Risk-averse Approach Research Articles

Coordinated preparation and recovery of a post-disaster Multi-energy distribution system considering thermal inertia and diverse uncertainties

Low-probability but high-impact extreme events, such as floods, earthquakes, hurricanes, etc., could threaten the security of a multi-energy system, especially on the distribution level, and cause severe energy supply outages. In this paper, a coordinated restoration method is presented for the renewable energy-integrated multi-energy distribution system (MDS) with several coupling points to coordinate the preparation and load recovery stages after the extreme event. First, the MDS restoration is comprehensively modeled with coupled power and thermal network constraints. Especially, the thermal inertia, which is in the form of pipe storage and thermal demand response of smart buildings to serve as a buffer when the source fails, is fully utilized to reduce the energy supply cost after disasters. Secondly, both preparation and load recovery stage measures are employed to facilitate efficient and reliable system restoration. Furthermore, multiple uncertainties from the renewable generation and power demands in the MDS restoration are dealt with via a risk-averse two-stage stochastic programming approach. Finally, simulation results validate the effectiveness of our method and its superiority over the traditional restoration methods.

Robust and resilience budget allocation for projects with a risk-averse approach: A case study in healthcare projects

One of the critical factors that force to complete projects in minimum time is Budget Allocation for Projects (BAP). This subject is vital in large-scale projects like provinces and healthcare projects that affect people's lives. The innovation of this research considers Robust, Resilience, and Risk-averse BAP (3RBAP). We utilize a hybrid robust stochastic optimization by considering worst-case in the objective function for robustness. This model minimizes the weighted expected and minimum progress functions to tackle budget fluctuation. The case study is about healthcare project construction. Finally, we assign the budget for healthcare projects with uncertain scenarios. After sensitivity analysis, we obtain that by employing 3R, we can see that the progress function is 17.07% less than without 3R. In addition, if the conservativity coefficient rises to 50%, the progress function will decrease to 7.32%, but the time computation changes a little. However, declining the resiliency coefficient makes decreases and disrupts the budget assignment. As a result, the progress function reduces. Finally, the time computation grows smoothly by increasing the scale of problems, and the progress function decreases. Because the number of projects is very much, maximizing the expected value of the progress function decreases. Eventually, by changing to exponential and sine functions, the progress function improved 41.46%.

A robust optimization approach to risk-averse routing of marine crude oil tankers

Marine accidents involving crude oil tankers are rare though such incidents have led to catastrophic socioeconomic and environmental consequences. This low probability – high consequence nature of marine oil spills renders the existing expected consequence technique ineffective and calls for the development of a risk-averse approach. We propose a conditional value-at-risk (CVaR) based methodology for selecting the appropriate size of crude oil tankers and then routing them over the intercontinental network such that the weighted sum of transport cost and CVaR based transport risk is minimized. In addition, we also develop a robust formulation of the mixed-integer routing problem, which not only facilitates tiding over imprecision in the risk data but also prepares risk-averse shipment plan. The proposed methodology was used to analyze the realistic marine network of a major oil supplier and to demonstrate its benefits, and to underscore the value of incorporating robustness.

Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intra-day multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.

Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards

Rainfall-induced slope failures disrupt the traffic and warrant urgent slope repair works. The impact of roadside slope failures can be minimized if slopes are proactively rehabilitated. Nonetheless, transportation agencies are constrained in their budget to rehabilitate a limited number of slope segments due to competing maintenance needs among different transportation assets. Therefore, the transportation agencies should identify the critical slope combination that should be proactively rehabilitated under constraint budgets to lessen the impact on the transportation network during extreme rainfall events. The decision-making approach for slope rehabilitation should also ensure low risk associated with the selected rehabilitation strategy. Current slope-rehabilitation decision models do not consider the risk associated with the rehabilitation strategies in the decision-making process. The objective of this study is to develop a risk-averse stochastic combinatorial optimization to facilitate the selection of slope rehabilitation strategies, which leads to the least expected cost and conditional value at risk (CVaR) for extreme rainfall events. The simulated annealing approach is used to solve the risk-averse combinatorial optimization rehabilitation problem with the objective function that measures the total cost of traffic disruption and slope restoration post-failures. The approach is demonstrated using a transportation network in Lamar County, Texas. Unlike a genetic algorithm-based approach in the literature that yields a single slope rehabilitation strategy, the proposed risk-averse simulated annealing approach identifies rehabilitation strategies along the Pareto efficient frontier facilitating the rehabilitation decisions based on the tradeoff between expected cost and CVaR. For the network in Lamar County, the proposed risk-averse simulated annealing provided a solution in the Pareto front that reduced CVaR by 2.0% compared to the solution obtained from the genetic algorithm-based approach while only increasing the expected cost by 0.8%. The risk-averse optimization approach will aid transportation agencies in determining slope rehabilitation strategies for minimizing the impact of rainfall-induced failures at appropriate risk aversion levels.

