Pennsylvania-New Jersey-Maryland Research Articles

Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application

Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the efficient management of electric systems. Precise forecasting offers advantages for future projects and economic activities of power system operators. In this study, a novel integrated model for short-term load forecasting has been developed, which combines the wavelet transform decomposition (WTD) model, a radial basis function network, and the Thermal Exchange Optimization (TEO) algorithm. The performance of this model was evaluated in two diverse deregulated power markets: the Pennsylvania-New Jersey-Maryland electricity market and the Spanish electricity market. The obtained results are compared with various acceptable standard forecasting models.

Does it matter when the wind blows? Differential impacts of wind generation on the PJM wholesale electricity market

As wind power continues to gain traction as a sustainable energy source, important questions arise as to how and when increasing penetration of wind power undermines its own revenues as well as revenues from other generating technologies in the wholesale electricity market. To answer this question, we analyzed data from the Pennsylvania-New Jersey-Maryland (PJM) market (with relatively low, approximately 3% wind penetration) from 2016 to 2019 using quantile regression methods that provide a comprehensive picture of market conditions. Our findings demonstrate that the merit order effect exists for wind power supply expansion with unit revenue declines across all quantiles and generators. These declines occur consistently throughout market conditions but at different magnitudes. Each additional GWh of wind power is associated with a fall in wind revenue ranging from $0.01/MWh to $0.08/MWh. Wind power's cross-cannibalization effect also reduces gas and baseload capacity revenues across all quantiles, ranging from $0.02/MWh to $0.06/MWh per additional GWh of wind power. Interestingly, our results also reveal positive impacts of wind power supply on value factors (a measure of the energy's worth to the market) at low price market conditions, but negative impacts at high price conditions of the 95% and 99% quantiles. These findings confirm differential impacts of wind power across market conditions – more wind increases its worth at low market values but decreases its worth at high market values. Although the observed effects are exceedingly small, the findings of negative and unequal impacts on unit revenue in this research provide some insights and possible policy recommendations for energy market analysts, wind developers, and energy policymakers.

Optimal bidding strategy of a gas-fired power plant in interdependent low-carbon electricity and natural gas markets

This work presents a bi-level optimization framework to determine the optimal bidding strategies for a strategic gas-fired power plant, exerting market power in interdependent pool-based electricity and natural gas markets, under a carbon emission trading scheme (CETS). The upper-level problem aims at maximizing the profits of the strategic player, while at the lower-level problem, the day-ahead electricity and natural gas markets are cleared sequentially, considering the provision of carbon emission allowances for conventional power producers and high penetration of wind power generation. The bi-level formulation is initially recast into a mathematical program with equilibrium constraints (MPEC), using the Karush-Kuhn-Tucker optimality conditions and duality theory, and is further reformulated into a mixed integer linear program. The proposed algorithm is applied to a Pennsylvania-New Jersey-Maryland (PJM) 5-bus power grid, incurred by transmission constraints and a single node natural gas network. Numerical simulations provide CETS-embedded electricity clearing prices and optimal bidding decisions for the strategic gas-fired power plant, under plausible power transmission congestions and natural gas prices increment scenarios.

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

New Real-Time Demand Response Market Co-Optimized With Conventional Energy Market

In addition to procuring energy, consumers in electricity markets procure demand response (DR) services. Demand and supply of energy in the electricity market drives the demand for DR services. Through the net benefits test (NBT), economic procurement of DR is limited to an amount that ensures that consumers benefit with the procurement of DR services. However, the NBT neither a) recognizes the coexistence of the DR market with the energy market; nor b) optimizes social welfare in the DR market in concert with that of the energy market. This lack of accounting for DR market surplus results in economic inefficiency. To address this shortcoming, we advance past works by: a) proposing a real-time DR market where the DR demand curve is a function of opportunity in the energy market; and b) co-optimizing energy and DR markets such that the total social welfare derived from both markets is maximized simultaneously. We also present an optimal power flow formulation and process to implement our ideas in real-time electricity markets. The formulation is tested on a simple test case and a system based on actual Pennsylvania-New Jersey-Maryland (PJM) data. For the PJM case, total social welfare is increased by 1.41% to 3.05% over existing DR procurement strategies, resulting in $14.5M to $30.9M additional benefits per hour.

