Risk-averse Strategy Research Articles

On the Investment Strategy, Effect of Inflation and Impact of Hedging on Pension Wealth during Accumulation and Distribution Phases

This paper studies the various results obtained in literature on the investment strategy, the effect of inflation and impact ofh edging on the Pension Wealth generation. The explicit solution of the constant relative risk aversion (CRRA) and constant absolute risk aversion (CARA) utility functions are obtained, both in the accumulation and distribution phase, using Legendre transform, dual theory, and change of variable techniques. It is established herein that the elastic parameter (beta) is not equal to one (beta neq 1), based on the assumption of our model. Theorems are constructed and proved on the various wealth investment strategies. Observations and significant results are made and obtained, respectively in the comparison of our various utility functions and some previous results in literature. Sensitivity analysis and Simulations on the various utility functions and optimal strategies during the accumulation and distribution phase are presented; when the existence of a elastic parameter that is not equal to one, when there is existence of modifying factors, when there is need for diversification of investment, when there is no significant effect of the choice of risk aversion strategy on investment returns during inflation period, when there is hedging ability of Inflation indexed Bond and Inflation-linked Stock and when there is insignificant effect of the orthogonal relationship between stock and time and nonpayment of pension benefits on the satisfaction of the investors.

Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios

With an increasing share of renewable energy resources participating in electricity markets, there is a growing dependence between renewable power production and clearing prices of spot markets. Modeling this dependence using bivariate analysis can result in underestimation of market risks and adverse effects for stakeholders’ risk management. To enable an undistorted risk assessment, we are using a copula approach to precisely and jointly model electricity prices and infeed volumes of wind power. We simulate the case of wind farm operators using power purchase agreements (PPAs) to shift the price risk to an energy trader, who integrates the infeed into its portfolio. The trader’s portfolio can either be geographically dispersed, or highly localized. Based on its portfolio, the energy trader can decide to use derivatives such as futures to manage its risk exposure. The trader decides on future volumes subject to its portfolio’s inherent volatility. With a given risk averse strategy, a sufficiently diverse portfolio can help reduce the necessity to trade futures and subsequently the disadvantage of having to pay potential risk premiums.

Multi-period optimal energy flow for electricity-gas integrated systems considering gas inertia and wind power uncertainties

The increasing integration of GFUs promotes the coordinated operation of power and gas systems to enhance the system efficiency and reliability. However, the gas inertia characteristic could have imperative impacts on the coordinated scheduling and this property has not been fully studied. This paper proposes a novel multi-period optimal energy flow (OEF) model considering gas inertia and wind power uncertainties for the scheduling and operation of integrated systems. The nonlinear transient natural gas transmission model is reformulated into a linear programming/quadratic programming (LP/QP) model via the reformulation linearization technique (RLT), yielding computational efficiency improvement. The gas inertia is molded as a time delay to study its impacts on the coordinated scheduling. Then, information gap decision theory (IGDT) based risk averse strategy is advocated to deal with the wind power uncertainties and the impact of the uncertainty level is quantified. Extensive simulation results on the modified 6-bus power system with 6-node gas system and the IEEE 39-bus power system integrated with a realistic 20-node natural gas network demonstrate the effectiveness of the proposed method.

Using Structured Decision-Making Tools With Marginalised Fishers to Promote System-Based Fisheries Management Approaches in South Africa

Fishers, and the communities they support face a range of challenges brought on by complexity and uncertainty in their social-ecological systems (SESs). This undermines their ability to achieve sustainability whilst hampering proactive planning and decision-making. To capacitate fishers to apply risk aversion strategies at smaller scales of operation and for managers to apply inclusive management approaches such as the ecosystem approach to fisheries management (EAF), a better understanding of the relationships and interactions in marine SES must be developed. At the same time, the EAF requires the inclusion of multiple stakeholders, disciplines and objectives into decision-making processes. Previous work in the southern Cape with fishers, identified drivers of change. Building on this previous research, and using causal mapping, fishers mapped out drivers of change in an iterative process in a problem framing exercise which also highlighted hidden drivers of change and feedback loops. To explore the relative importance of key drivers of change with participants, weighted hierarchies as well as a Bayesian Belief Network (BBN) were developed. By identifying and highlighting these hidden system interactions a more integrated systems view has been facilitated, adding to the understanding of this fishery system. Drivers identified in the weighted hierarchy were consistent with those identified in the causal maps and previous research, of interest is the relative weighting attributed to these drivers. Whereas the weighted hierarchies emphasised the political dimensions, group work already indicated the range of perceptions, reflecting the considerable uncertainties in this SES. While methodologically challenging at first, the individual approach behind the BBN construction yielded a better reflection of the diversity of views and a better balance of political, economic and climate dimensions of drivers of change. We show how, by using SDMTs, the most disenfranchised community members can engage meaningfully in a structured process. As structure is crucial to management processes, the research shows that where the appropriate groundwork, capacity building and resourcing takes place, disenfranchised stakeholders can be integrated into formal management processes; fulfilling a key requirement of an EAF.

