Real-time Locational Marginal Prices Research Articles

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants’ Performance: A Case of California ISO

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world. We address this open problem with focus on the electricity market that is operated by the California ISO. In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the existing real-world strategies to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used.

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.

Stealthy and profitable data injection attack on real time electricity market with network model uncertainties

The economic operations of real time (RT) electricity markets are vulnerable to false data injection (FDI) attacks, designed by cyber-attackers. Strategically, the RT locational marginal prices (LMPs) are stealthily altered by manipulating some of measurement data and it provides conditions for profitable financial misconduct in the electricity market. This paper proposes a new Monte Carlo-based FDI attack strategy for a cyber-attacker, who has very limited knowledge about the topology and parametric information of targeted network, which called an attacker with model topology-parametric uncertainties (TPUs). The main feature of the proposed attack is that despite the model errors, the attacker can guarantee the stealthy and profitable attack in advance, since the attack is designed based on an optimization problem of worst-case robust against uncertainties. Two 5-bus PJM and 30 IEEE bus systems are used to demonstrate the success of such cyber-attacks in real-time electricity markets.

Tracking Equilibrium Point Under Real-Time Price-Based Residential Demand Response

This letter proposes a model for tracking the equilibrium point of the real-time locational marginal price (LMP) based residential demand response program, where elastic demand is modeled as a monotonously decreasing linear function of the LMP. The resulting bi-level model contains both primary and dual variables, making it difficult to solve. Using duality, the dual model is formulated as a convex quadratic problem which is tractable to solve and find the global optimum. Furthermore, the condition for the existence of the equilibrium point is given. Numerical results on the IEEE 30-bus system verifies the effectiveness of the demand response model.

Analytical Model of Day-ahead and Real-time Price Correlation in Strategic Wind Power Offering

In this paper, the model of strategic wind power offering in the day-ahead (DA) market is proposed considering the uncertainties of wind power production, and price forecasting of DA and real-time (RT) market. The wind power deviation in the RT market is settled with the two-price mechanism based on the deviation direction and the relation between the locational marginal prices (LMPs) of DA and RT. Instead of using the point forecasting for the DA and RT LMPs, the uncertainties of LMP forecasting are modeled. In addition, the correlation between the forecasting errors of DA and RT LMP are directly modeled instead of generating the correlated scenarios. Finally, the optimal offered quantity of wind power in the DA market is derived using the probability theory based on the probabilistic wind power forecasting. The case study using the price data of actual DA and RT from Midcontinent Independent System Operator (MISO) validates the effectiveness of the proposed model. It shows that the correlation of the forecasting errors of DA and RT LMP has a significant impact on the wind power quantity offered by DA and revenue results.

Mean-Variance Optimization-Based Energy Storage Scheduling Considering Day-Ahead and Real-Time LMP Uncertainties

In this letter, a new mean-variance optimization-based energy storage scheduling method is proposed with the consideration of both day-ahead (DA) and real-time (RT) energy markets price uncertainties. It considers the locational marginal price (LMP) forecast uncertainties in DA and RT markets. The energy storage arbitrage risk associated with the LMP forecast uncertainty is explicitly modeled through the variance component in the objective function. The quadratic term of this variance is transformed into a second-order cone constraint using the charging and discharging power complementarity of the energy storage system. Finally, the proposed model is formulated as a mixed-integer conic programming problem. Numerical case studies demonstrate the effectiveness of the proposed model for energy storage scheduling considering price uncertainty.

Potential revenue and breakeven of energy storage systems in PJM energy markets.

The operation in energy arbitrage markets is an attractive possibility to energy storage systems developers and owners to justify an investment in this sector. The size and the point of connection to the grid can have significant impact on the net revenue in transmission and distribution systems. The decision to install an energy storage system cannot be based only on the cost of the equipment but also in its potential revenue, operation costs, and depreciation through its life cycle. This paper illustrates the potential revenue of a generic energy storage system with 70% round trip efficiency and 1-14h energy/power ratio, considering a price-taking dispatch. The breakeven overnight installed cost is also calculated to provide the cost below which energy arbitrage would have been profitable for a flow battery. The analysis of the potential revenue was performed for 13 locations within the PJM Real-time market. We considered hourly data of day-ahead and real-time locational marginal prices over 7years (2008-2014). Breakeven installed cost per MW ranged from $30 (1MW, 14 MWh, 2009) to $340 (1MW, 1MWh, 2008).

