52 research outputs found
A Projection-Based Approach for Distributed Energy Resources Aggregation
Aggregating distributed energy resources (DERs) is of great significance to
improve the overall operational efficiency of smart grid. The aggregation model
needs to consider various factors such as network constraints, operational
constraints, and economic characteristics of the DERs. This paper constructs a
multi-slot DER aggregation model that considers the above factors using
feasible region projection approach, which achieved the protection of DERs data
information and the elimination of internal variables. A system economic
dispatch (ED) model is established for the operators to make full use of the
DER clusters. We calculate the feasible regions with temporal coupling by
extending the Progressive Vertex Enumeration (PVE) algorithm to high dimension
by the Quickhull algorithm. Finally, an IEEE 39-bus distribution network is
simulated with DERs to verify the effectiveness of the proposed model. Results
show that the two-step ED derives the same results as the centralized ED
Transmission Congestion Management with Generalized Generation Shift Distribution Factors
A major concern in modern power systems is that the popularity and
fluctuating characteristics of renewable energy may cause more and more
transmission congestion events. Traditional congestion management modeling
involves AC or DC power flow equations, while the former equation always
accompanies great amount of computation, and the latter cannot consider voltage
amplitude and reactive power. Therefore, this paper proposes a congestion
management approach incorporating a specially-designed generalized generator
shift distribution factor (GSDF) to derive a computationally-efficient and
accurate management strategies. This congestion management strategy involves
multiple balancing generators for generation shift operation. The proposed
model is superior in a low computational complexity (linear equation) and
versatile modeling representation with full consideration of voltage amplitude
and reactive power.Comment: 5 pages, 4 figures. Accepted by conference: ICPES 202
A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe
in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late
March. As the U.S. begins to gradually resume economic activity, it is
imperative for policymakers and power system operators to take a scientific
approach to understanding and predicting the impact on the electricity sector.
Here, we release a first-of-its-kind cross-domain open-access data hub,
integrating data from across all existing U.S. wholesale electricity markets
with COVID-19 case, weather, cellular location, and satellite imaging data.
Leveraging cross-domain insights from public health and mobility data, we
uncover a significant reduction in electricity consumption across that is
strongly correlated with the rise in the number of COVID-19 cases, degree of
social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript
can also be accessed from EnerarXiv:
http://www.enerarxiv.org/page/thesis.html?id=198
Life cycle economic viability analysis of battery storage in electricity market
Battery storage is essential to enhance the flexibility and reliability of
electric power systems by providing auxiliary services and load shifting.
Storage owners typically gains incentives from quick responses to auxiliary
service prices, but frequent charging and discharging also reduce its lifetime.
Therefore, this paper embeds the battery degradation cost into the operation
simulation to avoid overestimated profits caused by an aggressive bidding
strategy. Based on an operation simulation model, this paper conducts the
economic viability analysis of whole life cycle using the internal rate of
return(IRR). A clustering method and a typical day method are developed to
reduce the huge computational burdens in the life-cycle simulation of battery
storage. Our models and algorithms are validated by the case study of two
mainstream technology routes currently: lithium nickel cobalt manganese oxide
(NCM) batteries and lithium iron phosphate (LFP) batteries. Then a sensitivity
analysis is presented to identify the critical factors that boost battery
storage in the future. We evaluate the IRR results of different types of
battery storage to provide guidance for investment portfolio.Comment: 17 pages, accepted by JP
Real-time scheduling of renewable power systems through planning-based reinforcement learning
The growing renewable energy sources have posed significant challenges to
traditional power scheduling. It is difficult for operators to obtain accurate
day-ahead forecasts of renewable generation, thereby requiring the future
scheduling system to make real-time scheduling decisions aligning with
ultra-short-term forecasts. Restricted by the computation speed, traditional
optimization-based methods can not solve this problem. Recent developments in
reinforcement learning (RL) have demonstrated the potential to solve this
challenge. However, the existing RL methods are inadequate in terms of
constraint complexity, algorithm performance, and environment fidelity. We are
the first to propose a systematic solution based on the state-of-the-art
reinforcement learning algorithm and the real power grid environment. The
proposed approach enables planning and finer time resolution adjustments of
power generators, including unit commitment and economic dispatch, thus
increasing the grid's ability to admit more renewable energy. The well-trained
scheduling agent significantly reduces renewable curtailment and load shedding,
which are issues arising from traditional scheduling's reliance on inaccurate
day-ahead forecasts. High-frequency control decisions exploit the existing
units' flexibility, reducing the power grid's dependence on hardware
transformations and saving investment and operating costs, as demonstrated in
experimental results. This research exhibits the potential of reinforcement
learning in promoting low-carbon and intelligent power systems and represents a
solid step toward sustainable electricity generation.Comment: 12 pages, 7 figure
Sharing Economy in Local Energy Markets
With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.</p
COVID-19 causes record decline in global CO2 emissions
The considerable cessation of human activities during the COVID-19 pandemic
has affected global energy use and CO2 emissions. Here we show the
unprecedented decrease in global fossil CO2 emissions from January to April
2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when
compared with the period last year. In addition other emerging estimates of
COVID impacts based on monthly energy supply or estimated parameters, this
study contributes to another step that constructed the near-real-time daily CO2
emission inventories based on activity from power generation (for 29
countries), industry (for 73 countries), road transportation (for 406 cities),
aviation and maritime transportation and commercial and residential sectors
emissions (for 206 countries). The estimates distinguished the decline of CO2
due to COVID-19 from the daily, weekly and seasonal variations as well as the
holiday events. The COVID-related decreases in CO2 emissions in road
transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to
2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%),
residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2,
-15%). Regionally, decreases in China were the largest and earliest (234.5 Mt
CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S.
(162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional
nitrogen oxides concentrations observed by satellites and ground-based
networks, but the calculated signal of emissions decreases (about 1Gt CO2) will
have little impacts (less than 0.13ppm by April 30, 2020) on the overserved
global CO2 concertation. However, with observed fast CO2 recovery in China and
partial re-opening globally, our findings suggest the longer-term effects on
CO2 emissions are unknown and should be carefully monitored using multiple
measures
Near-real-time monitoring of global COâ‚‚ emissions reveals the effects of the COVID-19 pandemic
The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO₂ emissions (−1551 Mt CO₂) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially
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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic
The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially
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