Assessing the CO2-emission risk due to wind-energy uncertainty

Wind power is a significant source of renewable energy, supporting reductions in emissions. However, the intermittency of wind power will cause uncertain carbon emissions and risks greenhouse-gas emission reduction. To address this issue, we analyzed the mechanism that converts wind-energy intermittency to carbon-emission uncertainty and developed metrics to assess the consequential risks of excessive carbon emissions. According to our empirical analyses on two American electricity markets, the Electric Reliability Council of Texas (ERCOT) and Pennsylvania-New Jersey-Maryland Interconnection (PJM), we found their carbon-emission risk is significant over the coming decade. The ten-year aggregated CO2 emissions between 2020 and 2030 have a 10% chance of exceeding their expected levels by 100 megatons. Our further analysis also shows that the CO2-emission risk is induced by the uncertainty of annual generation uncertainties and volatilities in the hourly wind power supply. Particularly, the two driving forces contribute heterogeneously to the carbon-emission risk in the two studied markets. Although the annual-generation randomness causes similar degrees of carbon-emission uncertainty in the two markets, the volatility-driven risk is only significant in the PJM market. According to the risk analyses, we suggested two types of investigations that are foundations for risk management: assessing the necessary installed capacity of wind energy that limits CO2-emission risk at desired confidence levels, and exploring hours when carbon emission is sensitive to wind-power volatility. The former helps set renewable-energy targets, while the latter is critical for designing policies hiring energy storage and zero-carbon generation technologies.

Potential greenhouse gas risk led by renewable energy crowding out nuclear power

SummaryIncreasing variable renewable energy (VRE) is one of the main approaches for greenhouse gas (GHG) mitigation. However, we find a GHG increase risk associated with increasing VRE: VRE crowds out nuclear power (VRECON) but cannot fully obtain the left market share, which is obtained by fossil energy. We developed an integrated dispatch-and-investment model to estimate the VRECON GHG-boosting effect in the Pennsylvania-New Jersey-Maryland Interconnection and the Electric Reliability Council of Texas. In the above two markets, VRECON could increase the annual GHG emission by up to 136 MTCO2eq totally. Furthermore, we find that the VRECON GHG-boosting effect can be mitigated by combining wind and solar power. We argue that, for GHG abatement, policymakers should require the proper mix of wind and solar power in renewable portfolio standards and control nuclear power’s retirement pace to match the progress of VRE growth.

Short-Term Load Forecasting and Temperature Load Extraction Based on CEEMDAN and TDIC

With the intensification of urbanization in various countries worldwide, the temperature load which is greatly affected by ambient temperature, such as summer cooling loads and winter heating loads, accounts for a rising proportion of the total urban load. It causes an increasing peak-to-valley load difference. However, due to the complex composition and strong randomness of the load, it is necessary to study the multi-scale and multi-period correlation between temperature. Based on this, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the temperature and load into multi-scale components. The time-dependent intrinsic correlation (TDIC) is proposed to analyze the local correlation between temperature and load in multiple periods under a multi-scale framework, and obtain the dynamic change characteristics of the correlation between temperature and load. Based on the TDIC analysis results, a suitable sample period for short-term load forecasting (STLF) and input temperature data can be selected. Finally, extreme learning machine optimized by particle swarm optimization (PSO-ELM) is used to forecast each component of the load. The proposed STLF method is validated on real-time data from the Pennsylvania-New Jersey-Maryland (PJM) Company in the United States. The proposed method has greatly reduced in both mean absolute percentage error (MAPE) and root mean square error (RMSE) compared with other traditional methods, and the temperature load that fluctuates with temperature in the day to be forecasted is extracted.

Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests

High penetration of intermittent renewables may cause safety and stability problems to the electricity grid. Building thermal loads could contribute to the grid stability in the era of renewable energy and smart grid, because they are high and flexible. This study proposes a model predictive control strategy to control a large constant speed centrifugal chiller to follow grid frequency regulation signals while providing sufficient cooling to the building. Onsite tests were conducted to identify critical parameters in the process including time delay and ramping speed of chiller power consumption. A dynamic platform was built based on the studied system and fine-tuned using onsite tests data. Validation tests were conducted using the 40 min Regulation A and Regulation D test signals provided by PJM (Pennsylvania - New Jersey - Maryland Interconnection). According to the onsite tests, the total delay time and the maximum ramping speed of chiller power were 20–25 s and 2.53 kW/s, respectively. Validation based on the 40 min test signals shown that the composite performance scores were 0.901 and 0.885 for Regulation A test and Regulation D test, respectively. Continuous 12 h validation tests in a working day shown that the composite scores were 0.917 and 0.893 for Reg A and Reg D tests, respectively. And the studied constant speed centrifugal chiller could provide a regulation capacity of 5–7.5% of its nominal power.

Market power analysis of the Indian power market

Emerging electricity reforms in the power market aims at removing the monopolistic oligopoly power market and promoting competition in the market by providing opportunities to more producers. This paper seeks to investigate various existing structures of the Power Market. Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL) facilitates transparent trading of electricity, a larger market spectrum and allows the participation of other players in the market. Market power is an indicator of an non-competitive market, that is increase in the market power will result in the degradation of competition. This research will help modify the current parameters (HHI Index and Concentration ratio) which are used for measuring the market power of the power markets. As there is always deviation in unconstrained cleared volume and actual cleared or scheduled volume, deviation arises due to the volume of electricity that could not be cleared because of congestion in the power exchange. The researchers have also examined the Indian Power market and analyzed different developed power markets of the world like the US Pennsylvania-New Jersey-Maryland Interconnection (PJM) and the Nordpool.

A temporal convolutional network based hybrid model of short-term electricity price forecasting

Electricity prices have complex features such as high frequency, multiple seasonality, and nonlinearity. These factors will make the prediction of electricity price difficult. However, Accurate electricity price prediction is important for energy producers and consumers to derive bidding strategies. To improve the accuracy of prediction by using each algorithms' advantages, this paper proposes a hybrid model that uses Empirical Mode Decomposition (EMD), Autoregressive Integrated Moving Average (ARIMA), and Temporal Convolutional Network (TCN). EMD is used to decompose the electricity prices into low and high frequency components. low frequency components are forecasted by the ARIMA model and high frequency series are predicted by the TCN model. Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland (PJM) electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm - Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics.

Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm

Nowadays, a basic commodity for a human being to lead a standard lifestyle with human comfort irrespective of the nature of environmental conditions is electric power. The electricity load demand increases tremendously especially for a metropolitan city due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal device usage, etc. Hence, the accuracy of electricity load and its price forecasting is a deciding factor for the power distribution network to retain as an efficient, sustainable, and secure consumer-friendly network. On the other hand, based on the volatile, intermittent, and uncertain behavior of electricity load and price, an accurate, and robust forecast model should be designed. In this paper, a new hybrid forecast model for short-term electricity load and price prediction has been developed. The proposed method includes three modules: wavelet transform that is used to eliminate fluctuation behaviors of the electricity load and price time series, feature selection based on entropy and mutual information has been proposed to rank candidate inputs and eliminate redundant inputs according to their information value, and a new learning algorithm. The proposed learning method consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions. The performance of the proposed method has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Also, for further test, the load data in Iran have been used.

Exactly how inefficient is the PJM capacity Market?

A common feature of electricity markets is the need to assure an appropriate amount of generating capacity to satisfy demand at any given moment. Some regional markets hold a capacity auction which compensates generation facilities for providing power at some point in the future. These auctions are intended to efficiently price capacity via competitive bidding. However, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) regional transmission organization (RTO) has been in litigation before the Federal Energy Regulatory Commission (FERC) over high prices and a lack of competition in their annual capacity market auctions. A peculiar feature of the PJM RTO is its segmented capacity market, where pivotal suppliers have successfully petitioned to separate from the larger market and create captive Locational Deliverability Areas (LDAs) in which they have a dominant market share. We analyze the competitiveness of the market, the growing capacity margins, as well as the PJM LDA market concentration via Hirschfield-Herfindahl Index (HHI), 4-Firm Concentration Ratio (CR4) and Three-Pivotal-Supplier (TPS) test. We find that many LDAs do not operate competitively and have a pivotal supplier with market power, able to set prices unilaterally. In addition, we find that although reserve margins are inordinately high in the PJM market, they are rising along with prices, an outcome that is only seen in monopolistic markets.