Wind power bidding strategy in the short-term electricity market

This paper presents an analytical trading electricity model for wind power producers (WPPs) in the short-term electricity market in the U.S. This model addresses four specific uncertainties: real-time (RT) wind power generation, day-ahead (DA) locational marginal prices (LMPs), RT LMPs, and deviation penalty rates. The model is designed to find the optimal bidding strategy to maximize the expected revenue under these uncertainties. In addition, this paper shows that advanced forecasting techniques could be used with the proposed bidding strategy to help WPPs trade energy in short-term markets. A case study is presented to illustrate the effectiveness of this proposed bidding strategy and advanced forecasting techniques using a set of real data taken from a wind farm in the PJM electricity market.

Robust and dynamic transactive energy system using Tsypkin–Polyak theorem

In this study, a robust and dynamic transactive energy system in smart grid (SG) environment is proposed based on the Tsypkin–Polyak theorem. A key factor of the proposed system is to consider the uncertainties in electrical grid parameters. Moreover, oligopolistic behaviours of agents, i.e. demands and generating units, are considered in the modelling. It is proved that selfish agents offer their true cost parameters in the proposed transactive energy system. Therefore, optimal power flow (OPF) of the electrical grid is obtained in the proposed transactive energy system. In addition, real-time locational marginal prices (LMPs) are also presented for demand-side management (DSM). Hence, all levels of demands side can interact and communicate with generation side through real-time LMPs. This characteristic is known as interoperability of transactive energy systems. However, in addition to all benefits of the proposed system, it has adverse impacts on the stability of the electrical grid. Here, to solve this challenge, a hierarchical and robust control system is proposed by using the Tsypkin–Polyak theorem to control stability and OPF simultaneously. Finally, the effectiveness of the proposed system is validated by implementing it on a test electrical grid.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price (LMP) in real-time power markets. We consider the case where the false status of circuit breakers (CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch. The main goal of this paper is to assess the economic impact of this misconfigured network topology on real-time LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Data Injection Attacks on Smart Grids With Multiple Adversaries: A Game-Theoretic Perspective

Data injection attacks have emerged as a significant threat on the smart power grid. By launching data injection attacks, an adversary can manipulate the real-time locational marginal prices to obtain economic benefits. Despite the surge of existing literature on data injection, most such works assume the presence of a single attacker and assume no cost for attack or defense. In contrast, in this paper, a model for data injection attacks with multiple adversaries and a single smart grid defender is introduced. To study the interactions between the defender and the attackers, two game models are considered. In the first, a Stackelberg game is proposed in which the defender acts as a leader that can anticipate the actions of the adversaries, that act as followers, before deciding on which measurements to protect. The existence and properties of the Stackelberg equilibrium of this game are studied. To find the equilibrium, a distributed learning algorithm that operates under limited system information is proposed and shown to converge to the game solution. In the second proposed game model, it is considered that the defender cannot anticipate the actions of the adversaries. To this end, a hybrid satisfaction equilibrium-Nash equilibrium game is proposed. To find the equilibrium of this hybrid game, a search-based algorithm is introduced. Numerical results using the IEEE 30-bus system are used to illustrate and analyze the strategic interactions between the attackers and defender. The results show that by defending a very small set of measurements, the grid operator can achieve an equilibrium through which the optimal attacks have no effect on the system. Moreover, the results also show how, at equilibrium, multiple attackers can play a destructive role toward each other by choosing to carry out attacks that cancel each other out, leaving the system unaffected. In addition, the obtained equilibrium strategies under the Stackelberg and the hybrid models are compared while characterizing the amount of loss that the defender endures due to its inability to anticipate the attackers' actions.

Probabilistic Forecasting of Real-Time LMP and Network Congestion

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

Sensitivity Analysis of Real-Time Locational Marginal Price to SCADA Sensor Data Corruption

This paper examines the impact of supervisory control and data acquisition (SCADA) data corruption on real-time locational marginal price (LMP) in electricity markets. We present an analytical framework to quantify LMP sensitivity with respect to changes in sensor data. This framework consists of a unified LMP sensitivity matrix subject to sensor data corruption. This sensitivity matrix reflects a coupling among the sensor data, an estimation of the power system states, and the real-time LMP. The proposed framework offers system operators an online tool to: 1) quantify the impact of corrupted data at any sensor on LMP variations at any bus; 2) identify buses with LMPs highly sensitive to data corruption; and 3) find sensors that impact LMP changes significantly and influentially. It also allows system operators to evaluate the impact of SCADA data accuracy on real-time LMP. The results of the proposed sensitivity based analysis are illustrated and verified with IEEE 14-bus and 118-bus systems with both Ex-ante and Ex-post real-time pricing models.

Impact of Data Quality on Real-Time Locational Marginal Price

The problem of characterizing impacts of data quality on real-time locational marginal price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.