Risk-averse design of tall buildings for uncertain wind conditions

Reducing the intensity of wind excitation via aerodynamic shape modification is a major strategy to mitigate the reaction forces on supertall buildings, reduce construction and maintenance costs, and improve the comfort of future occupants. To this end, computational fluid dynamics (CFD) combined with state-of-the-art stochastic optimization algorithms is more promising than the trial and error approach adopted by the industry. The present study proposes and investigates a novel approach to risk-averse shape optimization of tall building structures that incorporates site-specific uncertainties in the wind velocity, terrain conditions, and wind flow direction. A body-fitted finite element approximation is used for the CFD with different wind directions incorporated by re-meshing the fluid domain. The bending moment at the base of the building is minimized, resulting in a building with reduced cost, material, and hence, a reduced carbon footprint. Both risk-neutral (mean value) and risk-averse optimization of the twist and tapering of a representative building are presented under uncertain inflow wind conditions that have been calibrated to fit freely-available site-specific data from Basel, Switzerland. The risk-averse strategy uses the conditional value-at-risk to optimize for the low-probability high-consequence events appearing in the worst 10% of loading conditions. Adaptive sampling is used to accelerate the gradient-based stochastic optimization pipeline. The adaptive method is easy to implement and particularly helpful for compute-intensive simulations because the number of gradient samples grows only as the optimal design algorithm converges. The performance of the final risk-averse building geometry is exceptionally favorable when compared to the risk-neutral optimized geometry, thus, demonstrating the effectiveness of the risk-averse design approach in computational wind engineering.

Better Mechanisms Are Needed to Oversee HREC Reviews.

Hawe et al. raise concerns about Human Research Ethics Committees (HRECs) taking a risk-averse and litigation-sensitive approach to ethical review of research proposals. HRECs are tasked with reviewing proposals for compliance with the National Statement on Ethical Conduct in Human Research for the purpose of promoting the welfare of participants. While these guidelines intentionally include a significant degree of discretion in HREC decision making, there is also evidence that HRECs sometimes request changes that go beyond the guidance provided by the National Statement. When HRECs request changes outside their remit, inconsistencies between individual HRECs become more common, contributing to delays in ethical review and reducing the quality of HREC decision making. Improvements to the HREC regulatory system are needed to promote transparency and accountability.

Stochastic multi-stage multi-objective expansion of renewable resources and electrical energy storage units in distribution systems considering crypto-currency miners and responsive loads

In order to mitigate the influence of global warming and greenhouse gasses emission, different proceedings are suggested such as utilizing renewable energies and demand response programs. This paper investigates the expansion of renewable resources and electrical energy storage units in distribution systems towards reducing investment costs and environmental pollution. Since the development of components is not possible in a single-stage due to the limitation of staff and funds, a multi-stage programming is applied to consider various restrictions. According to the penetration of crypto-currency miners in recent years, their impact is evaluated on the problem as the high-rate energy consumers. Moreover, the demand side management strategy and risk-averse scenario-based approach are implemented to analyze the role of responsive loads and model the renewable resources uncertainty, respectively. The results approve that simultaneous expansion of wind turbines, photovoltaics and electrical storage systems decreases the total pollution and cost by 100% and 99.98% after the third year, respectively. The simulations also validate that crypto-currency miners reduce the total revenue by 4.16%, whereas the responsive loads increase it by 9.89%. As well, the fluctuations of wind and solar power decrease the total revenue by 13.27%, in return, the robustness notably improves.

