A Theory for the Operation of the Independent System Operator in a Smart Grid with Stochastic Renewables, Demand Response and Storage

TL;DR

This paper develops a decentralized framework for optimal demand response in smart grids with stochastic renewables, enabling agents to respond to pricing without full knowledge of each other's utilities, improving social welfare.

Contribution

It introduces a novel decentralized demand response scheme with price iteration for stochastic and deterministic cases, including approximations for complex scenarios.

Findings

Proposes a complete solution for deterministic and stochastic demand response.

Demonstrates improved social welfare through simulations.

Provides computationally feasible approximations for complex stochastic cases.

Abstract

In this paper, we address a key issue of designing architectures and algorithms which generate optimal demand response in a decentralized manner for a smart-grid consisting of several stochastic renewables and dynamic loads. By optimal demand response, we refer to the demand response which maximizes the utility of the agents connected to the smart-grid. By decentralized we refer to the desirable case where neither the independent system operator (ISO) needs to know the dynamics/utilities of the agents, nor do the agents need to have a knowledge of the dynamics/utilities of other agents connected to the grid. The communication between the ISO and agents is restricted to the ISO announcing a pricing policy and the agents responding with their energy generation/consumption bids in response to the pricing policy. We provide a complete solution for both the deterministic and stochastic cases. It features a price iteration scheme that results in optimality of social welfare. We also provide an optimal solution for the case where there is a common randomness affecting and observed by all agents. This solution can be computationally complex, and we pose approximations. For the more general partially observed randomness case, we exhibit a relaxation that significantly reduces complexity. We also provide an approximation strategy that leads to a model predictive control (MPC) approach. Simulation results comparing the resulting optimal demand response with the existing architectures employed by the ISO illustrate the benefit in social welfare utility realized by our scheme. To the best of the authors' knowledge, this is the first work of its kind to explicitly mark out the optimal response of dynamic demand.