Risk-averse Strategy Research Articles

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

Red pandas on the move: weather and disturbance effects on habitat specialists

Challenging weather events can make winters harsh for habitat and diet specialists. They may also incur high energetic costs due to reduced availability of food resources and elevated predation risk. Using GPS satellite collars we tracked the movement of the red panda Ailurus fulgens in the Himalayas, and evaluated the effects of weather and disturbances on their movement patterns and habitat use. We also analyzed the nutritional content of their key diet plant species. The mean daily distance travelled by red pandas was 748 ± 40 m (median 573 m), with no detectable effect of weather conditions and snow age. However, males travelled further than females when there was snow on the ground (β = 410.5, p < 0.02). Red pandas moved between 2528 and 3250 m during the study period with the mean elevation 2857 ± 107 m when snow was on the ground and 2816 ± 99 m without snow. A group of disturbances such as distance to settlements, herding stations, and roads, and geo‐physical variables affected their habitat use when the forest was covered with snow as they occupied areas away from human settlements (β = 0.36, p = 0.03), exhibited affinity for high elevations (β = 0.37, p = 0.02), and avoided steep slope (β = −0.21, p = 0.04). These movement patterns suggest a risk aversion strategy with males' behaviour appearing to be driven by reproductive instincts. Additionally, the distribution of their major dietary vegetation varied across the elevation gradient, leading to differences in the nutritional content of the diet, which might also have some effects on habitat use. Overall we found that despite red pandas exhibiting risk aversion behaviour, challenging weather events like snowfall could exacerbate the adverse effects on these habitat specialists.

A single and multiobjective robust optimization of a microgrid in distribution network considering uncertainty risk

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24 h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters.

Risky hybrid foraging: The impact of risk, reward value, and prevalence on foraging behavior in hybrid visual search.

In hybrid foraging, foragers search for multiple targets in multiple patches throughout the foraging session, mimicking a range of real-world scenarios. This research examines outcome uncertainty, the prevalence of different target types, and the reward value of targets in human hybrid foraging. Our empirical findings show a consistent tendency toward risk-averse behavior in hybrid foraging. That is, people display a preference for certainty and actively avoid taking risks. While altering the prevalence or reward value of the risky targets does influence people's aversion to risk, the overall effect of risk remains dominant. Additionally, simulation results suggest that the observed risk-averse strategy is suboptimal in the sense that it prevents foragers from maximizing their overall returns. These results underscore the crucial role of outcome uncertainty in shaping hybrid foraging behavior and shed light on potential theoretical developments bridging theories in decision making and hybrid foraging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimal scheduling of park-level integrated energy system considering multiple uncertainties: A comprehensive risk strategy-information gap decision theory method

Multiple uncertainties in the park-level integrated energy system (PIES) can affect the optimal operation of system. Information gap decision theory (IGDT) is a commonly used method of dealing with uncertainty by developing risky strategies to avoid risks or seek risky returns. However, there is no unified method or process for the selection of risk strategies and the setting of related parameters, leading to a certain blindness in the application of IGDT method. A comprehensive risk strategy (CRS)- IGDT approach is proposed for scheduling of PIES considering uncertainties of heat load, photovoltaic output and electric load. Risk averse strategy (RAS) and Risk seek strategy (RSS) scheduling models are constructed. Then an optimized solution method based on adaptive steps ratio (ASR) is proposed to solve the above two models. The CRS and comprehensive risk cost function are proposed from a risk equalization perspective. The target deviation coefficient and steps ratio in the IGDT model are automatically optimized with the objective of minimizing the comprehensive risk cost. Combining the two cases, the average cost reduction is 6.6 % compared to Risk-neutral (RN), 11 % compared to RAS-IGDT, and 4.1 % compared to RSS-IGDT. Moreover, the average costs of CRS-IGDT are lower compared to stochastic programming and robust optimization methods. The experiments verify the generalization and superiority of the proposed method in coping with different information gap situations caused by uncertainty. CRS-IGDT provides new research ideas for dealing with uncertainty problems in PIES and other fields.

