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Chen, Yang

Publications and source records attributed to Chen, Yang.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A comparison study on trading behavior and profit distribution in local energy transaction games

With the increasing integration of distributed energy resources, localized peer-to-peer energy transactions are receiving more attention since they could absorb more distributed renewables locally and stabilize the power grid. To better study the trading behavior and provide guidance for a viable local market design, different game-theoretical frameworks including cooperative and non-cooperative games are established and compared here. For the cooperative grand coalition, the maximum social welfare is guaranteed, the Shapley value and bargaining based approaches are implemented for further profit redistribution. It is shown that the asymmetrical bargaining power can be critical in the final payment clearing and it is equivalent to the Shapley value allocation if prosumers bargain based on their marginal contributions. Non-cooperative trading is formulated by using a Stackelberg bilevel model in which the upper level has the privilege in internal price setting. However, system optimum is not obtained due to competition, and the selfish leader could take away 89% of the collective benefit by price-gap based arbitraging. Aiming for a long-lasting trading market, a virtual benevolent leader and a uniform price structure in which purchase price equals selling price are tested. Experimental results indicate that maximum social welfare and a fairer profit allocation can be achieved with a uniform price structure, regardless of the selfish or benevolent attitude.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Distributed Solution Approach for a Stackelberg Pricing Game of Aggregated Demand Response

Demand-side management is a fundamental up-to-date strategy that transforms the traditional power grid to a modern smart grid where the flexible pricing mechanisms play a critical role in its successful implementation. In this paper, the pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is modeled as a Stackelberg game, where the DSO is the price maker that adjusts its strategy based on observed responses from LAs. With the concerns of computational cost and privacy protection, two distributed solution approaches, particle swarm optimization and pattern search algorithm, are conducted and compared with the classical centralized backward induction approach. Numerical results on a small case study demonstrate the effectiveness of the proposed distributed solution approaches in leveraging flexible demand response potential.

Chen, Yang↗
DOE OSTI2020

BI-LEVEL OPTIMIZATION FOR ELECTRICITY TRANSACTION IN SMART COMMUNITY WITH MODULAR PUMP HYDRO STORAGE

Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

Chen, Yang↗
DOE OSTI2020

The predicted properties of helium-enriched globular cluster progenitors at high redshift

Globular cluster progenitors may have been detected by Hubble Space Telescope, and are predicted to be observable with James Webb Space Telescope (JWST) and ground-based extremely large telescopes with adaptive optics. This has the potential to elucidate the issue of globular cluster formation and the origins of significantly helium-enriched subpopulations, a problem in Galactic astronomy with no satisfactory theoretical solution. Given this context, we use model stellar tracks and isochrones to investigate the predicted observational properties of helium-enriched stellar populations in globular cluster progenitors. We find that, relative to helium-normal populations, helium-enriched (ΔY = +0.12) stellar populations similar to those inferred in the most massive globular clusters, are expected, modulo some rapid fluctuations in the first ~30 Myr, to be brighter and redder in the rest frame. At fixed age, stellar mass, and metallicity, a helium-enriched population is predicted to converge to being ~0.40 mag brighter at $\lambda \approx 2.0\, {\mu \rm m}$, and to be 0.30-mag redder in the JWST–NIRCam colour (F070W - F200W), and to actually be fainter for $\lambda \lesssim 0.50 \, {\mu \rm m}$. Separately, we find that the time-integrated shift in ionizing radiation is a negligible $\sim \!5{{\ \rm per\ cent}}$, though we show that the Lyman-α escape fraction could end up higher for helium-enriched stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Genetic algorithm for demand response: a stackelberg game approach

Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

Amasyali, Kadir↗
DOE OSTI2020

Nonlinear δf particle in cell gyrokinetic simulations of the microtearing mode

Nonlinear simulations are carried out for the microtearing mode using particle-based δf gyrokinetic simulations for parameters relevant to spherical tokamaks. The present study finds that the microtearing mode can generate significant electron heat flux, which is predominantly carried out by the electromagnetic component of the heat flux with a negligible contribution from the electrostatic component. The mode sustains without the electrostatic component. We observe that the electron heat flux increases with the electron temperature gradient. The heat flux exhibits a very weak dependence on the collisions. It increases with electron β initially; however, at very high β, the electron heat flux is reduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Allocation and Operation of A Hydropneumatic Energy Storage with Building Microgrid

