Browsing by Author "Sun, Mucun"
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Item Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-Of-Use Rates(IEEE Computer Society) Sun, Mucun; Chang, Chih-Lun; Zhang, Jie; Mehmani, A.; Culligan, P.; Sun, Mucun; Chang, Chih-Lun; Zhang, JieAs energy consumption in residential and commercial buildings continues to grow, demand-side management (DSM) for energy systems becomes crucial, because DSM can shift energy use from peak to off-peak hours. In order to realize peak load shifting, energy storage systems (ESSs) can be integrated into buildings to store energy during off-peak hours and discharge energy in peak hours. However, installing a large number of ESSs in individual buildings can complicate DSM and increase the overall capital cost. In this paper, a cost-effective DSM strategy is proposed to address this energy management challenge. The break-even cost of battery storage in a building is explored through a process of two-step optimization in conjunction with different tariff structures. A number of scenarios are performed to conduct cost analyses of the storage-based building energy system under different time-of-use rate structures. The performance of the DSM strategy in the battery break-even cost, is explored using a particle swarm optimization algorithm based on the size of energy storage and priced-based constraints of the energy system. Results of a case study show that the proposed approach can reduce the peak-to-average ratio of the total energy demand to the total energy costs. In addition, as the percentage reductions in yearly maximum energy peaks increase, the optimal battery cost becomes progressively more economical to building owners.Item Grid Optimization of Shared Energy Storage Among Wind Farms Based on Wind Forecasting(Institute Of Electrical And Electronics Engineers Inc.) Zhu, K.; Chowdhury, S.; Sun, Mucun; Zhang, Jie; Sun, Mucun; Zhang, JieEnergy storage is crucial for source-side renewable energy power plants for enhancing output stability and reducing mismatch between power generation and demand. However, installing large size energy storage systems for renewable energy plants may not be economic, due to high capital cost and ever-increasing human resources and maintenance cost. As a result, in this paper, a shared energy storage system among multiple wind farms is proposed to address this energy management challenge. A state-of-the-art wind power forecasting method with ensemble numerical weather prediction models is used to optimally determine the size of a shared energy storage system (ESS). A number of scenarios are performed to optimize and explore the energy storage size under different economic and storage resource sharing circumstances. The performance of ESS, namely the net revenue of power plants, is explored subject to ESS size and operating constraints of wind farms and power systems. Results of a case study show that sharing of energy storage among multiple wind farms and lower cost of storage progressively enhance the economic benefits of using storage to mitigate over-production/under-forecasting (thus curtailment) and under-production/over-forecasting scenarios.Item Probabilistic Renewable Energy Forecasting by Considering Spatial-Temporal Correlation(2020-08) Sun, Mucun; 0000-0002-7527-6410 (Sun, M); Zhang, JieRenewable energy forecasts can help reduce the amount of operating reserves needed for the system, reducing costs of balancing the system, and improving the reliability of the system. Conventional deterministic forecasts might not be sufficient to characterize the inherent uncertainty of renewable energy. Probabilistic forecasts that provide quantitative uncertainty information associated with renewable energy are therefore expected to better assist power system operations. Meanwhile, studies have shown that the integration of geographically dispersed and correlated wind/solar farms could reduce extreme power output, which is referred to as smoothing effect. In addition, power produced from one wind/solar farm at different times is typically temporally correlated. The impacts of spatial-temporal correlation between wind/solar farms on renewable energy forecasting are not well studied in the literature. This dissertation aims at mitigating power system uncertainty by improving probabilistic renewable energy forecasting accuracy utilizing spatial-temporal correlation modeling. In this dissertation, a variety of methods, such as predictive distribution optimization, ensemble learning, deep learning, and scenario generation-based methods, are developed to assist spatial-temporal correlation modeling, and improve probabilistic forecasting accuracy. Computational experiments indicate that the presented study can provide scholars and engineers with critical insights to the usage of spatial-temporal modeling in probabilistic renewable energy forecasting and anomaly detection, and also serve as a valuable reference for practical industry forecasting systems.Item Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization(Institute of Electrical and Electronics Engineers Inc.) Sun, Mucun; Feng, Cong; Zhang, Jie; Chartan, E. K.; Hodge, B. -M; Sun, Mucun; Feng, Cong; Zhang, JieProbabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model. © 2018 IEEE.