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This study presents a novel tractable mixed-integer linear programming model for multiyear transmission expansion planning (TEP) problem coping with the uncertain capital costs and uncertain electricity demands using the information-gap decision theory (IGDT). As the uncertain capital costs and electricity demands compete to occupy the permissible uncertainty budget, the proposed IGDT-based TEP (IGDT-TEP) framework employs the augmented ɛ-constraint method to solve a multi-objective optimisation problem maximising the robust regions against the uncertain variables (i.e. capital costs and electricity demands) centred on their forecasted values. This framework enables the system's planner to control the immunisation level of the optimal expansion plan regarding the enforced planning uncertainties using a certain uncertainty budget. Also, a Latin hypercube sampling-based post-optimisation procedure is introduced to evaluate the robustness of an expansion plan obtained from the proposed IGDT-TEP framework. Simulation results demonstrate the effectiveness of the IGDT-TEP model to handle the uncertain nature of capital costs and electricity demands.

Summary In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information‐gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors' strategies. The TEP model is formulated for the risk‐averse and risk‐seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi‐stage time horizon. A genetic algorithm approach is employed to solve the bi‐level IGDT‐based optimization problem. Finally, this IGDT‐based model is applied to the simplified Iranian 400‐kV system, and the results are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Deregulation in power systems has created new uncertainties and increased the previous ones. The presence of these uncertainties causes the transmission network to remain monopoly and the private investors not being interested in investing in this section. This paper presents a new merchant‐based transmission expansion planning (TEP) formulation from the viewpoint of private investors. The information‐gap decision theory (IGDT) is used to model the inherent uncertainties associated with the estimated investment cost of candidate lines and the forecasted system load and NSGAII is utilized to solve the multi‐objective optimization problem. This algorithm helps private investors to select the best lines for investment in the presence of uncertainties. In order to verify the effectiveness of the proposed method, it has been applied to the IEEE RTS 24‐bus system and the simplified Iranian 400‐kV transmission system. Copyright © 2016 John Wiley & Sons, Ltd.

Summary This paper presents a risk‐constrained programming approach to solve a retailer's medium‐term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope‐bound information‐gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi‐level stochastic programming problem based on the information‐gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi‐level problem into a single‐level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Methods for reserve selection and conservation planning often ignore uncertainty. For example, presence-absence observations and predictions of habitat models are used as inputs but commonly assumed to be without error. We applied information-gap decision theory to develop uncertainty analysis methods for reserve selection. Our proposed method seeks a solution that is robust in achieving a given conservation target, despite uncertainty in the data. We maximized robustness in reserve selection through a novel method, \"distribution discounting, \" in which the site- and species-specific measure of conservation value (related to species-specific occupancy probabilities) was penalized by an error measure (in our study, related to accuracy of statistical prediction). Because distribution discounting can be implemented as a modification of input files, it is a computationally efficient solution for implementing uncertainty analysis into reserve selection. Thus, the method is particularly useful for high-dimensional decision problems characteristic of regional conservation assessment. We implemented distribution discounting in the zonation reserve-selection algorithm that produces a hierarchy of conservation priorities throughout the landscape. We applied it to reserve selection for seven priority fauna in a landscape in New South Wales, Australia. The distribution discounting method can be easily adapted for use with different kinds of data (e.g., probability of occurrence or abundance) and different landscape descriptions (grid or patch based) and incorporated into other reserve-selection algorithms and software.

Curiosity—the desire for information—underlies many human activities, from reading celebrity gossip to developing nuclear science. Curiosity is well recognized as a human blessing. Is it also a human curse? Tales about such things as Pandora's box suggest that it is, but scientific evidence is lacking. In four controlled experiments, we demonstrated that curiosity could lead humans to expose themselves to aversive stimuli (even electric shocks) for no apparent benefits. The research suggests that humans possess an inherent desire, independent of consequentialist considerations, to resolve uncertainty; when facing something uncertain and feeling curious, they will act to resolve the uncertainty even if they expect negative consequences. This research reveals the potential perverse side of curiosity, and is particularly relevant to the current epoch, the epoch of information, and to the scientific community, a community with high curiosity.

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.

Curiosity and interest are at the core of human inquiry. However, controversies remain about how best to conceptualize these constructs. I propose to derive definitions by attending to the common core of typical usages of the two terms. Using this approach, curiosity can be defined as a psychological state that includes three components: recognition of an information gap, anticipation that it may be possible to close it, and an intrinsically motivated desire to do so. Interest can be more broadly defined as intrinsically motivated engagement with any specific object, content, or activity. The two definitions imply that curiosity is a special case of interest. Furthermore, I propose to use the state-trait distinction to distinguish between momentary and enduring forms of both curiosity and interest, which makes it possible to treat state versus trait curiosity and interest in conceptually parallel ways. To make further progress in understanding the two constructs, research is needed that investigates their affective dynamics and their generalizability across age-related and socio-cultural contexts.

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock’s direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand.

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.