1,702 research outputs found
Is the Policy Window Open for High-Speed Rail in the United States: A Perspective from the Multiple Streams Model of Policymaking
Optimizing Energy Storage Participation in Emerging Power Markets
The growing amount of intermittent renewables in power generation creates
challenges for real-time matching of supply and demand in the power grid.
Emerging ancillary power markets provide new incentives to consumers (e.g.,
electrical vehicles, data centers, and others) to perform demand response to
help stabilize the electricity grid. A promising class of potential demand
response providers includes energy storage systems (ESSs). This paper evaluates
the benefits of using various types of novel ESS technologies for a variety of
emerging smart grid demand response programs, such as regulation services
reserves (RSRs), contingency reserves, and peak shaving. We model, formulate
and solve optimization problems to maximize the net profit of ESSs in providing
each demand response. Our solution selects the optimal power and energy
capacities of the ESS, determines the optimal reserve value to provide as well
as the ESS real-time operational policy for program participation. Our results
highlight that applying ultra-capacitors and flywheels in RSR has the potential
to be up to 30 times more profitable than using common battery technologies
such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC
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Optimizing energy storage participation in emerging power markets
The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving
The Coupling Coordination Degree Measurement of Society-Economy-Ecosystem of Regional National Forest Park in Heilongjiang Province
In order to estimate the comprehensive benefits brought by forest parks to the society, economy, and ecology of a certain area, this paper innovatively constructed a social-economic-ecological composite system of forest parks in Heilongjiang Province. The entropy method is used to determine the weight of each index, the coupling coordination degree model is used to analyze the coupling and coordination degree of the social, economic, and ecological benefits of forest parks in Heilongjiang Province from 2010 to 2018. In addition, the LSTM neural network model is used to predict the development trend of the coupling coordination degree of the composite system from 2019 to 2021. Research shows that from 2010 to 2018, the forest park composite system was in a state of "high coupling and low coordination" for a long time; from 2019 to 2021, it is predicted that the degree of coupling of the composite system will decrease slightly and the degree of coordination will increase
Modeling of Macroeconomics by a Novel Discrete Nonlinear Fractional Dynamical System
We propose a new nonlinear economic system with fractional derivative. According to the Jumarieās definition of fractional derivative, we obtain a discrete fractional nonlinear economic system. Three variables, the gross domestic production, inflation, and unemployment rate, are considered by this nonlinear system. Based on the concrete macroeconomic data of USA, the coefficients of this nonlinear system are estimated by the method of least squares. The application of discrete fractional economic model with linear and nonlinear structure is shown to illustrate the efficiency of modeling the macroeconomic data with discrete fractional dynamical system. The empirical study suggests that the nonlinear discrete fractional dynamical system can describe the actual economic data accurately and predict the future behavior more reasonably than the linear dynamic system. The method proposed in this paper can be applied to investigate other macroeconomic variables of more states
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