England Market Research Articles

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

Modeling Electricity Price Dynamics Using Flexible Distributions

We consider the wholesale electricity market prices in England and Wales during its complete history, where price-cap regulation and divestment series were introduced at different points in time. We compare the impact of these regulatory reforms on the dynamics of electricity prices. For this purpose, we apply flexible distributions that account for asymmetry, heavy tails, and excess kurtosis usually observed in data or model residuals. The application of skew generalized error distribution is appropriate for our case study. We find that after the second series of divestments, price level and volatility are lower than during price-cap regulation and after the first series of divestments. This finding implies that a sufficient horizontal restructuring through divestment series may be superior to price-cap regulation. The conclusion could be interesting to other countries because the England and Wales electricity market served as the benchmark model for liberalizing energy markets worldwide.

Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

The Impact of Regulatory Reforms on Demand Weighted Average Prices

Average prices are popularly used in the literature on price modeling. Calculating daily or weekly prices as an average over hourly or half-hourly trading periods assumes the same weight ignoring demand or traded volumes during those periods. Analyzing demand weighted average prices is important if producers may affect prices by decreasing them during low-demand periods and increasing them during high-demand periods within a day. The prediction of this price manipulation might have motivated the regulatory authority to introduce price caps not only on annual average prices but also on annual demand weighted average prices in the England and Wales wholesale electricity market. The dynamics of demand weighted average prices of electricity has been analyzed little in the literature. We show that skew generalized error distribution (SGED) is the appropriate assumption for model residuals. The estimated volatility model is used for evaluating the impact of regulatory reforms on demand weighted average prices during the complete history of the England and Wales wholesale electricity market.

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.

Issues on carcinogen contaminated antihypertensive drugs and constructing drug safety management system

European Medicines Agency withdrew valsartan from European market in July 2018 because it was contaminated with carcinogen, N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA). Medicines and Healthcare Products Regulatory Agency also found the same contamination and withdrew it from England market. US Food and Drug Administration followed the action after confirming its contamination. Ministry of Food and Drug Safety (MFDS) conducted testing all the valsartans at Korean market and withdrew some of them from market after confirming the contamination with NDMA. MFDS provided the pharmaceutical companies and laboratory institutions with the manual for testing both NDMA and NDEA and educated relevant personnels. MFDS also evaluated the health impact of the contaminated valsartan on the hypertensive patients who took the valsartan, which was shown to be very low risk of additional cancer incidence. MFDS pronounced strengthening of the safety management for the raw materials of the medicines. For guaranteeing the safety of medicines, more comprehensive drug safety management system from developing new drugs to consuming the medicines should be established. For achieving such a goal, active participation of all the stakeholders of the medicines including governmental agencies including MFDS and Ministry of Health and Welfare, the National Assembly, healthcare professionals, pharmaceutical companies, mass media, and general population including patients should be needed.

The Impact of Behavioral and Structural Remedies on Electricity Prices: The Case of the England and Wales Electricity Market

During the liberalization process the UK regulatory authority introduced a behavioral remedy (through price-cap regulation) and structural remedy (through divestment series) in order to mitigate an exercise of market power and lower the influence of incumbent producers on wholesale electricity prices. We study the impact of these remedies on the dynamics of the wholesale electricity price during the peak-demand period over trading days. An extended autoregressive and autoregressive conditional heteroscedasticity (AR–ARCH) model with a novel skew generalized error distribution is used. This distribution allows one to capture the features of asymmetry, excess kurtosis, and heavy tails. The model is extended to include individual incumbent producers’ market shares and other explanatory variables reflecting seasonal patterns and regulatory regimes. We find that the structural remedy was more successful than the behavioral remedy because the effect of market share of the previously larger incumbent producer on the wholesale price is statistically insignificant. Moreover, after the second series of divestments, price volatility reduced.

