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(a), (b) Normalized hourly electricity load profile of residential load (purple) and total SCE load (red) on two different Flex Alert days (solid lines) compared to the hourly load profiles on the comparable days (dashed lines). (c), (d) And hourly percent change in electricity demand on two Flex Alert days (outlined in black) and their corresponding comparable days.
Source publication
The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the effic...
Citations
... Incentive-based coordination mechanisms have received extensive attention and are one of the main features of power systems with communication capabilities. In the context of demand response in electricity markets, incentives can take many different forms, ranging from alert/text-based signals [4] to pricing [5]. In this paper, we focus on pricebased incentives: a system operator broadcasts prices, users respond by adjusting their consumption to minimize their individual costs, the operator adjusts the prices based on the user responses, etc. Ideally, this iterative interaction should converge to an optimal solution that balances user cost and system performance. ...
... x * (⃗ p) := (x * i (p i ), i ∈ N ) ∈ R n induced by individual user's optimization problem (4) in each iteration to gradually gear the demand profile ⃗ ...
Incentive-based coordination mechanisms for distributed energy consumption have shown promise in aligning individual user objectives with social welfare, especially under privacy constraints. Our prior work proposed a two-timescale adaptive pricing framework, where users respond to prices by minimizing their local cost, and the system operator iteratively updates the prices based on aggregate user responses. A key assumption was that the system cost need to smoothly depend on the aggregate of the user demands. In this paper, we relax this assumption by considering the more realistic model of where the cost are determined by solving a DCOPF problem with constraints. We present a generalization of the pricing update rule that leverages the generalized gradients of the system cost function, which may be nonsmooth due to the structure of DCOPF. We prove that the resulting dynamic system converges to a unique equilibrium, which solves the social welfare optimization problem. Our theoretical results provide guarantees on convergence and stability using tools from nonsmooth analysis and Lyapunov theory. Numerical simulations on networked energy systems illustrate the effectiveness and robustness of the proposed scheme.
... Typically, the goal is to provide incentives to selfish users such that the resulting equilibrium is closer to the socially optimal solution [13], [14], [15]. In the context of electricity markets, a large number of demand response strategies have been proposed, ranging from alert/text-based signals [16], to pricing [17], to direct load control [18]. In this paper, we focus on developing pricing mechanisms. ...
A central challenge in using price signals to coordinate the electricity consumption of a group of users is the operator's lack of knowledge of the users due to privacy concerns. In this paper, we develop a two-timescale incentive mechanism that alternately updates between the users and a system operator. As long as the users can optimize their own consumption subject to a given price, the operator does not need to know or attempt to learn any private information of the users for price design. Users adjust their consumption following the price and the system redesigns the price based on the users' consumption. We show that under mild assumptions, this iterative process converges to the social welfare solution. In particular, the cost of the users need not always be convex and its consumption can be the output of a machine learning-based load control algorithm.
... Mayes et al (2024) investigate the potential for precooling buildings under tight electricity supply conditions to reduce load and carbon dioxide emissions in California, USA. Peplinski and Sanders (2024) assess the performance of voluntary emergency demand response measures in the form of California, USA's Flex Alerts Programs. Akdemir et al (2024) present an open-source framework for improving speed in electricity supply production cost models while retaining sufficient modeling fidelity. ...
This issue formally launches Environmental Research: Energy, a new open-access interdisciplinary journal focused on energy systems as we grapple with the challenges and opportunities of decarbonization and advancing global social justice. As a society-owned journal in IOP’s Environmental Research portfolio, Environmental Research: Energy joins a publishing tradition focused on accessibility, fairness, and excellence, with a crucial topical focus on energy systems. Supported by an interdisciplinary and global editorial board, Environmental Research: Energy welcomes and is committed to creating conditions conducive to multi- and interdisciplinary research, particularly given the deep connections across society, technology, and culture that characterize energy systems. Our inaugural issue highlights the journal’s interest in both supply and demand side views of energy systems both adapting to and mitigating climate change across the world, and upcoming focus issues broaden and deepen our emphasis on multidisciplinary investigations into these crucial global issues. Thank you for your support!
Climate change, geopolitical tensions, and decarbonization targets are bringing the resilience of the European electric power system to the forefront of discussion. Among various regulatory and technological solutions, voluntary demand response can help balance generation and demand during periods of energy scarcity or renewable energy generation surplus. This work presents an open data service called Interoperable Recommender that leverages publicly accessible data to calculate a country-specific operational balancing risk, providing actionable recommendations to empower citizens toward adaptive energy consumption, considering interconnections and local grid constraints. Using semantic interoperability, it enables third-party services to enhance energy management and customize applications to consumers. Real-world pilots in Portugal, Greece, and Croatia with over 300 consumers demonstrated the effectiveness of providing signals across diverse contexts. For instance, in Portugal, 7% of the hours included actionable recommendations, and metering data revealed a consumption decrease of 4% during periods when consumers were requested to lower consumption.
