Contract Design for Energy Demand Response

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed in by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.Comment: full version of paper accepted to IJCAI'1

Adversarial contract design for private data commercialization

The proliferation of data collection and machine learning techniques has created an opportunity for commercialization of private data by data aggregators. In this paper, we study this data monetization problem using a contract-theoretic approach. Our proposed adversarial contract design framework accounts for the heterogeneity in honest buyers' demands for data, as well as the presence of adversarial buyers who may purchase data to compromise its privacy. We propose the notion of Price of Adversary (PoAdv) to quantify the effects of adversarial users on the data seller's revenue, and provide bounds on the PoAdv for various classes of adversary utility. We also provide a fast approximate technique to compute contracts in the presence of adversaries

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Models for efficient control and fair sharing of assets in energy communities

In recent times, energy communities have gained significant interest. These communities empower citizen prosumers by leveraging their own renewable energy generation and storage assets to manage their energy requirements and engage in the broader energy market. Such communities offer a promising solution for sustainable energy systems, promoting renewable integration and active user involvement. Within energy communities, members can engage in energy trading and invest in shared assets like production units, energy storage, and network infrastructure. However, efficiently controlling these assets in real-time and equitably distributing energy outputs among diverse members with varying needs remains a vital challenge. Addressing this concern is of both research and practical importance. It is essential to consider technical constraints like local low-voltage network characteristics and power ratings during this process. To tackle these challenges, this thesis presents a model that examines the techno-economic benefits of community-owned versus individually-owned energy assets, accounting for physical asset degradation and network constraints. Employing cooperative game theory principles, the thesis proposes a redistribution model for community benefits based on the marginal contribution of each household. This redistribution mechanism utilizes the concept of marginal value from coalitional game theory and distributed AI (specifically the multi-agent system). Study results demonstrate that the proposed marginal cost redistribution mechanism is fairer and more computationally manageable than existing state-of-the-art methods, thus providing a scalable approach for economic sharing of joint assets in community energy systems. However, integration of centrally shared community-owned energy assets may face limitations due to network/grid constraints. To address this issue, the thesis proposes a novel framework for a local peer-to-community (P2C) market mechanism as an alternative solution to investing in community-owned assets. The dynamics of the P2C market mechanism are studied for three different types of P2C sellers with non-uniform pricing schemes and tested across various community settings (comprising a mix of prosumers and consumers) and different rates of renewable energy adoption. All proposed models are validated and applied to a real case study from a large-scale smart energy demonstration project in the UK, using a substantial dataset of real renewable generation and demand. This practical case study provides confidence in the robustness of the experimental comparison results presented in the thesis.Engineering and Physical Sciences Council (EPSRC) Doctoral Training Programme (DTP) grant (EP/R513040/1