A probabilistic risk-averse approach for energy storage sizing in all-electric ship

In recent years, energy storage systems (ESS) are becoming an integral part of modern all-electric ships (AES). The topic of optimal ESS sizing is important as it determines the cost and effectiveness of the vessel operation. Conventional ESS sizing only considers the investment stage and ignores the operation stage and uncertainties. This research work adopts a risk-averse approach by coordinating investment and operation stages. The proposed two-stage method ensures optimal operation by reducing cost and emission while addressing the stochasticity present in voyage planning. Firstly, multi-objective ESS sizing and energy management scheduling are jointly optimized by considering the operation and sizing objectives with the probabilistic hydrodynamic data-oriented constraints from the voyage scenarios. In the second stage, the information-gap decision (IGD) decides the number of modules and configuration of the ESS to achieve power and energy requirements. Various ESS technologies and numerous scenarios are used to verify the effectiveness of the proposed strategy. The results indicate the validity and requirement of the proposed method.

Risk-averse approach for assessment of investment in a run-of-the-river power plants while considering reduced water availability

Investment in the facility for harnessing a stream of water is usually characterized by long payback period. Hence investment choice not only needs to be optimal, but it also needs to be robust enough to cope with uncertainties which occur during payback period. In this research, a family of flow duration curves is created to model stochastic nature of water availability. The risk-constrained approach for assessment of investments in cascaded hydropower plants is proposed in a form of a mixed-integer linear programming. Proposed approach will manage a risk of large financial losses induced by these uncertainties. The project of run-ofriver power plants on the Sava river stretch (Croatia) from border with Slovenia to the city of Sisak is analyzed.

A risk-averse approach for joint contract selection and slot allocation in liner container shipping

Liner shipping companies need to satisfy the shipping demand from both the long-term contracts and the spot market. In general, the container shipping demand of the long-term contract has more stable volumes but lower freight rates compared with the spot market demand. It is thus a critical problem for the shipping companies how to provide discriminative shipping services to these two types of shipping demand through contract selection and container slot allocation to simultaneously increase the shipping profit and control its variations resulting from the demand variations. To handle this problem, a two-stage stochastic programming model is constructed in this paper which adopts the Conditional-Value-at-Risk (CVaR) to measure the profit variations. In the first stage, the shipping company selects the long-term contracts from a candidate contract set to sign with customers under the uncertainties in the show-up rates of containers in long-term contracts, and the volumes and the freight rates of the spot market demands. In the second stage, after all uncertainties have been realized, the shipping company determines the slot allocation for both the contract and spot demands. With the introduction of the CVaR, classical Benders decomposition cannot be directly applied to solve the risk-averse model. This paper thus develops a risk-averse Benders decomposition method tailored for the model. Numerical experiments are also conducted to verify the effectiveness of the solution method and provide some meaningful managerial insights.

Is Attachment Theory the Answer to a Complex Healthcare System?

