FERTILIZANTES Y ACONDICIONADORES DEL SUELO

The majority of the work associated with the prediction of the power market status has

participants and operators. In contrast, no work has yet been reported regarding the prediction

of important power system statuses such as congestions and shift of marginal units in power

markets. The prediction of this type of information is also crucial in delivering more detailed

insights about the potential power system status in the projected time horizon.

LMP forecasting has been a hot research area as the forecasted price plays a key role in the

decision making process for both market participants and market operators. [45] lists a few of

the typical applications. Power suppliers and consumers use the forecasted price to optimize

the profit in the day-ahead market and bilateral contracts. Facility owners rely on the

forecasted price to make investment decisions. The ISO could use the forecasted price to

evaluate power market indices such as the Lerner Index. [49] presents an application from a

power producer‟s perspective to formulate an optimal bidding strategy utilizing the forecasted price.

Compared to the load, the electricity price in the power market is much more volatile.

Factors contributing to price volatility include change of fuel price, load uncertainty,

fluctuations in hydro and renewable power production, generation outages, transmission

congestion, market participant behavior and so on [6]. A study shows the price forecasting

error was 10% or more compared to a 3% error for load forecasting [6].

Price forecasting methods can be roughly classified into three groups. The first is

statistical method, for instance, the time-series model, econometric model, and regression

model, which fit a predefined mathematical formula to historical data. The underlying

assumption is that observations closer in time tend to be more closely related than

observations further apart. [48] employs the dynamic regression approach to establish a price

prediction model which relates the future price with the past price, and past demand as well.

Adjustments are made to the input data to minimize the effects of data outliers due to

report the 24 hour ahead price forecasting error is 5% for the Spanish market and 3% for the

California market, for the studied weeks.

The second method employs artificial intelligence (AI) techniques including the Neural

Network (NN) and fuzzy systems to predict price, which normally involves a training stage

based on historical data. [46] proposed an NN-based forecasting approach and uses a similar

day method to select the proper input data, through which each hour of the forecast day has a

separate set of similar days. The method was tested for the PJM market and reported to

produce accurate results. An adaptive wavelet NN-based price forecasting method is

presented in [47], which is capable of mapping the input-output space by adapting the shape

of the wavelet basis function, of the hidden layer neurons, to training data. The method was

tested on the Spanish market and concluded to be superior to other forecasting techniques,

such as the Auto wavelet-Regression Integrated Moving Average (ARIMA), multi-layer

perceptron (MLP), Radial Basis Function Neural Network (RBFNN), and Fuzzy Neural

Network (FNN). Reference [45] proposed a forecasting method which combines the fuzzy

inference system (FIS) and least squares estimation (LSE). Input data included temporal

indices, historical price, area loads at current and previous hours, and transmission constraints

of the current hour. This method claimed to have the advantage of high accuracy and explicit

reasoning.

The third model of price forecasting is the simulation method, which is presented in [44].

The simulation method utilizes a transmission constrained market simulation program that

mimics the actual dispatch and market clearing process while explicitly taking into account

the system operating constraints. However, this method requires intensive data input/output

such as transmission model, SCED, generation unit data, transaction data, and involves

Statistical and AI methods rely heavily on the data of past events, and prediction results

are less certain as the forecast lead time is longer [48]. The selection of input data is also

crucial for predicting performance since the input data should demonstrate the pattern that is

expected for the forecasted time. Manual picks, or techniques such as the similar day method,

may be needed toward this requirement. The reason for these limitations lies in the fact these

methods are basically black-box methods, which tend to discover the correlation between

future price and the most significant factors such as the past price, load, and congestion index

[6], while the internal model, which indeed relates these factors, is ignored. In fact, the

correlation could be discovered by studying the OPF model, which explicitly models the

interaction among all factors, including electricity price. Unlike the load, which is hard to

establish in a model to study its behavior, electricity price (namely, LMP) is the shadow price

of the OPF problem and has a definite formulation to study of price behavior. In addition,

although the future price could be estimated by drawing patterns from historical data, the

electricity price at any future time has no memory effect and is essentially independent of the

past price and system conditions. In other words, the price is only determined by the

operating conditions at that particular future time.

Therefore, instead of pursuing a black-box prediction method, this work provides a white-

box method for the prediction of electricity price, as well as important system status such as

congestions and change of marginal units. It should be pointed out that, although the white-

box method employs OPF as a prediction tool, similar to the aforementioned simulation

method in [44], it is different in the sense that it explores the solution feature of the OPF with

respect to parameter variation, and therefore, saves a significant amount of computational

time; while the simulation method performs intensive calculations on each presumed

condition, even if the condition has similar characteristics to a solved condition, and the new