 and 2008. The inflation is increasing as a response to positive output shocks after 12th quarter this rising trend is also evident during 2008 where the impact of output on inflation is long term.

Risk analysis of lithium battery energy storage systems under typical failures

Abstract With the rapid increase in the proportion of new energy installed capacity, to solve the problem of new energy output volatility, lithium-ion battery energy storage has developed rapidly by its electrical characteristics and economic advantages and has become a hot spot for the large-scale application of electrochemical energy storage, but it is also accompanied by safety problems such as battery short-circuit and thermal runaway. Based on the typical structure of the lithium battery energy storage system, this paper establishes a complete simulation model of the lithium battery energy storage system, calculates the change rule of battery system electrical parameters inside the battery module under different types of short-circuit faults, and summarizes the fault characteristics and risks of lithium battery energy storage system under different faults. The study shows that short-circuit faults inside the battery module will cause significant fluctuations in the terminal voltage and current of the battery system. The internal short-circuit faults will increase the inter-cluster circulating current. In contrast, the larger short-circuit current will not only cause the local battery cells to overcharge and over-discharge but also trigger the thermal runaway of the battery system.

Reliability analysis method of energy storage stations considering the correlation of battery clusters

In the context of dual carbon, in order to address the volatility of new energy output, energy storage power stations (ESPSs) are thriving. However, it is challenging to assess the dependability of ESPS operation because of the steady functioning of individual battery clusters (BCs), which will have an impact on how safely and steadily the power system is evaluated. Therefore, a reliability model for energy storage BC units based on the structural analysis of battery structure modules is constructed in this manuscript. Then, a method for constructing a correlation model of energy storage units based on the Copula function was proposed. Finally, a simulation analysis based on the ESPSs of pertinent demonstrative application projects in Fujian Province is used as an example to confirm the viability and efficacy of the proposed reliability analysis approach.

Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field Algorithm

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field Algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions; while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result.

Analyzing the Influence of Risk Models and Investor Risk-Aversion Disparity on Portfolio Selection in Community Solar Projects: A Comparative Case Study

This study examines the impact of risk models and investors’ risk aversion on the selection of community solar portfolios. Various risk models to account for the volatility in the electrical power output of community solar, namely variance (Var), SemiVariance (SemiVar), mean absolute deviation (MAD), and conditional value at risk (CVaR), were considered. A statistical model based on modern portfolio theory was employed to simulate investors’ risk aversion in the context of community solar portfolio selection. The results of this study showed that the choice of risk model that aligns with investors’ risk-aversion level plays a key role in realizing more return and safeguarding against volatility in power generation. In particular, the findings of this research revealed that the CVaR model provides higher returns at the cost of greater volatility in power generation compared to other risk models. In contrast, the MAD model offered a better tradeoff between risk and return, which can appeal more to risk-averse investors. Based on the simulation results, a new approach was proposed for optimizing the portfolio selection process for investors with divergent risk-aversion levels by averaging the utility functions of investors and identifying the most probable outcome.

Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Accurate prediction of global horizontal irradiance (GHI) is crucial for anticipating the volatility of solar power output. Under clear-sky conditions, aerosols play a significant role in influencing GHI, with different types of aerosols exhibiting distinct radiation effects. In this study, we employed the Planck mean aerosol optical depth (AOD) —Informer model to forecast future GHI, utilizing historical data of AOD, meteorological parameters, and GHI as input variables. Two data-fusion schemes were proposed, and the calculation results indicate that Scheme 1, organized in the order of Xianghe—Beijing—Shangdianzi (XBS) based on the day as a unit, demonstrates superior prediction performance. Additionally, the site information was blurred, allowing for the flexible selection of input and output lengths. The calculation results remained stable and reliable, suggesting that the inclusion of fuzzy site information does not degrade the prediction effect. Building upon this, we categorized aerosols into three types based on their time span and data volume. GHI predictions for different aerosol types revealed that the mean square error (0.145) for marine aerosols were significantly lower than those for the other types. The GHI predictions for continental aerosols were closest to the true values.

Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy.

Study on the Parallel Competition Governance Model of Multiple Subjects in the "Unveiling of the List of Commanders"

Study on the Parallel Competition Governance Model of Multiple Subjects in the "Unveiling of the List of Commanders"

Remittances as an Economic Stabilizer: How Remittance Inflows Reduce Output Volatility in Egypt

Remittances as an Economic Stabilizer: How Remittance Inflows Reduce Output Volatility in Egypt

Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networks.

This paper presents a novel approach to addressing the challenges associated with energy storage capacity allocation in high-permeability wind and solar distribution networks. The proposed method is a two-phase distributed robust energy storage capacity allocation method, which aims to regulate the stochasticity and volatility of net energy output. Firstly, an energy storage capacity allocation model is established, which considers energy storage's investment and operation costs to minimize the total cost. Then, a two-stage distributed robust energy storage capacity allocation model is established with the confidence set of uncertainty probability distribution constrained by 1-norm and ∞-norm. Finally, a Column and Constraint Generation (C&CG) algorithm is used to solve the problem. The validity of the proposed energy storage capacity allocation model is confirmed by examining different wind and solar penetration levels. Furthermore, the model's superiority is demonstrated by comparing it with deterministic and robust models.

The effects of precipitation shocks on rural labour markets and migration

ABSTRACT The welfare of workers in rural areas is highly affected by agricultural output volatility, caused in part by weather shocks. A volatile source of income is an important factor in inducing migration. While the impact of rainfall shocks on migration is well studied, the channels through which these shocks affect migration are underexplored. In this paper we study the hypothesis that the labour market is an avenue that carries the effect of precipitation shocks on migration. This paper first establishes the effect of precipitation shocks on the labour market. We use individual-level data combined with station-based precipitation data of Iran at the rural-agglomeration level in a fixed-effects panel-data model to find that workers in agriculture and industry sectors decrease their hours of work in response to negative shocks. We then use a linear probability model to show that negative shocks almost double the probability of migration and labour-migration for young men in Iran. Estimations of this paper indicate that as unemployment rate in rural areas increases, migration out of those regions rises as well. Additionally, controlling for the local labour market conditions at origin captures the impact of shocks on migration, implying the labour market is a channel through which precipitation shocks affect migratory decisions.