AimsThis article proposes the need for a theoretical framework that can be applied to underpin the varied idiosyncratic mental health systems.MethodsBowlby's Attachment Theory defines a set of values that are required for a developing child to acquire a stable base which allow for healthy psychological development into adulthood. These values and behaviours may serve as a caring and holistic framework for people using mental health services.ResultsThe outcomes in mental health remain unsatisfactory and services are overall fragmented and increasingly specialised. Ongoing recognition of the inter-related relationship between a person's immediate and social environment and their mental health are frequently overlooked as services become ever stretched in terms of finances, capacity and limited resources including support for staff. The emphasis of treatment is on illness instead of the multifactorial humanity of the individuals using the services. A key outcome of mental health provision is recovery but instead, recovery is compromised by a reductive approach to care that may paradoxically compromise rights, autonomy, confidence and self-belief when people are at their most vulnerable. This creates feelings of mistrust, uncertainty and a limited sense of safety toward services.Attachment theory takes into account the individual, their experiences, their social world and the significant people in their lives. The principles required for the developing child to develop a secure attachment from a stable base are similar to those required for people experiencing mental illness to facilitate recovery and develop resilience to help manage and reduce episodes of relapse.Systems that work well, frequently exhibit values underlying models of care that include continuity, consistency, respect, safety, autonomy, human rights, freedom, supportive, trusting relationships and collaboration. The opposite of that seen in what presents as autocratic and risk-averse approaches of many mental health services.The principles required to enable a child to develop into a psychologically well-adjusted adult are similar to those required when a person is at their most vulnerable. Episodes of mental illness can be a time for reflection and growth, with the right care and therapeutic intervention, illness can also be a time to learn and develop skills for greater resilience in future.ConclusionThis paper outlines the implications and cultural changes that are required so that the principles of attachment theory can serve as a theoretical framework across mental health services to provide a stable base for people using the services and staff providing the care.

Single-stage gradient-based stellarator coil design: stochastic optimization

We extend the single-stage stellarator coil design approach for quasi-symmetry on axis from (Giuliani et al 2020) to additionally take into account coil manufacturing errors. By modeling coil errors independently from the coil discretization, we have the flexibility to consider realistic forms of coil errors. The corresponding stochastic optimization problems are formulated using a risk-neutral approach and risk-averse approaches. We present an efficient, gradient-based descent algorithm which relies on analytical derivatives to solve these problems. In a comprehensive numerical study, we compare the coil designs resulting from deterministic and risk-neutral stochastic optimization and find that the risk-neutral formulation results in more robust configurations and reduces the number of local minima of the optimization problem. We also compare deterministic and risk-neutral approaches in terms of quasi-symmetry on and away from the magnetic axis, and in terms of the confinement of particles released close to the axis. Finally, we show that for the optimization problems we consider, a risk-averse objective using the conditional value-at-risk leads to results which are similar to the risk-neutral objective.

Wind–Thermal power generation scheduling with predictive transmission security margin

Optimal scheduling of generation and transmission resources in the day-ahead market is vital for the assurance of operational reliability in the case of with high wind penetration. To mitigate the ramifications of wind power uncertainty, this paper presents a risk-averse approach for the unit commitment (UC) model in a two-settlement market. A newly defined concept of predictive transmission security margin (PTSM) is formulated in the UC, which provides a measure for the risk margin of transmission congestion while accounting for wind power forecast error and contingencies. These procured PTSMs on each line are dynamically characterized based on the statistics of ex-post line flows in the real-time re-dispatch. The proposed framework is formed as a bi-level optimization problem, and is transformed into a mixed integer programming formulation using Karush–Kuhn–Tucher (KKT) optimality conditions for computational tractability. The effectiveness of the proposed method is tested using various case scenarios on a modified IEEE RTS 24-bus and a 118-bus system in terms of key performance metrics, including the total operation cost, congestion hours, and required and undelivered generation reserves. The results show that the proposed method can effectively reduce the total operation costs by suppressing congestion risk and improving reserve deliverability.

Two-stage risk-averse stochastic programming approach for multi-item single source ordering problem: CVaR minimisation with transportation cost

Integrating inventory and transportation decisions is vital in supply chain management and can enable decision-makers to achieve competitive advantages. This study considers a multi-item replenishment problem (MIRP) with a piece-wise linear transportation cost under demand uncertainty, which usually occurs both in retail and production environment when several items must be ordered from a single supplier. Conventionally, two-stage stochastic programming formulation is risk-neutral, and it lacks robustness in the presence of high data variability. Hence, we introduce the Conditional Value at Risk (CVaR) approach for MIRP. Additionally, we deploy both single and multi-cut L-shaped and the sample average approximation method to circumvent the computational complexity to solve large-scale instances. The data-driven simulation study is used to benchmark the results from deterministic, risk-neutral, and risk-averse stochastic models. The results indicate that under higher data variations, the risk-averse model provides better perspectives for a decision-maker. The results show a 40–50% reduction in lost sales with marginal growth in total cost while considering CVaR instead of a risk-neutral approach.

