Optimal Net-Load Balancing in Smart Grids with High PV Penetration

TL;DR

This paper presents a unified, fast, and fair net-load balancing framework for smart grids with high PV penetration, combining load and solar curtailment, with proven near-optimal solutions and online capabilities.

Contribution

It introduces a novel NP-hard problem formulation and develops approximation algorithms for efficient, fair, and online net-load balancing in high PV penetration smart grids.

Findings

Algorithms achieve solutions close to optimal.

Solutions are computed rapidly within grid timing constraints.

Framework effectively balances supply and demand with high PV integration.

Abstract

Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response (DR) have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for Smart Grids with high PV penetration. These grids can sometimes exhibit supply surplus causing over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are complex and computationally expensive. This increases the complexity of net-load balancing systems used by the grid operator and limits their scalability. Recently new technologies have been developed that enable the rapid and selective connection of PV modules of an installation to the grid. Taking advantage of these advancements, we develop a unified optimal net-load balancing framework which performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and develop bounded approximation algorithms for minimizing the curtailment cost. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation. We also incorporate the notion of fairness to ensure that curtailment is evenly distributed among all the nodes. Finally, we develop an online algorithm which performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions which are close to optimal in a small amount of time.