Midcontinent Independent System Operator Research Articles

Data-Driven Residential Electric Vehicle Charging Behavior and Load Profile Modeling for Demand Response in the Midcontinent Independent System Operator Region

Data-Driven Residential Electric Vehicle Charging Behavior and Load Profile Modeling for Demand Response in the Midcontinent Independent System Operator Region

The impacts of local wind power objection on the power system of the Midcontinent Independent System Operator area

Using the Regional Energy Deployment System (ReEDS) model, we estimate the deadweight loss imposed by county-level wind power development restrictions in the form of increased electricity costs due to suboptimal siting. This is accomplished by optimizing the power system of the United States' Midcontinent Independent System Operator (MISO) from 2020 to 2050. We perform the optimization with and without land-use constraints arising from simulated potential local ordinances restricting wind power development, and under multiple scenarios reflecting different renewable portfolio standards (RPS). We find that local restrictions on wind power increase the total system cost by 0.15%–0.3% and the wholesale electricity price by 1.8%–2.7%, depending on the RPS scenario. Changes in the generation and installed capacity mixes are more substantial and depend on both the level of county restrictions on wind power, and RPS requirements, thus indicating an interaction between RPS requirements and local wind power restrictions. We also find that plausible restrictions on wind development do not pose major barriers to meeting renewable energy targets in a cost-effective manner.

Hedging Effectiveness on the MISO Exchange

The purpose of this paper is to evaluate the effectiveness of four commonly used hedging techniques on wholesale electricity prices within the Midcontinent Independent System Operator (MISO) energy market. This analysis provides and in-depth assessment of hedging performance on one of the largest power markets in North America. In a break from extant literature, hourly time series are used to create hedge portfolios for each of the regional hubs within the MISO footprint. Each risk management strategy is employed on 24 hourly time series for each of the eight regional hubs within the MISO footprint. This results in a total of 768 hedged portfolios across 192 hourly time series. While the naïve approach tends to outperform the others in terms of variance reduction, none of the strategies examined meet the generally accepted standard for a highly effective hedge. In several instances, the hedged portfolio contains a higher amount of risk than the unhedged position. This may be explained by the unique characteristics of wholesale electricity and hedging pressure, particularly during peak demand hours.

Grid-scale demand-side flexibility services using commercial buildings lighting loads

Flexibility Services can be used to account for the high variability in electricity generation due to increasing renewable energy sources such as wind and solar energy. As buildings become smarter with the adoption of new technologies for sensing and control, more integration between buildings and the electric grid is possible. Building loads such as air conditioning and lighting in commercial buildings have the potential to provide these Flexibility Services. In commercial buildings, lighting accounts for approximately 10–15 % of the load at time. Past studies have shown that lighting can be dimmed by 15–20 % without causing visual discomfort to the occupants. Overall, this study aims to estimate the instantaneous demand reduction that can be provided due to lighting loads using prototypical building models aggregated across electric grid nodes in the Midcontinent Independent System Operator (MISO) region in the United States. Findings suggest, in a future case with 100 % LED fixtures and 30 % technology penetration for smart lighting controls, lighting loads can provide around 250 to 475 MW (0.21 to 0.39 % of MISO’s peak load) of Flexibility Services in the MISO region. The results can be used as inputs for grid levels models to predict future generation, transmission, and distribution investments for future high renewable energy scenarios.

Can the Basis Lead to Arbitrage Profits on the MISO Exchange?

Previous research has shown the basis contains information about future spot prices in wholesale electricity markets. This paper examines the profitability of a trading rule that utilizes the basis on the Midcontinent Independent System Operator (MISO) exchange. 24 hourly time series regressions across each of MISO’s eight regional hubs are estimated to determine the rule’s effectiveness. 67 of the 192 total regressions yield statistically significant results. The number of significant results as well as the size of the returns tend to be highest during the afternoon through early evening hours, which calls into question the efficiency of the market during peak demand times.