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the US Electricity Sector.

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US 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 US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Summary As the share of variable renewable energy (VRE) grows in the electric grid, so does the risk of curtailment. While energy storage and hydrogen production have been proposed as solutions to the curtailment problem, they often pose technological and economic challenges. Here, we analyze the potential of data center load migration for mitigating curtailment and greenhouse gas (GHG) emissions. Using historical hourly electricity generation, curtailment, and typical data center server utilization data, we simulate the effect of migrating data center workloads from the fossil-fuel-heavy PJM to the renewable-heavy CAISO. The results show that load migration within the existing data center capacity during the curtailment hours in CAISO has the potential to reduce 113–239 KtCO2e per year of GHG emissions and absorb up to 62% of the total curtailment with negative abatement costs in 2019. Our study demonstrates the overlooked role that data centers can play for VRE integration and GHG emissions mitigation.

Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

Electricity price forecasting is a key aspect for market participants to maximize their economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non-stationarity, the trend of the price is usually complicated to predict. On the other hand, the accuracy of short-term electricity price and load forecasting is fundamental for an efficient management of electric systems. An accurate prediction can benefit future plans and economic operations of the power systems’ operators. In this paper, a new and accurate combined model has been proposed for short-term load forecasting and short-term price forecasting in deregulated power markets. It includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm. A mixed data model for the price and load forecast has been considered and integrated with the original signal series of price and load and their decomposition. Throughout this model, the candidate input variables are chosen by a distinct hybrid feature selection. Two reliable electricity markets (Pennsylvania-New Jersey-Maryland and Spanish electricity markets) have been used to test the proposed forecasting model and the obtained results have been compared with different valid benchmark prediction models. Lastly, the real load data of Favignana Island's power grid have been considered to test the proposed model. The obtained results pinpointed that the proposed model’s precision and stability is higher than in other benchmark forecasting models.

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimized by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods.

Stochastic Up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management

Up to congestion (UTC) is a type of financial product available in the nodal electricity markets of the United States, based on which a financial participant can earn profits by utilizing the different congestion and loss components of the electricity prices in the day-ahead (DA) and real-time (RT) markets. This paper proposes the UTC bidding strategy by using stochastic optimization technique, where the uncertain electricity prices on the UTC transaction paths are represented via scenario sets. In the established stochastic model, the total expected profit and the Conditional Value at Risk (CVaR) of the UTC bidding strategy are maximized simultaneously considering risk management, where the risk preference of financial participant is characterized by using a risk aversion parameter. By solving the proposed stochastic model, non-increasing DA UTC bidding curves can be generated for all the time periods of the next operating day, where the credit requirements for UTC transactions are taken into account in detail. Finally, to verify the effectiveness of the proposed strategy, case studies are carried out based on the historical data and trading policies of the Pennsylvania-New Jersey-Maryland (PJM) electricity market, and the UTC bidding strategies generated by different models are analyzed. The numeral results indicate that, compared to the deterministic UTC bidding strategy, the proposed stochastic strategy can bring much higher expected profit and lower potential risks for the financial participant. Moreover, by adjusting the risk aversion parameter in the proposed model, the risks can be managed efficiently according to the financial participant’s preference.

Congestion in the Electricity Transmission System Redistributes Pollution Across Long Distances

Electricity transmission redistributes environmental impacts across space. We exploit episodes of high electricity transmission system congestion to explore changes in ambient concentrations of air pollutants in the eastern United States. Reducing electricity system congestion decreases ozone and PM2.5 concentrations in New England and New York and increases them in the western portions of the Pennsylvania-New Jersey-Maryland electricity market and much of the Midwestern states. We quantify the health impacts of changes in environmental pollution induced by a reduction in congestion and find overall health losses in central states such as Illinois, Indiana, and Ohio and health gains in Atlantic.