Enhancing resilience of distribution systems: Integrating mobile energy storage systems and information gap decision theory for uncertainty management

Power Distribution Systems (PDSs) have seen considerable disruption owing to events and the intrinsic uncertainty associated with renewable energy sources (RES). The fundamental purpose of this project is to identify methods to enhance the resilience of Mobile Energy Storage Systems (MESSs) against unexpected cyber and natural disasters. Information Gap Decision Theory (IGDT) is used to effectively handle the uncertainties linked with RES's outputs. It also applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict important aspects that affect the output of RES. The shift to microgrid mode prioritizes the delivery of critical loads through MESSs, which are crucial amid adverse situations. A risk-averse strategy mitigates risks associated with wind turbine and photovoltaic (PV) outputs, therefore enhancing the resilience of the PDS. An optimization model utilizing Mixed-Integer Linear Programming (MILP) was formulated. The approach's efficacy in reducing uncertainties in RES has been proved by simulations conducted on the IEEE 69 bus and Dakota transport system. Assuming the most severe situation, the Uncertainty Radius (UR) for wind turbines is 0.7385, but for PVs, it is 0.8753, thereby guaranteeing the robustness of the system against power failures and disturbances.

Reactive power management in distribution networks in the presence of distributed generation sources based on information gap decision theory

The presence of uncertain parameters in power systems has led to many challenges for the designers and operators of these systems. One of these challenges is reactive power management in the presence of distributed renewable generation sources.In this article, the management of reactive power in distribution networks in the electricity market and the presence of distributed renewable generation sources, including wind and solar power plants, is performed considering the uncertainties in the network load, power generation of distributed generation sources, and active and reactive power market prices. Furthermore, reactive power cost modeling of reactive power compensation equipment is carried out.A hybrid stochastic/robust optimization method is employed to model the uncertainties in the problem. Finally, the efficiency of the method is confirmed by numerical examinations using the IEEE 33-bus distribution network and the GAMS optimization software. Simulation results indicate that in the risk-averse strategy, for a certain increase in cost, the radius of uncertainty in the active and reactive power market prices increases. Also, in this strategy, as β increases, the total cost of network operating increases by 81.72 %, while in a risk-seeking strategy, with the increase of β, the total operating cost of the network decreases by 77.78 %.

Optimal risk management considering environmental and climatic changes.

Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equationsare used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model's superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climatepolicy.

Quantile frequency connectedness between crude oil volatility, geopolitical risk and major agriculture and livestock markets

ABSTRACT This article investigates the quantile frequency risk connectedness between the global geopolitical risk (GPR) index, the CBOE Crude Oil Volatility Index (OVX), and the major agricultural and livestock indexes using a quantile vector autoregression (QVAR)-based frequency connectedness approach. The empirical results suggest that spillover effects are higher in extreme market conditions than in normal market conditions. The GPR has a more significant impact on other markets in the long-term domain of extreme market conditions. Moreover, grain markets are the primary transmitters of spillovers, while OVX predominantly assumes the role of a net receiver of risk spillover effects in most instances. Meanwhile, although the spillover structure between frequencies is homogeneous at the same quantile level, the connection between major agricultural and livestock index markets and two uncertainty indexes is time-varying, and spillovers are heterogeneous at different quantile levels. Our research provides investors and policymakers with new understandings of the development of differentiated crude oil volatility and geopolitical risk aversion strategies under different market conditions, given the heterogeneity of volatility spillover connectedness under different market conditions and frequency domains.

Armed conflict, insecurity, and attitudes toward women's and girls' reproductive autonomy in Nigeria

