Optimizing Hotspot Areas for Ecological Planning and Management Based on Biodiversity and Ecosystem Services

The significance of biodiversity and ecosystem services are gradually recognized by human as an approach towards sustainability, so it is important to understand relationships and congruence between them to support conservation planning, especially in the hotspot areas with a prominent role in conservation. However, the management of most conservation hotspots mainly focused on biodiversity, and rarely concerned with ecosystem services. With the aim of proposing criteria for conservation strategies that contribute to the optimization of biodiversity and ecosystem services, in this study, a Geographic Information System(GIS)-based approach was designed to estimate and map the biodiversity and ecosystem services in Chongqing Municipality of China. Furthermore, the distributions of hotspot areas for biodiversity and ecosystem services were mapped based on the relationship between cumulative ecosystem services and areas. Finally the statistical analysis was processed focused on specific conservation objectives. The results showed that hotspot areas can conserve the most biodiversity but with the least ecosystem services under the conservation plans target to biodiversity conservation. In contrast, depending on the ecosystem services of interest, hotspot areas can conserve the largest ecosystem services but with the least biodiversity. By integrating biodiversity and ecosystem services into conservation plan, we found that the conservation and regeneration of these small areas, would contribute to a conservation of 44% of the biodiversity hotspot and 14%–42% of the ecosystem services hotspot. Moreover, the current nature reserve selection was not maximize the biodiversity and ecosystem services compared to integration strategy, indicating that hotspot areas conservation and selection is vital for optimization protection of biodiversity and ecosystem services, and has practical significance for natural resources and ecosystem management.

Automated Actuarial Data Analytics-Based Inflation Adjusted Frequency Severity Loss Reserving Model

In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.

Dynamic Setting Method of Assessment Indicators for Power Curves of Renewable Energy Sources Considering Scarcity of Reserve Resources

With the increasing proportion of renewable energy sources(RESs)in power grid,the reserve resource(RR)scarcity for correcting power deviation of RESs has become a potential issue.Consequently,the power curve of RES needs to be more rigorously assessed.The RR scarcity varies during different time periods,so the values of assessment indicators should be dynamically adjusted.The assessment indicators in this paper include two aspects,i.e.,deviation exemption ratio and penalty price.Firstly,this paper proposes a method for dynamically calculating the supply capacity and RR cost,primarily taking into account the operating status of thermal units,forecast information of RES,and load curve.Secondly,after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators,this paper establishes a mapping function between them.Based on this mapping function,a dynamic setting method for assessment indicators is proposed.In the future,RES will generally be equipped with battery energy storage systems(BESSs).Reasonably utilizing BESSs to reduce the power deviation of RESs can increase the expected income of RESs.Therefore,this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs.The case study demonstrates that the dynamic setting method of assessment indicators can increase the revenue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs.Additionally,the power curve optimization strategy can help RESs further increase income and fully utilize BESSs to reduce power deviation.

From Viewpoint of Reserve Provider:A Day-ahead Multi-stage Robust Optimization Reserve Provision Method for Microgrid with Energy Storage

Energy storage(ES),as a fast response technology,creates an opportunity for microgrid(MG)to participate in the reserve market such that MG with ES can act as an independent reserve provider.However,the potential value of MG with ES in the reserve market has not been well realized.From the viewpoint of reserve provider,a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process,which differs from the model that MG with ES acts as a reserve consumer in most existing studies.Based on the proposed model,MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling.Considering that the proposed model is just in concept and cannot be directly solved,a multi-stage robust optimization reserve provision method is proposed,which leverages the structure of model constraints.Next,the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility.Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.