Renewable power generators typically participate as price-takers in current electricity markets, and market clearing prices are determined by cost curves of conventional generators while treating renewable generation as negative load. In this paper, we analyze a new design of electricity markets where renewable power generators are able to bid their expectation-adjusted cost curves into the market, just like the cost curves of conventional generators. We formally define the new market model, and mathematically show how the renewable cost curves should be determined for the new market to be equivalent to the current market in terms of overall system cost. We further establish that on the average, the total revenue of both conventional and renewable generators, as well as the total cost to consumers, under the proposed market is the same as the current practice. While equivalent in terms of aggregate cost and revenue measures, the new market design allows renewables to be direct participants in the bidding process and bid according to their risk-return tolerance. We demonstrate our analytical results numerically using data obtained from the the Texas grid.
An increasing number of corporations and individuals are interested in minimizing the carbon footprint of their electricity use. In order to do this, they must have access to a ’carbon signal’ that quantifies the real time emissions intensity of their supply of electricity. One promising carbon signal is Locational Marginal Carbon Emissions (LMCE). However, as LMCE is often not available, Locational Marginal Price (LMP) is often suggested as an proxy for LMCE. This paper seeks to contribute to the evaluation of LMP as a potential signal for informing carbon-aware load-shifting. We evaluate the connection between LMCE and LMP via statistical analysis on real data from the PJM system operator. Additionally, we conduct data-center load-shifting case studies using each of the signals. Our results show that while LMP and LMCE have a positive relationship for many samples, there are also extremely high-magnitude exceptions to this pattern. Driven by such examples of ’misalignment’ between LMP and LMCE, our load-shifting case study shows the potential for large emissions increases when shifting based on LMP.
Special Protection Schemes (SPS) have been widely adopted to help power grids operate closer to their stability limits, maximizing efficiency and reducing costs. While SPS improves system stability, its heavy reliance on communication networks introduces significant cybersecurity challenges. A cyberattack against the cyber layer of SPS threatens the availability and integrity of the physical layer of power systems. This paper explores the impact of a Denial-of-Service (DoS) attack on a communication router within the Wide Area Network (WAN), focusing on how such an attack can disrupt SPS operations and compromise overall system stability. The study models three key components of SPS: the control center, MPLS-based WAN, and field devices. Two attack scenarios are simulated on the IEEE 9-bus test system. Simulation results and voltage stability analysis reveal that a successful DoS attack, by preventing the SPS from executing predefined protective actions, can lead to severe voltage drops and push the system into instability. These findings highlight the urgent need to address cyber-physical vulnerabilities when designing and deploying special protection schemes.
The energy market has undergone significant evolution, driven by modern energy systems. Nonetheless, the underlying infrastructure remains vulnerable to stealthy attack vectors and exploits, particularly False Data Injection Attacks (FDIAs). Profit-oriented FDIAs strategically manipulate Locational Marginal Prices (LMPs) to achieve long-term financial gains. The stealthy nature of such manipulations makes them difficult to distinguish from normal price fluctuations, posing severe threats to market stability. In parallel, the dynamic nature of energy markets shaped by seasonal variations, demand shifts, and generation changes adds complexity to the detection of these attacks. Given the aforementioned constraints, we introduce a novel market-level unsupervised model designed to detect stealthy LMP manipulations. We thus leverage non-parametric change-point detection principles to identify anomalous pricing behaviors without prior knowledge of attack patterns. In order to enhance robustness and higher confidence on our anomaly diagnosis, we propose a drift adaptive mechanism by adapting to evolving data distributions. Our extensive experiments on synthetic and real-world LMP datasets, showcase the effectiveness of our approach in reducing false alarm rates compared to state-of-the-art methods. Hence, we justify through our experimental outcomes the potential of adaptive unsupervised models in safeguarding modern energy markets against sophisticated and financially motivated cyber threat vectors.
The high penetration of renewables in power systems is responsible for the increased localisation of system dynamics. These changes undermine the reliability of traditional Single Machine Infinite Bus (SMIB)-based methods, highlighting the need for higher granularity in modelling these dynamics to prevent unforeseen locational violations. Existing alternatives are mainly analytical, often computationally intensive, and not predictive — only suitable for system monitoring and post-mortem analyses. We address this challenge by proposing a physics-informed learning approach to predict spatially distributed dynamics with minimal computational overhead. By embedding a multi-machine swing equation within its loss function, the proposed approach tracks the physics governing the dynamic system, facilitating convergence to reliable solutions that align with the physical characteristics of the network. The approach accounts for realistic system voltages, rather than assuming nominal values, by initialising steady-state conditions using the AC Optimal Power Flow (OPF), thereby demonstrating its potential for practical applications. Simulation studies on the IEEE 9-bus and IEEE 39-bus networks demonstrate that the proposed approach is both accurate and efficient in capturing rotor angle dynamics across the network, achieving over 10× computational speed-ups compared to traditional numerical solvers.
