# Research

Our research interest is in developing mathematical models and optimization algorithms to solve power system engineering and energy economics problems.

Power systems operation and planning

Decarbonization of power grids and renewable energy integration

Energy storage as grid flexibility resources

Demand-side resources - electric vehicles, smart buildings, and microgrids

Electricity market modeling and energy policy design

Stochastic and distributed optimization with energy applications

Grid resilience against extreme weather and cyber-attack

Power Grid Planning

Highlights:

Optimize renewable generation planning to decarbonize electric power girds while accounting for physical and economic impacts

Balancing between environmental benefits from low-carbon generation resources and power grid operational challenges

Stochastic & robust optimization for reliable power grid planning while capturing uncertain characteristics of renewables

Decomposition algorithms to overcome the computational complexity

Relevant paper:

J. Kim, R. Mieth, Y. Dvorkin, “Computing a Strategic Decarbonization Pathway: A Chance-Constrained Equilibrium Problem,” IEEE Transactions on Power Systems, vol. 36, no. 3, p. 1910, 2021.

J. Kim, B. Unel, S. Bialek, Y. Dvorkin, “Strategic Policymaking for Implementing Renewable Portfolio Standards: A Tri-level Optimization Approach,” IEEE Transactions on Power Systems, vol. 36, no. 6, p. 4915, 2021.

R. Mieth, J. Kim, Y. Dvorkin, “Risk-and Variance-Aware Electricity Pricing,” Electric Power Systems Research, vol. 189, p.106804, 2020.

Grid Resilience + ESS

Highlights:

Motivated by the recent progress in mobile ES technologies, i.e., ES units can be moved using public transportation routes, this paper proposes using this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and the locations where extra back-up capacity is necessary during disasters. [...]

Relevant paper:

J. Kim, Y. Dvorkin, “Enhancing Distribution System Resilience with Mobile Energy Storage and Microgrids,” IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 4996-5006, 2019.

Peer-to-Peer Energy Trading

Highlights:

Peer-to-peer energy trading reduces the portion of electricity supplied to end-customers by utilities and their revenue streams.

Utilities must ensure that peer-to-peer transactions comply with distribution network limits.

This article proposes a peer-to-peer energy trading architecture, in two configurations, that couples peer-to-peer interactions and distribution network operations. The first configuration assumes that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility. Both configurations use distribution locational marginal prices to compute network usage charges that peers must pay to the utility for using the distribution network. [...]

Relevant papers:

J. Kim, Y. Dvorkin, “A P2P-dominant Distribution System Architecture, ” IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 2716-2725, 2020.

Mathematical Energy Policymaking

Highlights:

Optimize clean energy policy, while incorporating conflicting interests and objectives of different stakeholders.

Investigate strategic regulatory competition and its effect on achieving renewable/decarbonization goals

Multi-level optimization models / Equilibrium models

Decomposition algorithms to overcome the computational complexity

Relevant papers:

J. Kim, S. Bialek, B. Ünel, Y. Dvorkin, “Impact of imperfect foresight on the optimal DER deployment, remuneration and policy,” Applied Energy, 2022.

J. Kim, R. Mieth, Y. Dvorkin, “Computing a Strategic Decarbonization Pathway: A Chance-Constrained Equilibrium Problem,” IEEE Transactions on Power Systems, vol. 36, no. 3, p. 1910, 2021.

J. Kim, B. Unel, S. Bialek, Y. Dvorkin, “Strategic Policymaking for Implementing Renewable Portfolio Standards: A Tri-level Optimization Approach,” IEEE Transactions on Power Systems, vol. 36, no. 6, p. 4915, 2021.