16,922 research outputs found
A looming revolution: Implications of self-generation for the risk exposure of retailers. ESRI WP597, September 2018
Managing the risk associated with uncertain load has always been a challenge for retailers in electricity markets. Yet
the load variability has been largely predictable in the past, especially when aggregating a large number of consumers. In
contrast, the increasing penetration of unpredictable, small-scale electricity generation by consumers, i.e. self-generation,
constitutes a new and yet greater volume risk. Using value-at-risk metrics and Monte Carlo simulations based on German
historical loads and prices, the contribution of decentralized solar PV self-generation to retailersâ load and revenue risks is
assessed. This analysis has implications for the consumersâ welfare and the overall efficiency of electricity markets
Forecasting Italian Electricity Zonal Prices with Exogenous Variables
In the last few years we have observed deregulation in electricity markets and an increasing interest of price dynamics has been developed especially to consider all stylized facts shown by spot prices. Only few papers have considered the Italian Electricity Spot market since it has been deregulated recently. Therefore, this contribution is an investigation with emphasis on price dynamics accounting for technologies, market concentration and congestions. We aim to understand how technologies, concentration and congestions affect the zonal prices since these ones combine to bring about the single national price (prezzo unico dâacquisto, PUN). Hence, understanding its features is important for drawing policy indications referred to production planning and selection of generation sources, pricing and riskâhedging problems, monitoring of market power positions and finally to motivate investment strategies in new power plants and grid interconnections. Implementing RegâARFIMAâGARCH models, we assess the forecasting performance of selected models showing that they perform better when these factors are considered.Electricity prices, Production technologies, Market power (HHI, RSI), Congestions, Fractional Integration, Forecasting
Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model
Long-term load forecasting plays a vital role for utilities and planners in
terms of grid development and expansion planning. An overestimate of long-term
electricity load will result in substantial wasted investment in the
construction of excess power facilities, while an underestimate of future load
will result in insufficient generation and unmet demand. This paper presents
first-of-its-kind approach to use multiplicative error model (MEM) in
forecasting load for long-term horizon. MEM originates from the structure of
autoregressive conditional heteroscedasticity (ARCH) model where conditional
variance is dynamically parameterized and it multiplicatively interacts with an
innovation term of time-series. Historical load data, accessed from a U.S.
regional transmission operator, and recession data for years 1993-2016 is used
in this study. The superiority of considering volatility is proven by
out-of-sample forecast results as well as directional accuracy during the great
economic recession of 2008. To incorporate future volatility, backtesting of
MEM model is performed. Two performance indicators used to assess the proposed
model are mean absolute percentage error (for both in-sample model fit and
out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table
Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models
Short-term electricity price forecasting has become important for demand side
management and power generation scheduling. Especially as the electricity
market becomes more competitive, a more accurate price prediction than the
day-ahead locational marginal price (DALMP) published by the independent system
operator (ISO) will benefit participants in the market by increasing profit or
improving load demand scheduling. Hence, the main idea of this paper is to use
autoregressive integrated moving average (ARIMA) models to obtain a better LMP
prediction than the DALMP by utilizing the published DALMP, historical
real-time LMP (RTLMP) and other useful information. First, a set of seasonal
ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed
and compared with autoregressive moving average (ARMA) models that use the
differences between DALMP and RTLMP on their forecasting capability. A
generalized autoregressive conditional heteroskedasticity (GARCH) model is
implemented to further improve the forecasting by accounting for the price
volatility. The models are trained and evaluated using real market data in the
Midcontinent Independent System Operator (MISO) region. The evaluation results
indicate that the ARMAX-GARCH model, where an exogenous time series indicates
weekend days, improves the short-term electricity price prediction accuracy and
outperforms the other proposed ARIMA modelsComment: IEEE PES 2017 General Meeting, Chicago, I
Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation
This paper aims to propose a two-step approach for day-ahead hourly
scheduling in a distribution system operation, which contains two operation
costs, the operation cost at substation level and feeder level. In the first
step, the objective is to minimize the electric power purchase from the
day-ahead market with the stochastic optimization. The historical data of
day-ahead hourly electric power consumption is used to provide the forecast
results with the forecasting error, which is presented by a chance constraint
and formulated into a deterministic form by Gaussian mixture model (GMM). In
the second step, the objective is to minimize the system loss. Considering the
nonconvexity of the three-phase balanced AC optimal power flow problem in
distribution systems, the second-order cone program (SOCP) is used to relax the
problem. Then, a distributed optimization approach is built based on the
alternating direction method of multiplier (ADMM). The results shows that the
validity and effectiveness method.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and
Computers 201
Collinsville solar thermal project: yield forecasting (draft report)
The final report has been published and is available here.
Executive Summary
1 Introduction
This reportâs primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia. However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace. The subsequent report called âEnergy economics and dispatch forecastingâ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.
2 Literature review
The literature review discusses the four drivers for yield for LFR technology:
DNI (Direct Normal Irradiance)
Temperature
Humidity
Pressure
Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville. However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates. This difference makes them unsuitable to act as proxies for yield calculations. Therefore, the review investigates modelling the four drivers for Collinsville.
We introduce the term âeffectiveâ DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology.
We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY). We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY.
2.1 Research questions
Research question arising from the literature review include:
The overarching research question:
Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites?
This overarching question has a number of smaller supporting research questions:
Is BoMâs DNI satellite dataset adequately adjusted for cloud cover at Collinsville?
Given the dust and dew effects, is using raw satellite data sufficient to model yield?
Does elevation between Collinsville and nearby sites affect yield?
How does the ENSO affect yield?
Given the 2007-2012 constraint, will the TMY process provide a âTypicalâ year over the ENSO cycle?
How does climate change affect yield?
A further research question arises in the methodology but is included here for completeness.
What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler?
3 Methodology
In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.
4 Results and analysis
In the results section we present the four driver models selected and the process that was undertaken to arrive at the models.
5 Discussion
We analyse the extent to which the research questions are informed by the results.
6 Conclusion
In this report, we have identified the key research questions and established a methodology to address these questions. The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville
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