Effects of Three Conservation Tillage Strategies on Yields and Soil Health in a Mixed Vegetable Production System

Most vegetable farms in southern New England market directly to consumers and are characterized by high crop diversity and intensive cultivation. Growers rely on tillage to prepare fields for planting and control weeds, but are concerned about the negative effects of tillage on soil health. This study evaluated three tillage reduction strategies in a market garden system producing tomatoes, melons, cucumbers, cabbage, carrots, and lettuce. Treatments of strip tillage into a killed cereal rye (Secale cereale) cover crop mulch, perennial white clover (Trifolium repens), and ryegrass (Lolium perenne) living mulch between planting rows, and annual crimson clover (Trifolium incarnatum) living mulch interseeded between vegetable rows were established in 2010 and compared over 3 years to a control system using tillage to maintain bare ground between rows. Treatments were evaluated for effects on vegetable yield and soil biological, chemical, and physical properties. The strip tillage treatment was the most effective at promoting soil health, resulting in significant increases in soil aggregate stability, potentially mineralizable nitrogen, active soil carbon, and microbial activity relative to the control, and significant decrease in loss of soil organic matter. However, it was not effective for production of vegetables, with the strip-tillage plots having the lowest yields throughout the study. The perennial living mulch treatment produced yields of carrots, melons, and cucumbers similar to the control yields, but reduced yields of tomatoes, cabbage, and lettuce. Microbial respiration was significantly higher than in the control, and nitrate levels, and loss of soil organic matter were significantly lower. The annual living mulch treatment produced yields similar to the control for all crops, and soil health was similar to the control for all variables except soil nitrate, which was significantly higher than the control. Perennial living mulch shows the most promise for improving soil health while maintaining yields in some vegetable crops, but challenges remain in preventing competition between vegetables and living mulches.

Medium-term retailer's planning and participation strategy considering electricity market uncertainties

This paper presents a risk-constrained programming approach to solve a retailer's medium-term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope-bound information-gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi-level stochastic programming problem based on the information-gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi-level problem into a single-level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Do producers apply a capacity cutting strategy to increase prices? The case of the England and Wales electricity market

Promoting competition among electricity producers is primarily targeted at ensuring fair electricity prices for consumers. Producers could, however, withhold part of production facilities (i.e., apply a capacity cutting strategy) and thereby push more expensive production facilities to satisfy demand for electricity. This behavior could lead to a higher price determined through a uniform price auction. Using the case of the England and Wales wholesale electricity market we empirically analyze whether producers indeed did apply a capacity cutting strategy. For this purpose we examine the bidding behavior of producers during high- and low-demand trading periods within a trading day. We find statistical evidence for the presence of capacity cutting by several producers, which is consistent with the regulatory authority's reports.

Development of a methodology for clustering electricity-price series to improve customer response initiatives

The aim of this study is to propose a methodology in order to obtain a better support management decisions in terms of planning of bids and energy offers in real-time energy markets. Specifically, the authors use self-organising maps and statistical Ward's linkage to classify electricity market prices into different clusters (high homogeneity inside each cluster). In the second stage, the authors use non-parametric estimation to extract some price patterns in the above mentioned clusters. The knowledge contained within these patterns supplies customers with market-based information on which to focus its energy use decisions. The methodology proposed has been applied to New England (USA) market.

DCOPF-Based Marginal Loss Pricing With Enhanced Power Flow Accuracy by Using Matrix Loss Distribution

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> On current days, many of the power markets are adopting the practice of marginal loss pricing. The dc optimal power flow (DCOPF) model is still mostly used for the purpose of market clearing owing to its simplicity, robustness and higher speed of convergence. Within the DCOPF framework, transmission line losses are represented as additional external loads on the system. In the classical DCOPF model of marginal loss pricing, as implemented in the New England market, the aggregated network loss is distributed over the system nodes in a fixed ratio. To gain more design flexibility in the distribution of system losses, a matrix loss distribution (MLD) framework is developed in this paper maintaining compatibility with financial transmission rights. In MLD, each individual line is considered separately for the purpose of loss distribution. The ratio in which the power loss in a certain line is distributed can be different from that in which the power loss in another line is distributed. The additional design flexibility thus obtained assists in making a more accurate representation of the power flow. The benefit of MLD is illustrated through a case study. An energy reference independent application of MLD is also suggested. </para>

Price forecast in the competitive electricity market by support vector machine

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C i and ε i assigned to each training data, the flexible C i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin R m , HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).

Resort Real Estate: Does Supply Prevent Appreciation?

This paper examines the behavior of ski resort property in a major New England market over the last 25 years. A constructed property price series reveals that nominal prices are quite volatile and only slightly higher today than in 1980. These fluctuations and trends are investigated with a time series VAR model. The findings indicate that (1) natural snowfall is crucial to business; (2) regional annual business is central to individual resort demand and hence price appreciation; and (3) resort supply responds so elastically to any movement in prices, that it effectively curtails any long-term property appreciation. Impulse responses reveal that positive demand shocks fail to generate any long-term (real) price appreciation because of excessive new development. This behavior could be typical of many other ski resorts.