BackgroundArmed conflict and insecurity have been linked to deteriorations in reproductive health and rights globally. In Nigeria, armed violence has taken a significant toll on women's and girls' health and safety. However, knowledge is limited about how conflict shapes attitudes surrounding their ability to make autonomous decisions on relationships and childbearing. Drawing on a socioecological framework and terror management theory, we aimed to investigate the association between conflict, insecurity, and attitudes toward women's and girls' reproductive autonomy in Nigeria. MethodsWe conducted a cross-sectional study using data from two sources: the World Values Survey (WVS) and the Uppsala Conflict Data Program-Georeferenced Event Dataset (UCDP-GED). Nationally representative data on attitudes of 559 men and 534 women was collected by WVS in 2017–2018. Linear probability models estimated the association between attitudes toward five dimensions of women and girl's reproductive autonomy (contraception, safe abortion, marital decision-making, delayed childbearing, early marriage), respondents' perceptions of neighborhood insecurity using WVS data, and geospatial measures of conflict exposure drawn from UCDP-GED. ResultsExposure to armed conflict and perceived neighborhood insecurity were associated with more supportive attitudes toward access to safe abortion among both men and women. Among women, conflict exposure was associated with higher support for contraception and the perception that early marriage can provide girls with security. Conflict-affected men were more likely to support a delay in girls’ childbearing. ConclusionOur findings suggest that conflict and insecurity pose a threat to, but also facilitate opportunities for, women's and girls' reproductive autonomy. Contraception, abortion, early marriage, and postponement or childbearing may be perceived as risk-aversion strategies in response to mortality threats, livelihood losses, and conflict-driven sexual violence. Our findings foreshadow changes in fertility and relationship patterns in conflict-affected Nigeria and highlight the need for health programming to ensure access to contraception and safe abortion services.

Innovative optimal risk and economic management of a hybrid electric/hydrogen refueling station with water electrolyze and steam methane reform technologies for hydrogen supply

This paper proposes a stochastic programming for the economic operation of an electric/hydrogen refueling station. To reduce the carbon footprint, the charging station is connected to solar power generation, electrolyzer, and fuel-cell device to interlink the electric and hydrogen energies. Extra hydrogen is procured through an innovative steam methane reform process, which charges additional gas prices and emission penalties in return. The optimal scheduling minimizes the operation cost of the charging station and the emissions produced. The decision-maker faces uncertainties of electricity price, solar power generation, and vehicle demand that are modeled using stochastic programming. The innovative method based on conditional value-at-risk method is proposed to develop risk-hedging strategies addressing financial risks using economic management. With this respect, the risk-aversion strategies increased the expected cost from $ 322.97 to $ 388.71 (about 17 %) and decreased the difference between the expected cost and the cost of the worst scenario from $ 78.04 to $ 12.30 (about 84.23 %). Moreover, expected risk reduced from $ 24.63 to $ 1.23.

Market volatility and crisis dynamics: a comprehensive analysis of U.S., China, India, and Pakistan stock markets with oil and gold interconnections during COVID-19 and Russia–Ukraine war periods

The objective of this paper is to explore the interconnectedness of volatility among the stock markets of U.S., China, India, and Pakistan in conjunction with oil and gold markets. Employing the novel Time-Varying Parameter Vector Autoregression (TVP-VAR) model for assessing connectedness, the study scrutinizes key patterns of dependency and interrelation between these markets. Furthermore, this study investigates the dynamic connectedness during the global health crisis due to COVID-19 and the geopolitical crisis due to Russia–Ukraine war periods to identify the changes in their relationship following the two crises episodes. The findings underscore the significance of volatility transmissions emanating from the U.S., a developed market, in shaping these dynamic linkages. It is observed that oil and gold returns play a limited role as sources of shocks for market returns in China, India, and Pakistan, suggesting a relatively lower contribution of oil and gold to equity market volatility. The results also emphasize the safe-haven characteristics of gold during periods of crisis such as the COVID-19 pandemic and the Russia–Ukraine war. Moreover, the study indicates that the volatility transmissions during the COVID-19 pandemic are more pronounced compared to the Russia–Ukraine war crisis. These findings hold notable implications for both investors and policymakers, emphasizing the need for a nuanced understanding of market dynamics and the development of risk-averse strategies, particularly in times of crisis.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

A risk-averse sustainable perishable food supply chain considering production and delivery times with real-world application.

In recent years, a relatively novel paradigm known as sustainable development has been introduced in response to concerns regarding the adverse impact of industrial activities on the environment and society. Managers in the food sector have been attempting to incorporate the principle of sustainable development in their supply chains owing to the paramount importance of social and environmental considerations in creating a competitive advantage for food products. To this end, we propose a multi-objective linear mathematical model considering the three dimensions of sustainability, i.e. economic, environmental, and social, to design a sustainable food supply chain. Given today's volatile business environment, we employ a robust optimization model by incorporating Conditional Value-at-Risk into the configuration of two-stage stochastic programming to tackle uncertainty and take up a risk-averse strategy in supply chain design. The model aims to identify the optimal production and delivery times of the products, investigate the effects of their perishability characteristic on inventory decisions, and assess the financial and environmental advantages of transportation decisions to improve the sustainability of logistics operations. A novel version of fuzzy goal programming approach is applied to solve the proposed model. Next, the applicability of the proposed model and its solution method is verified based on computational experiments on a real-world case study of a processed food company. Lastly, conflicts between the sustainability aspects are examined, and several sensitivity analyses on risk-aversion parameters are performed to provide managerial insights for industry executives seeking to optimize their network concerning sustainability issues and well-performance under worst-case scenarios.

