Microsoft Technology Licensing, LLC

Riyaz Pishori

Ranveer Chandra

Shivkumar Kalyanaraman

Srinivasan Iyengar

Peeyush Kumar

Upendra Singh

Anirudh Badam

Weiwei Yang

Shadi Abdollahian Noghabi

Swati Sharma

Lucien Werner

Tanuja Hrishikesh Ganu

Alireza Sadeghi

The techniques disclosed herein enable systems to optimize generation and dispatch of renewable energies using data-driven models. In many contexts, a renewable energy system is collocated with a local consumer such as a datacenter, a smart building, and so forth. The objective of the renewable energy system is to meet local power needs while participating in various energy markets of differing trading frequencies. To optimally manage the renewable energy system, a data-driven model is configured to analyze current conditions and generate policies to control renewable energy system operations. For instance, the model can retrieve current market prices, generation capacity, costs associated with generating energy, and so forth. Based on the collected information, the model can generate a policy that maximizes revenue obtained by the renewable energy system while meeting local demand. Through many iterations, the model can determine a realistically optimal policy for managing the renewable energy system.

Time-shifting optimizations for resource generation and dispatch

The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.

Multi-market resource dispatch optimization

A computer system that includes a plurality of compute clusters that are located at different geographical locations. Each compute cluster is powered by a local energy source at a geographical location of that compute cluster. Each local energy source has a pattern of energy supply that is variable over time based on an environmental factor. The computer system further includes a server system that executes a global scheduler that distributes virtual machines that perform compute tasks for server-executed software programs to the plurality of compute clusters of the distributed compute platform. To distribute virtual machines for a target server-executed software program, the global scheduler is configured to select a subset of compute clusters that have different complementary patterns of energy supply such that the subset of compute clusters aggregately provide a target compute resource availability for virtual machines for the target server-executed software program.

Distributed computing with variable energy source availability

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Sammamish, WA, United States of America

Riyaz Pishori