An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The efficiency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (PowerTrading Agent Competition). Specifically, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profits in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for a particular auction which is then used to generate suitable bids. We further demonstrate the efficacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 finals and through several controlled experiments.

Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars in the process. A bidder, participating in such PDAs, has to plan for bids in the current auction as well as for the future auctions, which highlights the necessity of good bidding strategies. In this paper, we perform an equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. We further derive the best response for a bidder with complete information in a single-shot double auction with ACPR. Leveraging the theory developed for single-shot double auction and taking the PowerTAC wholesale market PDA as our testbed, we proceed by modeling the PDA of PowerTAC as an MDP. We propose a novel bidding strategy, namely MDPLCPBS. We empirically show that MDPLCPBS follows the equilibrium strategy for double auctions that we previously analyze. In addition, we benchmark our strategy against the baseline and the state-of-the-art bidding strategies for the PowerTAC wholesale market PDAs, and show that MDPLCPBS outperforms most of them consistently.

A smart grid is an efficient and sustainable energy system that integrates diverse generation entities, distributed storage capacity, and smart appliances and buildings. A smart grid brings new kinds of participants in the energy market served by it, whose effect on the grid can only be determined through high fidelity simulations. Power TAC offers one such simulation platform using real-world weather data and complex state-of-the-art customer models. In Power TAC, autonomous energy brokers compete to make profits across tariff, wholesale and balancing markets while maintaining the stability of the grid. In this paper, we design an autonomous broker VidyutVanika, the runner-up in the 2018 Power TAC competition. VidyutVanika relies on reinforcement learning (RL) in the tariff market and dynamic programming in the wholesale market to solve modified versions of known Markov Decision Process (MDP) formulations in the respective markets. The novelty lies in defining the reward functions for MDPs, solving these MDPs, and the application of these solutions to real actions in the market. Unlike previous participating agents, VidyutVanika uses a neural network to predict the energy consumption of various customers using weather data. We use several heuristic ideas to bridge the gap between the restricted action spaces of the MDPs and the much more extensive action space available to VidyutVanika. These heuristics allow VidyutVanika to convert near-optimal fixed tariffs to time-of-use tariffs aimed at mitigating transmission capacity fees, spread out its orders across several auctions in the wholesale market to procure energy at a lower price, more accurately estimate parameters required for implementing the MDP solution in the wholesale market, and account for wholesale procurement costs while optimizing tariffs. We use Power TAC 2018 tournament data and controlled experiments to analyze the performance of VidyutVanika, and illustrate the efficacy of the above strategies.

Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction that works by identifying a smaller number of linearly uncorrelated variables (principal components) to explain the variation found in a data set. PCA as a technique has applications in almost all engineering disciplines including financial engineering. In this paper, we focus on PCA computations that arise in a (financial) real time risk management system (RTRM). A RTRM system has to simultaneously compute risk measures such as value-at-risk (VaR) or haircuts for large portfolios on a frequent basis. Typically, the portfolios on which these risk measures are computed, consist of thousands of financial assets. A general framework involves computation of the correlation matrix for the assets in the portfolio followed by the application of PCA to the correlation matrix and using the obtained principal components to eventually estimate the desired risk measure. In the case of large financial institutions, there could be several such portfolios corresponding to various clients and hence PCA has to be performed simultaneously on different correlation matrices. This scenario calls for an efficient high performance implementation and scheduling of concurrent PCA requests. This exposition addresses the stated scenario by proposing a solution for concurrent processing of multiple PCA requests. Concurrent processing of multiple PCA requests is achieved through suitable load distribution and scheduling mechanisms leading to optimal utilization of compute resources. The results are demonstrated on various high performance architectures including GP-GPUs and Intel based multi-core architectures. An important component of this work is a GPU based solution for PCA using CUDA streams.

We consider the problem of optimally hedging a (path-dependent) European contingent claim (ECC) with its underlying in a discrete-time framework. Specifically, we consider two quadratic optimal hedging strategies : minimum-variance hedging in a risk-neutral measure and optimal local-variance hedging in a market probability measure. The objective function for the former is the variance of the hedging error calculated in a risk-neutral measure and the latter optimizes the variance of the mark-to-market value of the portfolio over a trading interval in a market probability measure. The main aim of the paper is to derive explicit closed form solutions to hedge different types of ECCs using the above mentioned quadratic hedging schemes. To arrive at closed-form solutions, we assume geometric Brownian motion (GBM) as the stochastic model for the underlying asset prices. These explicit solutions when used instead of complex Monte-Carlo based solutions makes the proposed hedging solution well suited for computer implementation. In addition, we outline a mechanism to implement an automated trading position evaluation system based on the proposed hedging solutions.