Improving Market Clearing Price Prediction by Using a Committee Machine of Neural Networks

Predicting market clearing prices is an important but difficult task, and neural networks have been widely used. A single neural network, however, may misrepresent part of the input-output data mapping that could have been correctly represented by different networks. The use of a "committee machine" composed of multiple networks can in principle alleviate such a difficulty. A major challenge for using a committee machine is to properly combine predictions from multiple networks, since the performance of individual networks is input dependent due to mapping misrepresentation. This paper presents a new method in which weighting coefficients for combining network predictions are the probabilities that individual networks capture the true input-output relationship at that prediction instant. Testing of the New England market cleaning prices demonstrates that the new method performs better than individual networks, and better than committee machines using current ensemble-averaging methods.

Optimal Integrated Generation Bidding and Scheduling With Risk Management Under a Deregulated Power Market

In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through bidding at a daily market. Developing effective strategies to optimize hourly bid curves for a hydrothermal power system to maximize profits becomes one of the most important tasks of a GenCo. This paper presents a unified framework for optimizing energy and reserve bidding strategies under a deregulated market. In view of high volatilities of market clearing prices (MCP), the hourly MCPs and reserve prices are modeled as discrete random variables, whose probability mass functions are predicted with a classification based neural network approach. The mean-variance method is applied to manage bidding risks, where a risk penalty term related to MCP and reserve price variances is added to the objective function. To avoid buying too much power from the market at high prices, a GenCo may also require covering at least a certain percentage of its own customer load. This self-scheduling requirement is modeled similar to the system demand in traditional unit commitment problems. The formulation is a stochastic mixed-integer optimization with a separable structure. An optimization based algorithm combining Lagrangian relaxation and stochastic dynamic programming is presented to optimize bids for both energy and reserve markets. Numerical testing based on an 11-unit system in New England market shows that the method can significantly reduce profit variances and thus reduce bidding risks. Near-optimal energy and reserve bid curves are obtained in 4-5 minutes on a 600 Hz Pentium III PC, efficient for daily use.

Selecting input factors for clusters of gaussian radial basis function networks to improve market clearing price prediction

In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input-output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should be deleted because they mislead local learning and result in poor predictions. Existing pruning methods for neural networks examine the significance of connections between neurons, and are not applicable to deleting center and standard deviation parameters in a GRBF network since those parameters bear no sense of significance of connection. In this paper, the inverses of standard deviations are found to capture a sense of connection, and based on this finding, a new training method to identify and eliminate unimportant input factors is developed. Numerical testing results from two classroom problems and from New England Market Clearing Price prediction show that the new training method leads to significantly improved prediction performance with a smaller number of network parameters.

An Analysis of Factors That Influence the Purchasing Decision for Fresh Potoatoes: A Study of Consumers in a New England Market

Consumer interviews were held in Springfield, Massachusetts to gather information about how certain product and pack attributes influence the selection of fresh potatoes in the supermarket. Respondents rated eight different factors in terms of their importance in the purchasing decision. Ordinal probit models were estimated to test for differences in ratings between distinct respondent groups. The results showed that potato appearance was the most influential factor. Potato size and type were other key considerations while price was only moderately important. Differences in ratings were related to variations in demographic characteristics of respondents, including gender, age and income.

A Study of the Potato Purchasing Decision Among Consumers in a New England Market

Mallintercept interviews were conducted in Springfield, Massachusetts to gather data on consumers' potato purchasing habits, their responses to various attributes of potatoes and potato packs, and the influence these factors play in their purchasing decision. The results showed that these respondents bought round white potatoes most often, but they also purchased russet potatoes at least occasionally. When selecting potatoes, product origin was an important consideration. Variations in product prices were not significant, based on the tested price levels. However, the offer of a money back guarantee appeared to play a role in the purchasing decision at higher price levels.

A Conjoint Approach to Model Product Preferences: The New England Market for Fresh and Frozen Salmon

This paper illustrates how conjoint analysis can be used to model preference for food products, and applies the technique to the study of fresh and frozen salmon preference among buyers from two intermediary wholesale levels in New England. The degree of preference for specific attributes and levels of the products is compared. The paper also evaluates the performance and predictive validity of a traditional additive conjoint model, a hybrid model estimated using both ordinary least squares, and a maximum likelihood hybrid two-limit Tobit model.