Quantitative performance assessment and optimal design of microgrid systems considering supply-demand uncertainties

Abstract

Reducing carbon emissions and achieving carbon neutrality are urgent tasks for sustainable development. High renewable energy penetration in power generation is increasingly recognized as a solution to current environmental challenges and energy crises. Microgrids, as efficient means, have received increasing attention due to their potential to achieve high renewable energy penetration. Microgrid quantitative assessment and optimal design play significant roles in achieving high renewable energy penetration in power generation. However, quantitative approaches for the microgrid performance assessment and some key indexes to quantify the microgrid performance are still absent to provide the support and guideline for optimal microgrid design. In addition, existing microgrid optimal design methods optimize microgrids with simple assumptions for the demand-side variables. These methods are simple to implement but may result in reduced security/reliability and higher investment cost as the impacts of the demand-side systems on microgrid overall performance are not quantitatively considered. A trade-off between system reliability and the overall cost cannot be achieved. This study, therefore, aims to develop assessment approaches to quantify microgrid performance, including the economics, reliability (i.e., system adequacy and security), and renewable energy penetration, and to develop effective and comprehensive optimal design methods considering the supply-demand sides simultaneously. The contributions to the development of the microgrid quantitative assessment approaches are listed below. 1. A multi-dimensional performance assessment approach for the convenient assessment of microgrids is developed concerning their key performance indicators (i.e., economics, reliability, and renewable energy penetration). An empirical cost model is developed based on the Latin hypercube sampling (LHS) method, which can effectively reduce the computation cost and achieve acceptable accuracy compared with the conventional exhaustive method. The outputs of this work can effectively quantify the multi-dimensional performance of the microgrid. 2. A quantitative approach is proposed to assess the security of microgrids' dynamic load of power consumers. Two simplified generic transient models are developed based on the ANOVA (analysis of variance) method to quantify chiller motor startup performance, including inrush current and startup time. The microgrid blackout risk and system wear potential can be effectively quantified using the proposed quantitative approach and models. The quantitative approach and the utilization of the simplified generic transient startup power models are tested and verified using a hotel microgrid on a remote island. The outputs of this work can effectively quantify the system security and system wear potential in the real application of the microgrid design and chiller size determination. 3. A novel uncertainty-based reliability assessment approach is developed for microgrids considering uncertainties at both supply and demand sides. A new reliability index (named power inadequacy risk) is proposed, and a risk quantification method is developed to measure the risk/probability of power inadequacy under uncertainties. The uncertainties at both supply and demand sides are detailedly quantified using a bottom-up approach. As for developing the optimal microgrid design, we proposed two methods to enhance and trade off the microgrid reliability and economics, as summarized below. 1. A coordinated optimal design method is proposed for enhanced reliability and economics of microgrids. The designs of supply and demand systems are optimized simultaneously. Microgrid security is assessed quantitatively by considering the impacts of demand-side systems and considered as the optimization constraints together with the power supply adequacy. On the premise of ensuring power supply adequacy, the system security is enhanced significantly while achieving 5% of overall cost savings. 2. A robust optimal design method is proposed to obtain a trade-off between reliability enhancement and cost saving. As for enhancement of reliability, a novel indicator named power inadequacy risk is introduced by considering the supply-demand uncertainties simultaneously to quantify the probability of power supply inadequacy, which is used as a cost penalty in the optimization objective. In addition, the applicability and difference of two commonly-used robust optimal objective functions are analyzed, and their design solutions are compared with conventional optimization methods. The maximum overall cost saving is up to 16.5%, and power inadequacy risk is reduced by over 220 times maximum compared to existing optimal design methods.