The transmission ramifications of social and environmental siting considerations on wind energy deployment

Increasing the capacity of wind power is critical to achieving climate goals, however its continued deployment faces environmental and social siting challenges. For example, the United States government is increasingly emphasizing the importance of a just energy transition by considering the social impacts of energy and environmental justice (EEJ). In this study, we investigate the impact of considering available EEJ metrics and environmental impacts into siting wind power and transmission by applying SimWINDPRO. SimWINDPRO is an infrastructure optimization tool that can site wind energy technologies and transmission by concurrently considering wind resource potential, transmission costs, EEJ, and environmental impacts. We demonstrate the impacts of considering EEJ and environmental factors in the context of Midcontinent Independent System Operator’s (MISO) western region, which includes some of the best wind energy potential in the United States. We show that prioritizing EEJ and environmental considerations in wind deployment can result in exponentially more transmission deployment for the same amount of wind power delivered, and results in selecting different wind farm sites. Our results also show that, depending on how it is considered, it is possible that constraining sites based on EEJ and environmental factors can reduce the available capacity of wind energy enough that energy transition capacity targets cannot be met.

Incremental wind energy development in the Midcontinent Independent System Operator electricity markets of the United States

Incremental wind energy development in the Midcontinent Independent System Operator electricity markets of the United States

Grid Edge Visibility: Gaps and a road map

Behind-The-Meter (BTM) resources at the grid edge are rapidly becoming an important component of the electric grid, requiring a substantial reconfiguration of traditional grid practices, such as policy changes, market redesign, and infrastructure upgrades. This adjustment is challenged by the fact that, by definition, grid edge elements are not easily observable by grid control entities. Increasing the visibility of these resources is therefore an important goal, one that is experiencing much research and discussion by various power system stakeholders. For example, policy makers are analyzing the tradeoffs of using grid edge meters to impose charges on grid edge electricity generation. System operators, such as the Midcontinent Independent System Operator (MISO) in the United States, can identify visibility information on the electrical location and the size of the grid edge resources as a critical consideration across the transmission-and-distribution (T&D) spectrum. This article summarizes the challenges and needs of grid entities resulting from the introduction of grid edge resources as well the gaps in the extant grid edge visibility frameworks.

System integration costs and emission savings of high penetration of intermittent renewable electricity

Integrating intermittent wind and solar generation into the grid has system integration costs of spinning reserves, increased transmission, and storage. As the penetration of wind and solar generation increase, system integration costs need to be considered, but there are also emissions savings. A TIMES model for the Midcontinent Independent System Operator footprint using publicly available data was used to examine system integration costs and emissions savings benefits of high penetration of intermittent renewable electricity. TIMES is a bottom-up optimization model that minimizes the total system cost. The number of time slices was increased to capture wind and solar generation dynamics, to better understand spinning reserves costs, and to allow battery storage. Using candidate sites for solar and wind generation, transmission, investment and production costs were added. Total system costs increased as there were higher transmission and spinning reserve costs. The emissions savings of Carbon Dioxide equivalent, Sulphur Dioxide, and Particulate Matter were converted to economic benefits using the Social Costs of Emissions. As the penetration of wind and solar generation increases, emissions savings and economic benefits increase. The system integration costs are greater than the benefits of emissions savings. The economic benefits do not justify the system integration cost increases.

A European renewable supergrid for Europe’s future weather-dependent power system

Europe is facing an unprecedented energy challenge. Europe must transition to a renewables-dependent and thus weather-dependent energy system. Renewable generation technologies in solar and onshore and offshore wind have matured to become commercially and technologically feasible. However, the introduction of renewables at the speed necessary presents considerable challenges, most notably on the electricity grid. Grid technology and infrastructure are far from where they need to be to accommodate the needed level of renewables, especially in the offshore space where no grid currently exists. A more holistic approach to grid planning is required. Lessons are drawn from the Midcontinent Independent System Operator (MISO) in the US which delivers significant cost-savings across 15 US states. A European-wide supergrid with a significant offshore grid could provide similar benefits by alleviating the challenges of renewable energy and connecting the best offshore renewables to demand centres across the continent. The planning, development and operation of such a grid would face institutional barriers and would require a complex new European Governing Framework.

Predicting Real-Time Locational Marginal Prices: A GAN-Based Approach

Electricity market participants rely on data-driven methods using public market data to predict locational marginal prices (LMPs) and determine optimal bidding strategies, since they cannot access confidential power system models and operating details. In this paper, system-wide heterogeneous public market data are organized into a 3-dimensional (3D) tensor, which can store their spatio-temporal correlations. A generative adversarial network (GAN)-based approach is proposed to predict real-time locational marginal prices (RTLMPs) by learning the spatio-temporal correlations stored in the historical market data tensor. An autoregressive moving average (ARMA) calibration method is adopted to improve the prediction accuracy. Case studies using public market data from Midcontinent Independent System Operator (MISO) and Southwest Power Pool (SPP) demonstrate that the proposed method is able to learn spatio-temporal correlations among RTLMPs and perform accurate RTLMP prediction.