Behind-meter application for a recently invented hydropneumatic Ground-Level Integrated Diverse Energy Storage (GLIDES) is focused in this research. Considering possible uncertainties from solar radiation and electricity load in a building microgrid, two-stage stochastic programming is adopted for 15-min operation and a mixed-integer nonlinear stochastic model is built with integrated energy systems of GLIDES, solar panel and power generating unit. In order to make the model computationally tractable, we use sample average approximate method to decrease associated complexity with sample size and improve solving efficiency. The experimental results in deterministic and stochastic operation indicate that best candidate solution from SAA are superior to the solution from expected value problem in terms of optimality gap and variance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Re-splitting δ f method for electro-magnetic gyrokinetic particle-in-cell (PIC) simulation of tokamak plasmas

A new method based on a re-splitting δ f technique is proposed to mitigate the cancellation problem in electro-magnetic gyrokinetic PIC simulation. This new scheme, together with the pull-back mitigation (PBM) scheme (Mishchenko et al., 2014), have recently been implemented in the GEM code (Chen et al., 2003). These two algorithms are systematically compared with the original GEM algorithm for the simulation of the ion temperature gradient (ITG) mode, the kinetic ballooning mode (KBM), the toroidal Alfvén eigenmode (TAE) and the energetic particle mode (EPM). The three algorithms agree well for all of these modes. Moreover, both the re-splitting method and PBM can mitigate the cancellation problem and improve the computational efficiency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Machine learning to predict final fire size at the time of ignition

Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PART 1- techno-economic analysis of a grid scale Ground-Level Integrated Diverse Energy Storage (GLIDES) technology

In this paper, a techno-economic model / cost reduction analysis of a low-cost, dispatchable / scalable, efficient Ground-Level Integrated Diverse Energy Storage (GLIDES) system is analyzed, along with a review of existing state-of-the-art energy storage technologies. The introduced technology, GLIDES, which was invented at the Oak Ridge National Laboratory (ORNL), stores energy by compression and expansion of air using water as a liquid piston inside high-pressure reservoirs. GLIDES is introduced by combining Pumped Hydroelectric Storage (PHS) and Compressed Air Energy Storage (CAES) technologies. Here, by replacing inefficient gas compressor and turbine with higher efficiency liquid turbomachines for liquid-piston compression/expansion, GLIDES achieves higher roundtrip efficiency than other gas compression-based energy storage technologies and is a scalable energy storage system. For cost reduction purposes, various pressure reservoirs including steel vessels, carbon fiber vessels, pipe segments, and underground pressure reservoirs are analyzed in this paper. Based on the analyzed data using the models discussed in this paper, energy storage costs as low as ~$14/kWh and ~346/kWh (roundtrip efficiency (RTE) ~80%) can be achieved for a grid-scale GLIDES using depleted oil/gas reservoirs and high-pressure pipe segments respectively.

25 ENERGY STORAGE↗
DOE OSTI2019

Surrogate Modeling for Capacity Planning of Charging Station Equipped with PV and Hydropneumatic Energy Storage

Due to the promising potential for environmental sustainability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for EV charging stations is developed and aims to balance current capital investment costs and future operational revenue. The charging station considered in this work is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which could be electric battery or the recently invented hydro-pneumatic energy storage (GLIDES, Ground-Level Integrated Diverse Energy Storage) system. A co-optimization model that minimizes investment and operation cost is established to determine the global optimal solution while combining the capacity and operational decision making. The operational decision making considers EV mobility which is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of the EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in the design space and then fixed in solving the co-optimization model. The model can be used to provide insights for charging station placement in different practical situations. The sampled parameters include: the total number of EV charging slots, the PV area, the maximum capacity of the energy storage system, and daily mean EV arrival number in the Erlang-loss system. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models (RBF, Kriging etc). The effectiveness of proposed surrogate modeling approach is demonstrated in the numerical experiments.

Chen, Yang↗
DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Game-Theoretic Approach for Electricity Pricing Between Distribution System Operator and Load Aggregators

Transactive energy has emerged and gained more and more attention in recent years along with smart grid two-way communication technologies. Electricity pricing strategies play a critical role in influencing and shaping the customers’ energy load profile. In this paper, we investigate the electricity pricing strategy between a distribution system operator (DSO) and load aggregators (LAs) by adopting a bi-level Stackelberg game approach. With the purpose of maximizing its own operating revenue as well as better serving its customers, the upper level DSO will determine the price considering how lower level LAs will respond to it. Peak-to-average ratio of the total demand is modeled as a constraint and peak load is penalized at the upper optimization level. Classical backward induction is used to obtain a Nash equilibrium for the formulated bi-level Stackelberg game. Numerical results demonstrate the effectiveness of the proposed game approach in leveraging flexible demand potential to benefit both DSO and LAs.

Chen, Yang↗