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

Cooperative adaptive cruise control systems have the potential to improve fuel efficiency and safety. However, due to the large amount of uncertainties, which are encountered in platooning applications, typical controller calibrations are often not reliable. Therefore, in order to ensure a satisfying performance in the presence of various information topologies and relevant uncertainties such as errors in data transmission or extreme manoeuvres of the lead vehicle, a risk-averse stochastic optimisation approach for controller calibration is suggested and demonstrated for a pre-existing control scheme. Realistic vehicle dynamics simulation experiments with a prescribed set of uncertainties, such as transmission delays and different vehicle parameters, are performed. The results show that the collision probability and energy consumption are reduced by the risk-averse calibration of the controller and its spacing policy compared with classical calibration which assumes perfect communication.

The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions-A Case Study on the Microvascular Complications of Diabetes.

Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the 'best' prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies. To support this decision process, we developed the Selection of Machine-learning Algorithms with ReplicaTions (SMART) Framework that integrates building and selecting ML models with decision theory. We build ML models and estimate performance for multiple plausible future populations with a replicated nested cross-validation technique. We rank ML models by simulating decision-maker priorities, using a range of accuracy measures (e.g., AUC) and robustness metrics from decision theory (e.g., minimax Regret). We present the SMART Framework through a case study on the microvascular complications of diabetes using data from the ACCORD clinical trial. We compare selections made by risk-averse, -neutral, and -seeking decision-makers, finding agreement in 80% of the risk-averse and risk-neutral selections, with the risk-averse selections showing consistency for a given complication. We also found that the models that best predicted outcomes in the validation set were those with low performance variance on the testing set, indicating a risk-averse approach in model selection is ideal when there is a potential for high population feature variability. The SMART Framework is a powerful, interactive tool that incorporates various ML algorithms and stakeholder preferences, generalizable to new data and technological advancements.

Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery

Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.

Risk Averse Model Predictive Control of Bioreactors

Uncertainty is inherent in bioprocess modelling and control. Typically, uncertainties are handled using either the stochastic approach or the robust approach. Recently, the risk-averse approach, i.e., an interpolation between the stochastic and worst-case robust approach is gaining popularity. Risk-averse formulations are very useful in avoiding conservative solutions while still handling high-effect, low-probability events. In the bioreactor case considered in this paper, one such high effect low probability event is wash off caused by high feed rate or low inlet substrate concentration. A risk-averse, risk-constrained model predictive control formulation is proposed in this paper. The dynamic optimisation problem to be solved at every measurement instance is formulated using AV@R type risk objective. Similarly, probabilistic chance constraints are approximated by an AV@R-type risk constraints. The problem is then solved using the conic duality of the risk measure and an epigraphical decomposition of the nested multistage problem.

Risk-averse two-stage distributionally robust optimisation for logistics planning in disaster relief management

Relief logistics is vital to disaster relief management. Herein, a risk-averse two-stage distributionally robust programming model is proposed to provide decision support for planning disaster relief logistics. It is distinct from the conventional disaster relief logistics planning problem in that (i) the facility location-inventory model and the multi-commodity network flow formulation are integrated; (ii) the probability distribution information of the supply, demand, and road link capacity is partially known, and (iii) the two-stage distributionally robust optimisation (DRO) method based on the worst-case mean-conditional value-at-risk criterion is developed. For tractability, we reformulate the proposed DRO model as equivalent mixed-integer linear programs for box and polyhedral ambiguity sets, which can be directly solved to optimality using the CPLEX software. To evaluate the validity of the proposed DRO model, we conduct numerical experiments based on a real-world case study addressing hurricane threats in the Gulf of Mexico region of the United States. Furthermore, we compare the performance of the proposed DRO model with that of the conventional two-stage stochastic programming model. Finally, we report the managerial implications and insights of using the risk-averse two-stage DRO approach for disaster relief management.