Policy, Markets and Analytics

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Now showing 1 - 5 of 9
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    Weather and Random Forest-based Load Profiling Approximation Models and Their Transferability across Climate Zones
    ( 2021-01-05) Zhou, Huifen ; Hou, Z. Jason ; Liu, Yuan ; Etingov, Pavel
    This study is to provide predictive understanding of the associations of weather attributes with electricity load profiles across a variety of climate zones and seasons. Firstly, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked and screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).
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    Towards Financial Risk Management for Intermittent Renewable Generation with Battery Storage
    ( 2021-01-05) Henni, Sarah ; Staudt, Philipp ; Jaquart, Patrick ; Weinhardt, Christof
    As levelized costs of electricity for many renewable generation sources are continuing to fall and as feed-in tariffs are consequently being phased out, financial risk hedging for intermittent renewable generators takes a central stage. Battery storage as complementary capacity can support renewable generators regarding a more stable supply of electricity. In this study, we take first steps in modelling battery storage options as service products that are provided by battery storage operators to renewable generation operators. We model the situation theoretically, develop corresponding hedging strategies and apply the models to a fictional solar PV plant. The results show that battery storage options can reduce the risk for intermittent renewable generators and that the options can be financially beneficial for both the battery storage and the renewable capacity operator.
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    The effects of electricity tariffs on cost-minimal hydrogen supply chains and their impact on electricity prices and redispatch costs
    ( 2021-01-05) Vom Scheidt, Frederik ; Qu, Jingyi ; Staudt, Philipp ; Mallapragada, Dharik ; Weinhardt, Christof
    Hydrogen fueled transportation can contribute substantially to the reduction of global carbon emissions. However, the production of hydrogen through electrolysis creates interdependencies with electricity systems. Therefore, we present a new model which couples the hydrogen supply chain with the electricity system. We use this model to analyse a case study of Germany in 2030. We find that if efficient spatially resolved electricity tariffs are applied instead of existing uniform tariffs, electrolyzers are placed primarily at low-cost nodes and farther away from consumption centers. For hydrogen, this leads to higher transportation costs, but lower production costs, and lower total costs. Moreover, costs for congestion management decrease substantially.
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    Risk-based Stochastic Continuous-time Scheduling of Flexibility Reserve for Energy Storage Systems
    ( 2021-01-05) Parvania, Masood ; Li, Bosong ; Byrne, Raymond
    This paper develops a novel risk-based stochastic continuous-time model for optimizing the role of energy storage (ES) systems in managing the financial risk imposed to power system operation by large-scale integration of uncertain renewable energy sources (RES). The proposed model is formulated as a two-stage continuous-time stochastic optimization problem, where the generation of generating units, charging and discharging power of ES, as well as flexibility reserve capacity from both resources are scheduled in the first stage, while the flexibility reserve is deployed in the second stage to offset the uncertainty of RES generation in each scenario. The Conditional Value at Risk (CVaR) is integrated as the risk metric measuring the average of the higher tail of the system operation costs. The proposed model is implemented on the IEEE Reliability Test System using load and solar power data of CAISO. Numerical results demonstrate that the proposed model enables the system operators to effectively utilize the flexibility of ES and generating units to minimize the system operation cost and renewable energy curtailment at a given risk tolerance level.
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    Methodology for Calculation of the Marginal Emission Rates from a ComplexCogeneration Facility compared with that of the co-located NY ISO Bus
    ( 2021-01-05) Tabors, Richard
    Cogeneration facilities at commercial and institutional locations are significant emitters carbon dioxide. Many large universities, hospitals and large commercial complexes maintain combined heat and power facilities that are interfaced with wholesale power markets. These facilities both buy and sell electricity in the organized markets while maintaining what is their principle function of provision of thermal energy for heating and cooling. In this paper we provide the theoretical background to calculation of Marginal Emission Rates (MERs), provide an overview of the optimal operation of those facilities, and present the results of a detailed case analysis of the results of a comparison of the MER of an operating cogeneration facility at Cornell University compared with the MER for consumption of electricity at the closest wholesale bus of the New York Independent System Operator (NYISO).