The Promise of EV-Aware Multi-Period OPF Problem: Cost and Emission Benefits

In this paper, we study the Multi-Period Optimal Power Flow problem (MOPF) with electric vehicles (EV) under emission considerations. We integrate three different real-world datasets: household electricity consumption, marginal emission factors, and EV driving profiles. We present a systematic solution approach based on second-order cone programming to find globally optimal solutions for the resulting nonconvex optimization problem. To the best of our knowledge, our paper is the first to propose such a comprehensive model integrating multiple real datasets and a promising solution method for the EV-aware MOPF problem. Our computational experiments on various instances with up to 2000 buses demonstrate that our solution approach leads to high-quality feasible solutions with provably small optimality gaps. In addition, we show the importance of coordinated EV charging to achieve significant emission savings and reductions in cost. In turn, our findings can provide insights to decision-makers on how to incentivize EV drivers depending on the trade-off between cost and emission.Comment: 10 pages, 6 figures, 2 table

Towards Low-carbon Power Networks: Optimal Integration of Renewable Energy Sources and Hydrogen Storage

This paper proposes a new optimization model and solution method for determining optimal locations and sizing of renewable energy sources and hydrogen storage in a power network. We obtain these strategic decisions based on the multi-period alternating current optimal power (AC OPF) flow problem that considers the uncertainty of renewable output, electricity demand, and electricity prices. We develop a second-order cone programming approach within a Benders decomposition framework to provide globally optimal solutions. To the best of our knowledge, our paper is the first to propose a systematic optimization framework based on AC OPF that jointly analyzes power network, renewable, and hydrogen storage interactions in order to provide optimal locations and sizing decisions of renewables and hydrogen storage. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC OPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment costs, emission tax prices, and conversion efficiency

Second-order cone programming based methods for two variants of optimal power flow

Optimal Power Flow (OPF) is a fundamental optimization problem in power system operations. In this thesis, we focus on two variants of the OPF problem: Reactive Optimal Power Flow (ROPF) and Multi-Period Optimal Power Flow (MOPF). In Chapter 2, we provide an overview of the classical OPF formulations. In Chapter 3, we present an alternative mixed-integer non-linear programming formulation of the ROPF problem. We utilize a mixed-integer second-order cone programming (MISOCP) based approach to find globally optimal solutions of the proposed ROPF problem formulation. We strengthen the MISOCP relaxation via the addition of convex envelopes and cutting planes. Computational experiments on challenging test cases show that the MISOCP-based approach yields promising results with small optimality gaps compared to a semidefinite programming based approach from the literature. In Chapter 4, we focus on the MOPF problem with electric vehicles (EV) under emission considerations. Our model integrates three different real data sets: household electricity consumption, marginal emission factors, and EV driving profiles. We present a systematic solution approach based on SOCP to find globally optimal solutions. Our computational experiments on instances with up to 2000 buses demonstrate that our solution approach leads to globally optimal solutions with very small optimality gaps, in addition to significant emission savings and reductions in cost with the coordination of EV chargin

An MISOCP-based solution approach to the reactive optimal power flow problem

In this letter, we present an alternative mixed-integer non-linear programming formulation of the reactive optimal power flow (ROPF) problem. We utilize a mixed-integer second-order cone programming (MISOCP) based approach to find global optimal solutions of the proposed ROPF problem formulation. We strengthen the MISOCP relaxation via the addition of convex envelopes and cutting planes. Computational experiments on challenging test cases show that the MISOCP-based approach yields promising results compared to a semidefinite programming based approach from the literature

The promise of EV-aware multi-period optimal power flow problem: cost and emission benefits

Increased electric vehicle (EV) penetration brings considerable challenges to the daily planning of the power grid operations. A careful coordination of the grid operations and charging schedules is needed to alleviate these challenges, and turn them into opportunities. For this purpose, we study the Multi-Period Optimal Power Flow problem (MOPF) with electric vehicles under emission considerations. We integrate three different real-world datasets: household electricity consumption, marginal emission factors, and EV driving profiles. We present a systematic solution approach based on second-order cone programming to find globally optimal solutions for the resulting nonconvex optimization problem. To the best of our knowledge, our paper is the first to propose such a comprehensive model integrating multiple real datasets and a promising solution method for the EV-aware MOPF Problem. Our computational experiments on various instances with up to 2000 buses demonstrate that our solution approach leads to high-quality feasible solutions with provably small optimality gaps. In addition, we show the importance of coordinated EV charging to achieve significant emission savings and reductions in cost. In turn, our findings can provide quantitative insights to decision-makers on how to incentivize EV drivers depending on the trade-off between cost and emission

Towards Low-carbon Power Networks - Optimal Integration of Renewable Energy Sources and Hydrogen Storage

This paper proposes a new optimization model and solution method for determining optimal locations and sizing of renewable energy sources and hydrogen storage in a power network. We obtain these strategic decisions based on the multi-period alternating current optimal power (AC OPF) flow problem that considers the uncertainty of renewable output, electricity demand, and electricity prices. We develop a second-order cone programming approach within a Benders decomposition framework to provide globally optimal solutions. To the best of our knowledge, our paper is the first to propose a systematic optimization framework based on AC OPF that jointly analyzes power network, renewable, and hydrogen storage interactions in order to provide optimal locations and sizing decisions of renewables and hydrogen storage. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC OPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment costs, emission tax prices, and conversion efficiency