SLA-mechanisms for electricity trading under volatile supply and varying criticality of demand (Extended Abstract)

The increasing adoption of renewable power generation makes volatile quantities of electricity available, the delivery of which cannot be guaranteed, if sold. However, if not sold, the electricity might need to be curtailed, thus foregoing potential profits. In this paper we adapt service level agreements (SLAs) for the future smart electricity grid, where generation will primarily depend on volatile and istributed renewable power sources, and where buyers' ability to cope with uncertainty may vary significantly. We propose a contracting framework through SLAs to allocate uncertain power generation to buyers of varying preferences. These SLAs comprise quantity, reliability and price. We define a characterization of the value degradation of tolerant and critical buyers with regards to the uncertainty of electricity delivery (generalizing the Value of Lost Load, VoLL). We consider two mechanisms (sequential second-price auction and VCG) that allocate SLAs based on buyer bids. We further study the incentive compatibility of the proposed mechanisms, and show that both mechanisms ensure that no buyer has an incentive to misreport its valuation. We experimentally compare their performance and demonstrate that VCG dominates alternative allocations, while vastly improves the efficiency of the proposed system when compared to a baseline allocation considering only the VoLL. This article lays the ground work for distributed energy trading under uncertainty, thereby contributing an essential component to the future smart grid

Consider ethical and social challenges in smart grid research

Artificial Intelligence and Machine Learning are increasingly seen as key technologies for building more decentralised and resilient energy grids, but researchers must consider the ethical and social implications of their useComment: Preprint of paper published in Nature Machine Intelligence, vol. 1 (25 Nov. 2019

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

Rewarding Cooperative Virtual Power Plant Formation Using Scoring Rules 1

Abstract Virtual Power Plants (VPPs) are fast emerging as a viable means of integrating small and distributed energy resources (DERs), like wind and solar, into the electricity supply network (Grid). VPPs are formed via the aggregation of a large number of DERs, so that they exhibit the characteristics of a traditional generator in terms of predictability and robustness. In this work, we promote the formation of such "cooperative" VPPs (CVPPs) using techniques from the field of distributed Artificial Intelligence and game theory. In particular, we design a payment mechanism that encourages DERs to join CVPPs with increased size and visibility to the network operator. Our method is based on strictly proper scoring rules and incentivises the provision of accurate predictions of expected electricity generation from member DERs, which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK, and we show that it incentivises real DERs to form CVPPs, and outperforms the current state of the art payment mechanism developed for this problem