Fuzzy evaluation model of software project performance based on grey clustering analysis

This paper introduces the general principles that should be followed in project performance evaluation: through the analysis of the main factors affecting software project performance evaluation, it provides a guiding principle for the construction of project performance evaluation index system including software project characteristics and project characteristics. The empirical study shows that the model can effectively evaluate software project performance and knowledge. It provides decision support information for software organizations to formulate risk aversion strategies, improve project performance level and other risk factors.

Evaluating the impact of multi-carrier energy storage systems in optimal operation of integrated electricity, gas and district heating networks

Various energy networks such as electricity, natural gas, and district heating can be connected by emerging technologies for efficient application of renewable energy sources. On the other hand, the pressure shortage in the natural gas network and increasing heat loss in the district heating network by growth of gas and heat load in winter might play a significant role in the participation of combined heat and power units in the energy markets and operation cost of the whole integrated energy system. Hence, this paper presents a multi-network constrained unit commitment problem in the presence of multi-carrier energy storage technologies aiming to minimize the operation cost of an integrated electricity, gas and district heating system while satisfying the constraints of all three networks. In addition, an information gap decision theory is developed for studying the uncertainty of energy sources under risk-seeker and risk-averse strategies with no need for probability distribution function. Moreover, the role of multi-carrier energy storage technologies in integrated networks is investigated, which indicates decrement of total operation cost and reduction of the effect of wind power uncertainty on total operation cost in presence of the storage technologies.

Check dam agriculture on the mesa verde cuesta

Prehistoric water management in the northern US Southwest was integral to successful subsistence. On the Mesa Verde cuesta in southwestern Colorado, several types of water management features have been identified in the archaeological record, but research into these features has typically focused on the efficacy of reservoirs—a large-scale, labor-intensive, and community-oriented means of collecting and storing water. This focus on large-scale water management features has largely ignored the productive potential of small-scale and low-cost strategies for water management executed by individual households. There is considerable evidence, for example, that extensive check dam networks were constructed and used on the Mesa Verde cuesta, but their actual utility as a small-scale risk-aversion strategy to resource stress has not systematically been explored. This paper identifies all ephemeral drainages on the Mesa Verde cuesta where check dam construction was possible, then applies a maize growing niche model to estimate total yields from check dam farming plots for each year from AD 890–1285. A demographic reconstruction is then used to estimate the percentage of the total cuesta population that could have been supported using only check dam maize yields through time. Results suggest that check dam farming could have supplied a reliable source of surplus annual maize sufficient for household storage needs even during the most populous time periods across the cuesta landscape.

Optimal power management of dependent microgrid considering distribution market and unused power capacity

This study presents an optimal power management for a microgrid (MG) in distribution market environment. The MG operator is able to have interactions with the distribution market operator (DMO) and adjacent MG (AMG) operator to supply its local loads. The DMO regulates the electricity market, and assigns the electricity price and power profile for the MG. The motivation behind the use of The DMO is that it works as an entity between MG and independent system operator (ISO) in order to guarantee a flexible operation by reducing power fluctuation and reduce the unintentional peak loads in low market price hours. The unused power capacity in AMG is used during the islanded hours through an additional interconnection point connected to the MG. Using this method reduces the need for installing new generation resources, which will be a practical and economical solution for MG developer. This MG which is able to provide power from distribution market and the AMG with two interconnection points is named dependent MG (DMG). A market-based stochastic model containing distribution market constraints and AC power flow formulations employs conditional value at risk (CVaR) methodology to capture the loads and wind uncertainties.The effectiveness of the presented model is evaluated on a 20 kV test system using different case studies. In this test system, the operator is the owner of all generation units. The numerical analysis explicate that presented model reduces the operation cost of DMG with the aim of responsive loads, unused power capacity, energy storage system (ESS) and power generation units. The market-based scheduling also provides operational flexibility for distribution system by adjusting flexibility limit of market constraints. It is also shown that changing risk preferences level changes the power generation pattern in ESS and causes a costly operation of resources in risk-averse strategy. The competence of this model is significant when the preventive maintenance (PM) program is carried out, and the DMG should rely on its flexible resources and AMG’s available power capacity, which could reduce the load shedding.