A Second-Order Stochastic Dominance-Based Risk-Averse Strategy for Self-Scheduling of a Virtual Energy Hub in Multiple Energy Markets

A Second-Order Stochastic Dominance-Based Risk-Averse Strategy for Self-Scheduling of a Virtual Energy Hub in Multiple Energy Markets

Risk-aware two-stage stochastic short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station and end-user cooperation

The interconnection between different energy networks considerably improves their technical and economical aspects; however, it increases the complexity of decisions. This paper optimizes the short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station composed of charging facilities, battery swapping slots, parking lots and hydrogen and gasoline refueling parts. In order to achieve the optimal operation in such a complex system and consider the influence of day-ahead and real-time decisions under uncertainties, a risk-aware two-stage stochastic programming is suggested in the presented study. In this regard, fluctuations of wind and solar units and different types of electrical and thermal loads and markets are modeled using various scenarios and their risk is measured by the downside risk method. In addition, the impact of end-user cooperation is analyzed and a sensitivity analysis is implemented. The simulations demonstrate that the proposed model can successfully specify the behavior of all components under a risk-averse strategy with maximum robustness, where the profit is decreased by 8.4 % to achieve an expected risk in profit equal to 0 $. The results also validate that although the all-in-one station can act as a bulk storage, a multi-directional energy injection is required to provide its high demand.

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

Supplier selection and order allocation problems considering regional and supplier disruptions with a risk-averse strategy

This paper studies optimal supplier selection and order allocation problems considering regional and supplier disruptions. We suggest a pricing policy that takes into account disruption probability, which reflects the trade-off relationship between cost and risk. We present a risk-neutral model considering the expected cost and a risk-averse model considering the conditional value-at-risk (CVaR) measure. We also present a weighted CVaR model that considers various tolerance levels simultaneously. Several multi-objective models that consider both risk-averse and risk-neutral models are proposed. We develop methodologies to solve multi-objective models by applying a convex combination or a modified augmented Tchebycheff distance. Finally, we show the performance of solution approaches through numerical experiments and present the supplier dependency ratio, which can construct an appropriate portfolio. We offer a Pareto frontier as the result of the multi-objective model and derive managerial insights, suggesting a decision-making criterion between disruption risk and the expected cost.

A multi-agency coordination resource allocation and routing decision-making problem: A coordinated truck-and-drone DSS for improved wildfire detection coverage

This study proposes a novel coordinated truck-drone system as a decision support system to improve fire suppression operations. The problem in this study is formulated as a bi-objective mathematical model to minimize total monitoring cost and time, considering trucks as mobile drone depots and that drones can fly at various altitudes and access hard-to-reach areas. In addition, time and cost parameters in the mathematical model are deemed uncertain, rendering the model more realistic. Accelerated Benders' decomposition (ABD) is utilized to solve the model rapidly. Furthermore, a column-and-constraint generation (CCG) algorithm is employed, which is an effective method for solving scenario-based models under uncertainty. Two criteria are subsequently used to evaluate and compare proposed robust optimization approaches (risk averse and min-max relative regret). The results indicate that the proposed system can assist firefighters in determining the optimal number of drones and trucks to patrol and the time and cost required to visit all areas. Moreover, the findings demonstrate that the min-max relative regret approach can outperform other methods during the hot season when the risk of wildfire is elevated. In contrast, when the fire risk is lower during the cold season, time and cost can be effectively managed, and a risk-averse strategy can be implemented. Finally, the proposed solution framework can facilitate optimal strategic organizational decision planning.

A three-stage mechanism for flexibility-oriented energy management of renewable-based community microgrids with high penetration of smart homes and electric vehicles

A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%.