Distributed energy resources: Transitioning to a clean energy future [In My View

In my 40-year career as a utility consumer advocate, regulator, and consultant, I have participated in key power industry transitions, including <list list-type="bullet" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <list-item> <label>•</label> in the 1980s, advocating integrated resource planning and collaborating with utilities to design energy efficiency programs. </list-item> <list-item> <label>•</label> as a proponent of the acid rain cap and trade program in the 1990 Clean Air Act amendments and helping the U.S. Environmental Protection Agency develop the SO₂ allowance trading program. </list-item> <list-item> <label>•</label> leading modeling on the benefits of locational marginal pricing in the 1990s and later supporting the development of the Midcontinent Independent System Operator’s energy and ancillary service markets. </list-item> <list-item> <label>•</label> encouraging smart grid investments as a commissioner on the Public Utilities Commission of Ohio from 2007 to 2012 and helping guide standards development on the Board of the Smart Grid Interoperability Panel and Federal Advisory Committees for the Department of Energy and National Institute of Standards and Technology. </list-item> <list-item> <label>•</label> as a consultant, advising clients on aligning utility business and regulatory models with the transition to a clean energy economy. </list-item> </list>

Spot Prices and Forward Premia on the MISO Exchange

This paper explores information contained in the basis for wholesale electricity on the Midcontinent Independent System Operator (MISO) electricity exchange. Utilizing Fama and French’s (1987) approach, the basis is found to have predictive power on changes in real-time (spot) prices but provides only limited evidence of a time-varying forward premium. This result contradicts Huisman and Kilic’s (2012) theory that the basis in an electricity market which relies primarily on storable forms of power for electricity production (such as MISO) should contain information on both changes in spot prices and provide evidence of a time-varying forward premium.

Forecast Aggregated Supply Curves in Power Markets Based On LSTM Model

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties of individual bidding behaviors and can be used as reference for optimal bidding. In fact, the real ASC data contains bidding information from thousands of participants, which would be formulated with high dimensionality and unstructured formats, not applicable for general forecasting methods. Thus, a novel data-driven ASC forecasting framework based on long-short term memory (LSTM) model and corresponding data processing techniques is proposed in this paper. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, a LSTM model is customized to forecast ASCs. At last, real data from Midcontinent Independent System Operator market in the U.S. are utilized to demonstrate the forecasting performance of the proposed framework.

Comparison between Historical and Real-Time Techniques for Estimating Marginal Emissions Attributed to Electricity Generation

Electricity generation is tied to various environmental and social consequences. In prior studies, the environmental emissions associated with electricity generation were calculated using average emission factors (AEFs) whose use is different from the method of marginal emission factors (MEFs) in regard to the geographical redefinition and new policies applied to the US electricity grid in 2013. Moreover, the amount of emissions being released at a generation site depends on the technology of the generating units; it is important to take into account this factor as well. Thus, this paper provides comparisons between different historical and real-time approaches of estimating MEFs (i.e., CO2, SO2, and NOx) for the Midcontinent Independent System Operator (MISO) electricity region. The region under study is the same for all the scenarios, although the comparative time frames are different. The study is focused on the similarities observed in the data trends and system behaviors. We carry out different temporal comparisons whose results show the value of real-time approaches for estimating the MEFs for each location and at any time. These approaches can be extended to other regions to assist with proper investment and policy making, thereby increasing the grid efficiency, mitigating the environmental emissions, and clarifying the byproducts of energy consumption.

