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In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design--the Wholesale Power Market Platform ( WPMP )--for common adoption by all US wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. [PUBLICATION ABSTRACT]

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

Intermittent forms of renewable energy destabilize electricity grids unless adequate reliable generating capacity and storage are available, while instability of hybrid electricity grids and cost fluctuations in fossil fuel prices pose further challenges for policymakers. We examine the interaction between renewable and traditional fossil-fuel energy sources in the context of the Alberta electricity grid, where policymakers seek to eliminate coal and reduce reliance on natural gas. We develop a policy model of the Alberta grid and, unlike earlier models, calibrate the cost functions of thermal generation using positive mathematical programming. Rather than employing constant average and marginal costs, calibration determines upward sloping supply (marginal cost) functions. The calibrated model is then used to determine an optimal generation mix under different assumptions regarding carbon prices and policies to eliminate coal-fired capacity. Results indicate that significant wind capacity can enter the Alberta grid if carbon prices are high, but that it remains difficult to eliminate reliable baseload capacity. Adequate baseload coal and/or natural gas capacity is required, which is the case even if battery storage is allowed into the system. Further, significant peak-load gas capacity will also be required to backstop intermittent renewables.

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

Purpose of Review This paper explores the transition underway in competitive power markets in the USA and provides options for market operators to reliably manage through the current period of oversupply into a lower-cost, lower-carbon power system. Recent Findings There are several structural factors, as well as some more recent and short-term dynamic factors, contributing to oversupply in the power system. Summary Power market operators have options for thoughtfully managing the transition, which include updating resource adequacy frameworks and ensuring market product definitions remain technology neutral given emerging technologies. Load serving entities can also hedge on behalf of their customers by contracting for low-carbon, low-cost sources of grid flexibility.

As the world demand for energy continues to grow, a big question is where will all the energy come from and what will the price tag be. With such enormous sums needed, public-private partnerships (PPPs) could play a big role. But the financial crisis has raised worries about funding, and much is still not known about how best to attract PPPs. This report reviews the evidence to date with sectoral reforms and considers different approaches in varying circumstances to help outline the potential role of the private and public sector in: 1) strengthening the corporate governance of private and public utilities; 2) helping governments to establish legal, regulatory, contractual, and fiscal frameworks; and 3) improved market governance to attract private investment. Chapter one reviews the impact of the recent financial crisis on PPP investment compared with what happened in earlier financial crises. It also looks out the latest projections for additional power sector investment needed because of climate change and the possible sources of financing. Chapter two examines how PPP investment in the power sector has fared. It also gives the results of an econometric study that explores which types of incentives and variables matter most to PPPs when they are weighing entering the power sector, especially in renewables, and what influences the ongoing level of investment. The idea is to provide a powerful benchmarking tool at the sector and country levels against which governments and policy makers can evaluate progress on this issue. Chapter three examines four case studies-in China, Brazil, Peru, and Mexico-to identify, disseminate, and promote best practices on alternative ways to attract PPPs.

The current practices for detecting market power in Colombia’s wholesale power market are discussed in this paper. A miscalculation of the Lerner Index, the most common measure of market power, leads to an overestimation or underestimation of market power. Different alternatives to calculate the Lerner Index are proposed in order to have a more accurate estimate of market power. Numerical results indicate that it is necessary to carefully review the current approach in Colombia.

En este trabajo se analiza el estado actual de las diferentes metodologías para determinar posiciones dominantes en el mercado eléctrico mayorista Colombiano. Se indica la existencia de una aproximación en el cálculo del Índice de Lerner (IL), principal métrica para estimar poder de mercado, que conlleva en algunos casos a presentarse sobrestimaciones o subestimaciones de este índice. Se proponen varias alternativas con el fin de demostrar distintas maneras para estimar el poder de mercado y subsanar las dificultades encontradas. Los resultados numéricos indican que es preciso revisar cuidadosamente la metodología vigente en el país.

The PJM wholesale electricity market has evolved to promote open competition between existing generation resources, new generation resources, demand-response, and alternative technologies to supply services to support reliable power grid operations. PJM has adapted market rules and procedures to accommodate smaller alternative resources while maintaining and enhancing stringent reliability standards for grid operation. Although the supply resource mix has tended to be less operationally flexible, the development of smart grid technologies, breakthroughs in storage technologies, microgrid applications, distributed supply resources, and smart metering infrastructure have the potential to make power transmission, distribution, and consumption more flexible than in the past. Competitive market signals in forward capacity markets and grid service markets have resulted in substantial investment in demand-response and alternative technologies to provide reliability services to the grid operator. This chapter discusses these trends and the market mechanisms by which both system and market operators can manage and leverage these changes to maintain the reliability of the bulk electric power system.