Abstract-We present an architecture for peer-to-peer energy markets which can guarantee that operational constraints are respected and payments are fairly rendered, without relying on a centralized utility or microgrid aggregator. We demonstrate how to address trust, security, and transparency issues by using blockchains and smart contracts, two emerging technologies which can facilitate decentralized coordination between nontrusting agents. While blockchains are receiving considerable interest as a platform for distributed computation and data management, this is the first work to examine their use to facilitate distributed optimization and control. Using the Alternating Direction Method of Multipliers (ADMM), we pose a decentralized optimal power flow (OPF) model for scheduling a mix of batteries, shapable loads, and deferrable loads on an electricity distribution network. The DERs perform local optimization steps, and a smart contract on the blockchain serves as the ADMM coordinator, allowing the validity and optimality of the solution to be verified. The optimal schedule is securely stored on the blockchain, and payments can be automatically, securely, and trustlessly rendered without requiring a microgrid operator.

This paper studies the behavior of a strategic aggregator offering regulation capacity on behalf of a group of distributed energy resources (DERs, e.g. plug-in electric vehicles) in a power market. Our objective is to maximize the aggregator's revenue while controlling the risk of penalties due to poor service delivery. To achieve this goal, we propose data-driven risk-averse strategies to effectively handle uncertainties in: 1) The DER parameters (e.g., load demands and flexibilities) and 2) sub-hourly regulation signals (to the accuracy of every few seconds). We design both the day-ahead and the hour-ahead strategies. In the day-ahead model, we develop a two-stage stochastic program to roughly model the above uncertainties, which achieves computational efficiency by leveraging novel aggregate models of both DER parameters and sub-hourly regulation signals. In the hour-ahead model, we formulate a datadriven distributionally robust chance-constrained program to explicitly model the aforementioned uncertainties. This program can effectively control the quality of regulation service based on the aggregator's risk aversion. Furthermore, it learns the distributions of the uncertain parameters from empirical data so that it outperforms existing techniques, (e.g. robust optimization or traditional chance-constrained programming) in both modelling accuracy and cost of robustness. Finally, we derive a conic safe approximation for it which can be efficiently solved by commercial solvers. Numerical experiments are conducted to validate the proposed method.

The increased penetration of Distributed Energy Resources (DERs) on the distribution network creates local challenges in balancing consumption and generation. To coordinate the roll-out and the operation of DERs, distribution-level energy markets have been proposed, but there are currently few tools for simulating the operation of DERs in these proposed markets. We present a framework which utilizes a grid cosimulation platform (Mosaik) to simulation DER operation, while simulating market clearing operations with a blockchain network (Ethereum). The use of blockchains, an emerging technology for decentralized computing and data storage, allows us to model secure decentralized execution of market clearing functions and payment processes. By unifying simulation of market clearing rules and the physical grid, we are able to ensure that economic incentives are aligned with physical constraints, helping facilitate the development of more effective distributed energy markets. We demonstrate the use of this new simulation platform on a small feeder, for which a market mechanism to incentivize DER integration is explored.

Abstract-We present a convex model describing risk-averse strategies for electricity producers in congested electricity networks. Extending prior work on Cournot-Bertrand equilibria in Poolco-style spot markets with locational marginal pricing, we propose a formulation which integrates uncertainty through robust convex optimization. We find that producers uniformly benefit from robust strategies under small uncertainty intervals, and explore the impacts of congestion and network effects on strategic behavior. We demonstrate our results on a simple example and explore the impacts of robust strategies on social welfare, finding that these risk-averse strategies reduce welfare by restricting output and increasing prices. By formulating the equilibrium conditions as a convex optimization problem, we are able to scale our results to large networks and accommodate many sources of uncertainty.

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