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach.

Optimizing motor decision-making through competition with opponents

Although optimal decision-making is essential for sports performance and fine motor control, it has been repeatedly confirmed that humans show a strong risk-seeking bias, selecting a risky strategy over an optimal solution. Despite such evidence, the ideal method to promote optimal decision-making remains unclear. Here, we propose that interactions with other people can influence motor decision-making and improve risk-seeking bias. We developed a competitive reaching game (a variant of the “chicken game”) in which aiming for greater rewards increased the risk of no reward and subjects competed for the total reward with their opponent. The game resembles situations in sports, such as a penalty kick in soccer, service in tennis, the strike zone in baseball, or take-off in ski jumping. In five different experiments, we demonstrated that, at the beginning of the competitive game, the subjects robustly switched their risk-seeking strategy to a risk-averse strategy. Following the reversal of the strategy, the subjects achieved optimal decision-making when competing with risk-averse opponents. This optimality was achieved by a non-linear influence of an opponent’s decisions on a subject’s decisions. These results suggest that interactions with others can alter human motor decision strategies and that competition with a risk-averse opponent is key for optimizing motor decision-making.

Energy management in residential buildings using energy hub approach

Building-owned micro energy hubs (EHs) usually focus on optimal energy consumption cost and emission, whereas, macro energy hubs (MEHs) mainly concentrate on utility’s interest termed as network load deviation. Therefore, a bi-level MEH control capable of simultaneously attaining bilateral stakes is required. However, in real life energy distribution systems, MEH structure operates under uncertainties such as unpredictable solar photovoltaic (PV) irradiance and unplanned electric and natural gas (NG) network outages. Such uncertain conditions may affect the MEH’s performance (energy cost, emission and network load deviation) and resilience (capability of recovering quickly from disturbances) undesirably. Objective of this paper is to attain an optimal compromise between the performance and the resilience of a bi-level residential MEH, under uncertain conditions. In first step, risk neutral bi-level MEH is proposed to optimally reduce the network load deviation, while obeying the customer specified comfort, emission and cost constraints. However, this strategy disregards the risk introduced by the uncertainties. In second step, risk averse strategy is devised by incorporating conditional value at risk (CVaR) in the objective function, for improvement in resilience. Proposed linear bi-level risk averse MEH is mapped in conventional flower pollination algorithm (FPA). Resilience of the MEH is measured in terms of energy stored in the plug-in hybrid electric vehicle (PHEV) and thermal energy storage (TES). Third step concentrates on the development of an efficient solution technique for energy management system to obtain better solution. For this, an improved version of FPA, termed as 2-cored FPA is proposed and employed to solve the model. Proposed technique has a filtration layer (termed as core-1) that consists of random walk, local pollination and global pollination to obtain improved pollinators. Subsequently, these pollinators are injected into conventional FPA layer (termed as core-2) to obtain better solution. Comparison of results demonstrates that under risk averse approach with two cores, the energy retaining capability of the PHEV and the TES increases by 31.66% and 57.66%, respectively.

Risk-Based Performance of Combined Heat and Power Based Microgrid Using Information Gap Decision Theory

In this paper, to get optimal scheduling of microgrids (MGs) in short-term, info-gap decision-theory (IGDT) is applied to assess load uncertainty. In order to provide a comprehensive load uncertainty study, best and worst possible conditions are evaluated using IGDT's functions of opportunity and robustness, respectively, in which risk-taker and risk-averse strategies are obtained while deterministic case study is also carried-out under the risk-neutral strategy. Real-time pricing (RTP) and time-of-use pricing (TOU) of demand response program (DRP) are applied to peak-load management. For each strategy, three cases as without DRP, TOU-DRP, and RTP-DRP are investigated. According to the results in the risk-neutral strategy, by applying TOU-DRP and RTP-DRP, operating cost is reduced about 2.5% and 6.6%, respectively. In the risk-averse strategy, by considering MG operating cost equal to $6,000, the MG will robust against load uncertainty up to 18.37%, 21.52%, and 24.82% for without DRP, TOU-DRP, and RTP-DRP cases, respectively. In the risk-taker strategy, MG operating cost for 28% of load reduction, operating costs will be equal to $3,698.12, $3,605.21, and $3,338.87 for without DRP, TOU-DRP, and RTP-DRP, respectively, in comparison with the risk-neutral strategy.

