PJM Electricity Market Research Articles

On Electricity Spot Price Properties by t-innovation GARCH Model

The modeling of heteroskedasticities and kurtosises of electricity prices is crucial to forecast the future distribution of electricity prices, to understand the behavior of derivatives pricing and to quantify the risk in electricity markets. A GARCH model with t-innovations, which is solved by maximum likelihood estimation, is proposed. The model can explicitly address the relationship with system loads, seasonalities, heteroskedasticities, and kurtosises of electricity prices. The empirical analysis based on the historical data of the PJM electricity market shows that the system load squares have a significant effect on the average daily electricity prices, there exist volatility clustering and weekly, semi-monthly, monthly, bimonthly, quarterly and semi-annual periods, and the variances and kurtosises of electricity prices manifest clearly time-varying characteristics. The model holds parsimonious scale of estimated parameters, less computational costs, easy to select the orders and high practical application value. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3393

Potential Impact of Solar Energy Penetration on PJM Electricity Market

Renewable energy resources, such as wind and solar, have become important parts of today's generating resource mix. Along with them, these resources also bring their inherent characteristics of variability and uncertainty. Hence, resource planners must be adaptable to accommodate these resources in conducting resource planning. Understanding their impact on the electric grid must be prior and critical. In this paper, we have studied the potential impact of solar energy penetration in a particular location of PJM electricity market. Key economic and system variables that were analyzed include system production cost, market prices (average zonal prices), congestion (both hours and dollars), and transmission losses. We find that significant penetration of solar energy resources can produce system-wide economic benefits in the respective transmission areas as well as the entire system.

Incentives for Transmission Investment in the PJM Electricity Market: FTRs or Regulation (or Both?)

This paper presents an application of a mechanism that provides incentives to promote transmission network expansion in the area of the US electric system known as PJM. The applied mechanism combines the merchant and regulatory approaches to attract investment into transmission grids. It is based on rebalancing a two-part tariff in the framework of a wholesale electricity market with locational pricing. The expansion of the network is carried out through the sale of financial transmission rights for the congested lines. The mechanism is tested for 14-node and 17-node geographical coverage areas of PJM. Under Laspeyres weights, it is shown that prices converge to the marginal cost of generation, the congestion rent decreases, and the total social welfare increases. The mechanism is shown to adjust prices effectively given either non-peak or peak demand.

Incentives for transmission investment in the PJM electricity market: FTRs or regulation (or both?)

This paper presents an application of a mechanism that provides incentives to promote transmission network expansion in the area of the US electric system known as PJM. The applied mechanism combines the merchant and regulatory approaches to attract investment into transmission grids. It is based on rebalancing a two-part tariff in the framework of a wholesale electricity market with locational pricing. The expansion of the network is carried out through the sale of financial transmission rights for the congested lines. The mechanism is tested for 14-node and 17-node geographical coverage areas of PJM. Under Laspeyres weights, it is shown that prices converge to the marginal cost of generation, the congestion rent decreases, and the total social welfare increases. The mechanism is shown to adjust prices effectively given either non-peak or peak demand.

Multi-objective mean–variance–skewness model for generation portfolio allocation in electricity markets

This paper proposes an approach for generation portfolio allocation based on mean–variance–skewness (MVS) model which is an extension of the classical mean–variance (MV) portfolio theory, to deal with assets whose return distribution is non-normal. The MVS model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. Since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto-optimal solution in a single simulation run. Using a case study of the PJM electricity market, the performance of the MVS portfolio theory based method and the classical MV method is compared. It has been found that the MVS portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading.

A New Method for Next-Day Price Forecasting for PJM Electricity Market

In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.

Effect of Financial Hedging in Portfolio Selection in Electricity Markets

With the development of electricity markets, trading scheduling with risk management becomes more and more important for Generation Companies (Gencos) to protect themselves from the risk of extreme price volatility in the spot market. This paper investigates the effect of two financial instruments (ie, futures contracts and financial transmission rights) in trading scheduling based on the principles of the Modern Portfolio Theory and hedging. Historical data of the PJM electricity market are used to demonstrate the proposed approach to energy allocation. Simulation results indicate that futures contracts and financial transmission rights can reduce the risk of spot and bilateral transaction, respectively, and therefore affect physical trading in these two markets to some extent.

An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.

An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets

In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. An adaptive wavelet neural network (AWNN) is proposed in this paper for short-term price forecasting (STPF) in the electricity markets. A commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network (FFNN). To demonstrate the effectiveness of the proposed approach, day-ahead prediction of market clearing price (MCP) of Spain market, which is a duopoly market with a dominant player, and locational marginal price (LMP) forecasting in PJM electricity market, are considered. The forecasted results clearly show that AWNN has good prediction properties compared to other forecasting techniques, such as wavelet-ARIMA, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as recently proposed fuzzy neural network (FNN).

Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method

In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods.

Portfolio optimization in electricity markets

In a competitive electricity market, Generation companies (Gencos) face price risk and delivery risk that affect their profitability. Risk management is an important and essential part in the Genco's decision making. In this paper, risk management through diversification is considered. The problem of energy allocation between spot markets and bilateral contracts is formulated as a general portfolio optimization problem with a risk-free asset and n risky assets. Historical data of the PJM electricity market are used to demonstrate the approach.

The Short-Run Effects of Time-Varying Prices in Competitive Electricity Markets

We analyze the efficiency, distributional, and environmental effects of real-time pricing (RTP) adoption in the short run. Consistent with theory, our simulations of the PJM electricity market show that RTP adoption improves efficiency and compresses the distributions of loads and prices. Adoption increases average load but decreases operating profits with the largest decrease for oil-fired generation (59% when all customers adopt). Consumer surplus and welfare gains are modest (2.5% and 0.24% of the energy bill), and emissions of SO2 and NOx increase but CO2 emissions decrease. Approximately 30% of these efficiency gains could be captured by varying flat rates monthly instead of annually. Monthly flat rate adjustment has many of the same effects as RTP adoption, captures more of the deadweight loss than time of use (TOU) rates, and requires no new metering technology.

Electricity Forward Prices: A High‐Frequency Empirical Analysis

ABSTRACTWe conduct an empirical analysis of forward prices in the PJM electricity market using a high‐frequency data set of hourly spot and day‐ahead forward prices. We find that there are significant risk premia in electricity forward prices. These premia vary systematically throughout the day and are directly related to economic risk factors, such as the volatility of unexpected changes in demand, spot prices, and total revenues. These results support the hypothesis that electricity forward prices in the Pennsylvania, New Jersey, and Maryland market are determined rationally by risk‐averse economic agents.