Energy trading efficiency in the US Midcontinent electricity markets

Efficient trading under wholesale market competition reduces an electric grid’s energy costs for meeting time- and location-dependent demands. We analyse energy trading efficiency by estimating three newly developed sets of energy price difference regressions interconnected by their common root of energy price level regressions. Using a sample of ~ 0.6 million hourly observations for 01/01/2014 to 10/31/2020, our estimation documents energy trading efficiency of the 10 local resource zones across 15 American states administered by the Midcontinent Independent System Operator (MISO). We find MISO’s zonal energy markets integrated across space (zone j vs. zone k for j ≠ k) and time (day-ahead market vs. real-time market). However, these markets exhibit inter-day energy prices differences that move with the fundamental drivers (e.g., day-ahead forecasts for natural gas price and zonal wind generation) of day-ahead energy prices and their forecast errors. Inter-zonal day-ahead energy price differences move with the fundamental drivers and their zonal variations. Inter-zonal real-time energy price differences increase with inter-zonal day-ahead energy price differences and depend on zonal variations of the fundamental drivers’ forecast errors. Our empirics yield two important policy implications. First, enhancing inter-day trading efficiency requires accuracy improvements in (a) day-ahead forecasts for natural gas price, zonal load levels, and zonal wind generation, and (b) day-ahead scheduling of zonal nuclear and must-run generation. Second, improving inter-zonal trading efficiency requires mitigating inter-zonal transmission congestion via transmission capacity expansion, generation investment and demand reduction in a load pocket, and virtual bidding for inter-zonal energy price differences.

Why individual electric utilities cannot achieve 100 % clean energy

Establishing 100 % clean electricity goals is currently in vogue.11The term “clean” energy includes all resources that do not emit greenhouse gases (GHG). To-date in the United States (US), 19 large utilities, nearly 210 cities, and 150 corporations have established such an ambitious target.22S. Ptacek with A. Levin, “Race to 100% Clean”, Natural Resources Defense Council (NRDC), December 2, 2020, available at:https://www.nrdc.org/resources/race-100-clean. The authors are very supportive of the concept for addressing climate change. But with possibly only a few unique exceptions of which the authors are not aware, such a goal is currently physically and economically impossible unless certain policies and practices are changed.The term “Useful” clean energy refers to that amount of hourly clean energy supply that matches hourly customer electric load. In that regard, electric utility planners seeking 100 % clean energy are between a proverbial rock and a hard place. The rock is the fact that renewable energy is intermittent and non-dispatchable. The hard place is customer load, which, but for marginal adjustments with utility demand management, happens when customers want it to happen. Neither are sufficiently controllable by the utility to assure that supply exactly meets demand as required.Every utility has a hypothetical “inflection point” where increasing high levels of renewables begin to exceed hourly load and must be turned down or otherwise curtailed. Doing this wastes renewables investment. Further increases in renewable energy installations within or near the utility, whether grid-level or distributed, only increase this mismatch. As a result, diminishing returns for Useful energy increase as renewables increase.This article provides examples for four electric utilities in the Midcontinent Independent System Operator (MISO) market. All of them have inflection points. All of them would “stall out” in Useful energy when renewable energy reach levels at about 60 %–80 % of their annual load (equivalent to a Renewable Energy Standard (RES) or Renewable Portfolio Standard (RPS)).The cost of attaining levels of renewable energy higher than the “inflection point” gets very expensive for the utility retail customers as more local renewables are added. In addition to illustrating the issue, the authors propose potential solutions.

Data-Aided Offline and Online Screening for Security Constraint

Security constraint is a key component in unit commitment to guarantee reliable generator commitment. Large set of security constraints are notorious for making the problem difficult to solve. Constraint screening, i.e., filtering out non-dominating constraints, is regarded as a powerful tool to address this challenge. This work presents a security-constraint screening that can effectively integrate virtual transaction and capture changes online in real-time or look-ahead markets. The proposed approach takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness are verified using Midcontinent Independent System Operator (MISO) data.

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.

Market Implications of Short-Term Reserve Deliverability Enhancement

Midcontinent Independent System Operator (MISO) plans to introduce 30-minute short-term reserve (STR) to address 30 minutes system flexibility needs. Transmission bottlenecks in the power system may inhibit the deliverability of STR. Insufficient deliverable reserve may require additional operator manual adjustments, which may be uneconomic and distort the market price signal. To improve reserve deliverability, event-based zonal STR clearing model and nodal STR clearing model are proposed to distribute reserve across the grid. This paper compares reserve deliverability and market clearing prices between the zonal STR model and the nodal STR model. A new design on penalty function is proposed in this paper to reflect the true value of security constraints for reserve deployment with the consideration of multiple events. Analysis of MISO test cases shows that the zonal approach is imprecise and may not guarantee the actual post-event flows within the physical limits. Nodal STR model can improve the reserve deliverability, market efficiency, and price signal.