Offering Decision of Risk-Based Wind-Photovoltaic-Thermal GenCo Using Downside Risk Constraints Approach

Risk analysis and scheduling of a Generation Company (GenCo) under uncertain environments are challenging issues. So, stochastic optimization and downside risk constraints approaches are used in this paper to model and manage the risk associated with various uncertainties. The presented GenCo model comprised five thermal units, photovoltaic systems, and wind farms. It is assumed that all thermal units, photovoltaic systems, and wind farms can participate in the energy market. In contrast, only thermal units can participate in the reserve market. The uncertainty of electricity and reserve market prices and output power of photovoltaic systems and wind farms are modeled via a stochastic optimization approach. Afterward, the downside risk constraints method is used to manage the risks associated with various uncertainties. By analyzing the obtained results, it can be seen that the level of the average risk can plunge to 0 by gaining 4.68% less average profit. So, the GenCo can be immune against considered uncertainties with gaining a little bit less profit. Furthermore, the offering strategy is studied in two risk-neutral and risk-averse strategies. Finally, the CPLEX solver of GAMS software is used to optimize the studied linear-based model of GenCo.

Risk-Based Traded Demand Response Between Consumers’ Aggregator and Retailer Using Downside Risk Constraints Technique

Electricity retailer is the most critical player in the restructured electricity market. Retailer's electricity procurement problem is a big challenge for them. Electricity retailer can purchase their energy from various options such as pool market, forward contracts, and etc. A relatively new way with a lesser investigation is the demand response exchange market. In this paper, the risk-constrained stochastic power procurement problem of electricity retailers is formulated by modeling the uncertainty of pool-market price and consumers' electricity demand. The Downside risk constraints (DRC) risk-evaluation method is used to obtain risk-based power procurement scheduling of electricity retailers. By using the proposed method, conservative electricity retailer can experience a scenario-independent strategy over the stochastic optimization. In other words, the proposed risk strategy is such that impose more cost for electricity retailer while having an equal cost in all scenarios. Based on the obtained results, the operation cost of electricity retailers in all scenarios is closed to about $4827570, which is a relatively zero-risk strategy due to equality overall scenarios. Besides, results are compared in two cases to demonstrate the advantages of the proposed risk-evaluation method. Also, the Pareto front between risk-in-cost and expected cost can introduce an optimal risk-strategy for electricity retailers in the presence of uncertainties. Finally, the risk-averse strategy of electricity retailer is proposed to obtain the retailer's optimal conservative schedule during power procurement in the presence of uncertainties.

Irrigation, crop stress and drainage reduction under uncertainty: A scenario study

Two thirds of human water use is linked to agricultural practices including crop irrigation. Furthermore, excess irrigation leads to drainage problems. For this reason, reduced irrigation strategies need to be implemented to protect water resources. However, low irrigation may lead to crop water stress. A fast and inexpensive way to predict the necessary amount of irrigation required is by a model-based approach. With this approach, it is possible to explore the relation between irrigation, crop water stress and drainage. However, parameter uncertainty can reduce prediction accuracy. Therefore, the aims of this research were: (1) to develop and test a methodology that allows the analysis of uncertainty sources in irrigation strategies (2) to identify how much irrigation can be reduced while maintaining a low risk of crop stress, and (3) to explore the influence of uncertainty in soil parameters and evapotranspiration on model predictions. Results from a realistic case considered in this study indicated that, while maintaining a low risk of crop stress (<1 %), it is possible to reduce drainage (by 88 %) and water use (22 %) for a conventional irrigation strategy. This reduction is dependent on the type of risk aversion strategy and is specific for a case scenario where variations are certain.

