Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

A Proactive Approach Can Make Site Remediation Less Expensive

Without any incentive to clean up a contaminated site, remediation is often delayed until the site owner is compelled to act by regulatory agencies. In such a context, the selected technology is typically the one that will reach the remediation goals as quickly as possible. Unfortunately, this criterion is often met by overly expensive technologies, resulting in high and sometimes unaffordable total remediation costs, leading to a remediation with a negative net benefit. This study examines the effects of time constraint and benefit value on the optimal remediation strategy for a diesel-contaminated site. This strategy is developed using the technico-economic model METEORS, which takes into account the technology’s effectiveness, the uncertainty of the level of contamination, and the possibility of reducing this uncertainty through either an additional characterization (before selecting and applying a technology) or the monitoring of the remediation technology (during its use). Results of simulations with both economic and temporal constraints support a proactive approach to site remediation.     

Extension of EU Emissions Trading Scheme to Other Sectors and Gases: Consequences for Uncertainty of Total Tradable Amount

Emissions trading in the European Union (EU), covering the least uncertain emission sources of greenhouse gas emission inventories (CO₂ from combustion and selected industrial processes in large installations), began in 2005. During the first commitment period of the Kyoto Protocol (2008–2012), the emissions trading between Parties to the Protocol will cover all greenhouse gases (CO₂, CH₄, N₂O, HFCs, PFCs, and SF₆) and sectors (energy, industry, agriculture, waste, and selected land-use activities) included in the Protocol. In this paper, we estimate the uncertainties in different emissions trading schemes based on uncertainties in corresponding inventories. According to the results, uncertainty in emissions from the EU15 and the EU25 included in the first phase of the EU emissions trading scheme (2005–2007) is ±3% (at 95% confidence interval relative to the mean value). If the trading were extended to CH₄ and N₂O, in addition to CO₂, but no new emissions sectors were included, the tradable amount of emissions would increase by only 2% and the uncertainty in the emissions would range from −4 to +8%. Finally, uncertainty in emissions included in emissions trading under the Kyoto Protocol was estimated to vary from −6 to +21%. Inclusion of removals from forest-related activities under the Kyoto Protocol did not notably affect uncertainty, as the volume of these removals is estimated to be small.     

The Impact of Uncertainty on Banking Behavior: Evidence from the US Sulfur Dioxide Emissions Allowance Trading Program

In this paper, we study empirically whether uncertainty has an influence on trade in the US sulfur dioxide allowances market. In particular, we investigate the role of uncertainty on banking behavior. To do this, we introduce a tractable, structural model of trading permits under uncertainty. The model establishes a relation between banking behavior and risk preferences, especially prudence in the Kimball (1990) sense. We then test this model using data on allowances, for utilities submitted to the US Environmental Protection Agency’s Acid Rain Program, carried over from one year to the next. Evidence is found of imprudence, namely, utilities bank permits in order to favor higher profits. Another finding is that larger utilities do not adopt behavior significantly different from that of smaller ones. This paper was presented at the “International Workshop on Uncertainty in Greenhouse Gas Inventories: Verification, Compliance & Trading” in Warsaw, Poland, September 2004, under the title “Portfolio Management of Emissions Permits and Prudence Behavior.”     

Compliance and Emissions Trading under the Kyoto Protocol: Rules for Uncertain Inventories

A solution is proposed for proving compliance with emission targets and for emissions trading in the event of uncertainties in reported emission inventories. The solution is based on the undershooting concept, from which the mathematical conditions for both proving compliance with a risk α and calculating effective emissions for trading are derived. Based on the reported emission units, the number of permits granted is reduced in proportion to the uncertainty in the inventory. A country whose inventory has higher uncertainty is thereby allotted fewer permits than a country with the same inventory but smaller uncertainty.     

基于模糊综合方法的工业污水环境安全影响评价研究

在综合分析水环境影响因素的基础上,应用模糊综合评价方法对工业污水环境安全影响进行了研究。首先构建工业污水污染程度模糊综合评价的数学模型,然后利用综合评价方法对污水中重金属污染程度进行有效评价。研究结果表明:模糊综合评价方法可以全面考虑企业排放废水中影响环境安全的各种因素,充分体现评价因素和评价过程所固有的模糊性,适用于复杂参数水环境的不确定分析与评价,可以有效地评价水的质量;将模糊评价方法引入工业污水对环境安全影响的评价体系,是污水处理评价发展的一个新方向和新领域,它是污水处理评价方法的补充和完善,在该领域具有广阔的应用前景。     

DETERMINATION OF SEDIMENT YIELD BY TRANSFERRING RAINFALL DATA1

ABSTRACT: A methodology for obtaining the optimal design value to allow for sediment storage in a reservoir is presented for the situation where no data on sediment loads in the incoming streams are available. Information concerning the amount of sediment delivered to the reservoir over its life-time is obtained by a sediment yield model which uses data on rainfall amount and duration obtained from a nearby experimental watershed. Bayesian Decision Theory is used to obtain the optimal storage requirements in order to consider the natural variation of rainfall and the sampling error due to the short rainfall record available. The normally difficult calculations involved were made tractable by the use of simplifications and approximations valid in the context of the problem. Results show that sediment storage requirements can be calculated in this manner and that consideration of the uncertainties involved leads to a storage requirement substantially larger than that calculated without such consideration.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

洞穴次生化学碳酸盐沉积物-石笋的气候替代指标的意义与不确定性因素

洞穴次生化学碳酸盐(CaCO3)沉积物-石笋、钙华和流石,是一个具有丰富的陆地古气候信息的档案库。洞穴碳酸盐(CaCO3)-石笋古环境重建常用的代用指标有碳、氧稳定同位素、生长速度、纹层厚度和灰度、有机质荧光、微量元素等,利用多种替代指标的环境解译,增加了古气候环境重建的可靠性。本文从测年方法和石笋年代学的研究入手,对石笋的生长速度,沉积速率、纹层厚度、稳定同位素、微量元素等各代用指标指代的气候意义进行了简述,同时,也对洞穴次生化学碳酸盐(CaCO3)沉积物-石笋中各单一代用指标存在的不确定性影响因素进行了探讨。因此,我们认为洞穴动态监测可为洞穴碳酸盐(CaCO3)沉积物的各替代指标的解译提供可靠的依据,充分利用现代洞穴监测的碳酸盐(CaCO3)沉积物的各替代指标,并与现代器测气象资料进行相互对比、并用以校正,是精确或定量解释石笋气候替代指标的关键。