Differentially Private Optimal Power Flow for Distribution Grids

The growing interconnection between information networks and power grids enables a more efficient and economical operation, but also introduces significant challenges in modern cyber-physical power systems, such as malicious cyber-attacks. Several cyber-attacks have been reported in the recent decade, affecting hundreds of thousands of people. These attacks can lead to large power system blackouts, and optimization models are essential tools for optimal decision-making in reliable and secure operation of cyber-physical power systems. In this paper, optimization models in cyber-physical power systems are extensively reviewed and classified based on their applications, including cyber-security and optimal operation. One major application of optimization is in cyber-security evaluation of smart grids. This paper investigates the models to implement cyber-attacks against state estimation, coordinated cyber-physical attacks aiming to cover each other, and financially-motivated attacks in electricity markets; optimal defense strategies and interactions between the attacker and the defender are also introduced. Furthermore, optimization models used in the operation and dispatching of cyber-physical power and energy systems, optimal routing of information networks, and privacy-preserving models are presented. Finally, as solving optimization models is a crucial step in optimal decision-making, solvers in the literature are also introduced.

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“…Cloud-based energy management [17], [18], [21], [88]; 4. Privacy-preserving optimal decision making models [17], [18], [87], [89]- [92].…”

Section: Optimal Operation Of Cppsmentioning

confidence: 99%

Optimization Models in Cyber-Physical Power Systems: A Review

et al. 2022

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“…A balanced nonconvex AC OPF problem is adapted to restore feasibility when the obfuscation is problematic, and is solved with Ipopt. Dvorkin et al (2020) set up a DP problem in a distribution network context where the customer load values are considered sensitive. In this case, the balanced linearized DistFlow formulation (Baran & Wu, 1989) is used to build a chance-constrained optimization model where some of the decision variables are affine functions of the noise of added in the DP mechanism.…”

Section: Phase Identificationmentioning

confidence: 99%

Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

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“…The nonconvex constraint (2d) can be relaxed into multiple linear inequalities by first approximating v i,t ≈ 1 (which is a good approximation as long as v and v in (3) are close to 1 p.u. ), and then using a convex polygon inner approximation [20] to replace the quadratic terms as…”

Section: A Convex Relaxations Of Non-convex Constraints Convex Relaxa...mentioning

confidence: 99%

Load Restoration in Islanded Microgrids: Formulation and Solution Strategies

Bose

2021

Preprint

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Extreme weather events induced by climate change can cause significant disruptions to the normal operation of electric distribution systems (DS), including isolation of parts of the DS due to damaged transmission equipment. In this paper, we consider the problem of load restoration in a microgrid (MG) that is islanded from the upstream DS because of an extreme weather event. The MG contains sources of distributed generation such as microturbines and renewable energy sources, in addition to energy storage systems. We formulate the load restoration task as a non-convex optimization problem with complementarity constraints. We propose a convex relaxation of the problem that can be solved via model predictive control.In addition, we propose a data-driven policy-learning method called constrained policy optimization. The solutions from both methods are compared by evaluating their performance in load restoration, which is tested on a 12-bus MG.

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Differentially Private Optimal Power Flow for Distribution Grids

Cited by 36 publications

References 31 publications

Optimization Models in Cyber-Physical Power Systems: A Review

Optimization Models in Cyber-Physical Power Systems: A Review

Applications of optimization models for electricity distribution networks

Load Restoration in Islanded Microgrids: Formulation and Solution Strategies

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