Overseas Online Shopping Consumer Risk Perception Mechanism and Risk Aversion Strategy Research

This paper studies the overseas online shopping phenomenon from the perspective of consumer behavior. The methods of literature review, the research present situation of consumers' risk perception, the adoption of questionnaire and interview method to collect overseas online shopping consumer perception of risk dimension data, the relevant data processing method is adopted to build overseas online shopping consumer perceived risk impact on consumer buying behavior model, from overseas online shopping consumer perspective to explore the risk perception of consumers the internal influence mechanism of consumer purchase decisions and avoid risk strategy is put forward. Overseas online shopping refers to the online shopping behavior in which consumers browse and select commodities on overseas commercial websites through the Internet, submit electronic orders and complete the payment through electronic payment, deliver the goods directly through international express, or transfer companies collect the goods on behalf and forward them to consumers. Chinese consumers' overseas online shopping behavior originated from the 2008 Chinese dairy products contamination incident. With the development of economy, the demand for foreign products also increases, and overseas online shopping comes into being. With the help of Internet technology, payment means and logistics technology, overseas shopping becomes more and more convenient, and more Chinese consumers begin to "overseas online shopping".

RETRACTED: Risk-averse stochastic operation of a power system integrated with hydrogen storage system and wind generation in the presence of demand response program

Wind generation (WG) units as renewable energy sources (RESs) are increasing in the world due to environmental functions and lack of conventional energy sources. Also, hydrogen storage system (HSS) as an energy storage system (ESS) is used to cope with variable nature of RESs in which the concepts of power to hydrogen (P2H) and hydrogen to power (H2P) are defined. In this work, a risk-averse stochastic operation of HSS and WG is modeled using a scenario-based stochastic approach by considering price-responsive demand response (DR) program. All uncertainties are modeled via a scenario-based stochastic approach while the risk related uncertainties are modeled via the downside risk constraints (DRC) to capture the risk-averse operation of the HSS and WG. In order to investigate the impact of DRC implementation, a risk-averse strategy is compared versus risk-neutral strategy. Compared results show that the risk-in-cost (RIC) is reduced while the expected operation cost (EOC) is raised to deal with the risk of the uncertainty.

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

This paper proposes a new cooperative scheduling framework for demand response aggregators (DRAs) and electric vehicle aggregators (EVAs) in a day-ahead market. The proposed model implements the information-gap decision theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees obtaining the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenario-based and a bi-level IGDT based methods, respectively. The DR aggregator provides DR from two demand side management programs including time-of-use (TOU) and reward-based DR. Then, the obtained DR is offered into day-ahead markets. Furthermore, the EVA not only meet the EV owners’ demand economically, but also participates in the day-ahead mark while willing to set DR contracts with the DR aggregator. The objective function is to maximize the total profit of DR and EV aggregators perusing two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated on a case study with realistic data of electricity markets.

Risk-Constrained Stochastic Optimization of a Concentrating Solar Power Plant

A central concentrating solar power (CSP) plant is increasing in the power systems as novel technology in the solar energy sources. Also, solar thermal storage unit is combined with the CSP plant to increase flexibility and decrease the dependence on the instantaneous solar radiation. Furthermore, the CSP plant can be obtained the optimal offering strategies to submit to the electricity market in order to sell the produced power and increase the expected profit. In this paper, the uncertainty modeling of solar irradiation and electricity market price is a big challenge for a CSP plant. Therefore, the scenario-based stochastic framework is proposed for optimal scheduling of a CSP plant in the presence of uncertainties to obtain optimal offering curves in order to sell to power market. Also, the risk related to uncertainties is considered via the downside risk constraints (DRC), which leads to obtain risk-constrained stochastic optimization of a CSP plant. The proposed model is formulated as mixed-integer linear programming, which is solved via CPLEX solver under GAMS optimization software. Risk-averse strategy is introduced in comparison with risk-neutral strategy to investigate the impacts of DRC implementation, which leads to decrease the expected profit while the expected risk-in-profit reduced.

Novel load management for renewable generation sources/battery system through cut energy expenditure and generate revenue

The sources that provide energy to large electricity consumers include: pool market, bilateral contracts, micro-turbines, battery storage system, wind turbine and PV system as well as demand response program (DRP). Because of the uncertainty in the Pool Market the final cost of the large consumer is not identifiable. With the aim of improving the performance of large consumers in the risk-averse strategy, a strong optimisation method is recommended for pool markets in the presence of uncertainty. In large consumers due to high costs and with the aim of reducing it, the DRP method is used. By analysing the results, it can be concluded that DRP has had an important impact on total procurement cost of a large electricity consumer. By examining the results based on robust optimisation method, it can be discussed that the without using of DRP in the risk-neutral strategy is lower than the risk-averse strategy. Also, it can be said that the procurement cost of large electricity consumer with using of DRP program has been decreased 8.2% and 6.5% in the risk-neutral and risk-averse strategy, respectively.